Impact of thermal plant cycling on the cost-optimal composition of a regional electricity generation system

Applied Energy 197 (2017) 230–240

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Impact of thermal plant cycling on the cost-optimal composition of a regional electricity generation system Lisa Göransson ⇑, Joel Goop, Mikael Odenberger, Filip Johnsson Chalmers University of Technology, 412 96 Gothenburg, Sweden

h i g h l i g h t s
Thermal cycling impact the cost-optimal electricity system composition.
9–19% of investments are cycling dependent in systems studied with cap on CO2.
Cost-competitive, flexible thermal generation increases wind power investments.
System characteristics which result in cycling dependent capacity are identified.

a r t i c l e

i n f o

Article history: Received 16 September 2016 Received in revised form 27 March 2017 Accepted 8 April 2017

Keywords: Electricity system model Thermal cycling Intermittent generation Investment model

a b s t r a c t A regional cost-minimizing investment model that accounts for cycling properties (i.e., start-up time, minimum load level, start-up cost and emissions, and part-load costs and emissions) is developed to investigate the impact of thermal plant cycling on the cost-optimal composition of a regional electricity generation system. The model is applied to an electricity system that is rich in wind resources with and without accounting for cycling in two scenarios: one with favorable conditions for flexible bio-based generation (Bio scenario); and one in which base load is favored (Base load scenario) owing to high prices for biomass. Both scenarios are subject to a tight cap on carbon dioxide emissions, limiting the investment options to technologies that have low or no carbon emissions. We report that in the Bio scenario, the cost-optimal system is dominated by wind power and flexible bio-based generation, whereas base-load generation dominates the Base load scenario, in line with the assumptions made, and the level of wind power is reduced. In the Base load scenario, 19% of the capacity is cycling-dependent, i.e., for this share of installed capacity, the choice of technology is different if cycling properties are included, compared to a case in which they are omitted. In the Bio scenario, in which flexible bio-based generation is less costly, 9% of the capacity is cycling-dependent. We conclude that it is critical to include cycling properties in investment modeling, to assess investments in thermal generation technologies that compete at utilization times in the range of 2000–5000 h. Ó 2017 Published by Elsevier Ltd.

1. Introduction The last decade has seen a drastic reduction in the costs for wind and solar power, making these generation technologies highly cost-competitive with other generation technologies with low or no carbon dioxide emissions. Combined with support schemes for renewable energy source (RES)-based electricity generation, this has resulted in the expansion of wind and solar power in several regions of the world, in a development that is foreseen to continue. This increased adoption of wind and solar power motivates the development of electricity system modeling methods ⇑ Corresponding author. E-mail address: [email protected] (L. Göransson). http://dx.doi.org/10.1016/j.apenergy.2017.04.018 0306-2619/Ó 2017 Published by Elsevier Ltd.

that account for variability as well as variation management, such as thermal cycling. In dispatch models, cycling costs and cycling emissions from thermal generation are common features, as exemplified by the studies of Göransson et al. [1], Lew et al. [2], Meibom et al. [3], Bruce et al. [4], Troy et al. [5] and Mc Garrigle et al. [6]. Van Den Bergh et al. [7] present a further refined approach to account for thermal cycling in dispatch models. Göransson et al. [1] and Troy et al. [5] have shown how the inclusion of thermal cycling in dispatch modeling can modify the modeled dispatch order of the units in a wind-thermal electricity system. Van den Bergh et al. [8] show that cycling costs can be reduced with up to 40% if accounted for in the operation planning. Turconi et al. [9] show that, while cycling emissions do not negate the benefit of increased wind shares, emissions from cycling thermal

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generation are significant when evaluating life cycle emissions of the Irish electricity system. However, the variability of wind and solar generation not only impacts upon the electricity system operations analyzed in dispatch models, but also affects the optimal generation mix; the need for electricity system investment models that take into account load and generation variability is well-recognized [10–12]. Investment models typically have a much lower time resolution than dispatch models, which means that the ability to capture the variable nature of wind and solar power has to be addressed specifically. Strategies that account for variability in investment modeling may be categorized into three different families: (1) methods that select time steps or time periods to represent the variations and use criteria that assure sufficient capacity; (2) methods that soft-link low-time-resolution investment models with high-time-resolution dispatch models; and (3) methods that add investments to models that have a high time resolution. The latter strategy is chosen for the model developed in this work. A method that pertains to the first family of strategies is applied in the OSeMOSYS model described by Welsch et al. [11]. This method continues to rely on a low number of time steps but represents variability by gradually reducing the capacity credits of wind and solar power as investments in these technologies increase, while the required capacity is ensured by including reserve capacity. A second strategy from this family is the residual load duration curve (RLDC) approach of Ueckerdt et al. [13]. They derive an approximate load duration curve that consists of a peak that represents the peak load, a triangle that represents the intermediate load, and a box that represents the base load. They then evaluate, prior to optimization, how wind and solar power generation at different penetration levels affects the peak, the triangle, and the box. In the investment model, there are requirements for peak, intermediate, and base load capacities that depend on investments in renewable generation. A third strategy to account for variability in investment modeling is the ‘‘representative days” method proposed by Nahmmacher et al. [14], in which a number of days is modeled with chronological time and high time resolution within each day. The days are chosen to represent variations in load, as well as wind and solar resources. Methods that belong to the first family typically face challenges when accounting for variation management, since time does not follow a chronological order. The ‘‘representative days” method involves the chronological order of time within a day and can thus account for the diurnal variation management typically provided by batteries or demandside management. However, the inclusion of reservoir hydro power and thermal cycling in all the methods belonging to this family is difficult, since their modeling requires consecutive time over long time periods. The approach used in the SWITCH model [15] is a mixture of the method Families 1 and 2, and applies a method that contains a relatively high number of time steps (6 h per day for 2 days per month), accompanied by dispatch verification of capacity sufficiency. This approach could account for the reservoir hydro power, since the days follow a chronological order, although it would not be compatible with the cycling of thermal generation, which requires longer stretches of consecutive hours. Brouwer et al. [16] specifically addressed the role of thermal generation in future electricity systems by applying a method that pertains to Family 2 according to the categories suggested above. They combined a traditional investment model (MARKAL), which does not address variability, with a dispatch model that accounts for thermal cycling (Repowers). Deane et al. [17] have presented a similar approach using TIMES and PLEXOS to highlight the need for complements to traditional investment modeling, and Goransson et al. [18] have used the ELIN investment model together with the EPOD dispatch model to investigate the impact of demand-side management on

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transmission system congestion. Methods that belong to the family 2 models can take advantage of methods that account for variation management, including thermal cycling, as developed for dispatch models. The key challenge for methods in this family is the feedback of information from the dispatch step to the investment step. The lack of strategies to achieve convergence between investment and dispatch models implies in practice that the consequences of variations, in terms of (for example) thermal cycling, can be analyzed in detail but will not directly affect the investment decision. Vithayasrichareon et al. [19] evaluate a large set of system compositions, each optimal depending on which properties that are accounted for, with a dispatch modeling tool and calculate how cycling impacts on costs and emissions based on the modeling results. Their proposed approach, thus similar to other Family 2 approaches, combine a separate investment model and dispatch model, but avoid the need for iterations through the evaluation of a large number of system compositions. However, since accounting for cycling in the dispatch reduces the number of cycles [8], cycling costs and emissions from post-analysis risk to be overestimated. Also, the approach does not guarantee that a costoptimal system composition is identified. De Jonghe et al. [20] have applied a method (can be categorized into Family 3) to assess the cost-optimal generation mix. They have shown that wind power mainly increases the share of mid-load generation at the expense of base-load generation. The model used in their work has an hourly time resolution and is run for 1 year. Five technology options are considered in a single region (i.e., base, mid, peak, high peak, and wind power). Thermal cycling is accounted for by means of ramp rates. The main challenge facing this approach is the size of the model and the onerous calculation times, which impose limitations on the numbers of regions, years, and technologies that can be taken into account. Therefore, this approach is mainly suited to theoretical work on isolated systems and for single years, to reveal the characteristics of systems dynamics rather than for the modeling of existing systems. The modeling developed and applied in this work can also be assigned to the third family. However, in contrast to [20], the present model focuses on the impact of thermal cycling on the cost-optimal generation mix, with detailed modeling of 11 thermal generation technologies, and including properties such as minimum load level and start-up time, as well as costs and emissions from starting and operating thermal generation in part load. The results obtained from this work complement and facilitate the interpretation of results from investment models that pertain to Families 1 and 2, where the cycling of thermal generation has been disregarded (Family 1) or only accounted for subsequent to investment optimization by means of some dispatch analysis (Family 2). Thus, the present work aims to provide guidance regarding the further development of methods to account for variability and variation management in investment modeling of electricity systems. There are practical challenges linked to the inclusion of the cycling properties of thermal generation in investment models. The most straightforward way to model the cycling properties of thermal generation, as commonly applied in dispatch models, is to introduce one binary variable per unit and time step, which indicates whether a unit is online and ready for operation or not. Weber [21] has proposed a method to account for cycling properties that allows for the aggregation of units and that does not require binary variables. This approach was evaluated and compared to the binary approach in [22] and was found to provide good estimates of the total cycling costs for the system, as well as estimates of the full-load hours (FLH) on the technology level. The method proposed by Weber allows for the inclusion of cycling properties in models that analyze systems with a wide geographic scope, such as the EPOD dispatch model [22]. However, also for such an approach, accounting for cycling is costly in terms of

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computational time, due to the additional variables and constraints that must be included to account for the cycling properties. A high time resolution, to capture the variations, and consecutive timesteps are prerequisites for the inclusion of cycling properties. In the present study, we construct an investment model which is unique with regards to the level of detail on how it accounts for cycling properties. This level of detail can be achieved through trade-offs in terms of time horizon (one year) and geographical scope (one region). Accounting for thermal cycling, the model can investigate the impact of cycling costs towards the goals of: (1) outlining the dynamics between the capacity mix and cycling properties; and (2) determining whether the impact from thermal plant cycling is sufficiently important to necessitate the inclusion of cycling in investment models that cover a large geographic region and with a time-span of several decades.

2. Methodology The cost-minimizing investment model is developed and applied to a regional setting. A green field approach is applied, i.e., it is assumed that no capacity is in place prior to the optimization and that investments are driven by the need to meet the demand for electricity. To furnish the model with a realistic description of the variabilities of demand and generation, the demands for electricity and renewable resources (wind resources and solar irradiation) in western Denmark are used. Table 1 lists the cases that were investigated for each scenario. The two scenarios represent favorable conditions for bio-based generation and for base load, respectively. Thus, the Bio scenario in Table 1 represents favorable conditions for bio-based generation (biomass costs are 30 EUR/MWh), whereas the Base load scenario applies reduced costs for nuclear power investment (reduced by 10%) and a doubling of the biomass costs (to 60 EUR/MWh), i.e., reflecting a future with high competition for biomass. The region of western Denmark is rich in wind resources, which means that the main scenarios share good conditions for wind power generation (a potential of almost 7 GW at sites offering wind power at production costs of 42 EUR/MWh). Both scenarios are subject to a tight cap on carbon dioxide emissions, i.e., 128 ktonnes/year or less than 0.5% of Denmark’s 1990 levels. In addition, each scenario is subjected to variations (cases in Table 1), to investigate the degrees to which cycling emissions, as well as the increased variations in wind and solar power imposed on the system, influence the impact of cycling on the cost-optimal composition of the system. Thus, cases with uncapped emissions are also modeled (Bio uncapped w/ cycling, Bio uncapped w/o cycling), as well as

the more hypothetical cases with no option to invest in wind or solar power (Bio w/o vRES w/cycling, Bio w/o vRES w/o cycling, Base load w/o vRES w/ cycling, and Base load w/o vRES w/o cycling). 2.1. Optimization model The model developed in this work is a linear model that minimizes the total cost of the electricity generation system, i.e., the sum of the costs for investment, operation, and cycling. The cycling properties of thermal generation, i.e., minimum load level, start-up time, start-up costs and emissions, as well as the part-load costs and emissions, are accounted for according to the method proposed by Weber [21]. In line with the cases described in Table 1, the inclusion of cycling properties is optional in the model, and we compare the results from the model with and without cycling properties. The model differentiates between actual generation and ‘‘hot capacity” available for generation. Start-up costs and emissions are added as the amount of ‘‘hot capacity” is increased, whereas part-load costs and emissions are proportional to the difference between actual generation and ‘‘hot capacity”. The model is presented in the Appendix A. The model is run for 1 year, represented by four seasonal 3-week periods with 3-h time resolution. 2.2. Cycling-dependent capacity In the modeling results, we denote cycling-dependent capacity as that share of the total installed capacity that relies on whether or not the cycling properties are included. As indicated above, the concept of cycling includes both emissions and costs related to starting and shutting down power plants, as well as the operation of power plants at part load. The cycling dependency, icc, is the share of installed capacity from the positive difference in installed capacity if cycling is omitted (inocc ) or included (iwithcc ), summed over all the technologies and then divided by the total installed capacity in the cycling case (reference):

P

icc ¼

p maxð0; inocc

X

i p with

 iwithcc Þ

ð1Þ

cc

Thus, the case with cycling is used as the reference, and when the cycling case gives less capacity than the case without cycling the difference in total installed capacity is included as a fictitious technology. The set p⁄ include this fictious technology as well as all modeled technologies.

Table 1 Key properties of the cases investigated. Scenarios cases

Investment cost for nuclear power

Fuel cost for biomass (biogas)

Cycling properties

CO2 emission constraint

Wind and solar power investments allowed

Bio scenario Bio w/ cycling Bio w/o cycling Bio uncapped w/ cycling Bio uncapped w/o cycling Bio w/o vRES w/ cycling Bio w/o vRES w/o cycling

High High High High High High

Low Low Low Low Low Low

Yes No Yes No Yes No

Yes Yes No No Yes Yes

Yes Yes Yes Yes No No

Base load scenario Base load w/ cycling Base load w/o cycling Base load w/o cycling costs Base load w/o cycling emissions Base load w/o vRES w/ cycling Base load w/o vRES w/o cycling

Low Low Low Low Low Low

High High High High High High

Yes No Yes, except for cycling costs Yes, except for cycling emissions Yes No

Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes No No

w/, with; w/o, without. The four base cases, representing the two scenarios with and without cycling, are given in bold.

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ties are given by Jordan et al. [26], although with biogas as the start-up fuel. Start-up costs depend on down-time, i.e., the longer the unit has been idle the more costly it is to re-start. However, accounting for down-time in start-up cost estimates entail the introduction of non-linearity into the model. Therefore, start-up costs representing warm starts have been applied at all the startup occasions, corresponding to a down-time of around 8 h. Cycling properties for nuclear power are not provided by Jordan et al. [26]. The minimum load level of nuclear power is assumed to be 70% based on the work of Persson et al. [28]. The start-up cost for nuclear power is assumed to be the cost of running nuclear power at the minimum load level during the start-up time, which is assumed to be 20 h, as taken from Persson et al. [28]. Table 3 gives the fuel costs and carbon dioxide emissions per unit of fuel for the modeled fuels. In Table 3, there are two different fuel costs for biomass and biogas; the lower value pertains to the Bio scenario and the higher value is applied in the Base load scenario. The biogas used in the gas turbines and combined cycle plants is assumed to be gasified biomass with a 70% conversion efficiency and there are 20 EUR/MWh in additional running costs for the gasifying equipment, based on previous work [29]. The cost of the gasifier is included as an additional running cost rather than a capital cost, since biogas can be stored, which allows for more

2.3. Costs and assumptions Table 2 lists the key properties and costs for the modeled technologies, including the cycling properties. Investment costs and fixed operation and maintenance costs are taken from the IEA [23] and extrapolated to Year 2050 by assuming continuous cost reductions for wind and solar power. In Table 2, there are two different investment costs for nuclear power; the lower value is applied in the Base load scenario, whereas the higher value is applied in the Bio scenario. For biomass-only steam plants, we apply an electric efficiency based on recent advancements in Denmark [24,25], i.e., assuming that all future biomass-only steam plants will have such a level of efficiency. Traditional variable operation and maintenance costs include cycling costs to some extent [26]. In this work, the cycling costs are accounted for separately, so the variable operation and maintenance costs (excluding cycling) have been applied based on the work of Jordan et al. [26]. In addition to the variable operation and maintenance costs listed in Table 2, technologies with carbon capture and storage (CCS) are subject to carbon dioxide transportation and storage costs of 5.8 EUR/tCO2 and 5.4 EUR/tCO2, respectively (countryspecific figures taken from Kjärstad et al. [27]). When calculating annuities based on investment costs an 8% interest rate is applied. The start-up costs consist of two parts: (1) start-up costs linked to increased operation and maintenance costs for heating and cooling the plant; and (2) start-up costs for fuel consumption during the start-up phase. The cycling properties of all the modeled thermal generation with solid fuels apply cycling properties of large sub-critical coal, with oil as the start-up fuel. Thus, it is here assumed that the capture and storage of carbon dioxide does not imply reduced flexibility and that the cycling costs of natural gas-fired units with CCS apply the cycling properties of gas-fired steam plants with natural gas as the start-up fuel. Biogas-fired technologies are assumed to have the same cycling properties as their natural gas-fired counterparts, for which the cycling proper-

Table 3 Fuel costs and emission levels. Fuel

Cost [EUR/MWhfuel]

CO2 emissions [kgCO2/MWhfuel]

Coal Lignite Natural gas Biomass Biogas Uranium

9.8 5.5 34.3 30/60a 62.9/105.7a 8.1

342 400 207 0 0 0

a

Lower cost in the Bio scenario, higher cost in the Baseload scenario.

Table 2 Technologies included in the model and their properties. Fixed costs Technology

Cycling properties

Investment [EUR/kW]

O&M [EUR/kW yr]

Variable O&M [EUR/MWh]

Efficiency

Minimum load level

Start-up time [hours]

Start-up cost [EUR/MW]

Start-up emissions [tonne/MW]

Wind power Onshore Offshore

1192 1838

30 100

1 1

– –

– –

– –

– –

– –

Solar PV Fixed Tracking

724 923

20 20

1 1

– –

– –

– –

– –

– –

Thermal GT CCGT Steam Coal Steam Lignite Nuclear Steam Biomass

378 780 1560 1560 5148/4633c 1856

8/50a 13/50a 27 32 154 50

0.7 0.8 2 2 0 2

0.42 0.71 0.56 0.56 0.43 0.5

0.5 0.2 0.35 0.35 0.7 0.35

0.25 6 12 12 24 12

33/47a 44/46a 251 251 663 251

0.09/–b 0.01/–b 0.8 0.8 – 0.8

CCSd CCGT Steam Coal Steam Lignite

1800 3003 3003

35 90 90

1 1 1

0.53 0.43 0.43

0.35 0.35 0.35

12 12 12

251 251 251

0.8 0.8 0.8

Co-fire CCSe Steam Coal Steam Lignite

3463 3463

107 107

1 1

0.41 0.41

0.35 0.35

12 12

251 251

0.8 0.8

O&M = Operation and Maintenance. a Higher cost with biogas as the fuel, lower cost with natural gas as the fuel. b No emissions with biogas as the fuel, higher emissions with natural gas as the fuel. c Higher investment cost for nuclear in the Bio scenario and lower cost in the Baseload scenario. d Units with Carbon dioxide Capture and Storage. e Units with biomass co-firing. Biomass makes up 10% of the fuel mix during operation (not in the start-up phase).

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FLH of the gasifier (8000 FLH are assumed here), as compared to the power plant combusting the biogas to produce electricity. With respect to CO2 emissions, the capture rate for CCS technologies is assumed to be 90%. The modeled co-fired CCS technologies have biomass fractions of 10% to compensate for the non-captured fossil carbon, which implies that they are carbon-neutral when running on rated power. For the Base load scenario, the biomass cost is increased to 60 EUR/MWh, with a corresponding cost increase for biogas. However, the biomass used for co-firing in CCS plants is assumed not to be influenced by the biomass cost increase, meaning that low-grade biomass can be used in this case. The annual demand for electricity is set at 23 TWh, and the demand profile is based on electricity demand data for western Denmark for Year 2010 [30]. The wind and solar resources and profiles are based on the ERA-Interim dataset [31], with profiles for year 2010. Wind speeds from that dataset [31] are transformed into generation profiles based on the future lowland and offshore windfarm power curves developed within the TradeWind project [32]. For onshore wind power, wind sites are grouped into classes based on capacity factor, i.e., 35%, 30%, 25% etc. The capacity density is assumed to be 390 kW/km2, which is just above current capacity density of regions with extensive wind power generation, such as Schleswig-Holstein, Brandenburg, Niedersachsen and Jutland. Offshore wind power in this case pertains to a separate class with 3440 FLH based on average offshore site properties from the applied dataset for western Denmark. For fixed solar PV, the FLH are estimated as 1000 based on irradiance and temperature data from ECMWF [31], whereas this is increased to 1400 h for tracking devices based on data from the same reference.

Fig. 1 depicts the relationships between the annual running costs and the annualized fixed costs (i.e., the levelized cost of electricity) of the technology options with low or no carbon dioxide emissions, i.e., representing screening curves for the Bio scenario and Base load scenario. The screening curves give the annual costs, i.e., the annualized investment cost and the running cost, for different FLH. Running costs here include the costs associated with the transportation and storage of carbon dioxide for technologies with CCS. Fig. 1 indicates the impact on competitiveness of the higher cost of biomass (resulting in higher running costs for bio-based technologies) and lower investment cost for nuclear power in the Base load scenario (Fig. 1b), as compared to the Bio scenario (Fig. 1a). Generation technologies designed to supply peak load (i.e., gas turbines) have comparatively low annual costs for low FLH (to the left in the screening curve), whereas technologies designed to supply base load (i.e., nuclear) have low annual costs for high FLH (to the right in the screening curve). Cycling costs, which differ from hour to hour, are not included in the screening curves. 3. Results 3.1. The impacts of cycling in the Bio scenario and Base load scenario Figs. 2 and 3 show the cost-optimal system composition and the annual level of electricity generation as obtained from the model for the two main scenarios, with and without accounting for cycling properties (minimum load level, start-up time, costs and CO2 emissions for start-up and part-load). In the Bio scenario with

Fig. 1. Cost structure (fixed costs and running costs excluding cycling costs) for the CO2-lean and CO2-free thermal technologies considered in the model for the: (a) Bio scenario; and (b) base load scenario.

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Fig. 2. Cost-optimal system compositions obtained from the modeling in this work for: the Bio scenario with and without cycling; and the Base load scenario with and without cycling.

Fig. 3. Annual electricity generation levels for: the Bio scenario with and without cycling; and the Base load scenario with and without cycling.

cycling costs, the cost-optimal system is dominated by wind power (40% of the capacity and 40% of the generation), bio-based generation (30% of the capacity and 15% of the generation), and coal CCS co-fired with biomass (15% of capacity and 40% of the generation), whereas nuclear power dominates the Base load scenario (30% of capacity and 65% of the generation), while wind power is reduced. Taking the cycling costs into account, the wind power penetration levels (i.e., annual wind power generation relative to annual demand) are 40% in the Bio scenario and 20% in the Base load scenario As shown in Fig. 2, there is a lower cycling-dependent capacity (cf. Eq. (1)) in the Bio scenario (icc = 9%) than in the Base load scenario (icc = 19%). In the Base load scenario, the inclusion of cycling properties reduces the total amount of installed capacity mainly in terms of reduced wind power capacity. In the Bio scenario, cycling properties do not influence the total installed capacity, although in both scenarios, cycling properties have a substantial impact on system composition. In the Bio scenario (Fig. 2a), the model indicates that the inclusion of cycling properties (Bio scenario with cycling) results in an increase in the investments in biogas combined cycle units and coal CCS co-fired with biomass at the expense of biomass steam

plants, as compared to the Bio scenario without cycling. As can be seen in Fig. 1a, biomass steam units, biogas combined cycle units, and coal CCS units co-fired with biomass all compete for production in the 2000–5000 FLH interval. Biomass steam units and biogas combined cycle units have similar annual costs (i.e., running costs and annualized investment costs) at the lower end of this FLH interval, whereas biomass steam and coal CCS co-fired with biomass have similar annual costs at the upper end of the FLH interval. For these technologies, the inclusion of cycling properties modifies the cost relations in the 2000–5000 FLH interval and thereby affects the cost-optimal investment levels. The costoptimal level for biogas-fired gas turbines, which is the technology that offers the lowest costs at low FLH (see Fig. 1a), does not depend on whether or not the cycling properties are taken into account. Similarly, the cost-optimal level for wind power does not depend on whether or not the cycling properties are taken into account. At low FLH, a low investment cost is the key factor for competitiveness, and in this aspect, the biogas gas turbines are superior to the other options considered by the model. As is evident from Fig. 2a, the level of investment in wind power is not influenced by cycling in the Bio scenario, since technology options

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with low cycling costs, such as biogas combined cycle units, offer low annual costs compared to other technology options for a wide range of FLH. In the Base load scenario (Fig. 2b), the inclusion of cycling properties affects the cost-optimal level of wind power investments. As shown in Fig. 1b, nuclear and coal CCS co-fired with biomass have annual costs that are lower than the other technology options from 2000 FLH and above, and are increasingly lower with increasing FLH. Given the cost structure of the base load technologies, with high investment costs and low running costs, the cost-optimal level of wind power is lower in the Base load scenario than in the Bio scenario, even if cycling properties are not taken into account. The cost-optimal level is further reduced as cycling costs are accounted for, since both nuclear and coal CCS co-fired with biomass have high cycling costs of 663 EUR/MW and 251 EUR/ MW, respectively (see Table 2). The offset of wind power is mainly compensated for by the higher cost-optimal level of nuclear power in the cycling case. There are also additional investments in biogas combined cycle units. The Base load scenario also illustrates the impact of accounting for cycling emissions. When cycling emissions are included, and under the assumption that there is no carbon capture during the start-up phase for all the CCS units, there is no room for CCS units without co-firing (cf. natural gas CCS in Fig. 2).

biomass as load-following plants, and the installed capacity of this technology is overestimated (compare Base load w/o cycling emissions to Base load w/ cycling). Even more striking are the investments in natural gas turbines and natural gas with CCS in the Base load w/o cycling emissions case, which are associated with carbon dioxide emissions under normal operation, an effect from neglecting the emissions from cycling coal CCS co-fired with biomass. Based on the results shown in Fig. 4, it is found that 7% of the capacity is cycling emission-dependent and 12% is cycling cost-dependent. Fig. 5 gives the cost-optimal system composition in the emissions uncapped case of the Bio scenario with and without cycling. The overall installed capacity is much lower than in the cases with wind and solar power, which is obviously due to the fact that the system is composed of thermal generation to a much greater extent. As the cap on carbon dioxide is removed, cycling exerts a very low impact on the cost-optimal system composition. For the Bio scenario, only 1% of the installed capacity is cyclingdependent (Eq. (1)), as compared to 9% with capped emissions. There are two main reasons for this difference: the relative cost structures of the competing technology; and the competitiveness

3.2. Cycling emissions and emission constraints Fig. 4 gives the cost-optimal system compositions for the Base load scenario with cycling costs and cycling emissions (Base load w/ cycling), without cycling costs but with cycling emissions (Base load w/o cycling costs), with cycling costs but without cycling emissions (Base load w/o cycling emissions), and without either cycling costs or cycling emissions (Base load w/o cycling). It is clear that in the Base load scenario, the inclusion of cycling costs is of greater importance than the inclusion of cycling emissions when assessing the system composition. However, under the assumptions made here, where emissions are constrained to remain below 128 kt of carbon dioxide emissions annually (corresponding to less than 0.5% of Denmark’s Year 1990 carbon dioxide emission levels) and with all the fossil-fired units running without CCS during the start-up phase, the impact of cycling emissions on the costoptimal system composition is significant. If cycling emissions are omitted, there is room to operate coal CCS plants co-fired with

Fig. 5. Cost-optimal system composition for the Bio scenario without a cap on carbon dioxide emissions, with and without cycling being taken into account.

Fig. 4. Cost-optimal system composition in the Base load scenario, as obtained from the modeling in this work, with (from the left) cycling costs and emissions, with cycling costs but without cycling emissions, with cycling emissions but not cycling costs, and without cycling costs or cycling emissions.

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Fig. 6. Cost structure for thermal technology options, including technologies that generate carbon dioxide emissions.

Fig. 7. Cost-optimal system composition without wind power and solar power (w/o vRES) as investment options in (from the left): the Bio scenario with cycling; the Bio scenario without cycling; the Base load scenario with cycling; and the Base load scenario without cycling.

of wind power relative to that of thermal plants. The first difference can be seen from the screening curves in Fig. 6 for the Bio scenario in the cases without any cap on emissions, which shows how coal is the technology that offers the lowest costs for 2000 FLH. Competition between the technologies exists only in the first quarter of the screening curve, where investment costs dominate the relationship between technologies. These cost relationships are different to the cost relationships in Fig. 1, where the different biomass- and biogas-based technologies compete in the midquarters of the screening curve and the inclusion of cycling costs shifts their positions. For the Bio scenario in the cases without a cap on emissions (Bio uncapped w/cycling and Bio uncapped w/o cycling), the cost of thermal base load is low and wind investments remain low and do not challenge the cycling properties of the baseload units, which also explains why the level of investment in wind power is not influenced by cycling in these cases. 3.3. Cycling dependency in systems without vRES Fig. 7 gives the optimal system composition for the Bio scenario and the Base load scenario with and without cycling if, hypothetically, wind and solar investments are not possible (referred to as the Bio w/o vRES and Baseload w/o vRES cases, respectively, in Table 1), although there is still a cap on emissions. As in the cases

without a cap on carbon dioxide emissions (shown in Fig. 5), here the overall capacity is much lower than in the cases with wind and solar power. In these cases, this is obviously due to the fact that the system consists exclusively of thermal generation. As Fig. 7 shows, substantial differences in system composition appear when cycling is accounted for compared to when cycling is omitted, even if variable renewables are excluded from the system. In the Bio scenario, more of the capacity is cycling-dependent [Eq. (1)] if wind power and solar power are excluded from the system, i.e., 14% versus 9% when wind power and solar power are included. This is the case because the bio-based technologies that compete in the 2000– 5000 FLH range represent a relatively larger share of the system when wind power and solar power are excluded compared to when wind power and solar power are included. For the Base load scenario, the impact of cycling is reduced as wind power and solar power are excluded, from 19% down to 12% cycling-dependent capacity. The remaining differences between the cycling and no cycling cases are mainly emission-related. Due to the emissions cap, the share that can be supplied by coal CCS co-fired with biomass (which is assumed to be started with oil without capture) in the 2000–5000 FLH range is limited if cycling emissions are included. In the case without cycling, the emissions space is filled by operating with natural gas with CCS.

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4. Discussion In this study, we show that it is critical to include cycling properties in investment modeling, in order to assess investments in thermal generation technologies that compete at utilization times in the range of 2000–5000 h. Within this range, we identify three situations that create cycling-dependent capacity. First, in the Bio scenario with a cap on emissions (Bio w/cycling compared to Bio w/o cycling and Bio w/o vRES w/ cycling compared to Bio w/o vRES w/ cycling), biomass steam and biogas combined cycle plants, with similar relationships between fixed and running costs and low levelized costs for the 2000–5000 full-load-hour range, create cycling-dependent capacity due to their significantly different cycling properties. Second, in the Base load scenario (Base load w/ cycling compared to Base load w/o cycling), a cyclingdependent capacity arises due to the fact that thermal generation technologies with high cycling costs (i.e., nuclear power) have low levelized costs in this full-load-hour range relative to other thermal generation technologies in a system with costcompetitive variable renewables. Third, again in the Base load scenario, low-emissions-associated generation technologies with a relatively low levelized cost in this full-load-hour range with significant cycling emissions (i.e., coal CCS co-fired with biomass) give rise to cycling-dependent capacity, albeit only in a system that is subject to an emission cap. This is particularly apparent in the cases without variable renewables (Base load w/o vRES w/cycling compared to Base load w/o vRES w/o cycling). The results of the modeling presented in this work reveal that cycling mainly affects technologies with low levelized cost in the 2000–5000 full-load-hour range. This can be explained by the fact that units with 2000–5000 FLH are typically cycled frequently, in contrast to base-load generation, while running costs make up a substantial part of the annual cost, in contrast to peak-load generation for which investment costs generally are of greater relevance. The finding that cycling properties can have a significant impact on the cost-optimal composition of systems with low or no potential for variable generation deserves to be highlighted. Only one out of three of the system characteristics identified as having modeled capacity that is cycling-dependent is related to variable generation. However, none of these characteristics is present for the system investigated when there is no cap on carbon emissions. A tight cap on carbon emissions could trigger the appearance of all three characteristics by eliminating technology options that traditionally have had the lowest levelized cost for utilization times in the range of 2000–5000 h, by increasing the relative competitiveness of variable renewable generation, and by reducing the cycling ability of thermal generation with cycling emissions. To model the cycling properties, the time-steps need to be in chronological order, as employed in the modeling of this work. In addition, due to the magnitude of the start-up costs relative to the running costs, the modeled timeframe for which the chronology is maintained needs to be long (i.e., weeks), to avoid boundary effects, such as low utilization of base load with high start-up costs. Therefore, there are clear limitations as to the representative days and time-slice approaches for the purpose of modeling the costoptimal composition of an electricity system operated under a tight carbon emissions cap. The wind farm power curve applied in the model dates back a number of years [32]. New wind turbines are often dimensioned with lower specific power than older ones (i.e., the rotor is larger relative to the generator in new turbines), resulting in higher capacity factors for new wind farms and generation profiles that differ from those on which the power curve applied in this work are based. As the specific power of wind turbines is reduced, the FLH of complementary generation, such as thermal generation,

are likely reduced. The specific impact of reducing the specific power of wind turbines on the cost-optimal composition of the systems is a topic for future research. 5. Conclusions A regional, cost-minimizing, investment model that accounts for start-up time, minimum load level, start-up costs and emissions and part load costs and emissions is presented, and it is applied to investigate the impact of thermal plant cycling on the cost-optimal composition of a regional electricity generation system. It is found that future biomass and biogas prices may have a substantial impact on the cost-optimal wind penetration level. If bio-based generation is cost-competitive (in terms of levelized cost) for utilization times in the range of 2000–5000 h, the costoptimal wind power penetration is substantially higher than for a system in which the base-load generation is cost-competitive for this utilization time-span. The inclusion of cycling properties enhances this effect. Furthermore, cycling dependent capacity is quantified and coupled to system characteristics. The magnitude of the impact of thermal cycling on the cost-optimal system composition and the system characteristics which cause cycling dependency is crucial information when choosing method for investment modeling and when evaluating model results. We conclude that it is critical to include cycling properties in investment modeling in order to assess investments in thermal generation technologies, especially in the case of generation that is competitive at utilization times in the range of 2000–5000 h. Within this range, we find the following characteristics of thermal plants linked to cycling-dependent capacity: d when technologies with similar cost structures but with significantly different cycling properties are the lowest cost options; d when technologies with high cycling costs (i.e., nuclear power) are the lowest cost options and wind power is cost-competitive; and d when low emission generation technologies with significant cycling emissions (i.e., coal CCS co-fired with biomass) are the lowest cost options and there is a cap on carbon emissions. For the systems investigated with the above characteristics, 9–19% of the capacity is found to be cycling-dependent [defined by Eq. (1)]. Acknowledgments This work was co-financed by the Swedish Energy Agency and the research programme Pathways to Sustainable European Energy Systems. Appendix A. Model formulation A regional cost-minimizing investment model is developed to investigate the impact of thermal-plant cycling on the costoptimal electricity composition. The model accounts for cycling properties of thermal generation. Specifically, minimum load level, start-up time, start-up costs and emissions as well as part load costs and emissions are taken into consideration. The model is run for 1 region and 1 year, represented by 4 seasonal 3-week periods with 3-h time resolution. In order to not overestimate the impact of cycling while optimizing over a set of separate 3-week periods, units can be taken online without cycling cost penalty or

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start-up time considerations the first time step in each 3-week period. With this strategy the impact of cycling is slightly underestimated. The objective function of the model can be expressed as;

Once capacity deactivated, it cannot be active again during the interval K encompassing the time steps k in the start-up interval.

ðA1Þ

There is a cap on total carbon dioxide emissions from electricity generation in the model, Ecap . Emissions from generation, Ep;t , as well as from starting plants, Eon p;t , and operating them at part load,

MIN C tot

X inv XX run ¼ C p ip þ ðC p;t g p;t þ ccycl p;t Þ p2P

p2P t2T

where P T TT v C in p

ip C run p;t g p;t ccycl p;t

is the set of all technology aggregates is the set of all time steps is the set of all time steps except for the first time step in each week is the investment cost of technology p is the investment in technology p is the running costs of technology p at time t is the generation in technology aggregate p in time step t is the cycling costs (summed start-up costs and part load costs) of technology aggregate p at time step t.

The demand for electricity, Dt , has to be met at all times. Thus,

ðA2Þ

p2P

Generation has to stay below installed capacity, weighted by profile, W p;t , which is weather dependent for wind and solar power (but constantly equal to one for thermal technologies).

g p;t 6 ip  W p;t ; 8t 2 T; 8p 2 P

ðA3Þ

Investments in wind and solar power capacity cannot exceed regional resources on respective technology, Rp; . For onshore wind, sites are ordered into classes depending on wind conditions and there is a resource constraint to every class. Offshore wind pertain to an own class.

ip 6 Rp; 8p 2 Pwind

ðA4Þ

where P wind is the set of wind classes. For solar there is a total resource constraint for the modeled region.

p2P solar

ðA10Þ

Epart p;t , hare accounted for.

X g p;t P Dt ; 8t 2 T

X

activ e g on p;t 6 ip  g p;tk ; 8k 2 K

ip 6

X

Rp

ðA5Þ

p2P solar

In order to account for cycling properties of thermal generation there is a separate variable for capacity which is active and available for generation in each technology aggregate for each time v e . Thus, step, g acti p;t activ e g p;t 6 g p;t ; 8t 2 T; 8p 2 P

ðA6Þ

The active capacity is used to assure that generation is greater : than the minimum load level, Lmin p activ e Lmin  g p;t 6 g p;t ; 8t 2 T; 8p 2 P p

ðA7Þ

The amount of capacity started is controlled by the variable g on p;t : This constraint does not apply to the first time steps in each week in order to not overestimate the impact of cycling. activ e activ e g on  g p;t1 ; 8t 2 TT; 8p 2 P p;t P g p;t

ðA8Þ

The start-up cost is proportional to started capacity g on p;t and the part load cost is proportional to the difference between active capacity and generation: on on activ e ccycl  g p;t ÞC part p;t P g p;t  C p;t þ ðg p;t p;t ; 8t 2 T; 8p 2 P

ðA9Þ

XX on cap activ e g p;t Ep;t þ g on  g p;t ÞEpart p;t  Ep;t þ ðg p;t p;t 6 E t

ðA11Þ

p2P

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