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Solar power in district heating. P2H flexibility concept
Energy 181 (2019) 1023e1035
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Solar power in district heating. P2H flexibility concept Armands Gravelsins, Ieva Pakere*, Anrijs Tukulis, Dagnija Blumberga Riga Technical University, Institute of Energy Systems and Environment, Azenes street 12/1, Riga, Latvia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 January 2019 Received in revised form 22 May 2019 Accepted 30 May 2019 Available online 3 June 2019
District heating (DH) systems present a great opportunity to increase the proportion of renewable energy used in both heating and cooling. Renewable energy can be integrated into DH to account for heat load and also for power production, which in cogeneration systems is used for both heat generation and transmission. The main aim of this research is to determine whether (and also how) to integrate solar PV panels into DH systems to achieve an economically feasible, flexible energy production solution by using a power-toheat concept. To reach greater depth in this research, the system dynamics (SD) approach was used. The results show that it is not profitable to install larger PV panels than are needed to provide for summer electricity use. However, use of the power-to-heat concept can expand the flexibility of a solar power system and increase the overall efficiency or economic feasibility when the power generated exceeds consumption. When integrating a larger PV area, the surplus power can either be transferred back to the grid or used for heat production via a heat pump. On average, about 47% of excess power is converted into heat and 53% is sent to the grid. Results show that policies both in the form of subsidies for PV panels and increases in the price of heat can significantly influence the results. © 2019 Elsevier Ltd. All rights reserved.
Keywords: District heating system Power-to-heat Solar power System dynamics modelling
1. Introduction Globally, nearly 80% of produced energy comes from fossil fuels, which promotes climate change, water and air pollution, and natural resource depletion. Renewable energy sources (RES) play an increasingly important role in the energy sectors in most European countries, particularly in those wishing both to improve the overall quality of the environment and to decrease their dependency on imported fossil fuels. Due to the rapid development of technologies, solar energy is becoming increasingly widespread in various countries, but particularly in Europe. The price of solar power panels has decreased by around 85% between 2009 and 2018, which has resulted in a sharp increase of installed solar power capacity. Around 140 million Euros have been invested in PV technologies, which is the largest share from all investments in RES [1]. Solar power is an important element in 100% cost effective renewable power systems. However, generated solar power is dependent on the available solar radiation and not on actual power consumption, which indicates the necessity redesign the operation of energy
* Corresponding author. E-mail address: [email protected] (I. Pakere). https://doi.org/10.1016/j.energy.2019.05.224 0360-5442/© 2019 Elsevier Ltd. All rights reserved.
systems. The flexibility of energy systems that use integrated intermittent energy sources is crucial [2]. As the installed capacity of solar energy production technologies has grown, the alignment of energy production and consumption has lately become a muchdiscussed topic [3]. There are several ways of increasing the capability of systems to adjust production to consumption: accumulate the power for periods when the demand increases, apply demand side management to align power consumption with power production and/or convert the power to different types of energy [3]. The main technology for direct power accumulation is the battery. Power can be stored in a variety of ways such as compressed air energy storage, flywheels, superconducting magnetic energy storage, etc. [4]. However, most of these technologies are not yet fully adapted for commercial use and are often not cost effective. 1.1. Development of power-to-heat concept There is another solution for utilizing the power from RES when the power consumption is lower than the production rate. This amount of generated solar power can be defined as surplus power, which due to legislative or economic reasons cannot be fed into the grid. When this occurs, one possibility is to convert power to heat (power-to-heat (P2H) concept) via electric boilers or heat pumps
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Nomenclature B1eB3 CA CAPP2H CCDR CCIR CCPV CHP COP CRHP CS DH DR EBTG ED EG EHD EHP EMAX EP2H EPV ES ESC FLHPV FOCPV HP HTC HTH HTP2H
balancing loops avoided costs for grid power, EUR/hr power-to-heat capacity, kW capital cost discard rate, EUR/yr/yr capital cost increment rate, EUR/yr/yr PV capital costs, EUR/yr combined heat and power heat pumps coefficient of performance heat pump capacity requirement, kW/m2 system costs, EUR/hr district heating discount rate, % surplus power back to the grid, kWh/hr power demand for self-consumption, kWh/hr power from grid, kWh/hr heat demand, kWh/hr power-to-heat production, kWh/hr maximum power demand possible for P2H, kWh/hr surplus power for P2H, kWh/hr PV power production, kWh/hr PV power surplus, kWh/hr PV power for self-consumption, kWh/hr PV full load hours, hr/yr PV fixed operating costs, EUR/MW/yr heat pump comparable heat tariff, EUR/MWhe company's heat tariff, EUR/MWhth indicated P2H tariff, EUR/MWhth
Heat production costs
(HP) [5] and use it in either the local [6] or the large-scale heating systems [7]. Fig. 1 shows the interaction between heat production costs and electricity prices for different heat generation technologies. This demonstrates that P2H is feasible when the electricity price is lower than the heat production costs incurred when using other heat generation technologies [8]. In the past few years, a number of authors have developed the smart grid concept [9] where power, heating and cooling networks are combined and analysed as an integrated system. Consumption
Heat only boilers
P2H potential
i IR LHP OCPV PPV PRA P2H PUC R1-R3 RBTG RES RP2H RS SB SCOM SD SDE SHPR SI SO SOPV SPV TGP TMC TME TMPV TSP VOCPV
hPV
order of delay function investment rate, EUR/yr heat pump load, % PV operating costs, EUR/hr PV price, EUR/m2 accumulated profit, EUR power-to-heat public utilities commission reinforcing loops revenue from selling power back-to-grid, EUR/hr renewable energy sources revenue from selling P2H heat, EUR/hr system revenue, EUR/hr Subsidies in PV technologies, % PV commissioning, m2/yr system dynamics PV decommissioning, m2/yr profit share in new PV, % solar irradiation, kWh/m2/hr ordering of PV area, m2/yr ordered PV area, m2 PV area, m2 grid power price, EUR/MWh commissioning time, yr economic lifetime of investment, yr PV service life, yr power sales price, EUR/MWhe variable operating costs of PV, EUR/MWh PV efficiency, %
load management and grid-interconnectivity are the key factors in developing more flexible renewable energy system solutions [10]. The potential of P2H systems in different European countries have been analysed by Yilmaz et al. [11]. Authors have calculated the P2H potential for Germany, Denmark, France, Austria, the Netherlands and Italy by analysing changes in hourly power and heat load. Other authors have applied a similar methodology for estimating the potential of P2H technology for Sweden [12] and other Nordic countries [13]. Sandberg et al. [14] have lately discussed P2H as a solution using different power tariff structures and conclude that, while this could increase the share of wind power in DH, it would also decrease the share of energy through cogeneration. The P2H concept has been analysed in a microeconomic perspective by Ehrlich et al. [15]. The authors conclude that, without additional government support, the financial benefits of P2H systems at the household level are negligible. Therefore, it is reasonable to analyse such systems at the district or regional level, but until now, there has been little research on large-scale P2H integration in district heating systems [8]. Hypothetically, DH would be a suitable application for P2H because electricity can be directly used for heat generation and transmission in a boiler house, while the surplus power can be converted to heat and fed into the heat supply system. 1.2. Solar power and P2H concept in baltic countries
Electricity price Fig. 1. Heat production cost and electricity price interact for different heat generation technologies (CHP-combined heat and power) [8].
The use of solar and wind energy in Baltic countries is not yet widespread for energy production [16,17]. Solar panels are mainly used in household applications and there have been several research papers analysing the operation of solar power systems in
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Latvia [18,19]. The authors of these papers identify the main parameters affecting solar system feasibility including the efficiency rate and price of solar panels, the level of government support and the structure of electricity tariffs. In order to initiate the installation of RES, there should be wellplanned regulatory framework and national support for carbonfree technologies [20]. Best & Burk [21] present the roles of policies and preferences in the national adoption of solar and wind energy technologies. These authors have found that carbon pricing is an important factor in the early adoption of solar energy. Similar support schemes should be analysed for Baltic countries in order to achieve a higher share for solar and wind energy in the energy market. Moller et al. [8] analysed the possible increase in system flexibility by implementing policy incentives for DH in Baltic countries. The authors compare the levelized costs of heat for several scenarios combining biomass CHP, electric boilers, heat storage and oil boilers for covering the peak load. The results showed that integration of different energy storage systems results in the lower levelized costs of heat. However, the current policies in the Baltic countries do not support increased system flexibility from coupling DH and power generation. Taking into account the significant decrease of PV prices, the development of the smart grid and P2H, the goal of this research is to determine whether the integration of solar PV panels into DH systems is economically feasible. The authors evaluate if it is reasonable to integrate the PV system for on-site power consumption (that is, the energy used to produce power) and convert the surplus solar power to heat. The P2H concept is a possible solution to increase the flexibility of solar power systems. 2. Methodology For more in-depth research of the PV technology integration into DH, the authors have used the system dynamics (SD) approach. SD solves complex problems such as the development of renewable energy systems that use several dynamic variables [22]. SD modelling is useful for evaluating the relationship between dynamic factors (dependency, energy demand, cost changes, etc. [23]) and the effect of different policies on RES [24]. SD theory is based on the study of the relationship between system behaviour and the underlying structure of the system. This means that an analysis of the system's structure may lead to a deeper understanding of the causes which lead to certain behaviour which, in turn, allows any problematic behaviour existing in the system [25] to be further addressed. For the purposes of this research, the SD model was developed to analyse the complexity of the DH company's energy balance and tariffs for different energy types. It also include forecasts for energy and changes in the price of PV technology. This model is based on solar power integration, which is used to cover power used to produce heat for a DH company. 2.1. System dynamic behaviour The aim of this research is to evaluate whether it is reasonable to integrate a PV system into a DH company's system in order to cover part of the company's power self-consumption and convert the surplus solar energy into heat. In addition, the model makes it possible to compare different technologies that can be used to produce heat energy from surplus energy. To identify the main driving forces of the system, the causal loop diagram was constructed. The causal loop diagram describes mutual connections among the elements of the system. The system (see Fig. 2.) consists of three reinforcing loops (R1 e
1025
R3), which promote the development of the system. However, two balancing loops (B1 and B2) hinder the development of the system. The main measurement of the system is profitability. A higher profitability level leads to a higher interest on the part of the Company to invest in additional PV capacity. This, in turn, leads to more solar energy being produced, which further leads to more surplus power when the PV is producing close to maximum capacity. If there is a significant amount of surplus energy, there is the option to use it for heat generation. Thus, with more surplus energy, more heat can be generated. This results in higher income levels, which make the system more profitable. This is the first reinforcing loop (R1). The 2nd reinforcing loop (R2) is similar. If there is more surplus energy, more power can be sold back to the grid (BTG). This increases the income from the PV system making it more profitable. The 3rd reinforcing loop (R3) shows that by increasing the PV energy production rate, the company is able to cover a greater share of power self-consumption. This allows it to avoid the costs that would result from purchasing power from the grid. This also helps to make the system more profitable. Although the reinforcing loops promote the development of the system, there are also two balancing loops trying to hinder the development of the system. Balancing Loop 1 (B1) is related to the PV costs. The installation of more PV panels results in higher capital and operating costs, making the system less profitable. Balancing Loop 2 (B2) works by the same principle, but it takes into account the P2H technology costs and their impact on the system. The system is profitable when the reinforcing loops are stronger than the balancing loops, meaning that revenues need to be higher than costs. Therefore, it is necessary to determine the capacity at which maximum revenue is generated at a minimal cost. Based on literature, it is assumed that technology costs for PV and P2H technologies will decrease [1]. The hourly data of a DH company's heat demand, power consumption and solar irradiation is used in the model. The dynamic hypothesis is that, by integrating solar energy and P2H technologies, it is possible to improve the economic indicators of the DH company. 2.2. Model structure The SD model consists of five connected sub-models. Stocks and flows are the main elements of every sub-model. The stocks accumulate the particular value over time, while flows increase or decrease this accumulated value. 2.2.1. Installed PV area sub-model The installed PV area sub-model describes the total installed PV area. Fig. 3 shows the model structure. In cases where the PV system operation is profitable, more PV is installed to cover a larger area. If not, then the PV area slowly decreases due to decommissioning of the existing PV panels. Two flows regulate the PV area stocke PV commissioning and decommissioning. This structure shows the extent of PV area at any given moment. Stock value is calculated according to (1) below:
ðt SPV ðtÞ ¼
½SCOM SDE dt þ SPV ðt0 Þ
(1)
t0
where SPV e PV area, m2; SCOM e PV commissioning, m2/yr; SDE e PV decommissioning, m2/yr. Other stocks of the system are calculated using the same principle.
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Income from BTG + Income from P2H
+ System income +
+
+
+
+ + P2H costs
+ System costs +
+ System Profitability
P2H
Power sales price
B2 PV energy surplus
+
Heat tariff
+ PV CAPEX
+
+
+ +
+ PV costs
B1
+
PV efficiency + PV energy
+ Avoided costs from + + grid power
+
Other PV costs
PV capacity
Grid power purchase price
BTG
P2H CAPEX
R1
Other P2H costs
R2
R3
+
PV energy share in self consumption
+ Solar irradiation
-
Power demand for self consumption
Fig. 2. Causal loop diagram for PV panel integration in a DH company.
Profit share in new PV
Ordering of PV area Accumulated profit
Commissioning time
Ordered PV area
PV commissioning
PV area
PV service life
PV decommissioning
PV price PV price reduction rate
Reduction rate % per year
Fig. 3. Installed PV area sub-model.
The decision about additional PV panel area installation is based on economic considerations such as total revenue and costs of installed PV system. If the operation is profitable, it is assumed that part of the profit will be redirected towards installation of new PV panels. The amount of installed area is calculated by dividing the available funds with the PV price. The ordering of new PV panels is determined by the following “if” function:
SO ¼ if ðPRA > 0; PRA , SHPR =PPV ; 0Þ
(2) 2
where SO e ordering of PV area, m /yr; PRA e accumulated profit, EUR; SHPR e profit share in new PV, %; PPV e PV price, EUR/m2. There is a time delay before PV panels are delivered, installed and become fully functional. Therefore, there are two stocks e ordered PV area and PV area. These stocks are regulated by the flow e PV commissioning. After the commissioning time (a time necessary to deliver and install the equipment) passes, the ordered PV panels move to the PV area stock:
SCOM ¼
SOPV TMC
(3)
where SOPV e ordered PV area, m2; TMC e commissioning time, yr. PV decommissioning describes the amount of PV panels decommissioned after the end of their service life:
SDE ¼
SPV TMPV
(4)
where TMPV e PV service life, yr. The PV panel price is assumed to decrease in the future [1]. The decrease is modelled by building one stock with one outflow. Fig. 4 shows the modelled PV panel price decrease during a 10 year period (see Fig. 5). The main input parameters (profit share in new PV, commissioning time, PV service life, PV price, etc.) are shown in Table 1.
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PV price, EUR/m2
200 150 100 50 0 2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
Fig. 4. Simulated PV price decrease.
PV efficiency
PV area
Solar irradiation
PV power production
PV power for self consumption Surplus power BTG Power from grid
P2H capacity
Power demand for self consumption
Indicated P2H tariff
Heat demand
Max power demand for P2H
Power sales price
COP
Comparable heat tariff
Surplus power for P2H
PV power surplus
Companie s heat tariff
Heat tariff increase
Heat tariff increase rate
Fig. 5. PV power production and consumption sub-model.
2.2.2. Power production and consumption sub-model The power production and consumption sub-model reveals the following flows: how much power is produced with the installed PV area; how much of the produced power is used for self-consumption; how much of the produced power remains as surplus power, which can be either BTG, or used in heat generation via HP.
The generated PV power is calculated as:
EPV ¼ SPV ,hPV ,SI
(5)
where EPV e PV power production, kWh/hr; hPV e PV efficiency, %; SI e solar irradiation. The PV panel efficiency is based on previous research [26,27], but the hourly solar irradiation data are obtained from the
Table 1 Input data for baseline scenario. Input parameter Starting price for PV panels PV panel price reduction PV commissioning time Service life of PV PV panel efficiency Profit share in new PV Solar irradiation Electricity consumption Electricity needed for heat demand Heat tariff increase Heat tariff for end users HP COP Electricity price HP price Service life of HP equipment HP capacity requirement Full electricity price
Unit 2
EUR/m %/year years years % % kWh/m2/hr kWh kWh % EUR/MWh e EUR/MWh EUR/kW years kWh/m2 EUR/MWh
Value
Source
180 5 1 25 16 25 Hourly Hourly Hourly 2 50 3 Hourly 800 20 0.2 Hourly
[27] [27,28] [29] [29] [30] [28] Meteorological database [31] Data from DH company Data from DH company Data from DH company Data from DH company [32,33] Data from electricity market [32,33] [32] [32] Average mark-up
data data data
data
data
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meteorological database [31]. To calculate how much of a DH company's power demand can be covered by generated PV energy, it is necessary to take into account how much power is needed from the grid, as well as how much surplus solar power is still left after self-consumption is covered (6e8).
ESC ¼ if ðEPV ED ; ED ; EPV Þ
(6)
EG ¼ if ðED > EPV ; ED EPV ; 0Þ
(7)
ES ¼ if ðEPV > ED ; EPV ED ; 0Þ
(8)
where ESC e PV power for self-consumption, kWh/hr; EG e power from grid, kWh/hr; ES e PV. Surplus power, kWh/hr; ED e power demand for selfconsumption, kWh/hr. The hourly power consumption data of the particular DH company studied is used in the modelling process. The surplus energy (PV power production minus the DH company's self-consumption) is transformed into heat or fed into the grid. The decision on whether to feed surplus power BTG or to use it in heat generation is based on economic considerations. The potential income from both options is compared, and the more profitable option is selected. If the heat tariff is higher, the surplus PV power is converted to heat taking into account the restrictions of heat demand and installed P2H technological capacity. The decision uses the following functions:
HTC ¼ if ðHTH HTPtH ; HTH , COP; 0Þ
(11)
where HTH e company's heat tariff, EUR/MWhth; HTP2H e indicated P2H tariff, EUR/MWhth; COP e HP coefficient of performance. The variable, called maximum power demand possible for P2H, was introduced into the model in order to prevent a situation whereby more surplus energy would be transformed into heat than required by the heat demand.
EMAX ¼
EHD COP
(12)
where EHD eheat demand, kWh/hr. It is assumed that the DH Company's regular tariff will increase over time. The tariff increase is modelled the same way as the PV price decrease in section 2.2.1. 2.2.3. P2H capacity sub-model The HP is used as P2H technology to convert PV surplus energy into heat. Fig. 6 shows the end-to-end relationships in the P2H capacity sub-model. To understand how much capacity is necessary for P2H technologies, a HP capacity factor is introduced. This variable describes the HP capacity requirement per one square meter of installed PV area that is necessary to cover the full load when PV is working at peak capacity. As PV panels are working at full capacity only a few hours per year, it is impractical and expensive to install HP capacity to cover the full power load. Thus, the HP capacity factor describes how much of the full HP capacity requirement is installed (e.g. if HP capacity requirement is 0.2 kW/m2 and HP load
EBTG ¼ if ðTSP > HTC ; ES ; if ðES > EMAX ; if ðEMAX > CAPPtH ; ES CAPPtH ; ES EMAX Þ; if ðES > CAPPtH ; ES CAPPtH ; 0ÞÞÞ
EPtH ¼ if ðTSP >HTC ;0;if ðES >EMAX ;if ðEMAX >CAPPtH ;CAPPtH ;EMAX Þ; if ðES >CAPPtH ;CAPPtH ;ES ÞÞÞ (10) where EBTG e surplus power B, kWh/hr; EP2H e surplus power for P2H, kWh/hr; TSP e price of power for sale, EUR/MWhe; HTC e comparable heat tariff, EUR/MWhe; EMAX e maximum power demand possible for P2H, kWh/hr; CAPP2H e P2H capacity, kW. The hourly electricity price is determined using historical data from the power market with future price forecasts. To compare which of the options are more profitable, it was necessary to compare the power tariff with the heat tariff. Since the heat tariff is expressed per heat energy produced, in order to compare it with the power tariff, the coefficient of performance (COP) for HP, which would be used to transform surplus power into heat, was introduced. In addition, two different heat tariffs were compared e the DH company's regular tariff (approved by the public utilities commission (PUC)) and the calculated P2H tariff. In cases when the P2H tariff is higher than the one approved by the PUC, it is disadvantageous to transform surplus PV power to heat. However, if the P2H tariff is lower than the tariff approved by PUC and when it is more profitable than selling power BTG, surplus PV energy is transformed into heat by using P2H technologies. The heat produced by the P2H technology is sold at the PUC approved tariff.
(9)
is 10%, only 0.02 kW/m2 will be installed). The HP load is one of the variables the impact of which is analysed in different scenarios. The initial P2H capacity is calculated as follows:
CAPPtH ¼ CRHP ,SPV ,LHP
(13)
where CRHP eHP capacity requirement, kW/m2; LHP e HP load, %. The P2H technology capacity is modelled as a stock with one inflow and one outflow. The inflow P2H commissioning is calculated the same as initial capacity (13) except that the additional P2H
PV area
Heat pump capacity requirement
P2H
P2H service life
capacity P2H P2H commissioning decommissioning Ordering of PV area Heat pump load Fig. 6. P2H capacity sub-model.
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Economic lifetime Discount rate
Capital cost increment rate Ordering of PV area
the different scenarios when PV panels are subsidized (same is done in the P2H technology capital cost sub-model). The capital cost increment rate and discard rate is calculated in the following manner: PV area
PV capital costs
CCIR ¼
Capital cost discard rate
Cummulative investment
SPV ,PPV ,ð100 SBÞ,DR 1 1=ðð1 þ DRÞTME
OCPV ¼ EPV ,ðVOCPV þ FOCPV =FLHPV Þ
where CCPV e PV capital costs, EUR/yr; SB e subsidies in PV technologies, %; DR e discount rate, %; TME e economic lifetime of investment, yr. The subsidies are implemented in the model to later compare
P2H full load hours
PV capital costs
P2H heat production
Fixed operating costs P2H
RBTG ¼ EBTG ,TSP
Variable operating costs PV
Surplus power BTG
(19)
Companies heat tariff
Power sales price Revenue from selling power BTG
System costs
P2H heat production
Revenue from selling P2H heat
PV costs
P2H capital costs
(18)
where OCPV e PV operating costs, EUR/hr; VOCPV e variable operating costs PV, EUR/MWh; FOCPV e fixed operating costs PV, EUR/ MW/yr; FLHPV e PV full load hours, hr/yr. The same approach is used when calculating HP operating costs. Total revenue consist of two elements: revenue from electricity sold BTG and revenue from HP heat energy sold to consumers.
(14)
PV power production
(17)
2.2.5. Cost, revenue and profit sub-model To evaluate the economic benefits, it is important to calculate the costs and revenue of PV system integration in the DH system. The costs of the system include PV costs and HP costs. Costs consists of capital costs (section 2.2.4.) and operating costs e both fixed and variable. The operating costs for the PV panels are calculated according to (18).
2.2.4. Capital cost sub-model For PV and P2H technology implementation, one of the main barriers is high capital costs which cannot be overlooked. The capital cost sub-model (see Figs. 7 and 8) shows calculations of the capital costs for existing and new capacities. The capital costs is a stock with two flows e inflow, which increases the capital costs when a new PV area is installed, and outflow, which decreases the capital costs, when those are refunded (see Fig. 7). The initial capital costs for the initial PV area are calculated according to (14):
PV full load hours
System revenue
PV power for self consumption
Avoided costs for grid power
P2H costs
P2H operating costs
(16)
In this example, the capital cost calculation is displayed only for PV panels, because the calculation for the HP is similar.
capacity is installed for the new PV area, not the initial one. The P2H decommissioning is calculated similar to formula (4).
PV operating costs
(15)
IR ¼ PPV ,SO ,ð1 SBÞ
Fig. 7. PV capital cost sub-model.
Fixed operating costs PV
where CCIR e capital cost increment rate, EUR/yr/yr; CCDR e capital cost discard rate, EUR/yr/yr; IR e investment rate, EUR/yr; i e order of delay function (i ¼ 6). The investment rate calculates the necessary capital investment for new capacities in later steps of the simulation (17).
Subsidies PV
CCPV ¼
IR,DR 1 1=ðð1 þ DRÞTME
CCDR ¼ DELAYMTRðCCIR ; TME ; i; CCPV =TME Þ
PV price
Investment rate
1029
Variable operating costs P2H
Fig. 8. Cost, revenue and profit sub-model.
Profit
Accumulated profit
Grid power price
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RPtH ¼ EHP ,HTH
(20)
where RBTG e revenue from selling power BTG, EUR/hr; RP2H e revenue from selling P2H heat, EUR/hr; EHP e P2H heat production, kWh/hr. There is an additional category called “avoided costs”. Since the company is producing its own power with PV, the amount of power generated is the same amount they do not have to buy from the grid. Avoided costs are calculated as follows:
CA ¼ ESC ,TGP
(21)
where CA e avoided costs for grid power, EUR/hr; TGP e grid power price, EUR/MWh. Profit is calculated, by taking into account costs, revenue and avoided costs:
PR ¼ RS CS þ CA
(22)
where RS e system revenue, EUR/hr; CS e system costs, EUR/hr. The revenue growth is only observed at times when the PV system is producing power, while fixed costs are the same throughout the year. To compare revenue and costs, it is necessary to compare the annual values. Each year based on the annual profit, the investment decision is made whether to invest in additional PV panels or not. Therefore, the revenue and costs are reset. 2.3. Assumptions and input data Table 1 shows the main input data for the baseline scenario and also includes the unit, the exact value and the source for each input parameter. 2.4. Analysed scenarios The developed SD model evaluates different solar power system configurations and policy instruments for renewable energy support. Table 2 summarizes the analysed scenarios. Scenarios 1, 2 and 5 represent the situation with different PV areas and without integrated HP for P2H concept. Solar power is only used for self-consumption coverage or sent BTG. Scenarios 3,4, 6 and 7 show the impact of different installed HP capacities for PV areas of 1000 m2 and 500 m2. The used values describe the part of the PV load that could be directly used for heat
production via HP without covering self-consumption. Scenarios 8 to 12 describe the situation with additional support for renewable technologies in the form of subsidies for investment in PV panels and HP. Scenarios 13 to 16 show the impact of increases in power and/or heat tariff as they directly impact the economic benefits of power conversion to heat. 2.5. Model validation outlier The extreme conditions test was done to see how the model responds to extreme values. This test showed that by radically changing the main input data, the model behaviour is normal and the balancing loops limit the outlying results. Fig. 9 shows the changes in installed PV capacity by putting extreme values (10 times larger e 10 000 m2) for the initial PV area. Fig. 9 shows that by installing too great a PV capacity from the initial launch of the system, the system cannot be economically justified and the total accumulated profit is negative. That is to say, there is a loss. This shows that the balancing loops (costs) are stronger than reinforcing loops (profit). In cases where the extreme condition test was done for all input parameters, the model showed that it can balance itself and deliver reliable results even for extremely low and high input values. 3. Results This section presents the results for different solar power system configurations and the impact of applied policies and increases in energy tariffs. The simulation is conducted for a 10 year period to show the long term dynamic of the obtained results. 3.1. Baseline scenario The baseline scenario represents the configuration of a 1000 m2 PV area and the HP with a starting capacity of 20 kW. The solar power production is modelled on an hourly basis according to the available solar radiation. It is further aligned with the hourly power consumption and the market price of electricity. Fig. 10 shows the results of solar power production and utilisation of surplus solar power. The solar fraction reaches around 20% of total power consumption in the first year and drops to 13% in 10th year. Around 81% of produced solar power is used directly for self-consumption; therefore, the remaining part is surplus power, which can be either transmitted BTG or converted to heat via HP
Table 2 Overview of analysed scenarios. Scenario
Base scenario Sc 1 Sc 2 Sc 3 Sc 4 Sc 5 Sc 6 Sc 7 Sc 8 Sc 9 Sc 10 Sc 11 Sc 12 Sc 13 Sc 14 Sc 15 Sc 16
Initial PV area
HP capacity factor
PV subsidies
HP subsidies
Electricity price increase
Heat price increase
m2
e
%
%
%
%
1000 100 1000 1000 1000 500 500 500 1000 1000 1000 1000 1000 1000 1000 1000 1000
0,1 0 0 0,05 0,2 0 0,05 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
0 0 0 0 0 0 0 0 20 40 0 0 40 0 0 0 0
0 0 0 0 0 0 0 0 0 0 20 40 40 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 20 50 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 50
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Fig. 9. Extreme condition test for initial installed PV area 10 000 m2. a-PV energy share in self-consumption and share of surplus PV power; b-changes of accumulated profit.
Fig. 10. Solar power production, surplus power utilisation and changes of PV area in the Base scenario.
when the electricity price is low. The installed PV area within a 10-year period decreases from 1000 m2 to 630 m2, because it is assumed that not all PV panels will last the whole lifetime predefined by manufacturer. If PV panels would prove to be economically beneficial, the company would be interested in investing in more PV panels, and the PV area would increase in the case when the investment rate is larger than the decommissioning rate. Around 47% of surplus power is converted to heat and fed into the DH network, and the rest is transmitted BTG. However, the use of power for heat production is strongly limited by the HP capacity. Part of the surplus power is sold BTG due to insufficient HP capacity.
3.2. Installed PV area and generated solar power One of the main parameters affecting the operation of the solar
panel system is the installed PV area, which is usually chosen according to power consumption. In this research, several scenarios with different installed PV areas are analysed (see Table 2). If the installed area is below a certain size, all of the generated solar power is directly used on site for self-consumption (Scenario 1). In order to increase the share of solar energy in the total power consumption, it is necessary to install a larger area of solar panels. In this case, there will be periods when the generated solar power exceeds the actual power consumption and a surplus of power occurs. Fig. 11 shows the share of PV energy in self-consumption and Fig. 12 shows the surplus solar power in different PV area scenarios. There is no surplus power in Scenario 1 where only 100 m2 of PV panels are installed. For the installed area of 1000 m2, the share of surplus power reaches around 16% in total power consumption. This amount is smaller after 10 years in 2026 as the installed PV
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PV energy in self consumption, %
18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 2017
2019
2021 Sc 2
Sc 1
2023 Sc 5
2025
PV energy surplus, %
Fig. 11. PV power in self-consumption for different PV area scenarios.
25% 20% 15% 10% 5% 0% 2017
2018
2019
2020
2021
2022
Sc 2
2023
2024
2025
2026
Sc 5
Fig. 12. Surplus power in different PV area scenarios.
PV surplus power share for P2H
area decreases. The main factors that determine the use of surplus power in this research is the hourly electricity price and the HP capacity to convert P2H. Around 20% of solar power exceeds the consumption and the power needs to be redirected when analysing the 1000 m2 PV panel area scenario. Fig. 13 shows the surplus power converted to heat depending on the installed HP capacity. When the HP capacity factor is 0.1, almost half of the surplus power is converted to heat. In case of higher HP capacity, up to around 74% of surplus power is used for heat production as it is more beneficial than the BTG option. In this particular case study, all of this generated heat can be used in DH in order to cover the summer heat load. In order to compare the different scenarios with and without HP
installation, the accumulated profit per m2 of PV area is used as the main indicator. Fig. 14 and Fig. 15 show the results for different scenarios without use of HP when all the surplus power is sent to BTG. The highest value is obtained for Scenario 1 with an installed solar panel area of 100 m2. The total accumulated profit after 10 year period is 18 EUR/m2. Fig. 14 shows that Scenario 2 is not profitable during the analysed period, but PV installation of 500 m2 results in 10 EUR/m2 accumulated profit. When comparing scenarios with the same area but different policies for the surplus power utilisation (Scenario 2 without HP and Base scenario with HP), the results show that it is more profitable to convert part of the surplus power to heat. Scenario 2 results in 5.5 EUR/m2 accumulated profit, but Base scenario results in a 3.7 EUR/m2 profit.
100% 80% 60% 40% 20% 0% 2017
2018
0 (Sc 2)
2019
2020
0,05 (Sc 3)
2021
2022
2023
Base scenario
2024
2025
0,2 (Sc 4)
Fig. 13. Surplus power used in heat production depending on the installed HP capacity factor.
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Fig. 14. Specific accumulated profit for scenarios without HP installation.
Fig. 15. Specific accumulated profit for scenarios with HP installation.
As indicated in Fig. 15, higher accumulated profit values are obtained for the Base scenario and Scenario 6 (PV 500 m2 combined with HP). In addition, lower specific accumulated profit is evident in Scenario 4 and Scenario 7 in which case higher HP capacities are taken into account.
different grant policies. The obtained results are compared with the Base scenario. The subsidies for PV panel installations (20% and 40%) show higher accumulated profit increases compared with those that include support for HP. The highest value of 88.25 EUR/ m2 is obtained for Scenario 12 when support is considered for both PV panels and HP. 40% subsidies for PV panel installation only (Scenario 9) show profits reaching 78.26 EUR/m2. The leading factor that influences profitability of the PV installation is when the costs of power are replaced by solar energy costs. The heat tariff and electricity price also significantly impact the benefits of power transformation to heat. Fig. 17 shows the impact that increases in the price of heat and power have on the accumulated profit of PV system. If the price of heat increases by 20%, the accumulated profit increases by 71% in Scenario 15. However, an increase in the power price does not result in such a steady increase of accumulated profit. The share of surplus power converted to heat does not increase in cases of additional support for investment costs and higher heat tariff. This is due to low HP capacity, which is insufficient to convert all the power to heat.
3.3. Support policies and tariff changes 4. Conclusions Several authors concluded that the P2H concept could be more beneficial with additional support policies or different tariff structures [8,12,13]. Therefore, this research include scenarios with additional support in the form of subsidies both for the purchase and installation of PV panels and HP. Fig. 16 shows the accumulated profit per installed PV area for
Solar energy is widely discussed as a solution for sustainable development of the DH and energy sectors. Many DH systems use solar energy for heat production, which is economically justified. The significant decrease of the price of PV panels promotes expansion of solar power integration for different applications. In
Fig. 16. Specific accumulated profit for different scenarios with support policies.
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Accumulated pro it, EUR/m2
30 20 10 0 2017 -10
2018
2019
2020
2021
2022
2023
2024
2025
2026
-20 -30
Base scenario Power price 50% (Sc 14) Heat price 50% (Sc 16)
Power price 20% (Sc 13) Heat price 20% (Sc 15)
Fig. 17. Specific accumulated profit for different scenarios with increased power and heat tariffs.
this research, the authors evaluated the PV installation for DH system self-consumption with additional P2H technology for surplus power utilisation when it is not beneficial to transmit power back to the grid. The results of the Baseline scenario showed that it is not profitable to install a larger PV area than needed for summer electricity consumption. In the Baseline scenario, the total installed PV decreased in total area by 37% in 10 years’ time, because of insufficient profit from produced power and decommissioning of PV panels. The highest accumulated profit value was obtained for the scenario with a smaller PV area when all the produced solar power is directly used for self-consumption. However, P2H can increase the flexibility of solar power systems when there is surplus power. When integrating larger initially installed PV area, the surplus power can be transferred BTG or used for heat production via HP. On average, about 45% of overproduced power is converted into heat and the rest is sent BTG. This distribution depends on installed HP capacity as well on the prices of both electricity and heat. The analyses included scenarios with additional support in the form of subsidies for the purchase and installation of PV panels and HP. A 40% subsidy for PV panel installation indicates that the accumulated profit could reach 78.26 EUR/m2 compared to 3.69 (EUR/m2) in the Baseline scenario. The key factor which influences the profitability of the PV installation and amount of power converted to heat is the electricity and heat tariff. The scenario with the higher heat tariff (40% increase) shows that the accumulated profit increases 7 times. However, the share of surplus power converted to heat does not increase due to low HP capacity. The use of electric boilers could be one of the options for further analysis, since boilers have a lower initial investment cost. The developed SD model is applicable for different DH companies as well as for testing different PV integration scenarios. This model can help to determine the optimal PV integration area and P2H capacity for a particular DH system. The main input data for the model use is the hourly power and heat consumption, as well as the electricity market price and solar radiation levels. To investigate different scenarios for PV integration, there are many parameters in the model that can be changed. In further research, other government support mechanisms as well as different applications for produced power could be integrated into the model. Taking a broader perspective, the district cooling systems can lead to higher profit where a larger part of solar energy could be used in total energy production.
Acknowledgement This research is funded by the Ministry of Economics of the Republic of Latvia, project “Development of heat supply and cooling systems in Latvia” (project No. VPP-EM-EE-2018/1e0002). References €ger-Waldau A. PV status report 2018, EUR 29463 EN. Luxembourg: Publi[1] Ja cations Office of the European Union; 2018. Available online: http:// publications.jrc.ec.europa.eu/repository/bitstream/JRC113626/pv_status_ report_2018_online.pdf. [Accessed 21 March 2019]. [2] Carpaneto E, Lazzeroni P, Repetto M. Optimal integration of solar energy in a district heating network. Renew Energy 2015;75:714e21. [3] Bertsch J, Growitsch C, Lorenczik S, Nagl S. Flexibility in Europe's power sector d an additional requirement or an automatic complement? Energy Econ 2016;53:118e31. [4] Park A, Lappas P. Evaluating demand charge reduction for commercial-scale solar PV coupled with battery storage. Renew Energy 2017;108:523e32. [5] Averfalka H, Ingvarssonb P, Perssona U, Gonga M, Werner S. Large heat pumps in Swedish district heating systems. Renew Sustain Energy Rev 2017;79: 1275e84. n K. Zero energy level and economic po[6] Hirvonen J, Kayo G, Hasan A, Sire tential of small-scale building-integrated PV with different heating systems in Nordic conditions. Appl Energy 2016;167:255e69. [7] Salpakari J, Mikkola J, Lund PD. Improved flexibility with large-scale variable renewable power in cities through optimal demand side management and power-to-heat conversion. Energy Convers Manag 2016;126:649e61. [8] Moller Sneum D, Sandberg E, Koduvere H, Olsend OJ, Blumberga D. Policy incentives for flexible district heating in the Baltic countries. Utilities Policies 2018;51:61e72. [9] Yang L, Entchev E, Rosato A, Sibilio S. Smart thermal grid with integration of distributed and centralized solar energy systems. Energy 2017;122:471e81. [10] Elcia M, Oliva A, Herkel S, Klein K, Ripka A. Grid-interactivity of a solar combined heat and power district heating system. Energy Procedia 2015;70: 560e7. [11] Yilmaz HU, Keles D, Chiodi A, Hartel R, Mikuli M. Analysis of the power-toheat potential in the European energy system. Energy Strategy Reviews 2018;20:6e19. [12] Schweiger G, Rantzer J, Ericsson K, Lauenburg P. The potential of power-toheat in Swedish district heating systems Energy 2017;137:661e9. [13] Kirkerud GJ, Bolkesjø TF, Trømborg E. Power-to-heat as a flexibility measure for integration of renewable energy. Energy 2017;128:776784. [14] Sandberg E, Kirkerud G, Trømborg E, Bolkesjø T. Energy system impacts of grid tariff structures for flexible power-to-district heat. Energy 2019;168:772e81. [15] Ehrlich LR, Klamka J, Wolf A. The potential of decentralized power-to-heat as a flexibility option for the German electricity system: a microeconomic perspective. Energy Policy 2015;87:417e28. [16] Kveselis V, Dzenajavi ciene_ EF, Masaitis S. Analysis of energy development sustainability: the example of the Lithuanian district heating sector. Energy Policy 2017;100:227e36. [17] Ziemele J, Pakere I, Blumberga D. The future competitiveness of the nonEmissions Trading Scheme district heating systems in the Baltic States. Appl Energy 2016;162:1579e85. [18] Rozentale L, Lauka D, Blumberga D. Accelerating power generation with solar panels. Case in Latvia, Energy Procedia 2018;147:600e6. [19] Lauka D, Pakere I, Blumberga D. First solar power plant in Latvia. Analysis of operational data. Energy Procedia 2018;147:162e5. [20] Lund H, Hvelplund F, Ingermann K, Kask U. Estonian energy system Proposals for the implementation of a cogeneration strategy. Energy Policy 2000;28: 729e36.
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Solar power in district heating. P2H flexibility concept Armands Gravelsins, Ieva Pakere*, Anrijs Tukulis, Dagnija Blumberga Riga Technical University, Institute of Energy Systems and Environment, Azenes street 12/1, Riga, Latvia
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 January 2019 Received in revised form 22 May 2019 Accepted 30 May 2019 Available online 3 June 2019
District heating (DH) systems present a great opportunity to increase the proportion of renewable energy used in both heating and cooling. Renewable energy can be integrated into DH to account for heat load and also for power production, which in cogeneration systems is used for both heat generation and transmission. The main aim of this research is to determine whether (and also how) to integrate solar PV panels into DH systems to achieve an economically feasible, flexible energy production solution by using a power-toheat concept. To reach greater depth in this research, the system dynamics (SD) approach was used. The results show that it is not profitable to install larger PV panels than are needed to provide for summer electricity use. However, use of the power-to-heat concept can expand the flexibility of a solar power system and increase the overall efficiency or economic feasibility when the power generated exceeds consumption. When integrating a larger PV area, the surplus power can either be transferred back to the grid or used for heat production via a heat pump. On average, about 47% of excess power is converted into heat and 53% is sent to the grid. Results show that policies both in the form of subsidies for PV panels and increases in the price of heat can significantly influence the results. © 2019 Elsevier Ltd. All rights reserved.
Keywords: District heating system Power-to-heat Solar power System dynamics modelling
1. Introduction Globally, nearly 80% of produced energy comes from fossil fuels, which promotes climate change, water and air pollution, and natural resource depletion. Renewable energy sources (RES) play an increasingly important role in the energy sectors in most European countries, particularly in those wishing both to improve the overall quality of the environment and to decrease their dependency on imported fossil fuels. Due to the rapid development of technologies, solar energy is becoming increasingly widespread in various countries, but particularly in Europe. The price of solar power panels has decreased by around 85% between 2009 and 2018, which has resulted in a sharp increase of installed solar power capacity. Around 140 million Euros have been invested in PV technologies, which is the largest share from all investments in RES [1]. Solar power is an important element in 100% cost effective renewable power systems. However, generated solar power is dependent on the available solar radiation and not on actual power consumption, which indicates the necessity redesign the operation of energy
* Corresponding author. E-mail address: [email protected] (I. Pakere). https://doi.org/10.1016/j.energy.2019.05.224 0360-5442/© 2019 Elsevier Ltd. All rights reserved.
systems. The flexibility of energy systems that use integrated intermittent energy sources is crucial [2]. As the installed capacity of solar energy production technologies has grown, the alignment of energy production and consumption has lately become a muchdiscussed topic [3]. There are several ways of increasing the capability of systems to adjust production to consumption: accumulate the power for periods when the demand increases, apply demand side management to align power consumption with power production and/or convert the power to different types of energy [3]. The main technology for direct power accumulation is the battery. Power can be stored in a variety of ways such as compressed air energy storage, flywheels, superconducting magnetic energy storage, etc. [4]. However, most of these technologies are not yet fully adapted for commercial use and are often not cost effective. 1.1. Development of power-to-heat concept There is another solution for utilizing the power from RES when the power consumption is lower than the production rate. This amount of generated solar power can be defined as surplus power, which due to legislative or economic reasons cannot be fed into the grid. When this occurs, one possibility is to convert power to heat (power-to-heat (P2H) concept) via electric boilers or heat pumps
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Nomenclature B1eB3 CA CAPP2H CCDR CCIR CCPV CHP COP CRHP CS DH DR EBTG ED EG EHD EHP EMAX EP2H EPV ES ESC FLHPV FOCPV HP HTC HTH HTP2H
balancing loops avoided costs for grid power, EUR/hr power-to-heat capacity, kW capital cost discard rate, EUR/yr/yr capital cost increment rate, EUR/yr/yr PV capital costs, EUR/yr combined heat and power heat pumps coefficient of performance heat pump capacity requirement, kW/m2 system costs, EUR/hr district heating discount rate, % surplus power back to the grid, kWh/hr power demand for self-consumption, kWh/hr power from grid, kWh/hr heat demand, kWh/hr power-to-heat production, kWh/hr maximum power demand possible for P2H, kWh/hr surplus power for P2H, kWh/hr PV power production, kWh/hr PV power surplus, kWh/hr PV power for self-consumption, kWh/hr PV full load hours, hr/yr PV fixed operating costs, EUR/MW/yr heat pump comparable heat tariff, EUR/MWhe company's heat tariff, EUR/MWhth indicated P2H tariff, EUR/MWhth
Heat production costs
(HP) [5] and use it in either the local [6] or the large-scale heating systems [7]. Fig. 1 shows the interaction between heat production costs and electricity prices for different heat generation technologies. This demonstrates that P2H is feasible when the electricity price is lower than the heat production costs incurred when using other heat generation technologies [8]. In the past few years, a number of authors have developed the smart grid concept [9] where power, heating and cooling networks are combined and analysed as an integrated system. Consumption
Heat only boilers
P2H potential
i IR LHP OCPV PPV PRA P2H PUC R1-R3 RBTG RES RP2H RS SB SCOM SD SDE SHPR SI SO SOPV SPV TGP TMC TME TMPV TSP VOCPV
hPV
order of delay function investment rate, EUR/yr heat pump load, % PV operating costs, EUR/hr PV price, EUR/m2 accumulated profit, EUR power-to-heat public utilities commission reinforcing loops revenue from selling power back-to-grid, EUR/hr renewable energy sources revenue from selling P2H heat, EUR/hr system revenue, EUR/hr Subsidies in PV technologies, % PV commissioning, m2/yr system dynamics PV decommissioning, m2/yr profit share in new PV, % solar irradiation, kWh/m2/hr ordering of PV area, m2/yr ordered PV area, m2 PV area, m2 grid power price, EUR/MWh commissioning time, yr economic lifetime of investment, yr PV service life, yr power sales price, EUR/MWhe variable operating costs of PV, EUR/MWh PV efficiency, %
load management and grid-interconnectivity are the key factors in developing more flexible renewable energy system solutions [10]. The potential of P2H systems in different European countries have been analysed by Yilmaz et al. [11]. Authors have calculated the P2H potential for Germany, Denmark, France, Austria, the Netherlands and Italy by analysing changes in hourly power and heat load. Other authors have applied a similar methodology for estimating the potential of P2H technology for Sweden [12] and other Nordic countries [13]. Sandberg et al. [14] have lately discussed P2H as a solution using different power tariff structures and conclude that, while this could increase the share of wind power in DH, it would also decrease the share of energy through cogeneration. The P2H concept has been analysed in a microeconomic perspective by Ehrlich et al. [15]. The authors conclude that, without additional government support, the financial benefits of P2H systems at the household level are negligible. Therefore, it is reasonable to analyse such systems at the district or regional level, but until now, there has been little research on large-scale P2H integration in district heating systems [8]. Hypothetically, DH would be a suitable application for P2H because electricity can be directly used for heat generation and transmission in a boiler house, while the surplus power can be converted to heat and fed into the heat supply system. 1.2. Solar power and P2H concept in baltic countries
Electricity price Fig. 1. Heat production cost and electricity price interact for different heat generation technologies (CHP-combined heat and power) [8].
The use of solar and wind energy in Baltic countries is not yet widespread for energy production [16,17]. Solar panels are mainly used in household applications and there have been several research papers analysing the operation of solar power systems in
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Latvia [18,19]. The authors of these papers identify the main parameters affecting solar system feasibility including the efficiency rate and price of solar panels, the level of government support and the structure of electricity tariffs. In order to initiate the installation of RES, there should be wellplanned regulatory framework and national support for carbonfree technologies [20]. Best & Burk [21] present the roles of policies and preferences in the national adoption of solar and wind energy technologies. These authors have found that carbon pricing is an important factor in the early adoption of solar energy. Similar support schemes should be analysed for Baltic countries in order to achieve a higher share for solar and wind energy in the energy market. Moller et al. [8] analysed the possible increase in system flexibility by implementing policy incentives for DH in Baltic countries. The authors compare the levelized costs of heat for several scenarios combining biomass CHP, electric boilers, heat storage and oil boilers for covering the peak load. The results showed that integration of different energy storage systems results in the lower levelized costs of heat. However, the current policies in the Baltic countries do not support increased system flexibility from coupling DH and power generation. Taking into account the significant decrease of PV prices, the development of the smart grid and P2H, the goal of this research is to determine whether the integration of solar PV panels into DH systems is economically feasible. The authors evaluate if it is reasonable to integrate the PV system for on-site power consumption (that is, the energy used to produce power) and convert the surplus solar power to heat. The P2H concept is a possible solution to increase the flexibility of solar power systems. 2. Methodology For more in-depth research of the PV technology integration into DH, the authors have used the system dynamics (SD) approach. SD solves complex problems such as the development of renewable energy systems that use several dynamic variables [22]. SD modelling is useful for evaluating the relationship between dynamic factors (dependency, energy demand, cost changes, etc. [23]) and the effect of different policies on RES [24]. SD theory is based on the study of the relationship between system behaviour and the underlying structure of the system. This means that an analysis of the system's structure may lead to a deeper understanding of the causes which lead to certain behaviour which, in turn, allows any problematic behaviour existing in the system [25] to be further addressed. For the purposes of this research, the SD model was developed to analyse the complexity of the DH company's energy balance and tariffs for different energy types. It also include forecasts for energy and changes in the price of PV technology. This model is based on solar power integration, which is used to cover power used to produce heat for a DH company. 2.1. System dynamic behaviour The aim of this research is to evaluate whether it is reasonable to integrate a PV system into a DH company's system in order to cover part of the company's power self-consumption and convert the surplus solar energy into heat. In addition, the model makes it possible to compare different technologies that can be used to produce heat energy from surplus energy. To identify the main driving forces of the system, the causal loop diagram was constructed. The causal loop diagram describes mutual connections among the elements of the system. The system (see Fig. 2.) consists of three reinforcing loops (R1 e
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R3), which promote the development of the system. However, two balancing loops (B1 and B2) hinder the development of the system. The main measurement of the system is profitability. A higher profitability level leads to a higher interest on the part of the Company to invest in additional PV capacity. This, in turn, leads to more solar energy being produced, which further leads to more surplus power when the PV is producing close to maximum capacity. If there is a significant amount of surplus energy, there is the option to use it for heat generation. Thus, with more surplus energy, more heat can be generated. This results in higher income levels, which make the system more profitable. This is the first reinforcing loop (R1). The 2nd reinforcing loop (R2) is similar. If there is more surplus energy, more power can be sold back to the grid (BTG). This increases the income from the PV system making it more profitable. The 3rd reinforcing loop (R3) shows that by increasing the PV energy production rate, the company is able to cover a greater share of power self-consumption. This allows it to avoid the costs that would result from purchasing power from the grid. This also helps to make the system more profitable. Although the reinforcing loops promote the development of the system, there are also two balancing loops trying to hinder the development of the system. Balancing Loop 1 (B1) is related to the PV costs. The installation of more PV panels results in higher capital and operating costs, making the system less profitable. Balancing Loop 2 (B2) works by the same principle, but it takes into account the P2H technology costs and their impact on the system. The system is profitable when the reinforcing loops are stronger than the balancing loops, meaning that revenues need to be higher than costs. Therefore, it is necessary to determine the capacity at which maximum revenue is generated at a minimal cost. Based on literature, it is assumed that technology costs for PV and P2H technologies will decrease [1]. The hourly data of a DH company's heat demand, power consumption and solar irradiation is used in the model. The dynamic hypothesis is that, by integrating solar energy and P2H technologies, it is possible to improve the economic indicators of the DH company. 2.2. Model structure The SD model consists of five connected sub-models. Stocks and flows are the main elements of every sub-model. The stocks accumulate the particular value over time, while flows increase or decrease this accumulated value. 2.2.1. Installed PV area sub-model The installed PV area sub-model describes the total installed PV area. Fig. 3 shows the model structure. In cases where the PV system operation is profitable, more PV is installed to cover a larger area. If not, then the PV area slowly decreases due to decommissioning of the existing PV panels. Two flows regulate the PV area stocke PV commissioning and decommissioning. This structure shows the extent of PV area at any given moment. Stock value is calculated according to (1) below:
ðt SPV ðtÞ ¼
½SCOM SDE dt þ SPV ðt0 Þ
(1)
t0
where SPV e PV area, m2; SCOM e PV commissioning, m2/yr; SDE e PV decommissioning, m2/yr. Other stocks of the system are calculated using the same principle.
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Income from BTG + Income from P2H
+ System income +
+
+
+
+ + P2H costs
+ System costs +
+ System Profitability
P2H
Power sales price
B2 PV energy surplus
+
Heat tariff
+ PV CAPEX
+
+
+ +
+ PV costs
B1
+
PV efficiency + PV energy
+ Avoided costs from + + grid power
+
Other PV costs
PV capacity
Grid power purchase price
BTG
P2H CAPEX
R1
Other P2H costs
R2
R3
+
PV energy share in self consumption
+ Solar irradiation
-
Power demand for self consumption
Fig. 2. Causal loop diagram for PV panel integration in a DH company.
Profit share in new PV
Ordering of PV area Accumulated profit
Commissioning time
Ordered PV area
PV commissioning
PV area
PV service life
PV decommissioning
PV price PV price reduction rate
Reduction rate % per year
Fig. 3. Installed PV area sub-model.
The decision about additional PV panel area installation is based on economic considerations such as total revenue and costs of installed PV system. If the operation is profitable, it is assumed that part of the profit will be redirected towards installation of new PV panels. The amount of installed area is calculated by dividing the available funds with the PV price. The ordering of new PV panels is determined by the following “if” function:
SO ¼ if ðPRA > 0; PRA , SHPR =PPV ; 0Þ
(2) 2
where SO e ordering of PV area, m /yr; PRA e accumulated profit, EUR; SHPR e profit share in new PV, %; PPV e PV price, EUR/m2. There is a time delay before PV panels are delivered, installed and become fully functional. Therefore, there are two stocks e ordered PV area and PV area. These stocks are regulated by the flow e PV commissioning. After the commissioning time (a time necessary to deliver and install the equipment) passes, the ordered PV panels move to the PV area stock:
SCOM ¼
SOPV TMC
(3)
where SOPV e ordered PV area, m2; TMC e commissioning time, yr. PV decommissioning describes the amount of PV panels decommissioned after the end of their service life:
SDE ¼
SPV TMPV
(4)
where TMPV e PV service life, yr. The PV panel price is assumed to decrease in the future [1]. The decrease is modelled by building one stock with one outflow. Fig. 4 shows the modelled PV panel price decrease during a 10 year period (see Fig. 5). The main input parameters (profit share in new PV, commissioning time, PV service life, PV price, etc.) are shown in Table 1.
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PV price, EUR/m2
200 150 100 50 0 2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
Fig. 4. Simulated PV price decrease.
PV efficiency
PV area
Solar irradiation
PV power production
PV power for self consumption Surplus power BTG Power from grid
P2H capacity
Power demand for self consumption
Indicated P2H tariff
Heat demand
Max power demand for P2H
Power sales price
COP
Comparable heat tariff
Surplus power for P2H
PV power surplus
Companie s heat tariff
Heat tariff increase
Heat tariff increase rate
Fig. 5. PV power production and consumption sub-model.
2.2.2. Power production and consumption sub-model The power production and consumption sub-model reveals the following flows: how much power is produced with the installed PV area; how much of the produced power is used for self-consumption; how much of the produced power remains as surplus power, which can be either BTG, or used in heat generation via HP.
The generated PV power is calculated as:
EPV ¼ SPV ,hPV ,SI
(5)
where EPV e PV power production, kWh/hr; hPV e PV efficiency, %; SI e solar irradiation. The PV panel efficiency is based on previous research [26,27], but the hourly solar irradiation data are obtained from the
Table 1 Input data for baseline scenario. Input parameter Starting price for PV panels PV panel price reduction PV commissioning time Service life of PV PV panel efficiency Profit share in new PV Solar irradiation Electricity consumption Electricity needed for heat demand Heat tariff increase Heat tariff for end users HP COP Electricity price HP price Service life of HP equipment HP capacity requirement Full electricity price
Unit 2
EUR/m %/year years years % % kWh/m2/hr kWh kWh % EUR/MWh e EUR/MWh EUR/kW years kWh/m2 EUR/MWh
Value
Source
180 5 1 25 16 25 Hourly Hourly Hourly 2 50 3 Hourly 800 20 0.2 Hourly
[27] [27,28] [29] [29] [30] [28] Meteorological database [31] Data from DH company Data from DH company Data from DH company Data from DH company [32,33] Data from electricity market [32,33] [32] [32] Average mark-up
data data data
data
data
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meteorological database [31]. To calculate how much of a DH company's power demand can be covered by generated PV energy, it is necessary to take into account how much power is needed from the grid, as well as how much surplus solar power is still left after self-consumption is covered (6e8).
ESC ¼ if ðEPV ED ; ED ; EPV Þ
(6)
EG ¼ if ðED > EPV ; ED EPV ; 0Þ
(7)
ES ¼ if ðEPV > ED ; EPV ED ; 0Þ
(8)
where ESC e PV power for self-consumption, kWh/hr; EG e power from grid, kWh/hr; ES e PV. Surplus power, kWh/hr; ED e power demand for selfconsumption, kWh/hr. The hourly power consumption data of the particular DH company studied is used in the modelling process. The surplus energy (PV power production minus the DH company's self-consumption) is transformed into heat or fed into the grid. The decision on whether to feed surplus power BTG or to use it in heat generation is based on economic considerations. The potential income from both options is compared, and the more profitable option is selected. If the heat tariff is higher, the surplus PV power is converted to heat taking into account the restrictions of heat demand and installed P2H technological capacity. The decision uses the following functions:
HTC ¼ if ðHTH HTPtH ; HTH , COP; 0Þ
(11)
where HTH e company's heat tariff, EUR/MWhth; HTP2H e indicated P2H tariff, EUR/MWhth; COP e HP coefficient of performance. The variable, called maximum power demand possible for P2H, was introduced into the model in order to prevent a situation whereby more surplus energy would be transformed into heat than required by the heat demand.
EMAX ¼
EHD COP
(12)
where EHD eheat demand, kWh/hr. It is assumed that the DH Company's regular tariff will increase over time. The tariff increase is modelled the same way as the PV price decrease in section 2.2.1. 2.2.3. P2H capacity sub-model The HP is used as P2H technology to convert PV surplus energy into heat. Fig. 6 shows the end-to-end relationships in the P2H capacity sub-model. To understand how much capacity is necessary for P2H technologies, a HP capacity factor is introduced. This variable describes the HP capacity requirement per one square meter of installed PV area that is necessary to cover the full load when PV is working at peak capacity. As PV panels are working at full capacity only a few hours per year, it is impractical and expensive to install HP capacity to cover the full power load. Thus, the HP capacity factor describes how much of the full HP capacity requirement is installed (e.g. if HP capacity requirement is 0.2 kW/m2 and HP load
EBTG ¼ if ðTSP > HTC ; ES ; if ðES > EMAX ; if ðEMAX > CAPPtH ; ES CAPPtH ; ES EMAX Þ; if ðES > CAPPtH ; ES CAPPtH ; 0ÞÞÞ
EPtH ¼ if ðTSP >HTC ;0;if ðES >EMAX ;if ðEMAX >CAPPtH ;CAPPtH ;EMAX Þ; if ðES >CAPPtH ;CAPPtH ;ES ÞÞÞ (10) where EBTG e surplus power B, kWh/hr; EP2H e surplus power for P2H, kWh/hr; TSP e price of power for sale, EUR/MWhe; HTC e comparable heat tariff, EUR/MWhe; EMAX e maximum power demand possible for P2H, kWh/hr; CAPP2H e P2H capacity, kW. The hourly electricity price is determined using historical data from the power market with future price forecasts. To compare which of the options are more profitable, it was necessary to compare the power tariff with the heat tariff. Since the heat tariff is expressed per heat energy produced, in order to compare it with the power tariff, the coefficient of performance (COP) for HP, which would be used to transform surplus power into heat, was introduced. In addition, two different heat tariffs were compared e the DH company's regular tariff (approved by the public utilities commission (PUC)) and the calculated P2H tariff. In cases when the P2H tariff is higher than the one approved by the PUC, it is disadvantageous to transform surplus PV power to heat. However, if the P2H tariff is lower than the tariff approved by PUC and when it is more profitable than selling power BTG, surplus PV energy is transformed into heat by using P2H technologies. The heat produced by the P2H technology is sold at the PUC approved tariff.
(9)
is 10%, only 0.02 kW/m2 will be installed). The HP load is one of the variables the impact of which is analysed in different scenarios. The initial P2H capacity is calculated as follows:
CAPPtH ¼ CRHP ,SPV ,LHP
(13)
where CRHP eHP capacity requirement, kW/m2; LHP e HP load, %. The P2H technology capacity is modelled as a stock with one inflow and one outflow. The inflow P2H commissioning is calculated the same as initial capacity (13) except that the additional P2H
PV area
Heat pump capacity requirement
P2H
P2H service life
capacity P2H P2H commissioning decommissioning Ordering of PV area Heat pump load Fig. 6. P2H capacity sub-model.
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Economic lifetime Discount rate
Capital cost increment rate Ordering of PV area
the different scenarios when PV panels are subsidized (same is done in the P2H technology capital cost sub-model). The capital cost increment rate and discard rate is calculated in the following manner: PV area
PV capital costs
CCIR ¼
Capital cost discard rate
Cummulative investment
SPV ,PPV ,ð100 SBÞ,DR 1 1=ðð1 þ DRÞTME
OCPV ¼ EPV ,ðVOCPV þ FOCPV =FLHPV Þ
where CCPV e PV capital costs, EUR/yr; SB e subsidies in PV technologies, %; DR e discount rate, %; TME e economic lifetime of investment, yr. The subsidies are implemented in the model to later compare
P2H full load hours
PV capital costs
P2H heat production
Fixed operating costs P2H
RBTG ¼ EBTG ,TSP
Variable operating costs PV
Surplus power BTG
(19)
Companies heat tariff
Power sales price Revenue from selling power BTG
System costs
P2H heat production
Revenue from selling P2H heat
PV costs
P2H capital costs
(18)
where OCPV e PV operating costs, EUR/hr; VOCPV e variable operating costs PV, EUR/MWh; FOCPV e fixed operating costs PV, EUR/ MW/yr; FLHPV e PV full load hours, hr/yr. The same approach is used when calculating HP operating costs. Total revenue consist of two elements: revenue from electricity sold BTG and revenue from HP heat energy sold to consumers.
(14)
PV power production
(17)
2.2.5. Cost, revenue and profit sub-model To evaluate the economic benefits, it is important to calculate the costs and revenue of PV system integration in the DH system. The costs of the system include PV costs and HP costs. Costs consists of capital costs (section 2.2.4.) and operating costs e both fixed and variable. The operating costs for the PV panels are calculated according to (18).
2.2.4. Capital cost sub-model For PV and P2H technology implementation, one of the main barriers is high capital costs which cannot be overlooked. The capital cost sub-model (see Figs. 7 and 8) shows calculations of the capital costs for existing and new capacities. The capital costs is a stock with two flows e inflow, which increases the capital costs when a new PV area is installed, and outflow, which decreases the capital costs, when those are refunded (see Fig. 7). The initial capital costs for the initial PV area are calculated according to (14):
PV full load hours
System revenue
PV power for self consumption
Avoided costs for grid power
P2H costs
P2H operating costs
(16)
In this example, the capital cost calculation is displayed only for PV panels, because the calculation for the HP is similar.
capacity is installed for the new PV area, not the initial one. The P2H decommissioning is calculated similar to formula (4).
PV operating costs
(15)
IR ¼ PPV ,SO ,ð1 SBÞ
Fig. 7. PV capital cost sub-model.
Fixed operating costs PV
where CCIR e capital cost increment rate, EUR/yr/yr; CCDR e capital cost discard rate, EUR/yr/yr; IR e investment rate, EUR/yr; i e order of delay function (i ¼ 6). The investment rate calculates the necessary capital investment for new capacities in later steps of the simulation (17).
Subsidies PV
CCPV ¼
IR,DR 1 1=ðð1 þ DRÞTME
CCDR ¼ DELAYMTRðCCIR ; TME ; i; CCPV =TME Þ
PV price
Investment rate
1029
Variable operating costs P2H
Fig. 8. Cost, revenue and profit sub-model.
Profit
Accumulated profit
Grid power price
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RPtH ¼ EHP ,HTH
(20)
where RBTG e revenue from selling power BTG, EUR/hr; RP2H e revenue from selling P2H heat, EUR/hr; EHP e P2H heat production, kWh/hr. There is an additional category called “avoided costs”. Since the company is producing its own power with PV, the amount of power generated is the same amount they do not have to buy from the grid. Avoided costs are calculated as follows:
CA ¼ ESC ,TGP
(21)
where CA e avoided costs for grid power, EUR/hr; TGP e grid power price, EUR/MWh. Profit is calculated, by taking into account costs, revenue and avoided costs:
PR ¼ RS CS þ CA
(22)
where RS e system revenue, EUR/hr; CS e system costs, EUR/hr. The revenue growth is only observed at times when the PV system is producing power, while fixed costs are the same throughout the year. To compare revenue and costs, it is necessary to compare the annual values. Each year based on the annual profit, the investment decision is made whether to invest in additional PV panels or not. Therefore, the revenue and costs are reset. 2.3. Assumptions and input data Table 1 shows the main input data for the baseline scenario and also includes the unit, the exact value and the source for each input parameter. 2.4. Analysed scenarios The developed SD model evaluates different solar power system configurations and policy instruments for renewable energy support. Table 2 summarizes the analysed scenarios. Scenarios 1, 2 and 5 represent the situation with different PV areas and without integrated HP for P2H concept. Solar power is only used for self-consumption coverage or sent BTG. Scenarios 3,4, 6 and 7 show the impact of different installed HP capacities for PV areas of 1000 m2 and 500 m2. The used values describe the part of the PV load that could be directly used for heat
production via HP without covering self-consumption. Scenarios 8 to 12 describe the situation with additional support for renewable technologies in the form of subsidies for investment in PV panels and HP. Scenarios 13 to 16 show the impact of increases in power and/or heat tariff as they directly impact the economic benefits of power conversion to heat. 2.5. Model validation outlier The extreme conditions test was done to see how the model responds to extreme values. This test showed that by radically changing the main input data, the model behaviour is normal and the balancing loops limit the outlying results. Fig. 9 shows the changes in installed PV capacity by putting extreme values (10 times larger e 10 000 m2) for the initial PV area. Fig. 9 shows that by installing too great a PV capacity from the initial launch of the system, the system cannot be economically justified and the total accumulated profit is negative. That is to say, there is a loss. This shows that the balancing loops (costs) are stronger than reinforcing loops (profit). In cases where the extreme condition test was done for all input parameters, the model showed that it can balance itself and deliver reliable results even for extremely low and high input values. 3. Results This section presents the results for different solar power system configurations and the impact of applied policies and increases in energy tariffs. The simulation is conducted for a 10 year period to show the long term dynamic of the obtained results. 3.1. Baseline scenario The baseline scenario represents the configuration of a 1000 m2 PV area and the HP with a starting capacity of 20 kW. The solar power production is modelled on an hourly basis according to the available solar radiation. It is further aligned with the hourly power consumption and the market price of electricity. Fig. 10 shows the results of solar power production and utilisation of surplus solar power. The solar fraction reaches around 20% of total power consumption in the first year and drops to 13% in 10th year. Around 81% of produced solar power is used directly for self-consumption; therefore, the remaining part is surplus power, which can be either transmitted BTG or converted to heat via HP
Table 2 Overview of analysed scenarios. Scenario
Base scenario Sc 1 Sc 2 Sc 3 Sc 4 Sc 5 Sc 6 Sc 7 Sc 8 Sc 9 Sc 10 Sc 11 Sc 12 Sc 13 Sc 14 Sc 15 Sc 16
Initial PV area
HP capacity factor
PV subsidies
HP subsidies
Electricity price increase
Heat price increase
m2
e
%
%
%
%
1000 100 1000 1000 1000 500 500 500 1000 1000 1000 1000 1000 1000 1000 1000 1000
0,1 0 0 0,05 0,2 0 0,05 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
0 0 0 0 0 0 0 0 20 40 0 0 40 0 0 0 0
0 0 0 0 0 0 0 0 0 0 20 40 40 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 20 50 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 50
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Fig. 9. Extreme condition test for initial installed PV area 10 000 m2. a-PV energy share in self-consumption and share of surplus PV power; b-changes of accumulated profit.
Fig. 10. Solar power production, surplus power utilisation and changes of PV area in the Base scenario.
when the electricity price is low. The installed PV area within a 10-year period decreases from 1000 m2 to 630 m2, because it is assumed that not all PV panels will last the whole lifetime predefined by manufacturer. If PV panels would prove to be economically beneficial, the company would be interested in investing in more PV panels, and the PV area would increase in the case when the investment rate is larger than the decommissioning rate. Around 47% of surplus power is converted to heat and fed into the DH network, and the rest is transmitted BTG. However, the use of power for heat production is strongly limited by the HP capacity. Part of the surplus power is sold BTG due to insufficient HP capacity.
3.2. Installed PV area and generated solar power One of the main parameters affecting the operation of the solar
panel system is the installed PV area, which is usually chosen according to power consumption. In this research, several scenarios with different installed PV areas are analysed (see Table 2). If the installed area is below a certain size, all of the generated solar power is directly used on site for self-consumption (Scenario 1). In order to increase the share of solar energy in the total power consumption, it is necessary to install a larger area of solar panels. In this case, there will be periods when the generated solar power exceeds the actual power consumption and a surplus of power occurs. Fig. 11 shows the share of PV energy in self-consumption and Fig. 12 shows the surplus solar power in different PV area scenarios. There is no surplus power in Scenario 1 where only 100 m2 of PV panels are installed. For the installed area of 1000 m2, the share of surplus power reaches around 16% in total power consumption. This amount is smaller after 10 years in 2026 as the installed PV
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PV energy in self consumption, %
18% 16% 14% 12% 10% 8% 6% 4% 2% 0% 2017
2019
2021 Sc 2
Sc 1
2023 Sc 5
2025
PV energy surplus, %
Fig. 11. PV power in self-consumption for different PV area scenarios.
25% 20% 15% 10% 5% 0% 2017
2018
2019
2020
2021
2022
Sc 2
2023
2024
2025
2026
Sc 5
Fig. 12. Surplus power in different PV area scenarios.
PV surplus power share for P2H
area decreases. The main factors that determine the use of surplus power in this research is the hourly electricity price and the HP capacity to convert P2H. Around 20% of solar power exceeds the consumption and the power needs to be redirected when analysing the 1000 m2 PV panel area scenario. Fig. 13 shows the surplus power converted to heat depending on the installed HP capacity. When the HP capacity factor is 0.1, almost half of the surplus power is converted to heat. In case of higher HP capacity, up to around 74% of surplus power is used for heat production as it is more beneficial than the BTG option. In this particular case study, all of this generated heat can be used in DH in order to cover the summer heat load. In order to compare the different scenarios with and without HP
installation, the accumulated profit per m2 of PV area is used as the main indicator. Fig. 14 and Fig. 15 show the results for different scenarios without use of HP when all the surplus power is sent to BTG. The highest value is obtained for Scenario 1 with an installed solar panel area of 100 m2. The total accumulated profit after 10 year period is 18 EUR/m2. Fig. 14 shows that Scenario 2 is not profitable during the analysed period, but PV installation of 500 m2 results in 10 EUR/m2 accumulated profit. When comparing scenarios with the same area but different policies for the surplus power utilisation (Scenario 2 without HP and Base scenario with HP), the results show that it is more profitable to convert part of the surplus power to heat. Scenario 2 results in 5.5 EUR/m2 accumulated profit, but Base scenario results in a 3.7 EUR/m2 profit.
100% 80% 60% 40% 20% 0% 2017
2018
0 (Sc 2)
2019
2020
0,05 (Sc 3)
2021
2022
2023
Base scenario
2024
2025
0,2 (Sc 4)
Fig. 13. Surplus power used in heat production depending on the installed HP capacity factor.
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Fig. 14. Specific accumulated profit for scenarios without HP installation.
Fig. 15. Specific accumulated profit for scenarios with HP installation.
As indicated in Fig. 15, higher accumulated profit values are obtained for the Base scenario and Scenario 6 (PV 500 m2 combined with HP). In addition, lower specific accumulated profit is evident in Scenario 4 and Scenario 7 in which case higher HP capacities are taken into account.
different grant policies. The obtained results are compared with the Base scenario. The subsidies for PV panel installations (20% and 40%) show higher accumulated profit increases compared with those that include support for HP. The highest value of 88.25 EUR/ m2 is obtained for Scenario 12 when support is considered for both PV panels and HP. 40% subsidies for PV panel installation only (Scenario 9) show profits reaching 78.26 EUR/m2. The leading factor that influences profitability of the PV installation is when the costs of power are replaced by solar energy costs. The heat tariff and electricity price also significantly impact the benefits of power transformation to heat. Fig. 17 shows the impact that increases in the price of heat and power have on the accumulated profit of PV system. If the price of heat increases by 20%, the accumulated profit increases by 71% in Scenario 15. However, an increase in the power price does not result in such a steady increase of accumulated profit. The share of surplus power converted to heat does not increase in cases of additional support for investment costs and higher heat tariff. This is due to low HP capacity, which is insufficient to convert all the power to heat.
3.3. Support policies and tariff changes 4. Conclusions Several authors concluded that the P2H concept could be more beneficial with additional support policies or different tariff structures [8,12,13]. Therefore, this research include scenarios with additional support in the form of subsidies both for the purchase and installation of PV panels and HP. Fig. 16 shows the accumulated profit per installed PV area for
Solar energy is widely discussed as a solution for sustainable development of the DH and energy sectors. Many DH systems use solar energy for heat production, which is economically justified. The significant decrease of the price of PV panels promotes expansion of solar power integration for different applications. In
Fig. 16. Specific accumulated profit for different scenarios with support policies.
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Accumulated pro it, EUR/m2
30 20 10 0 2017 -10
2018
2019
2020
2021
2022
2023
2024
2025
2026
-20 -30
Base scenario Power price 50% (Sc 14) Heat price 50% (Sc 16)
Power price 20% (Sc 13) Heat price 20% (Sc 15)
Fig. 17. Specific accumulated profit for different scenarios with increased power and heat tariffs.
this research, the authors evaluated the PV installation for DH system self-consumption with additional P2H technology for surplus power utilisation when it is not beneficial to transmit power back to the grid. The results of the Baseline scenario showed that it is not profitable to install a larger PV area than needed for summer electricity consumption. In the Baseline scenario, the total installed PV decreased in total area by 37% in 10 years’ time, because of insufficient profit from produced power and decommissioning of PV panels. The highest accumulated profit value was obtained for the scenario with a smaller PV area when all the produced solar power is directly used for self-consumption. However, P2H can increase the flexibility of solar power systems when there is surplus power. When integrating larger initially installed PV area, the surplus power can be transferred BTG or used for heat production via HP. On average, about 45% of overproduced power is converted into heat and the rest is sent BTG. This distribution depends on installed HP capacity as well on the prices of both electricity and heat. The analyses included scenarios with additional support in the form of subsidies for the purchase and installation of PV panels and HP. A 40% subsidy for PV panel installation indicates that the accumulated profit could reach 78.26 EUR/m2 compared to 3.69 (EUR/m2) in the Baseline scenario. The key factor which influences the profitability of the PV installation and amount of power converted to heat is the electricity and heat tariff. The scenario with the higher heat tariff (40% increase) shows that the accumulated profit increases 7 times. However, the share of surplus power converted to heat does not increase due to low HP capacity. The use of electric boilers could be one of the options for further analysis, since boilers have a lower initial investment cost. The developed SD model is applicable for different DH companies as well as for testing different PV integration scenarios. This model can help to determine the optimal PV integration area and P2H capacity for a particular DH system. The main input data for the model use is the hourly power and heat consumption, as well as the electricity market price and solar radiation levels. To investigate different scenarios for PV integration, there are many parameters in the model that can be changed. In further research, other government support mechanisms as well as different applications for produced power could be integrated into the model. Taking a broader perspective, the district cooling systems can lead to higher profit where a larger part of solar energy could be used in total energy production.
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