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Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market
Energy Conversion and Management xxx (2015) xxx–xxx
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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman
Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market q Henryk Wolisz ⇑, Carl Punkenburg, Rita Streblow, Dirk Müller RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, 52074 Aachen, Germany
a r t i c l e
i n f o
Article history: Available online xxxx Keywords: Demand side management (DSM) Energy policy Scenario analysis Smart grid Thermal storage
a b s t r a c t A transition in the electricity market is required to manage the volatility of increasing renewable energy generation. These fluctuations can be faced with flexible consumption through Demand Side Management (DSM), establishment of further centralized storage capacities and provisioning of dynamic back up generation capacities. At least the latter two options can impose large establishment and operation costs upon the electricity market. Therefore, the feasibility and the resulting potential of coupling the electricity grid with the thermal supply of residential buildings is analysed in this paper. Thereby, inexpensive and widespread thermal storage capacities could be used to improve the integration of dynamic renewable electricity generation. In this paper the technical and economical key impact factors for such thermal DSM approach are elaborated. Based on a literature review, the identified key factors are aggregated to form consistent scenarios of the German ‘‘Energiewende’’ (turnaround in energy policy). The practicability and possible magnitude of the intended DSM is then analysed based on the identified scenarios. All resulting scenarios highlight the growing demand for a flexible electricity market. Especially in scenarios with strong growth of renewable electricity generation, up to 45 GW of flexible electric capacities would be required in Germany by the year 2030. Furthermore, the analysis demonstrates that independently of the energy market development, it is very likely that electricity coupled supply systems will continuously have installed capacities of at least 14 GW. It is found that especially the strong dissemination of smart metering and smart control infrastructure is crucial to incorporate these capacities into DSM activities. Furthermore, the necessity and the resulting benefits of residential end-user participation in the electricity market need to be communicated to all stakeholders to ensure the availability and acceptance of dynamic pricing schemes in the future. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction The vastly growing renewable energy sector causes problems such as highly fluctuating and unpredictable renewable energy generation (i.e. photovoltaic (PV) and wind). Due to the inflexibility of electricity demand, it is not always possible to match the renewable energy generation with the demand. The rising share of wind and PV in the total energy portfolio will extend that challenge. Residential and commercial buildings, which employ up to 30% of Germany’s final energy consumption [1], could provide
q This paper is an edited and extended version of the original paper ‘‘Feasibility and potential of thermal demand side management in residential buildings considering different developments in the energy market’’ which was presented at the 27th ECOS Conference 2014 in Turku, Finland. ⇑ Corresponding author. Tel.: +49 241 80 49808. E-mail address: [email protected] (H. Wolisz).
flexibility to counter the imbalances between supply and demand in the electric grid. However, there are technical and energy political issues which strongly impact that potential. Within the Dual Demand Side Management (2DSM) project an approach is developed, which manages the total energy demand of buildings [4]. It links thermal and electrical demand on city district level and offers a communication platform for dynamic energy management. In addition to electrical supply systems (e.g. heat pumps), 2DSM analyses the potential for thermal energy storage in buildings, integrating conventional storage technologies like hot water tanks as well as the structural thermal storage capacity of a building itself. 2DSM control is based on a thermal building simulation in Dymola/Modelica combined with electric grid simulations in Neplan. The focus of this work is the analysis of future potential for residential thermal demand side management concepts like 2DSM. This research is performed based on the experience about key
http://dx.doi.org/10.1016/j.enconman.2015.06.059 0196-8904/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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Fig. 1. Five-phase scenario process (based on [2]).
factors for successful DSM gained within our project and the evaluation of existing studies on the development of energy policy and management. However, even when focusing just on Germany it is very hard to foresee how the technical and political framework for the energy sector will develop. In the existing studies, there are many different predictions and estimations about future electricity generation, the electricity market, and the residential electricity consumption. In the following, based on an extensive literature research of current laws, governmental support schemes, technological road maps and dissemination forecasts, reasonable and consistent developments of the German ‘‘Energiewende’’ (turnaround in energy policy) are modelled using the scenario technique. In the next chapters, first the chosen scenario technique is presented. Afterwards, the key impact factors for such dynamic energy management are elaborated and three consistent scenarios are developed. Thereby, a best-case scenario represents a smooth transition to a modern and flexible electricity market with high share of renewable energies and good development of all key factors. A Medium scenario presents a faltering transition in the energy market, leading to an unbalanced constellation of energy generation, storage, and consumption mechanisms. To complete the range of possible developments, a worst-case scenario describes a poor energy market development in a stagnating economical surrounding. For these scenarios, the possibilities for integration of thermal demand side management are then evaluated and discussed. Finally, it will be stated which conditions and developments seem to be most favourable for the intended DSM approach and analysed which drivers are most important to improve and support these conditions. 2. Scenario approach For the evaluation of the residential thermal DSM potential, scenarios of the future are created. As a futures technique, scenario analysis estimates values that cannot be precisely defined at the current point in time, therefore the results can potentially vary a lot from the predicted reality. However, scenario analysis is the most persuasive futures technique, when projections of very dynamic and not easily predictable developments are required, since it depicts several possible futures in contrast to most other methods (e.g. Delphi method) that try to decide on the one most probable future. Furthermore, scenario analysis also takes into account the significance of interaction between all identified factors, in contrast to e.g. sensitivity analysis, thus increasing the chances of generating consistent projection of the future. However, the width extent of the scenario field, which is evaluated and used to generate scenario projections, limits the level of detail that can be applied to each single impact factor. Moreover, in some cases the manifestation of the key factors even requires some generalization to create scenarios that are consistent across all factors. Thus, scenario analysis is typically not used to describe a given development as precisely as possible but, as in this analysis, to
evaluate a given question considering different developments of the surrounding conditions. In this work the scenario analysis is performed according to a five-phase scenario process proposed by the Institute for Future Studies and Technology Assessment (IZT) [2]. Fig. 1 illustrates the approach used for the scenario generation. The time horizon of the scenarios is chosen between 2020 and 2030 with a focus on 2030 in the evaluation and discussion. The first phase of the scenario process defines the scenario field. The scenario field describes the segment of the entire future which is of concern for this evaluation. The second phase covers the identification of key factors, also called scenario drivers, which have substantial impact upon the scenario field. Thereby, the objective is to consider the given question at its entirety by using as few descriptive factors as possible. The selection of scenario drivers is supported by the application of an impact analysis, which provides a ranking of possible key drivers. Therefore, all potentially relevant factors are gathered during an unbiased analysis process. Subsequently, the relation between these factors is investigated. Thereby, factors which critically influence others and have proved to be widely linked are likely to be key factors. In the end, the key factors are chosen with respect to their rated importance in the context of the scenario field as described in [2,3]. In the third phase of the scenario analysis, the previously described key factors are analysed. Possible developments of each factor are examined. As a basis, external studies and scenarios that discuss possible evolutions of the key factors were consulted. Hence, for each factor possible developments are conflated to an upper, a medium, and a lower trend. After the examination of each factor, global scenarios are created in the fourth phase. Based on consistent combinations of the developments of the different drivers, alternative images of the future are constructed. Finally, the fifth phase of the scenario process covers the scenario transfer, which emphasises the conclusions drawn from the scenario process.
3. Scenario analysis 3.1. Scenario field In the case at hand, all trends affecting the potential of combining the electricity grid with thermal DSM are within the scope of the scenario field. These are especially the developments on the electricity market and within the heat supply sector. The latter focuses particularly on private households. Furthermore, energy policy and the general development of the economy are also within scope. Altogether, 18 driving factors were identified and used to describe the scenario field. These factors were analysed using a cross impact matrix, which is a particular characteristic of a sensitivity analysis. Following, the identification of key factors and the analysis of potential key factor developments, is presented.
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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H. Wolisz et al. / Energy Conversion and Management xxx (2015) xxx–xxx Table 1 Cross impact matrix of the scenario field. Impact factor
Active sum (AS)
Passive sum (PS)
Fluctuations in electricity generation Wind and PV energy share in gross electricity generation Expansion of the German electricity transmission networks Trans border electricity exchange in Europe Dissemination of electric heat pumps Dissemination electrical driven supply systems Dissemination of small scale CHP Building refurbishment rate Availability of thermal storage Energy storage systems (electrical) Oil price Gas price Electricity price Electricity market design Dissemination of smart energy management Macroeconomic development Legislation Acceptance of ‘‘Energiewende’’
21 24 9 4 6 6 9 15 5 16 13 15 22 15 13 21 31 18
12 13 15 13 24 28 24 13 11 20 2 4 20 13 17 6 6 22
3.2. Identification of key factors Within a cross impact matrix the stability and the interdependencies of different factors within the scenario field are assessed [3]. As a result, the factors are classified as active or passive and dynamic or inert. Within the evaluation each factor’s effect upon on all the others is evaluated on a scale of 0–3, where 0 means no impact, 1 – a weak relationship, 2 – an average relationship, and 3 – a strong relationship. As a reference value for the overall effect of one factor on the others, we add up the corresponding impact factors into the so-called active sum (AS). To describe the effect of all other factors on the observed factor, the passive sum (PS) is calculated. The active factors having a high active sum and a small passive sum play a very important role and are often considered as ‘‘levers’’ or ‘‘switches’’ because they can change relatively independent from other factors but have themselves high influence on others [2]. Factors with both, high active and passive sum, represent dynamic factors. Since they greatly influence the scenario field while being strongly linked, they should be observed very carefully and are typically chosen as key impact factors. The evaluation of the cross impact matrix is presented in Table 1 and performed according to the following mathematical conditions for the determination of active, passive, dynamic, and inert factors [2]:
AS > PS ? active AS < PS ? passive AS PS > (n 1)2 ? dynamic AS PS < (n 1)2 ? inert
(n being the number of factors within the scenario field, in this case n = 18). As a result, the first key factor is the fluctuation and unpredictability in electricity generation. The development is highly important, because particularly fluctuating and unpredictable energy generation makes the wide use of thermal DSM interesting. Since the main focus of this analysis is the evaluation of domestic thermal DSM, it is necessary to observe the complete shiftable electrical energy potential of the household heating sector. Therefore, the electricity demand and the generation capacity of technologies linking heat supply and electric energy (e.g. electric heating, Heat Pumps (HP) and Combined Heat and Power (CHP)) is analysed, even though this factors are neither active nor
AS– PS 9 11 6 9 18 22 15 2 6 4 11 11 2 2 4 15 25 4
Active or passive
AS PS – (n
Active Active Passive Passive Passive Passive Passive Active Passive Passive Active Active Active Active Passive Active Active Passive
37 23 154 237 145 121 73 94 234 31 263 229 151 94 68 163 103 107
1)2
Dynamic or inert Inert Dynamic Inert Inert Inert Inert Inert Inert Inert Dynamic Inert Inert Dynamic Inert Inert Inert Inert Dynamic
dynamic. The distribution levels for these technologies are key factors two and three. Another important impact factor is the availability of electricity storage options. The capacity of electricity storages is an alternative to thermal storage solutions like hot water buffer storages or the buildings structural mass itself. Accordingly, the capacity of future electricity storage systems forms the fourth key factor. The electricity market, with special emphasis on the market structure, price level, pricing mechanisms, and consumer involvement, is the fifth key factor. It strongly influences the acceptance of alternative energy generation (e.g. wind and PV distribution) [49] as well as the use of electrical driven heating and CHP on a household level. In addition to these rather technical and measurable factors, a couple of surrounding factors have to be analysed. For example, changes in national and European energy policy, as well as the economic situation in the energy sector, can affect investments in power plants and storage solutions. Considering that, the last key factor contains all surrounding ‘‘soft’’ factors like policy, economic situation, business models, interfaces, and the link to the user. An overview of the key factors and the associated main sources is shown below in Table 2.
3.3. Key factor analysis In the following subchapters, the development of each key factor identified in Section 3.2 is analysed. If a given key factor is quantifiable and enough data sources were available, an upper, a medium, and a lower development scenario are presented for the years 2020 and 2030. While the upper development pictures a positive extreme scenario, the lower one represents a worst case scenario. The medium development describes an average
Table 2 Key factors and sources used for the analysis. Key factor
Sources
Fluctuation in electricity generation Electrical driven supply systems Small scale CHP Energy storage systems Electricity market and prices Surrounding conditions
[5–17,39,43] [18–21] [5,22–25] [5,22,26–32] [5,11,33–37,39–42] [5,11,38–40,49]
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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Table 3 Wind and PV share in different scenarios.
Table 4 Scenarios for installed heat pump capacity.
Wind and PV share in gross electricity generation
Upper (‘target’) scenario [10] (%)
Medium (‘reference’) scenario [9] (%)
Lower scenario [9,47] (%)
2012 2020 2030
12.3 24.5 38.8
12.3 21.6 30.8
12.3 19 25
advancement of the trend. In this way, a scenario funnel encompassing the future extreme developments is created. 3.3.1. Fluctuation in electricity generation To simplify this factor, it is represented by the wind and PV energy share in gross electricity generation. That share, however, is influenced by several sub-factors. First of all, the political plans for the diffusion of these technologies are of great importance. So far the German Renewable Energy Act (EEG) [5], which came into effect in the year 2000, was the main driver for the diffusion of wind and PV installations. Besides the growth of the installed wind and PV capacity, the development of the total electricity demand in Germany directly influences the share renewable electricity sources have. Furthermore, numerous studies [6] show that the electrical network has to adapt and expand to enable the governmental extension plans for renewables. Also, the development of electricity generation costs for each competing technology is highly relevant. Thus, also the underlying fuel costs for fossil fuelled power plants are observed. Concerning the development of electricity generation costs, a study [7] predicts that PV will have competitive costs in relation to hard coal and gas fuelled power stations by 2020. In 2030, optimal onshore wind power installations will have even smaller generation costs than lignite power plants. The governmental plan [8] aims for a share of renewable in electricity generation between 40% and 45% by 2025. German governmental goals are described in ‘target-’ and ‘reference scenarios’ for a future energy concept [9,10], which were created on behalf of the German ministries BMWi and BMU.1 The corresponding developments for the wind and PV share in gross electricity production are depicted in Table 3, with an additional worst case scenario. While the governmental target scenario is a positive extreme scenario, the reference scenario represents the medium development. A possible negative extreme scenario is characterized by a smaller wind and PV share due to initial difficulties and delays in offshore wind installations as well as problems with the expansion of the electrical grid as forecasted by [47]. Additionally, the currently announced reform of the EEG [5,11] could result in major cuts of the wind and PV subsidies. 3.3.2. Electricity driven heat supply systems The second key factor focuses on the possible shiftable electricity demand of electric heating and heat pumps used in private households. Electric heating in the form of night storage heaters was broadly introduced in Germany during the late 1950s. The concept was based on the use of cheap electricity supplied by base load power stations during nights to equalize electricity demand across the whole day and avoid expensive shutting down or throttling of plants. In the past years, such electric heating was widely criticized for its low efficiency and low primary energy factor, considering that the storage heaters were charged mainly with electricity from
1 BMWi: Federal Ministry of Economics and Energy, BMU: Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety.
Electric capacity installed
Upper scenario [20] (GW)
Medium scenario [20] (GW)
Lower scenario [19] (GW)
2011 2020 2030
1.7 4.6 9.1
1.7 3.6 5.7
1.7 2.9 3.7
Table 5 Scenarios of installed small CHP capacity. Electric capacity of installed small scale CHP
Upper scenario [22] (GW)
Medium scenario [22] (GW)
Lower scenario [22] (GW)
2012 2020 2030
0.9 2.7 6.3
0.9 1.8 2.6
0.9 0.9 0.9
conventional thermal plants. Therefore, in Germany a legal prohibition for the use of electric night storage heating after 2019 was resolved. However, due to an upcoming awareness of load management requirements the prohibition was suspended in 2013. If strategically controlled and based on renewable generation, the currently installed electric storage heating capacities of approximately 19 GW would reveal a huge storage potential. Yet, new installations of electric heating are not expected to be made in a significant amount. It is rather likely that the installed capacity will decline. Beside political decisions, the development of the electricity price and the refurbishment rate will strongly influence the future of this technology. The share of heat pump supply systems in new build housing increased from 0.8% in 2000 to 24.5% in 2012 [18]. Still, the major part of the heat demand in existing buildings is covered by fossil-fuel-powered installations. Overall, natural gas-fuelled solutions, currently covering 50% of the new installations, are the main competitors for heat pumps and electric heating in general. The potential of heat pumps for load management was investigated by two different scenario based studies [19,20]. The aggregated results of these scenarios are displayed in Table 4. Even though the dissemination trend for heat pumps is positive, the lower scenario might occur due to climbing electricity prices in relation to natural gas prices, which would make heat pumps economically less attractive. Moreover, a decrease in the refurbishment rate could seriously impact the diffusion of heat pumps. Nevertheless, a total growth of heat pump installations is most likely. 3.3.3. Small scale CHP The analysis of the future dissemination of cogeneration in the household sector focuses on small-scale units with electric power rating of less than 50 kW. The governmental goal for all types and power ratings of cogeneration is set as 25% share in net electricity production by 2020 [8]. Several factors influence the future of CHP distribution. Mainly political decisions, structural and price developments on the electricity stock market, and fuel prices have a strong impact. Considering the current economic situation, even including governmental incentives, small-scale cogeneration units are struggling to be competitive [22]. Therefore, the further development will strongly depend on governmental reconsideration about promotions for CHPs. The technical potential of cogeneration in compliance with the future energy supply system of the ‘‘Energiewende’’ was investigated by a study [22]. According to three different scenarios for the development of Germany’s thermal demand, the technical electricity generation potential of CHP was projected. As Table 5
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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shows, installed small scale CHP capacity evolves strongly in the medium and upper scenario, while it stagnates in the lower scenario. The high diffusion scenario implies tighter mandatory CO2 emissions for buildings which lead to wide use of CHPs, especially for buildings with historic monument protection or high costs for insulation refurbishments. The study further proposes the option to equip the already available heat storages associated with CHP systems with additional electrical heating units, used to absorb excessive renewable electricity generation [22]. Another advantage of decentralised CHP installations is a reduction of the necessary grid expansion [23]. 3.3.4. Energy storage systems Considering the steadily rising share of wind and PV generation, it is sure that the demand for electricity storage, especially with diurnal application, will increase. Currently, the most economic options for large scale stationary energy storage are pump storage power plants. In 2012 all German plants of this technology had a capacity of 7 GW and were able to store a maximum of 40 GW h [16]. Including international plants, which are connected to the German grid, the currently accessible electric storage capacity reaches approximately 9 GW [48]. Studies on behalf of the Federal Ministry of Economics and Energy show, that the technical potential for pump storage power plants in Germany is almost exhausted [26]. However, the currently installed storage capacities will not be sufficient to react to future fluctuations in the grid [27]. Possible alternative large scale storage technologies are dependent on fossil fuels (diabatic Compressed Air Energy Storage, CAES), lack efficiency (hydrogen storage/methanisation) or market maturity and immediate technical feasibility (adiabatic CAES). The major obstacles for all storage solutions are the current economic boundary conditions, which do not facilitate capacity extensions. All described plants have huge investment costs. This leads to long payback periods and thereby makes the big scale storage plants risky investments [28]. Especially low electricity prices on the stock market and the present uncertainties on the energy market deny such investments. Therefore, even implying expected price reductions for most energy storage technologies, it is likely that there will not be enough electricity storage capacities in 2030. Under good circumstances the currently installed electrical energy storage capacity could be doubled to 14 GW. The demand for flexible energy on the other hand will increase progressively together with the wind and PV share in gross electricity generation (considering a cost-effective integration of the renewables). This leads to an estimated demand for electricity storage between 22 GW and 45 GW in 2030 [26,29]. However, domestic heating systems like HPs or CHPs are usually equipped with water based thermal storage buffer tanks. If flexible control of these appliances was established, this existing storage potential could be accessed. A recent study [29] calculates the specific thermal storage costs to be approximately 30% of those for pump storage power plants. Additionally, active usage of the structural thermal mass in buildings could further extend the available storage capacities. First studies have shown very promising potential and achievable shiftable capacities in the GW range, when the thermal supply systems are used to dynamically pre-condition residential buildings [44,45]. Since these potentials cannot be precisely quantified yet, it will be assumed in this paper that in the best case scenario such additional domestic storage allows to exploit the total load shifting potential of electricity coupled heat generation. 3.3.5. Electricity market structure and resulting pricing The development of the electricity market is one of the most important key factors. As it can be seen today, the trend of the
5
consumer electricity price can diverge from the evolution of the wholesale electricity price determined by the European Energy Exchange (EEX). However, these prices along with the associated fees and taxes will highly influence the electricity market. Further, factors like the dissemination of dynamic pricing and the possible implementation of a capacity mechanism are of great importance. Moreover, the development of the consumer´s electricity price in relation to the gas price is of special concern for the scenarios. Three developments are considered. Electricity price increases more, respectively less than the gas prices does, or both prices increase equally. The wholesale price of electricity is determined by the marginal costs of energy generation and evolves at the EEX. In general, the total electricity generation costs of renewable energy sources are still higher than these of fossil power plants. However, current subsidies and market regulations lead to overcapacities in the current generation market and a growing amount of renewable energy sources which can offer electricity at marginal costs of approximately zero. Because of the merit-order of power plants, this enforces lower prices on the electricity market. Consequently thermal power plants have declining operation hours and lower profits and therefore struggle to cover their total costs. This phenomenon is commonly referred to as the missing-money-problem, which often also leads to decommissioning of power plants and denies investments in peak load power generation. Nevertheless, flexible conventional power plants are needed to supply service security. Capacity mechanisms are supposed to counter the missing-money-problem by creating incentives to keep power plants, which are essential for the supply security, in operation or encouraging new installations of peak load power generation. There are different capacity mechanisms which are currently discussed. Each one has a different impact on the scenario field. A simple but temporary solution is the creation of a strategic reserve. A central coordinator manages old power plants, which do not participate on the market any more, and just uses these plants if the market cannot satisfy the demand for electricity. Another option, is the implementation of a market for secure supply capacities, called capacity market. A selective capacity market promotes new plants of desired technologies (e.g. gas turbines) and is managed by a central authority. A demand-induced capacity market, on the other hand, works as a liberal, market based solution and treats supply security as a private good, which all energy supplying companies have to buy from a capacity market. This market structure additionally incentivises the integration of a controllable demand side. While a strategic reserve is considered to be just a temporary but cheap solution, capacity markets are more expensive. Overall it is likely that if any type of capacity mechanism is introduced, the costs will be passed on to the end-consumer [34,35]. Currently approximately 50% of the consumer’s electricity price in Germany is caused by taxes and dues [33]. The other share is triggered by the generation and distribution of the energy. The development of the taxes’ and dues’ share in the electricity price is hard to forecast, because it is based on uncertain political decisions. One main governmental goal of the EEG reform is to stop the excessive rise of the EEG-levy and the electricity prices in general. Instead of such generalized charges, dynamic adaption of electricity fees and taxes could be used to promote consumption of renewable energy at times of its availability. However, this would require the introduction of dynamic pricing schemes. Theoretically, since 2010 energy supplying companies are obligated to offer at least one tariff with dynamic pricing.2 However, most companies just renamed the longstanding day and night rates with fixed time intervals to fulfil this expectation. Yet, this does neither help
2
EnWG §40 para. 3 [42].
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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Table 6 Scenario descriptions. Factor
2012
2030 Scenario A (positive)
2030 Scenario B (medium)
2030 Scenario C (negative)
Macroeconomic development Consumer electricity price Capacity mechanism on electricity market Smart meter distribution (households with smart energy management) Electrical storage capacity Wind and PV share in gross electricity production Demand for flexible electric capacity Electric HP capacity Electric heating capacity Small CHP capacity Capacity of domestic electric appliances Controllable domestic supply systems
/ 0.26 €/kW h None <1%
Significant sustainable growth Rises less than gas prices do Demand induced capacity market 90%
Slow steady growth Rises more than gas prices do Selective capacity market 30%
Stagnation and crisis Rises equally to gas prices Strategic reserve 20%
9 GW 12% 8 GW 2 GW 19 GW 1 GW 22 GW 0 GW
17 GW 39% 45 GW 9 GW 15 GW 2.5 GW 26.5 GW 24 GW
14 GW 31% 30 GW 5.7 GW 5 GW 6.3 GW 17 GW 5 GW
11 GW 25% 22 GW 3.7 GW 10 GW 1 GW 14.7 GW 3 GW
managing fluctuating renewable energy generation, nor is it a motivations for end-customers to perform dynamic DSM. Besides that, a survey conducted by Fraunhofer ISI [40] shows that currently German electricity consumers prefer simple and static pricing to risky dynamic pricing models. The study concludes that dynamic pricing will only have a positive effect if an automatic control system is implemented at the same time. Also, active utilization of dynamic pricing would require a stronger dissemination of smart meters. Due to a new law,3 smart meters have to be installed in new and extensively refurbished buildings ever since 2010. Still, in addition to smart meters, DSM-concepts require a uniformed communication and interaction between supply systems from different manufacturers. Thus, smart control in the required extent is often not fulfilled by many of today’s smart meter implementations, based on digital counters with none or mono-directional communication. A smart energy market requires smart meters, which offer continuous bidirectional communication enabling real dynamic pricing as well as optimization and control of local supply systems. Since the impact of such metering and control components on the electricity market is huge, different scenarios for an extensive rollout of smart meters and the resulting macroeconomic value are currently under investigation [41]. Moreover, along with the smart metering concept comes the discussion about data privacy. This important aspect needs to be clarified. Secure und trustworthy options for data management have to be developed and implemented to improve the chances of public support for that technology.
3.3.6. Surrounding conditions It is evident that the German electricity market is currently influenced by a lot of uncertainties. The lack of reliable forecasts creates risks that deny long term investments. The CEO of RWE (one of the major German energy providers) calls the current situation the biggest structural crisis German and European energy providers have ever encountered [38]. According to Gabriel [39], Federal Minister of Economics and Energy, securing reliable market conditions, that enable risk minimized investment projections and the market integration of renewable energy are major goals of the EEG reform 2014. However, policy is always highly influenced by its impact and acceptance among the population and industry. At the present time, the sharp increase in consumer’s electricity price caused by the EEG subsidies for renewables could undermine the public support for further dissemination of renewable energies. Moreover, a possible decline in stability and availability of power supply and necessary structural
3
EnWG §21 [42].
reorganizations of the electricity market threaten the acceptance of the ‘‘Energiewende’’ by population and industry. 3.4. Scenario generation In the following section the presented key factors and surrounding market conditions are aggregated to form three consistent scenarios for the development of the energy market. First the underlying factors and the calculated outcomes for the scenarios are listed together in Table 6. Afterwards, the positive, medium, and negative developments of the ‘‘Energiewende’’ are presented in a descriptive way. 3.4.1. Scenario A – a well implemented transition In scenario A the surrounding factors evolve in a most positive manner. Significant economic growth supplies the monetary capability to invest in a large-scale turnaround of the energy sector. The governmental goals are reached for the ‘‘Energiewende’’ in general and especially for the desired penetrations of renewable energies. Major cost reductions in the wind (especially offshore) and PV sector make future promotion of renewables less expensive and renewable electricity generation competitive on a broad spectrum. Due to this, the rise of the EEG-levy is limited. Actually, consumer’s electricity price will rise slightly, but the increase in oil and gas price will be considerably higher. Combined with a high refurbishment rate [9], this leads to an additional growth of electric heat pump installations. Also, the capacity of electric heating only slightly declines, because of acceptable electricity prices and attractive rewards for load management. To enable widespread utilization of domestic load management, changes are made in the EEG boosting smart meter installations by following the suggestions made in [41]. By 2030 the smart meter rollout is completed and 90% of all households in Germany are equipped with intelligent electricity management. This reduces the demand for grid expansions and conventional electricity storage capacity. Accessible capacities of hydraulic pump storage plants have almost doubled, but the potential for Germany is exhausted. First adiabatic CAES and hydrogen storage solutions are built and tested [29]. The price of Lithium-ion batteries declines drastically and first vehicle-to-grid storage solutions are initiated. Still, thermal storage is the most cost efficient option to store energy for heating purposes. Due to the high refurbishment rate of households, smart control of domestic buffer tanks and structural thermal mass is widespread, supplying additional storage capacities at negligible costs. Taking into account the huge share of renewable energies, a demand-induced capacity market is implemented to ensure supply security. The market structure indirectly promotes the further integration of controllable demand. Despite the opportunity to participate in the capacity market, sinking heat demand and rising fuel
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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prices lead to just a moderate increase in small scale CHP installations. 3.4.2. Scenario B – a struggling unbalanced market Due to moderate economic growth, further uncertainty in governmental promotions, and legal situation on the energy market cost intensive long term investments are partially evaded. Therefore, the energy market struggles to overcome the current crisis and the governmental goals of the ‘‘Energiewende’’ are only partially reached. Nevertheless, financed by continuous EEG subsidies a large amount of wind and PV generation capacities is connected to the power grid, driving the consumer’s electricity price very high. In total, the electricity price rises much more than the gas and oil prices do. This and just a small increase in the refurbishment rate lead to a slowdown in electric heat pump installations resulting in a moderate increase, as assumed in the conservative HP expansion scenarios. Distribution of direct electric heating suffers drastically from the high electricity prices. In 2030, only approximately 25% of the conventional electric heating capacity of 2012 is left. However, small CHP units profit from the high electricity prices, the still relatively high thermal energy demand of buildings, governmental promotions, and stricter obligations concerning building CO2 emissions. These widely spread CHP units are equipped with thermal storages which can be additionally supplied with electric heating. Thus, these systems could potentially offer required dynamic load management capacities. Yet, the rollout of smart meters proceeds only at the current rate, resulting in 30% of Germany´s households being equipped with smart meters by 2030 [41]. This drastically limits the actual load management potential of electricity coupled heating systems. Although, the exploration and development of new storage solutions is limited, the electricity storage capacity rises by approximately 50% due to the realization of almost all possible pumped water storages in Germany [22]. Since the market itself cannot fulfil the flexibility requirements evolving from the weighty renewable generation, a selective capacity market, compensating operators of new and secure supply capacities, is implemented. The resulting costs are further increasing the electricity price for the end-consumer. These high prices and the possibility to engage in the capacity market further support the utilization of cogeneration and lead to the development of local electricity and heating grids [22]. 3.4.3. Scenario C – stagnation in the energy market In scenario C negative macroeconomic developments limit investments for a long period. The high costs of the ‘‘Energiewende’’, especially during difficult economic times build up resistance in politics, population, and industry. Electricity and fuel prices both rise in a similar magnitude. Due to decreasing governmental promotions and delayed grid extensions, the share of wind and PV electricity generation reaches only 25% in 2030, thus lagging far behind governmental plans. Trying to avoid an expensive capacity market, a strategic reserve is used to ensure supply security. Because of the poor economic situation and uncertainties, long term investments are avoided by energy companies and house owners’ restraint lowers the refurbishment rate. The annual sales of electric heat pumps remain on the current annual level. Even though electric heating is economically not attractive, the installed capacity just diminishes by approximately 50% due to the slow exchange of existing systems. The capacity of installed small scale CHP units also stays on the level of 2012. Similarly to the dissemination of electrically coupled heating technologies, also the rollout of smart meters suffers from the low refurbishment rate. Considering this as well as the current regulations, about 20% of the German households will be equipped with smart energy management in 2030. Therefore, the potential for decentralised load management is very small. Still, whenever possible, cheap thermal
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storages are used to absorb some of the renewable energy. Urgently needed storage capacities are barely available. Caused by residential and political resistance, just a small share of the currently planned expansions of pump storage plants is realized. New storage technologies with high investments are not brought forward at all. 4. Discussion 4.1. Evaluation of the current market situation The electricity market is currently in a critical stage. Changes made in the next few years will seriously impact the whole energy sector and determine the development until 2030. Phenomena like the missing-money-problem, call into question the long term viability of the energy-only-market. Warranted revenues for fluctuating and not controllable renewable energy sources (wind and PV) shift the market risks to conventional power plants. As a result, a possible shortage of 5 GW to 15 GW of secure supply capacity is projected for 2022 [36]. This leads to discussions about the general market structure and the implementation of capacity mechanisms in particular. Thereby, one major threat to supply security and an effective market is the strong lack of flexibility in electricity demand. Transition to a more flexible demand of electricity can be provided by large-scale electrical storages or by decentralized DSM activities. Though, it is unlikely that DSM can completely solve the problem, it definitely can complement the limited potential of pump water storage and the expensive efforts to develop new storage technologies. Furthermore, load management can reduce the need for, or even completely substitute the expensive capacity mechanisms, allowing electricity consumers to actively participate in the energy market instead of just imposing the costs of the market transition upon them. Since decreases in governmental promotion of the ‘‘Energiewende’’ are likely at the present time, it is crucial to focus on the acceptance of market transition by all relevant stakeholders. Consumers are mainly interested in affordable and secure energy supply which does not reduce their comfort. Especially German consumers are extremely risk-averse [46], therefore reliability of service and simple predictable monetary incentives are crucial elements of future concepts that include private end-consumers. Furthermore, the industry needs to be closely integrated into the market transition. 4.2. Scenario transfer Scenario A presents a balanced electricity market, where extensive renewable energy generation is faced by a flexible energy market and distinct diversified energy storage potentials. The ‘‘Energiewende’’ is very successful and conditions for further transition towards a renewable electricity market are good. Associated technologies become an important factor of Germany’s industry and economic growth. Scenario B depicts an unbalanced and inefficient electricity market, where still quite marked renewable generation is not accompanied by the required flexibility and storage capacities in the market. High electricity costs induce local efforts towards grid autonomy, leading to a wide reaching CHP dissemination. While CHP growth is generally desirable, the development of parallel supply networks is further undermining the profitability of renewable electricity generation. Still, Germany profits from some associated technological developments and pushes forward to improve the situation on the energy market. Finally, scenario C illustrates the results of a stagnating market and resulting poor development of the key factors as well as the
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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surrounding factors. This scenario describes a future where all stakeholders avoid further actions and risks, which leads to few alterations to the current situation. Only highly subsidised extensions of renewable electricity generation are implemented, however, the market is not prepared to manage fluctuating energies. Germany is not a leading driver of the energy transition anymore and struggles to keep pace with energy market advances in other countries. In spite of the vast differences between the presented scenarios, all of them show the growing demand for a flexible electricity market coming along with the increasing renewable generation. In Table 6, the assumed demand for flexible electric capacity is faced with the assumed centralised and decentralised electric management capacities. Thus, it becomes visible that available centralised storage alone cannot offer enough flexibility to effectively incorporate renewable electricity generation. Therefore, either a large share of renewable generation can only be used when availability and demand are matching or the renewables keep generation priority and large dynamic conventional back up capacities have to be established. Both options dramatically undermine the profitability of renewable electricity generation. However, coupling the electricity market with the demand for thermal energy through smart integration of electricity driven supply systems, smart meters and DSM programs would improve the situation [4]. The possibility to utilize widely available and cost efficient thermal storages would allow incorporating distinctly larger amounts of renewable energy, without strong subsidies and political prioritisation of technologies [44,45]. The scenario analysis demonstrates that, independent of the energy market development it is very likely that electricity coupled supply systems will continuously have installation capacities of 14–27 GW. This corresponds to approximately 40–75% of the maximum renewable electricity generation occurring on a sunny and windy day in 2013 [43]. However, the potential to include these supply systems into energy management is only given if a communication and control infrastructure is supplied. Therefore, the rollout of smart control and metering appliances as well as the dissemination of dynamic pricing schemes will be crucial. Yet, this requires a well elaborated concept for the involvement of end-consumers into the market. Today, customers and energy providers are still very skeptical and hesitant about dynamic electricity rates, being afraid of the large-scale implementation complexities. Furthermore, manufacturers of supply systems are still far away from agreeing upon uniform communication interfaces for the HVAC equipment. 4.3. Scenario credibility In this section we seek to estimate and discuss the consequences of forecast inaccuracies upon the generated scenarios. While a typical error propagation calculation is not applicable for an analysis which strives to project possibly most diverging manifestations of input values, the risk and the result of inaccurate key factor forecasts is discussed. Our analysis shows, that there is an existing large stock of electrical driven and potentially controllable supply systems (key factor 2) in Germany as of today (i.e. night storage heaters). Since there is no law or other incentive to exchange these systems and the German refurbishment rate is very low, the assumption that these systems will persist for many years is considered reliable. However, it cannot be excluded that disruptive market conditions could potentially change the framework of these systems. For example, due to very high electricity prices (key factor 5) or the introduction of strong supply system refurbishment subsidies (key factor 6). Based on the evaluation of current supply system installations and the foreseeable price development of supply
system technologies, there is a strong indication that these systems will be exchanged by combined heat and power systems (key factor 3) or heat pumps (key factor 2). Both cases, and thus most possible manifestations of key factors 2, 3, 5 & 6, are considered and evaluated in the presented scenarios. The extent of fluctuating electricity generation (key factor 1) and the availability of energy storage systems, particularly domestic battery systems (key factor 4), are correlated to still evolving and very dynamic technologies. In the past the dissemination rate and the resulting capacities of renewable energies were underestimated by almost all forecast studies, as shown by a report that evaluates 50 studies published between 1980 and 2007 predicting the development of renewable energies [50]. Therefore, it can be expected that these factors are still underestimated by current studies. If this should hold for the share of renewable generation and thus also for the amount of fluctuating electricity generation, the general findings of this paper are strongly reinforced, underlining the importance of introducing thermal demand side management. In turn, the underestimation of the availability of energy storage systems would reduce the requirement for DSM. However, the high availability of energy storage and particularly domestic batteries would increase the profitability of decentralized renewable energy generation, thus having implications on the installation of fluctuating energy generation [26,27]. Therefore, even the potential underestimation of key factors 1 and 4 would not undermine the general statement of the elaborated scenario projections. 5. Conclusions The challenge of the electricity market transition is mainly given by the volatility of renewable energy generation. These fluctuations can be faced with flexible consumption through DSM, establishment of further centralized storage capacities and provisioning of dynamic back up generation capacities. The latter, however, leads to an inefficient market with great overcapacities, high electricity prices and imposed governmental policies to keep the power grid stable. Thus, it can be seen that an adequate availability of storage capacities and load management enables the transition from an electricity market withstanding the increasing renewable generation to a market efficiently incorporating renewable electricity generation. Among many other factors, the smart interconnection of electricity market with electricity driven supply systems and associated thermal storage capacities can provide some of the required demand flexibility. While it can be expected that a growing amount of electricity coupled supply systems will be available in the future, a strong dissemination effort for smart metering and control infrastructure is required. Thereby, besides of unresolved technical challenges, like the need for uniform communication interfaces between energy provider and supply systems, the motivation of all stakeholders to enable a dynamic market is crucial. First, it is important that energy providers perceive the opportunities for domestic demand management as facilitation for energy balancing and not as a further unwanted complication of the market. Second, end-consumers need to recognize possible benefits of dynamic electricity pricing. Thereby, automated processing of price signals as well as reliability of service and simple predictable monetary incentives are essential factors. Finally, governmental authorities could support residential load management by introducing dynamic taxation of electricity and further incentives for building refurbishment and smart meter installations. Therefore, further research on viable concepts for a dynamic electricity market, as well as the conception and development of communication and control approaches for smart appliances is necessary. While thermal demand side management is only one
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman
Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market q Henryk Wolisz ⇑, Carl Punkenburg, Rita Streblow, Dirk Müller RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, 52074 Aachen, Germany
a r t i c l e
i n f o
Article history: Available online xxxx Keywords: Demand side management (DSM) Energy policy Scenario analysis Smart grid Thermal storage
a b s t r a c t A transition in the electricity market is required to manage the volatility of increasing renewable energy generation. These fluctuations can be faced with flexible consumption through Demand Side Management (DSM), establishment of further centralized storage capacities and provisioning of dynamic back up generation capacities. At least the latter two options can impose large establishment and operation costs upon the electricity market. Therefore, the feasibility and the resulting potential of coupling the electricity grid with the thermal supply of residential buildings is analysed in this paper. Thereby, inexpensive and widespread thermal storage capacities could be used to improve the integration of dynamic renewable electricity generation. In this paper the technical and economical key impact factors for such thermal DSM approach are elaborated. Based on a literature review, the identified key factors are aggregated to form consistent scenarios of the German ‘‘Energiewende’’ (turnaround in energy policy). The practicability and possible magnitude of the intended DSM is then analysed based on the identified scenarios. All resulting scenarios highlight the growing demand for a flexible electricity market. Especially in scenarios with strong growth of renewable electricity generation, up to 45 GW of flexible electric capacities would be required in Germany by the year 2030. Furthermore, the analysis demonstrates that independently of the energy market development, it is very likely that electricity coupled supply systems will continuously have installed capacities of at least 14 GW. It is found that especially the strong dissemination of smart metering and smart control infrastructure is crucial to incorporate these capacities into DSM activities. Furthermore, the necessity and the resulting benefits of residential end-user participation in the electricity market need to be communicated to all stakeholders to ensure the availability and acceptance of dynamic pricing schemes in the future. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction The vastly growing renewable energy sector causes problems such as highly fluctuating and unpredictable renewable energy generation (i.e. photovoltaic (PV) and wind). Due to the inflexibility of electricity demand, it is not always possible to match the renewable energy generation with the demand. The rising share of wind and PV in the total energy portfolio will extend that challenge. Residential and commercial buildings, which employ up to 30% of Germany’s final energy consumption [1], could provide
q This paper is an edited and extended version of the original paper ‘‘Feasibility and potential of thermal demand side management in residential buildings considering different developments in the energy market’’ which was presented at the 27th ECOS Conference 2014 in Turku, Finland. ⇑ Corresponding author. Tel.: +49 241 80 49808. E-mail address: [email protected] (H. Wolisz).
flexibility to counter the imbalances between supply and demand in the electric grid. However, there are technical and energy political issues which strongly impact that potential. Within the Dual Demand Side Management (2DSM) project an approach is developed, which manages the total energy demand of buildings [4]. It links thermal and electrical demand on city district level and offers a communication platform for dynamic energy management. In addition to electrical supply systems (e.g. heat pumps), 2DSM analyses the potential for thermal energy storage in buildings, integrating conventional storage technologies like hot water tanks as well as the structural thermal storage capacity of a building itself. 2DSM control is based on a thermal building simulation in Dymola/Modelica combined with electric grid simulations in Neplan. The focus of this work is the analysis of future potential for residential thermal demand side management concepts like 2DSM. This research is performed based on the experience about key
http://dx.doi.org/10.1016/j.enconman.2015.06.059 0196-8904/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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Fig. 1. Five-phase scenario process (based on [2]).
factors for successful DSM gained within our project and the evaluation of existing studies on the development of energy policy and management. However, even when focusing just on Germany it is very hard to foresee how the technical and political framework for the energy sector will develop. In the existing studies, there are many different predictions and estimations about future electricity generation, the electricity market, and the residential electricity consumption. In the following, based on an extensive literature research of current laws, governmental support schemes, technological road maps and dissemination forecasts, reasonable and consistent developments of the German ‘‘Energiewende’’ (turnaround in energy policy) are modelled using the scenario technique. In the next chapters, first the chosen scenario technique is presented. Afterwards, the key impact factors for such dynamic energy management are elaborated and three consistent scenarios are developed. Thereby, a best-case scenario represents a smooth transition to a modern and flexible electricity market with high share of renewable energies and good development of all key factors. A Medium scenario presents a faltering transition in the energy market, leading to an unbalanced constellation of energy generation, storage, and consumption mechanisms. To complete the range of possible developments, a worst-case scenario describes a poor energy market development in a stagnating economical surrounding. For these scenarios, the possibilities for integration of thermal demand side management are then evaluated and discussed. Finally, it will be stated which conditions and developments seem to be most favourable for the intended DSM approach and analysed which drivers are most important to improve and support these conditions. 2. Scenario approach For the evaluation of the residential thermal DSM potential, scenarios of the future are created. As a futures technique, scenario analysis estimates values that cannot be precisely defined at the current point in time, therefore the results can potentially vary a lot from the predicted reality. However, scenario analysis is the most persuasive futures technique, when projections of very dynamic and not easily predictable developments are required, since it depicts several possible futures in contrast to most other methods (e.g. Delphi method) that try to decide on the one most probable future. Furthermore, scenario analysis also takes into account the significance of interaction between all identified factors, in contrast to e.g. sensitivity analysis, thus increasing the chances of generating consistent projection of the future. However, the width extent of the scenario field, which is evaluated and used to generate scenario projections, limits the level of detail that can be applied to each single impact factor. Moreover, in some cases the manifestation of the key factors even requires some generalization to create scenarios that are consistent across all factors. Thus, scenario analysis is typically not used to describe a given development as precisely as possible but, as in this analysis, to
evaluate a given question considering different developments of the surrounding conditions. In this work the scenario analysis is performed according to a five-phase scenario process proposed by the Institute for Future Studies and Technology Assessment (IZT) [2]. Fig. 1 illustrates the approach used for the scenario generation. The time horizon of the scenarios is chosen between 2020 and 2030 with a focus on 2030 in the evaluation and discussion. The first phase of the scenario process defines the scenario field. The scenario field describes the segment of the entire future which is of concern for this evaluation. The second phase covers the identification of key factors, also called scenario drivers, which have substantial impact upon the scenario field. Thereby, the objective is to consider the given question at its entirety by using as few descriptive factors as possible. The selection of scenario drivers is supported by the application of an impact analysis, which provides a ranking of possible key drivers. Therefore, all potentially relevant factors are gathered during an unbiased analysis process. Subsequently, the relation between these factors is investigated. Thereby, factors which critically influence others and have proved to be widely linked are likely to be key factors. In the end, the key factors are chosen with respect to their rated importance in the context of the scenario field as described in [2,3]. In the third phase of the scenario analysis, the previously described key factors are analysed. Possible developments of each factor are examined. As a basis, external studies and scenarios that discuss possible evolutions of the key factors were consulted. Hence, for each factor possible developments are conflated to an upper, a medium, and a lower trend. After the examination of each factor, global scenarios are created in the fourth phase. Based on consistent combinations of the developments of the different drivers, alternative images of the future are constructed. Finally, the fifth phase of the scenario process covers the scenario transfer, which emphasises the conclusions drawn from the scenario process.
3. Scenario analysis 3.1. Scenario field In the case at hand, all trends affecting the potential of combining the electricity grid with thermal DSM are within the scope of the scenario field. These are especially the developments on the electricity market and within the heat supply sector. The latter focuses particularly on private households. Furthermore, energy policy and the general development of the economy are also within scope. Altogether, 18 driving factors were identified and used to describe the scenario field. These factors were analysed using a cross impact matrix, which is a particular characteristic of a sensitivity analysis. Following, the identification of key factors and the analysis of potential key factor developments, is presented.
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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H. Wolisz et al. / Energy Conversion and Management xxx (2015) xxx–xxx Table 1 Cross impact matrix of the scenario field. Impact factor
Active sum (AS)
Passive sum (PS)
Fluctuations in electricity generation Wind and PV energy share in gross electricity generation Expansion of the German electricity transmission networks Trans border electricity exchange in Europe Dissemination of electric heat pumps Dissemination electrical driven supply systems Dissemination of small scale CHP Building refurbishment rate Availability of thermal storage Energy storage systems (electrical) Oil price Gas price Electricity price Electricity market design Dissemination of smart energy management Macroeconomic development Legislation Acceptance of ‘‘Energiewende’’
21 24 9 4 6 6 9 15 5 16 13 15 22 15 13 21 31 18
12 13 15 13 24 28 24 13 11 20 2 4 20 13 17 6 6 22
3.2. Identification of key factors Within a cross impact matrix the stability and the interdependencies of different factors within the scenario field are assessed [3]. As a result, the factors are classified as active or passive and dynamic or inert. Within the evaluation each factor’s effect upon on all the others is evaluated on a scale of 0–3, where 0 means no impact, 1 – a weak relationship, 2 – an average relationship, and 3 – a strong relationship. As a reference value for the overall effect of one factor on the others, we add up the corresponding impact factors into the so-called active sum (AS). To describe the effect of all other factors on the observed factor, the passive sum (PS) is calculated. The active factors having a high active sum and a small passive sum play a very important role and are often considered as ‘‘levers’’ or ‘‘switches’’ because they can change relatively independent from other factors but have themselves high influence on others [2]. Factors with both, high active and passive sum, represent dynamic factors. Since they greatly influence the scenario field while being strongly linked, they should be observed very carefully and are typically chosen as key impact factors. The evaluation of the cross impact matrix is presented in Table 1 and performed according to the following mathematical conditions for the determination of active, passive, dynamic, and inert factors [2]:
AS > PS ? active AS < PS ? passive AS PS > (n 1)2 ? dynamic AS PS < (n 1)2 ? inert
(n being the number of factors within the scenario field, in this case n = 18). As a result, the first key factor is the fluctuation and unpredictability in electricity generation. The development is highly important, because particularly fluctuating and unpredictable energy generation makes the wide use of thermal DSM interesting. Since the main focus of this analysis is the evaluation of domestic thermal DSM, it is necessary to observe the complete shiftable electrical energy potential of the household heating sector. Therefore, the electricity demand and the generation capacity of technologies linking heat supply and electric energy (e.g. electric heating, Heat Pumps (HP) and Combined Heat and Power (CHP)) is analysed, even though this factors are neither active nor
AS– PS 9 11 6 9 18 22 15 2 6 4 11 11 2 2 4 15 25 4
Active or passive
AS PS – (n
Active Active Passive Passive Passive Passive Passive Active Passive Passive Active Active Active Active Passive Active Active Passive
37 23 154 237 145 121 73 94 234 31 263 229 151 94 68 163 103 107
1)2
Dynamic or inert Inert Dynamic Inert Inert Inert Inert Inert Inert Inert Dynamic Inert Inert Dynamic Inert Inert Inert Inert Dynamic
dynamic. The distribution levels for these technologies are key factors two and three. Another important impact factor is the availability of electricity storage options. The capacity of electricity storages is an alternative to thermal storage solutions like hot water buffer storages or the buildings structural mass itself. Accordingly, the capacity of future electricity storage systems forms the fourth key factor. The electricity market, with special emphasis on the market structure, price level, pricing mechanisms, and consumer involvement, is the fifth key factor. It strongly influences the acceptance of alternative energy generation (e.g. wind and PV distribution) [49] as well as the use of electrical driven heating and CHP on a household level. In addition to these rather technical and measurable factors, a couple of surrounding factors have to be analysed. For example, changes in national and European energy policy, as well as the economic situation in the energy sector, can affect investments in power plants and storage solutions. Considering that, the last key factor contains all surrounding ‘‘soft’’ factors like policy, economic situation, business models, interfaces, and the link to the user. An overview of the key factors and the associated main sources is shown below in Table 2.
3.3. Key factor analysis In the following subchapters, the development of each key factor identified in Section 3.2 is analysed. If a given key factor is quantifiable and enough data sources were available, an upper, a medium, and a lower development scenario are presented for the years 2020 and 2030. While the upper development pictures a positive extreme scenario, the lower one represents a worst case scenario. The medium development describes an average
Table 2 Key factors and sources used for the analysis. Key factor
Sources
Fluctuation in electricity generation Electrical driven supply systems Small scale CHP Energy storage systems Electricity market and prices Surrounding conditions
[5–17,39,43] [18–21] [5,22–25] [5,22,26–32] [5,11,33–37,39–42] [5,11,38–40,49]
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Table 3 Wind and PV share in different scenarios.
Table 4 Scenarios for installed heat pump capacity.
Wind and PV share in gross electricity generation
Upper (‘target’) scenario [10] (%)
Medium (‘reference’) scenario [9] (%)
Lower scenario [9,47] (%)
2012 2020 2030
12.3 24.5 38.8
12.3 21.6 30.8
12.3 19 25
advancement of the trend. In this way, a scenario funnel encompassing the future extreme developments is created. 3.3.1. Fluctuation in electricity generation To simplify this factor, it is represented by the wind and PV energy share in gross electricity generation. That share, however, is influenced by several sub-factors. First of all, the political plans for the diffusion of these technologies are of great importance. So far the German Renewable Energy Act (EEG) [5], which came into effect in the year 2000, was the main driver for the diffusion of wind and PV installations. Besides the growth of the installed wind and PV capacity, the development of the total electricity demand in Germany directly influences the share renewable electricity sources have. Furthermore, numerous studies [6] show that the electrical network has to adapt and expand to enable the governmental extension plans for renewables. Also, the development of electricity generation costs for each competing technology is highly relevant. Thus, also the underlying fuel costs for fossil fuelled power plants are observed. Concerning the development of electricity generation costs, a study [7] predicts that PV will have competitive costs in relation to hard coal and gas fuelled power stations by 2020. In 2030, optimal onshore wind power installations will have even smaller generation costs than lignite power plants. The governmental plan [8] aims for a share of renewable in electricity generation between 40% and 45% by 2025. German governmental goals are described in ‘target-’ and ‘reference scenarios’ for a future energy concept [9,10], which were created on behalf of the German ministries BMWi and BMU.1 The corresponding developments for the wind and PV share in gross electricity production are depicted in Table 3, with an additional worst case scenario. While the governmental target scenario is a positive extreme scenario, the reference scenario represents the medium development. A possible negative extreme scenario is characterized by a smaller wind and PV share due to initial difficulties and delays in offshore wind installations as well as problems with the expansion of the electrical grid as forecasted by [47]. Additionally, the currently announced reform of the EEG [5,11] could result in major cuts of the wind and PV subsidies. 3.3.2. Electricity driven heat supply systems The second key factor focuses on the possible shiftable electricity demand of electric heating and heat pumps used in private households. Electric heating in the form of night storage heaters was broadly introduced in Germany during the late 1950s. The concept was based on the use of cheap electricity supplied by base load power stations during nights to equalize electricity demand across the whole day and avoid expensive shutting down or throttling of plants. In the past years, such electric heating was widely criticized for its low efficiency and low primary energy factor, considering that the storage heaters were charged mainly with electricity from
1 BMWi: Federal Ministry of Economics and Energy, BMU: Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety.
Electric capacity installed
Upper scenario [20] (GW)
Medium scenario [20] (GW)
Lower scenario [19] (GW)
2011 2020 2030
1.7 4.6 9.1
1.7 3.6 5.7
1.7 2.9 3.7
Table 5 Scenarios of installed small CHP capacity. Electric capacity of installed small scale CHP
Upper scenario [22] (GW)
Medium scenario [22] (GW)
Lower scenario [22] (GW)
2012 2020 2030
0.9 2.7 6.3
0.9 1.8 2.6
0.9 0.9 0.9
conventional thermal plants. Therefore, in Germany a legal prohibition for the use of electric night storage heating after 2019 was resolved. However, due to an upcoming awareness of load management requirements the prohibition was suspended in 2013. If strategically controlled and based on renewable generation, the currently installed electric storage heating capacities of approximately 19 GW would reveal a huge storage potential. Yet, new installations of electric heating are not expected to be made in a significant amount. It is rather likely that the installed capacity will decline. Beside political decisions, the development of the electricity price and the refurbishment rate will strongly influence the future of this technology. The share of heat pump supply systems in new build housing increased from 0.8% in 2000 to 24.5% in 2012 [18]. Still, the major part of the heat demand in existing buildings is covered by fossil-fuel-powered installations. Overall, natural gas-fuelled solutions, currently covering 50% of the new installations, are the main competitors for heat pumps and electric heating in general. The potential of heat pumps for load management was investigated by two different scenario based studies [19,20]. The aggregated results of these scenarios are displayed in Table 4. Even though the dissemination trend for heat pumps is positive, the lower scenario might occur due to climbing electricity prices in relation to natural gas prices, which would make heat pumps economically less attractive. Moreover, a decrease in the refurbishment rate could seriously impact the diffusion of heat pumps. Nevertheless, a total growth of heat pump installations is most likely. 3.3.3. Small scale CHP The analysis of the future dissemination of cogeneration in the household sector focuses on small-scale units with electric power rating of less than 50 kW. The governmental goal for all types and power ratings of cogeneration is set as 25% share in net electricity production by 2020 [8]. Several factors influence the future of CHP distribution. Mainly political decisions, structural and price developments on the electricity stock market, and fuel prices have a strong impact. Considering the current economic situation, even including governmental incentives, small-scale cogeneration units are struggling to be competitive [22]. Therefore, the further development will strongly depend on governmental reconsideration about promotions for CHPs. The technical potential of cogeneration in compliance with the future energy supply system of the ‘‘Energiewende’’ was investigated by a study [22]. According to three different scenarios for the development of Germany’s thermal demand, the technical electricity generation potential of CHP was projected. As Table 5
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shows, installed small scale CHP capacity evolves strongly in the medium and upper scenario, while it stagnates in the lower scenario. The high diffusion scenario implies tighter mandatory CO2 emissions for buildings which lead to wide use of CHPs, especially for buildings with historic monument protection or high costs for insulation refurbishments. The study further proposes the option to equip the already available heat storages associated with CHP systems with additional electrical heating units, used to absorb excessive renewable electricity generation [22]. Another advantage of decentralised CHP installations is a reduction of the necessary grid expansion [23]. 3.3.4. Energy storage systems Considering the steadily rising share of wind and PV generation, it is sure that the demand for electricity storage, especially with diurnal application, will increase. Currently, the most economic options for large scale stationary energy storage are pump storage power plants. In 2012 all German plants of this technology had a capacity of 7 GW and were able to store a maximum of 40 GW h [16]. Including international plants, which are connected to the German grid, the currently accessible electric storage capacity reaches approximately 9 GW [48]. Studies on behalf of the Federal Ministry of Economics and Energy show, that the technical potential for pump storage power plants in Germany is almost exhausted [26]. However, the currently installed storage capacities will not be sufficient to react to future fluctuations in the grid [27]. Possible alternative large scale storage technologies are dependent on fossil fuels (diabatic Compressed Air Energy Storage, CAES), lack efficiency (hydrogen storage/methanisation) or market maturity and immediate technical feasibility (adiabatic CAES). The major obstacles for all storage solutions are the current economic boundary conditions, which do not facilitate capacity extensions. All described plants have huge investment costs. This leads to long payback periods and thereby makes the big scale storage plants risky investments [28]. Especially low electricity prices on the stock market and the present uncertainties on the energy market deny such investments. Therefore, even implying expected price reductions for most energy storage technologies, it is likely that there will not be enough electricity storage capacities in 2030. Under good circumstances the currently installed electrical energy storage capacity could be doubled to 14 GW. The demand for flexible energy on the other hand will increase progressively together with the wind and PV share in gross electricity generation (considering a cost-effective integration of the renewables). This leads to an estimated demand for electricity storage between 22 GW and 45 GW in 2030 [26,29]. However, domestic heating systems like HPs or CHPs are usually equipped with water based thermal storage buffer tanks. If flexible control of these appliances was established, this existing storage potential could be accessed. A recent study [29] calculates the specific thermal storage costs to be approximately 30% of those for pump storage power plants. Additionally, active usage of the structural thermal mass in buildings could further extend the available storage capacities. First studies have shown very promising potential and achievable shiftable capacities in the GW range, when the thermal supply systems are used to dynamically pre-condition residential buildings [44,45]. Since these potentials cannot be precisely quantified yet, it will be assumed in this paper that in the best case scenario such additional domestic storage allows to exploit the total load shifting potential of electricity coupled heat generation. 3.3.5. Electricity market structure and resulting pricing The development of the electricity market is one of the most important key factors. As it can be seen today, the trend of the
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consumer electricity price can diverge from the evolution of the wholesale electricity price determined by the European Energy Exchange (EEX). However, these prices along with the associated fees and taxes will highly influence the electricity market. Further, factors like the dissemination of dynamic pricing and the possible implementation of a capacity mechanism are of great importance. Moreover, the development of the consumer´s electricity price in relation to the gas price is of special concern for the scenarios. Three developments are considered. Electricity price increases more, respectively less than the gas prices does, or both prices increase equally. The wholesale price of electricity is determined by the marginal costs of energy generation and evolves at the EEX. In general, the total electricity generation costs of renewable energy sources are still higher than these of fossil power plants. However, current subsidies and market regulations lead to overcapacities in the current generation market and a growing amount of renewable energy sources which can offer electricity at marginal costs of approximately zero. Because of the merit-order of power plants, this enforces lower prices on the electricity market. Consequently thermal power plants have declining operation hours and lower profits and therefore struggle to cover their total costs. This phenomenon is commonly referred to as the missing-money-problem, which often also leads to decommissioning of power plants and denies investments in peak load power generation. Nevertheless, flexible conventional power plants are needed to supply service security. Capacity mechanisms are supposed to counter the missing-money-problem by creating incentives to keep power plants, which are essential for the supply security, in operation or encouraging new installations of peak load power generation. There are different capacity mechanisms which are currently discussed. Each one has a different impact on the scenario field. A simple but temporary solution is the creation of a strategic reserve. A central coordinator manages old power plants, which do not participate on the market any more, and just uses these plants if the market cannot satisfy the demand for electricity. Another option, is the implementation of a market for secure supply capacities, called capacity market. A selective capacity market promotes new plants of desired technologies (e.g. gas turbines) and is managed by a central authority. A demand-induced capacity market, on the other hand, works as a liberal, market based solution and treats supply security as a private good, which all energy supplying companies have to buy from a capacity market. This market structure additionally incentivises the integration of a controllable demand side. While a strategic reserve is considered to be just a temporary but cheap solution, capacity markets are more expensive. Overall it is likely that if any type of capacity mechanism is introduced, the costs will be passed on to the end-consumer [34,35]. Currently approximately 50% of the consumer’s electricity price in Germany is caused by taxes and dues [33]. The other share is triggered by the generation and distribution of the energy. The development of the taxes’ and dues’ share in the electricity price is hard to forecast, because it is based on uncertain political decisions. One main governmental goal of the EEG reform is to stop the excessive rise of the EEG-levy and the electricity prices in general. Instead of such generalized charges, dynamic adaption of electricity fees and taxes could be used to promote consumption of renewable energy at times of its availability. However, this would require the introduction of dynamic pricing schemes. Theoretically, since 2010 energy supplying companies are obligated to offer at least one tariff with dynamic pricing.2 However, most companies just renamed the longstanding day and night rates with fixed time intervals to fulfil this expectation. Yet, this does neither help
2
EnWG §40 para. 3 [42].
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Table 6 Scenario descriptions. Factor
2012
2030 Scenario A (positive)
2030 Scenario B (medium)
2030 Scenario C (negative)
Macroeconomic development Consumer electricity price Capacity mechanism on electricity market Smart meter distribution (households with smart energy management) Electrical storage capacity Wind and PV share in gross electricity production Demand for flexible electric capacity Electric HP capacity Electric heating capacity Small CHP capacity Capacity of domestic electric appliances Controllable domestic supply systems
/ 0.26 €/kW h None <1%
Significant sustainable growth Rises less than gas prices do Demand induced capacity market 90%
Slow steady growth Rises more than gas prices do Selective capacity market 30%
Stagnation and crisis Rises equally to gas prices Strategic reserve 20%
9 GW 12% 8 GW 2 GW 19 GW 1 GW 22 GW 0 GW
17 GW 39% 45 GW 9 GW 15 GW 2.5 GW 26.5 GW 24 GW
14 GW 31% 30 GW 5.7 GW 5 GW 6.3 GW 17 GW 5 GW
11 GW 25% 22 GW 3.7 GW 10 GW 1 GW 14.7 GW 3 GW
managing fluctuating renewable energy generation, nor is it a motivations for end-customers to perform dynamic DSM. Besides that, a survey conducted by Fraunhofer ISI [40] shows that currently German electricity consumers prefer simple and static pricing to risky dynamic pricing models. The study concludes that dynamic pricing will only have a positive effect if an automatic control system is implemented at the same time. Also, active utilization of dynamic pricing would require a stronger dissemination of smart meters. Due to a new law,3 smart meters have to be installed in new and extensively refurbished buildings ever since 2010. Still, in addition to smart meters, DSM-concepts require a uniformed communication and interaction between supply systems from different manufacturers. Thus, smart control in the required extent is often not fulfilled by many of today’s smart meter implementations, based on digital counters with none or mono-directional communication. A smart energy market requires smart meters, which offer continuous bidirectional communication enabling real dynamic pricing as well as optimization and control of local supply systems. Since the impact of such metering and control components on the electricity market is huge, different scenarios for an extensive rollout of smart meters and the resulting macroeconomic value are currently under investigation [41]. Moreover, along with the smart metering concept comes the discussion about data privacy. This important aspect needs to be clarified. Secure und trustworthy options for data management have to be developed and implemented to improve the chances of public support for that technology.
3.3.6. Surrounding conditions It is evident that the German electricity market is currently influenced by a lot of uncertainties. The lack of reliable forecasts creates risks that deny long term investments. The CEO of RWE (one of the major German energy providers) calls the current situation the biggest structural crisis German and European energy providers have ever encountered [38]. According to Gabriel [39], Federal Minister of Economics and Energy, securing reliable market conditions, that enable risk minimized investment projections and the market integration of renewable energy are major goals of the EEG reform 2014. However, policy is always highly influenced by its impact and acceptance among the population and industry. At the present time, the sharp increase in consumer’s electricity price caused by the EEG subsidies for renewables could undermine the public support for further dissemination of renewable energies. Moreover, a possible decline in stability and availability of power supply and necessary structural
3
EnWG §21 [42].
reorganizations of the electricity market threaten the acceptance of the ‘‘Energiewende’’ by population and industry. 3.4. Scenario generation In the following section the presented key factors and surrounding market conditions are aggregated to form three consistent scenarios for the development of the energy market. First the underlying factors and the calculated outcomes for the scenarios are listed together in Table 6. Afterwards, the positive, medium, and negative developments of the ‘‘Energiewende’’ are presented in a descriptive way. 3.4.1. Scenario A – a well implemented transition In scenario A the surrounding factors evolve in a most positive manner. Significant economic growth supplies the monetary capability to invest in a large-scale turnaround of the energy sector. The governmental goals are reached for the ‘‘Energiewende’’ in general and especially for the desired penetrations of renewable energies. Major cost reductions in the wind (especially offshore) and PV sector make future promotion of renewables less expensive and renewable electricity generation competitive on a broad spectrum. Due to this, the rise of the EEG-levy is limited. Actually, consumer’s electricity price will rise slightly, but the increase in oil and gas price will be considerably higher. Combined with a high refurbishment rate [9], this leads to an additional growth of electric heat pump installations. Also, the capacity of electric heating only slightly declines, because of acceptable electricity prices and attractive rewards for load management. To enable widespread utilization of domestic load management, changes are made in the EEG boosting smart meter installations by following the suggestions made in [41]. By 2030 the smart meter rollout is completed and 90% of all households in Germany are equipped with intelligent electricity management. This reduces the demand for grid expansions and conventional electricity storage capacity. Accessible capacities of hydraulic pump storage plants have almost doubled, but the potential for Germany is exhausted. First adiabatic CAES and hydrogen storage solutions are built and tested [29]. The price of Lithium-ion batteries declines drastically and first vehicle-to-grid storage solutions are initiated. Still, thermal storage is the most cost efficient option to store energy for heating purposes. Due to the high refurbishment rate of households, smart control of domestic buffer tanks and structural thermal mass is widespread, supplying additional storage capacities at negligible costs. Taking into account the huge share of renewable energies, a demand-induced capacity market is implemented to ensure supply security. The market structure indirectly promotes the further integration of controllable demand. Despite the opportunity to participate in the capacity market, sinking heat demand and rising fuel
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prices lead to just a moderate increase in small scale CHP installations. 3.4.2. Scenario B – a struggling unbalanced market Due to moderate economic growth, further uncertainty in governmental promotions, and legal situation on the energy market cost intensive long term investments are partially evaded. Therefore, the energy market struggles to overcome the current crisis and the governmental goals of the ‘‘Energiewende’’ are only partially reached. Nevertheless, financed by continuous EEG subsidies a large amount of wind and PV generation capacities is connected to the power grid, driving the consumer’s electricity price very high. In total, the electricity price rises much more than the gas and oil prices do. This and just a small increase in the refurbishment rate lead to a slowdown in electric heat pump installations resulting in a moderate increase, as assumed in the conservative HP expansion scenarios. Distribution of direct electric heating suffers drastically from the high electricity prices. In 2030, only approximately 25% of the conventional electric heating capacity of 2012 is left. However, small CHP units profit from the high electricity prices, the still relatively high thermal energy demand of buildings, governmental promotions, and stricter obligations concerning building CO2 emissions. These widely spread CHP units are equipped with thermal storages which can be additionally supplied with electric heating. Thus, these systems could potentially offer required dynamic load management capacities. Yet, the rollout of smart meters proceeds only at the current rate, resulting in 30% of Germany´s households being equipped with smart meters by 2030 [41]. This drastically limits the actual load management potential of electricity coupled heating systems. Although, the exploration and development of new storage solutions is limited, the electricity storage capacity rises by approximately 50% due to the realization of almost all possible pumped water storages in Germany [22]. Since the market itself cannot fulfil the flexibility requirements evolving from the weighty renewable generation, a selective capacity market, compensating operators of new and secure supply capacities, is implemented. The resulting costs are further increasing the electricity price for the end-consumer. These high prices and the possibility to engage in the capacity market further support the utilization of cogeneration and lead to the development of local electricity and heating grids [22]. 3.4.3. Scenario C – stagnation in the energy market In scenario C negative macroeconomic developments limit investments for a long period. The high costs of the ‘‘Energiewende’’, especially during difficult economic times build up resistance in politics, population, and industry. Electricity and fuel prices both rise in a similar magnitude. Due to decreasing governmental promotions and delayed grid extensions, the share of wind and PV electricity generation reaches only 25% in 2030, thus lagging far behind governmental plans. Trying to avoid an expensive capacity market, a strategic reserve is used to ensure supply security. Because of the poor economic situation and uncertainties, long term investments are avoided by energy companies and house owners’ restraint lowers the refurbishment rate. The annual sales of electric heat pumps remain on the current annual level. Even though electric heating is economically not attractive, the installed capacity just diminishes by approximately 50% due to the slow exchange of existing systems. The capacity of installed small scale CHP units also stays on the level of 2012. Similarly to the dissemination of electrically coupled heating technologies, also the rollout of smart meters suffers from the low refurbishment rate. Considering this as well as the current regulations, about 20% of the German households will be equipped with smart energy management in 2030. Therefore, the potential for decentralised load management is very small. Still, whenever possible, cheap thermal
7
storages are used to absorb some of the renewable energy. Urgently needed storage capacities are barely available. Caused by residential and political resistance, just a small share of the currently planned expansions of pump storage plants is realized. New storage technologies with high investments are not brought forward at all. 4. Discussion 4.1. Evaluation of the current market situation The electricity market is currently in a critical stage. Changes made in the next few years will seriously impact the whole energy sector and determine the development until 2030. Phenomena like the missing-money-problem, call into question the long term viability of the energy-only-market. Warranted revenues for fluctuating and not controllable renewable energy sources (wind and PV) shift the market risks to conventional power plants. As a result, a possible shortage of 5 GW to 15 GW of secure supply capacity is projected for 2022 [36]. This leads to discussions about the general market structure and the implementation of capacity mechanisms in particular. Thereby, one major threat to supply security and an effective market is the strong lack of flexibility in electricity demand. Transition to a more flexible demand of electricity can be provided by large-scale electrical storages or by decentralized DSM activities. Though, it is unlikely that DSM can completely solve the problem, it definitely can complement the limited potential of pump water storage and the expensive efforts to develop new storage technologies. Furthermore, load management can reduce the need for, or even completely substitute the expensive capacity mechanisms, allowing electricity consumers to actively participate in the energy market instead of just imposing the costs of the market transition upon them. Since decreases in governmental promotion of the ‘‘Energiewende’’ are likely at the present time, it is crucial to focus on the acceptance of market transition by all relevant stakeholders. Consumers are mainly interested in affordable and secure energy supply which does not reduce their comfort. Especially German consumers are extremely risk-averse [46], therefore reliability of service and simple predictable monetary incentives are crucial elements of future concepts that include private end-consumers. Furthermore, the industry needs to be closely integrated into the market transition. 4.2. Scenario transfer Scenario A presents a balanced electricity market, where extensive renewable energy generation is faced by a flexible energy market and distinct diversified energy storage potentials. The ‘‘Energiewende’’ is very successful and conditions for further transition towards a renewable electricity market are good. Associated technologies become an important factor of Germany’s industry and economic growth. Scenario B depicts an unbalanced and inefficient electricity market, where still quite marked renewable generation is not accompanied by the required flexibility and storage capacities in the market. High electricity costs induce local efforts towards grid autonomy, leading to a wide reaching CHP dissemination. While CHP growth is generally desirable, the development of parallel supply networks is further undermining the profitability of renewable electricity generation. Still, Germany profits from some associated technological developments and pushes forward to improve the situation on the energy market. Finally, scenario C illustrates the results of a stagnating market and resulting poor development of the key factors as well as the
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surrounding factors. This scenario describes a future where all stakeholders avoid further actions and risks, which leads to few alterations to the current situation. Only highly subsidised extensions of renewable electricity generation are implemented, however, the market is not prepared to manage fluctuating energies. Germany is not a leading driver of the energy transition anymore and struggles to keep pace with energy market advances in other countries. In spite of the vast differences between the presented scenarios, all of them show the growing demand for a flexible electricity market coming along with the increasing renewable generation. In Table 6, the assumed demand for flexible electric capacity is faced with the assumed centralised and decentralised electric management capacities. Thus, it becomes visible that available centralised storage alone cannot offer enough flexibility to effectively incorporate renewable electricity generation. Therefore, either a large share of renewable generation can only be used when availability and demand are matching or the renewables keep generation priority and large dynamic conventional back up capacities have to be established. Both options dramatically undermine the profitability of renewable electricity generation. However, coupling the electricity market with the demand for thermal energy through smart integration of electricity driven supply systems, smart meters and DSM programs would improve the situation [4]. The possibility to utilize widely available and cost efficient thermal storages would allow incorporating distinctly larger amounts of renewable energy, without strong subsidies and political prioritisation of technologies [44,45]. The scenario analysis demonstrates that, independent of the energy market development it is very likely that electricity coupled supply systems will continuously have installation capacities of 14–27 GW. This corresponds to approximately 40–75% of the maximum renewable electricity generation occurring on a sunny and windy day in 2013 [43]. However, the potential to include these supply systems into energy management is only given if a communication and control infrastructure is supplied. Therefore, the rollout of smart control and metering appliances as well as the dissemination of dynamic pricing schemes will be crucial. Yet, this requires a well elaborated concept for the involvement of end-consumers into the market. Today, customers and energy providers are still very skeptical and hesitant about dynamic electricity rates, being afraid of the large-scale implementation complexities. Furthermore, manufacturers of supply systems are still far away from agreeing upon uniform communication interfaces for the HVAC equipment. 4.3. Scenario credibility In this section we seek to estimate and discuss the consequences of forecast inaccuracies upon the generated scenarios. While a typical error propagation calculation is not applicable for an analysis which strives to project possibly most diverging manifestations of input values, the risk and the result of inaccurate key factor forecasts is discussed. Our analysis shows, that there is an existing large stock of electrical driven and potentially controllable supply systems (key factor 2) in Germany as of today (i.e. night storage heaters). Since there is no law or other incentive to exchange these systems and the German refurbishment rate is very low, the assumption that these systems will persist for many years is considered reliable. However, it cannot be excluded that disruptive market conditions could potentially change the framework of these systems. For example, due to very high electricity prices (key factor 5) or the introduction of strong supply system refurbishment subsidies (key factor 6). Based on the evaluation of current supply system installations and the foreseeable price development of supply
system technologies, there is a strong indication that these systems will be exchanged by combined heat and power systems (key factor 3) or heat pumps (key factor 2). Both cases, and thus most possible manifestations of key factors 2, 3, 5 & 6, are considered and evaluated in the presented scenarios. The extent of fluctuating electricity generation (key factor 1) and the availability of energy storage systems, particularly domestic battery systems (key factor 4), are correlated to still evolving and very dynamic technologies. In the past the dissemination rate and the resulting capacities of renewable energies were underestimated by almost all forecast studies, as shown by a report that evaluates 50 studies published between 1980 and 2007 predicting the development of renewable energies [50]. Therefore, it can be expected that these factors are still underestimated by current studies. If this should hold for the share of renewable generation and thus also for the amount of fluctuating electricity generation, the general findings of this paper are strongly reinforced, underlining the importance of introducing thermal demand side management. In turn, the underestimation of the availability of energy storage systems would reduce the requirement for DSM. However, the high availability of energy storage and particularly domestic batteries would increase the profitability of decentralized renewable energy generation, thus having implications on the installation of fluctuating energy generation [26,27]. Therefore, even the potential underestimation of key factors 1 and 4 would not undermine the general statement of the elaborated scenario projections. 5. Conclusions The challenge of the electricity market transition is mainly given by the volatility of renewable energy generation. These fluctuations can be faced with flexible consumption through DSM, establishment of further centralized storage capacities and provisioning of dynamic back up generation capacities. The latter, however, leads to an inefficient market with great overcapacities, high electricity prices and imposed governmental policies to keep the power grid stable. Thus, it can be seen that an adequate availability of storage capacities and load management enables the transition from an electricity market withstanding the increasing renewable generation to a market efficiently incorporating renewable electricity generation. Among many other factors, the smart interconnection of electricity market with electricity driven supply systems and associated thermal storage capacities can provide some of the required demand flexibility. While it can be expected that a growing amount of electricity coupled supply systems will be available in the future, a strong dissemination effort for smart metering and control infrastructure is required. Thereby, besides of unresolved technical challenges, like the need for uniform communication interfaces between energy provider and supply systems, the motivation of all stakeholders to enable a dynamic market is crucial. First, it is important that energy providers perceive the opportunities for domestic demand management as facilitation for energy balancing and not as a further unwanted complication of the market. Second, end-consumers need to recognize possible benefits of dynamic electricity pricing. Thereby, automated processing of price signals as well as reliability of service and simple predictable monetary incentives are essential factors. Finally, governmental authorities could support residential load management by introducing dynamic taxation of electricity and further incentives for building refurbishment and smart meter installations. Therefore, further research on viable concepts for a dynamic electricity market, as well as the conception and development of communication and control approaches for smart appliances is necessary. While thermal demand side management is only one
Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059
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Please cite this article in press as: Wolisz H et al. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manage (2015), http://dx.doi.org/10.1016/j.enconman.2015.06.059