WP EN2014-20

The Importance of Integrating theVariability of Renewables in

Long-term Energy Planning Models

K. Poncelet, E. Delarue, J. Duerinck, D. Six, W. D’haeseleer

TME Working Paper - Energy and Environment

Last update: October 2014

An electronic version of the paper may be downloaded from the TME website:http://www.mech.kuleuven.be/tme/research/

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The Importance of Integrating the Variability of Renewables in

Long-term Energy Planning Models

Kris Poncelet*,#, Erik Delarue*, Jan Duerinck+, Daan Six+ and William D’haeseleer*

#Ir. Kris Poncelet, Leuven, Belgium, kris.poncelet@mech.kuleuven.be*University of Leuven (KU Leuven) Energy Institute TME Branch, EnergyVille

+VITO, EnergyVille

Abstract

Long-term (LT) energy system planning models are frequently used in studies analyzing the transitiontowards a sustainable energy system. To limit the computational cost, these LT models typically have alow temporal resolution. In addition, these models typically operate at a technology level, rather thanconsidering individual power plants. Therefore, operational constraints and cycling costs of power plantscannot be represented, i.e. the operational detail is low. For energy systems with a high penetrationof intermittent renewables (IRES), these simplifications could have a substantial impact on the modeloutcome. This work analyzes and quantifies the impact of both the low temporal resolution and thelimited operational detail for an increasing share of intermittent renewables in the generation mix. Tothis end, the results of an LT planning model using a low temporal resolution are compared to an LTmodel using hour-by-hour data. Furthermore, to assess the impact of the limited operational detail, thedispatch of the LT models is re-evaluated using a mixed integer linear unit-commitment (UC) model.Using a low temporal resolution is shown to lead to an inaccurate representation of the residual loadduration curve (RLDC), in turn leading to sub-optimal investments, an over-estimation of the potentialuptake of IRES and an underestimation of operational costs. Neglecting operational detail of powerplants leads to similar effects, though to a lesser degree. These effects are quantified for a case studybased on the Belgian power system. In this study, operational costs in the LT model with at a lowtemporal resolution are shown to be 3-58% lower than in the corresponding UC model depending on theshare of IRES (highest deviations are observed for the largest share of IRES). For the model with a hightemporal resolution the underestimation of operational costs is only 3-23%.

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1 Introduction

Bottom-up, long-term (LT) energy system planning models are frequently used to analyze pathways for the tran-sition of the energy system and to deduce policy advice for policy makers. In this category of models, popular ex-amples are, amongst others, the MARKAL/TIMES framework, PRIMES, EnergyPLAN, IKARUS and PERSEUS[1]. Recently, these types of models were used in a variety of studies to analyze (aspects of) the evolution towardslow-carbon energy systems [2]-[10].

To decarbonize the energy system, the share of renewable generation is expected to increase significantly,especially in the electricity sector [11]. Some of these renewable energy sources, such as wind energy and solarphotovoltaic (PV) energy, are characterized by an intermittent character, i.e. they are (highly) variable and havea limited predictability. Due to these characteristics, a large penetration of intermittent renewables (IRES) canhave a significant impact on the operation of the power system. First, as demand and supply have to be incontinuous balance, the intermittency of IRES causes the residual load (i.e. the load that has to be supplied bydispatchable generators) to become increasingly volatile. Short-term variations in wind and solar PV power outputcan be severe [10], increasing the need for cycling of dispatchable power plants (i.e. changing the power output byramping up/down or by switching on/off). Furthermore, as the residual load is also lowered by the penetration ofIRES, a shift towards increased cycling of baseload plants can be observed [12],[13]. From the perspective of theplant operator, this increased cycling imposes additional costs. These cycling costs can roughly be divided intoadditional fuel usage during start-ups and part-load operation, on the one hand, and increased capital, maintenanceand opportunity costs due to increased wear and tear of components, on the other hand [14]. Furthermore, theload-following capabilities of power plants are limited. As the share of IRES in the electricity system is increased,the increased short-term volatility of the residual demand pushes plants against their operational constraints (e.g.,limited ramping rates, minimal up and down times etc.) [15]. From a system perspective, an additional costcomponent must be considered as these technical constraints can cause a shift from baseload generation towardsmore expensive mid/peakload generation. Second, sufficient back-up capacity is needed to deal with periods inwhich IRES output is low. For this reason, the IRES capacity cannot contribute to meeting long-term generationadequacy to the same extent as dispatchable power plants. In other words, IRES capacity is less firm. Third, theoutput of IRES can only be predicted to a limited extent. Uncertainty does not only exist for IRES output, butalso for the aggregated demand and equipment availability. To cope with this uncertainty, transmission systemoperators (TSOs) contract reserves to provide balancing services. An overview of different studies determining theadditional need for reserves resulting from high levels of wind penetration is included in [16], whereas the resultingcosts of these uncertainties for the Belgian electricity system are presented in [17].

Long-term energy system planning models typically use a low temporal resolution (i.e., a year is typicallyrepresented by 1-12 so-called time slices) and do not preserve the chronology of intra-annual demand and supplyvariations. Furthermore, these models typically operate at a technology-type level, rather than considering individ-ual power plants and corresponding technical load-following constraints and cycling costs. This level of detail waspreserved for operational models (i.e., unit-commitment models) which focus on the commitment and dispatch of agiven set of power plants for a period up to 1 year. Historically, these details could be ignored in planning modelswithout a major impact on the quality of the results due to the highly predictable and fairly slow dynamics of loadvariations [15],[18]. However, recently it is becoming more and more clear that the classical approach used in LTplanning models falls short of accurately representing the unit commitment and dispatch for electricity systemswith high shares of IRES. In this regard, a myriad of approaches attempting to bridge the gap between planningmodels and operational models is currently being developed [10],[15],[18]-[24].

In this work, the focus lies on analyzing the impact of the simplifications typically used in LT planning modelsfor generation expansion planning with a high share of variable renewables (VRES1). The analysis is split up intwo parts: a first part treats the impact of temporal aspects while the second part focusses on the impact of thelimited level of operational detail2. The temporal aspect is addressed by comparing the model results for twoplanning models differing only in the considered temporal resolution. A planning model that uses 8736 intra-annual time slices (52weeks×7days×24hours) serves as the reference for this comparison. To analyze the effect ofincluding operational detail, the investments in generation technologies in the planning model are translated to aset of individual generating units which serve as input data in a UC model. This allows re-evaluating the dispatchdecisions and corresponding operational costs.

The remainder of this paper is organized as follows: section 2 presents a literature review on the impact of the

1This work only considers the variability of intermittent renewables, i.e. deterministic profiles are assumed and uncertainty is nottaken into account. For this reason, the term VRES is used in the remainder of this work.

2In this work, operational detail refers to both operational constraints of power plants (i.e., limited ramping rates, minimum up anddown times and a minimum operating point) and costs related to cycling (i.e., start-up costs, ramping costs and reduced efficienciesin part-load operation).

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temporal resolution and operational aspects in planning models. Subsequently, section 3 gives an overview of themethodology used in this work. The different models, used data and assumptions are presented in section 4. Themain results from the analysis are discussed in section 5 and finally a conclusion is presented in section 6.

2 Literature Review

In this section, a review of the relevant literature is presented. A first section focusses on the temporal aspect,while the second section focusses on the inclusion of technical operational detail.

2.1 Temporal Resolution

Recently, much attention has gone to the temporal representation of load and VRES feed-in variations in planningmodels. Following Haydt et. al. [25], we distinguish between three methods of dealing with the time dimension.In a first method, referred to as the ’integral’ method, the aim is to approximate the (residual) load duration curveby dividing a year into a limited number of time slices (typically 4-12) to represent seasonal, daily and diurnalvariations in demand and supply3. This method is used most frequent by energy-system planning models. A recentapplication of this method can be found in the JRC-EU-TIMES model in which 4 seasonal and 3 diurnal time slicesare used. To deal with the limitations of this time-slice division for modeling the variability of VRES, additionalequations have been implemented [9]. In a study assessing the possibilities for achieving a 100% renewable energysystem in Belgium by 2050, the number of time slices is increased to 78 (26 two-week periods and 3 diurnallevels) [2]. A second method, labeled the ’semi-dynamic’ method, is based on selecting typical days (or weeks)to represent an entire year. These selected days are in turned divided into a number of diurnal time slices. Bydoing so, this method aims to include short-term variations in VRES output (and the load) without drasticallyincreasing the number of time slices (and thus the computing time). This approach has one main advantage: therange of VRES generation levels is retained. In contrast, in the integral approach, the VRES generation in a specifictime slice is the result of taking the average value of all VRES generation levels occurring at times belonging tothis specific time slice. Besides clear advantages, the semi-dynamic approach also has some drawbacks. First,Pfluger and Wietschel rightfully point out that this approach does not capture the influence of weather phenomenalasting longer than one day/week [26]. For example, In Northern Europe, longer periods during which wind speedsare continuously very low have been observed during winter periods (in which demand in this region is high).Securing energy provision in these situations would require sufficient dispatchable back-up capacity or large hydroreservoirs, whereas short periods without wind can be managed more easily with short-term storage systems.Second, de Sisternes and Webster remark that there is no consistent criterion to select days/weeks or to assess thevalidity of the assumption [27]. A last method, labeled the dynamic method, uses sequential hourly data for theentire year. Due to computational limits, the use of this method is typically restricted to static4 power systemmodels.

Kannan and Turton [28] evaluate the effect of the temporal resolution on the dispatch capabilities of anelectricity generation planning model. For this reason, a planning model STEM-E for the Swiss electricity systemis created in the TIMES framework. The results of a first model version aggregating input data into 8 intra-annualtime slices is compared to the results of a model that operates at an hourly resolution by using 4 seasonal, 3daily (weekday, Saturday and Sunday) and 24 diurnal time slices (i.e. a total of 288 intra-annual time slicesare considered). However, due to the lack of hourly wind resource data, only seasonal variations in wind poweroutput are modeled. Nevertheless, the analysis reveals significant differences between both model versions dueto the aggregation of demand, solar PV availability and exogenously defined import and export prices. Ludiget. al. show to which extent the variability in load, solar PV generation and wind generation is captured fortime-slice divisions using the integral approach. The results show that increasing the number of diurnal time slicesis sufficient to capture most of the variability in load and solar PV generation. However, due to the stochasticnature of wind, increasing the number of diurnal time slices does not suffice for capturing the variability of windgeneration [24]. Pina et. al., [19] use an identical time-slice division as Kannan and Turton (288 time slices), butuse data of three selected typical days per season (semi-dynamic approach) rather than average data for each timeslice. Hourly wind and load data are used for each day. Model results are contrasted to models with less diurnaltime slices. Their analysis reveals a significant impact on the expected electricity generation from wind turbines.Similar results follow from a study of Haydt et. al., [25] in which the quality of the electricity dispatch is comparedfor all three methods: the integral method using 8 time slices, implemented in LEAP, the semi-dynamic methodusing the 288 time slices, implemented in a TIMES model and the dynamic method, implemented in EnergyPLAN.The dispatch results are compared for different levels of wind power penetration in the Flores island. However, due

3If data are available at a finer resolution, data values are aggregated into this limited number of time slices. On the other hand, ifdata are not available at a finer resolution, data can be disaggregated into values for each time slice, following a logic rule.

4Static power system models provide a snapshot of the equilibrium state of a power system. In contrast, dynamic power systemmodels provide information on the evolution of a power system over time.

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to the specifics of the case study (small system, electricity in the Flores island is generated by wind, run-of-riverhydro and very flexible diesel generators), the implications of the model results cannot easily be transferred tolarger power systems in Europe or the United States.

2.2 Operational characteristics and constraints.

An overview of the technical constraints of power plants and the corresponding mathematical formulation (in amixed-integer linear programming (MILP) formulation of the UC problem) is presented in [29]:

• Generation limits: When a plant is running, it is bounded upward by its capacity and downward by a minimalstable generation level.

• Ramping limits: A running plant can change its output with a limited rate, both upward and downward.

• Minimum up and down times: When a plant is switched on/off, it has to remain switched on/off for a certainamount of time.

Furthermore, costs are attached to start-ups and power ramps. Modeling these start-up costs and technicalconstraints involves tracking the on/off (commitment) status of individual power plants, thus requiring one integervariable for each plant in each period. In addition, tracking the on/off status is a prerequisite for modelling spinningreserves. Finally, to accurately model generation costs at partial loading, one would need to consider the non-linearrelationship between the efficiency and the power output of a plant. This relationship can be approximated viaa piecewise linear function [29]. Using more than one interval for the linearization involves the use of additionalinteger variables in case of concave production-cost curves. Determining the unit commitment and dispatch ofa set of power plants is thus a complex problem, requiring high-resolution data series and the use of numerousinteger variables. Therefore, (MILP) UC models are computationally demanding. Solving the unit-commitmentproblem for long periods (e.g. one year) requires splitting up the modeled horizon into smaller periods, whichcan be solved with a rolling horizon approach [15]. Incorporating yearly data series at an hourly resolution whilemodeling technical operational constraints at a power-plant level is therefore not an option for planning models[15],[30].

To integrate technical operational aspects of the power system into energy-system-planning models, a distinctioncan be made between two different approaches. In a first approach, additional constraints are directly integratedin the LT model. However, to limit computational complexity, these additional constraints are often very stylized.As such, these stylized constraints do not directly represent the physical processes, but rather aim to mimic theimpact of these physical constraints on the generation scheduling. Therefore, a deep understanding of the electricitysystem and underlying processes is needed to calibrate these constraints to the specific energy system that is beingstudied. Care is needed transferring these constraints to models for different energy systems. A second approachis to soft-link the LT planning model with an operational model. In this approach, the LT model is solved and theresulting capacities are used as input data for the operational model. This operational model typically operates atan hourly (or higher) resolution and includes operational constraints both at the system level (e.g. supply-demandbalance, operating reserve requirements) as well as the power plant level (e.g. minimal operating levels, maximalramping rates, etc.). Based on the results of the operational model, parameters used in the LT planning modelare adapted and/or additional constraints are generated and the cycle is repeated. By solving both models in aniterative approach, the aim is to obtain a solution for the LT model that accurately reflect the short-term dynamicsof the electricity system [10],[20].

In a static power-system model, Palmintier shows that not including operational constraints results in a sub-optimal capacity, in turn leading to higher operating costs and carbon emissions [15]. Nweke et. al., show in acase study of the South Australian power system that integration of operational constraints in planning modelshas a significant impact on the investment decisions [31].

The added value of this work is that it provides a comprehensive analysis of the impact of typical modelingsimplifications related to the operational side of power systems used in LT models.

3 Methodology

The methodology consists of two steps. In a first step, the consequences of operating at a low temporal resolutionare analyzed. To do this, an LT energy-system model based on the Belgian electricity system, as a typical example,is created with a temporal resolution common to the resolutions typically used in LT energy-system models (referredto as TS low - for time slices low). The results of this model are compared to the results of a second model, whichuses hourly data for an entire year and serves as a benchmark (referred to as TS ref). In the analysis, the differencesin investment decisions, dispatch and total system cost are discussed.

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In the second step, the generation portfolios stemming from the LT models are split up in a set of individualpower plants, which are used as input in a UC model operating at a high temporal resolution and operationaldetail. Focus in this step lies on the differences in the dispatch between both models. Since both the LT modelTS Ref and the UC model operate at hourly resolution, differences in model results can be solely attributed tothe difference in modeling operational constraints, cycling costs, and periods of unavailability (due to periodicmaintenance and outages).

Finally, by gradually increasing the share of VRES in the generation mix, the relation between this share ofVRES and the impact of the temporal resolution and the inclusion of operational constraints of power plants inLT planning models is analyzed.

4 Model, data and assumptions

4.1 A Long-term power-system model in TIMES

The LT models based on the Belgian electricity system are created in the TIMES environment. TIMES (an acronymfor The Integrated MARKAL-EFOM System) is an economic model generator for local, national or multi-regionalenergy systems [32]. The term model generator is used to reflect that, although the mathematical structure is fixed,different model instances are generated depending on the input data specified by the user. The full description ofthe mathematical structure in the TIMES model generator is presented in [33]. A so-called reference energy systemis defined by the user by specifying four types of input data: energy service demands, primary resource potentials,a policy setting and the description of a set of energy technologies. Given this input, the model will maximize thetotal discounted surplus by simultaneously making investment and operational decisions for the specified modelhorizon. TIMES models are therefore bottom-up (technology explicit), dynamic partial equilibrium models ofenergy markets [32].

With respect to the time dimension, the model horizon is split up into periods of which the duration can bespecified freely. In the LT models used in this work, the time horizon is 2014-2055 and divided into 5 periods, asshown in table 1. Each period is represented by a single year (so-called ’milestone year’)5. These milestone yearscan be divided into a set of user-defined time slices, meant to represent an entire year as adequately as possible.An example of a time slice tree within one model period is represented in figure 1.

Model horizon

Period 4Period 3Period 2Period 1

Seasons

Weekly

Daynite

WISP SU FA

SP_WD SP_WE SU_WD SU_WE FA_WD FA_WE WI_WD WI_WE

SU_W

E_N

SU_W

E_D

SU_W

D_N

SU_W

D_D

SP_W

E_N

SP_W

E_D

SP_W

D_N

SP_W

D_D

WI_WE_N

WI_WE_D

WI_WD_N

WI_WD_D

FA_W

E_N

FA_W

E_D

FA_W

D_N

FA_W

D_D

Annual

Figure 1: An example of the temporal structure in TIMES models [32].

5Yearly dispatch and corresponding operational costs as well as installed capacities are assumed to be constant within one periodand equal to the values in the milestone year. Investment costs can differ for each year within a period following a pattern dependingon the lifetime and lead time of the investment and the period duration. Yearly costs are discounted individually for each year of themodel horizon.

6The name of the time slice levels corresponds to the terminology in TIMES. No constraints are imposed on how these levels areused (e.g. for TS Ref, it would have been possible to specify 8736 hourly time slices at the level labeled seasonal). However, theterminology reveals how the different levels of time slices are typically used, i.e., to introduce seasonal, weekly/daily (often the levellabeled weekly is used to make a distinction between different days within one week rather than between certain weeks) and diurnalvariations in demand and supply.

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Period Duration Milestone year

2014 1 20142015-2025 11 20202026-2035 10 20302036-2045 10 20402046-2055 10 2050

(a) Disaggregation of the time horizon into periods of differentlength.

TS low TS ref

Seasonal6 4 Seasons/year 52 Weeks/yearWeekly - 7 days/weekDaynite Day, Night, Peak 24 hours/day# Time Slices 12 8736

(b) Disaggregation of each milestone year into time slices.

Table 1: Time horizon disaggregation in the TIMES models.

As mentioned in section 3, the importance of the temporal resolution is analyzed by setting up two distinctmodels differing only in the respective time-slice division used (and correspondingly, in the way hourly input datais aggregated into these time slices). The first time-slice division (TS low), disaggregates a year into 4 seasonsand each season is in turn disaggregated in a peak period, a day period and a night period. This corresponds toa time-slice division typically used in LT energy-system models7. The second time-slice division comprises everyhour of an entire year and serves as a benchmark for comparison in terms of the temporal resolution. An overviewof both time-slice divisions is presented in table 1.

Furthermore, in both models, Belgium is represented as an island, i.e., no import or export of electricity isallowed. For discounting costs, a constant discount rate of 5% is assumed. The policy setting consists of a taxon CO2 emissions, which varies linearly between 0 EUR/ton in 2010 and 50 EUR/ton in 2050, and a target forthe share of renewable electricity generation, which varies linearly between 0% in 2010 and 50% in 2050. Finally,to ensure generation adequacy, a so-called peaking equation is introduced (equation 1). This equation statesthat the sum of installed capacities of each technology weighted by the technology’s capacity credit should be atleast 5% higher than the peak demand occuring in that year. In equation 1, capi,t and cci,t are respectively the

installed capacity and the capacity credit of technology i in period t. Dpeakt is the peak demand occurring in

period t. The capacity credit of dispatchable technologies is taken to be equal to 1. For the VRES, the capacitycredit is dependent on the amount and type of VRES installed (given the electricity demand) and is approximatediteratively8. ∑

i

capi,t · cci,t ≥ 1.05 ·Dpeakt ∀t (1)

The set of technologies in the models is restricted to 7 dispatchable technologies and 3 types of VRES. Theset of dispatchable technologies consists of nuclear plants (of generation III+), coal-fired subcritical and (ultra)supercritical steam turbines, coal-fired integrated gasification combined cycle (IGCC) plants, combined cycle gasturbines (CCGT) and regular and advanced open cycle gas turbines (OCGT). Furthermore, investments in onshoreand offshore wind turbines and solar PV panels (for buildings) are allowed. Finally, the pumped-storage plantof Coo is included in the model, but given the limited geographical potential for new pumped-storage plants, noinvestments in additional pumped-storage plants are allowed.

4.2 Unit-Commitment model

The UC model used in this work is based on the mixed integer linear formulation of the unit-commitment problemas described by Van den Bergh et al. [29]. The UC model is set to operate at an hourly resolution and takesinto account a minimal stable generation level, ramping limits, minimum up and down times as well as plannedoutages. Part-load efficiencies are modeled via a stepwise linear approximation. Grids and cross-border trade aredisregarded in our model (i.e., island operation with a single node). Furthermore, operational reserves are nottaken into account in this work. This allows analyzing the impact of the variability of IRES on the quality of theresults of LT models in a deterministic setting. In a proposed next step, operational reserves could be added toanalyze the impact of the increasing uncertainty due to the large-scale penetration of IRES.

7As the time slices can be defined freely within the TIMES environment, these time-slice divisions differ to some extent from modelto model. Similar time-slice divisions use e.g. 4 seasonal and 2 daynite time slices, or add a time-slice level to separate weekdays fromSaturdays and Sundays.

8The capacity credit is defined in [35] as the equivalent conventional generation that the variable generating source represents toguarantee the same overall system security. In this work, the capacity credit of VRES is approximated by dividing the decrease inpeak residual demand due to the integration of one type of VRES -ceteris paribus- by the installed capacity of this type of VRES. Thisapproach implicitly assumes that system security is driven by the peak residual demand (i.e., demand subtracted by VRES generationpotential), i.e., two systems with the same peak residual demand are assumed to have the same system security.

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4.3 Data

With the exception of the nuclear plants, data on investment costs, fixed O&M costs, life times and efficiencies aretaken from [9]. Data on nuclear plants and lead times are taken from [34]. Variable O&M costs and availabilitiesof different technologies correspond to [36]. Finally, technical characteristics of power plants are adopted from[36]. An overview of the most relevant technology data used in this work is presented in tables 2 and 4 in theappendix. Regarding VRES, generation profiles for onshore and offshore wind turbines and solar PV panels aretaken from measured output in 2013, as provided by Elia9 [37]. This generation profile is scaled to the installedcapacity in future years. Due to the small geographical scope of Belgium, no additional smoothing effects areconsidered as the installed capacity increases. The generation system in the base year (2014), documented by Elia[37], is taken as the current Belgian electricity production park. The age of existing power plants is assumed to beequally distributed between 0 year (new) and the respective technology’s lifetime. Similar to the VRES generationprofiles, the future electricity demand profile is considered identical to the one observed in 2013 [37] and a constantelectricity demand growth rate of 1% per year is assumed. Fuel prices in the first period are adopted from [34],while fuel price evolutions are derived from [38]. The assumed price of lost load equals 3000 EUR/MWh.

5 Results and Discussion

This section comprises two parts. In a first part, the impact of the temporal resolution used in LT energy-system-planning models is discussed. A second section focusses on the impact of including techno-economic operationaldetail.

5.1 Temporal resolution

The effect of the different time-slice divisions on the investments is shown in figure 2. This figure shows thecomposition of the total installed capacity in each milestone year. The left and right bar, respectively, correspondto the model with low (TS low) and high (TS Ref) temporal resolution. As can be seen, the capacity that hasbeen installed prior to the model time horizon (existing capacity, referred to as Ex.) is gradually replaced asplants reach the end of their techno-economic lifetime. Furthermore, for both TS divisions, new investments aredominated by onshore wind turbines. The high level of investments in onshore wind turbines does not mean thatwind turbines become cost-attractive, but rather that this technology is identified by the model to be the leastexpensive option to meet the imposed targets for renewable electricity generation. Besides investments in VRES,the bulk of investments in conventional dispatchable technologies consists of nuclear power plants and OCGTs. Inboth models, there are no investments in coal power plants, advanced OCGTs or offshore wind turbines. At thispoint, it must be stressed that the aim of this work is not to present scenarios for the transition of the Belgianelectricity system, but rather to analyze the impact of modeling assumptions typically used in LT planning models.In this regard, our interest lies in the difference in results between both models, and not on the model results assuch.

2014 2020 2030 2040 20500

10

20

30

40

50

60

Period

InstalledCap

acity[G

W]

Ex. Cap.Nuclear PPCCGTOCGTWind OnshorePV

Figure 2: Breakdown of the installed capacity for each milestone year. The leftand right bar respectively show the results of the model with low (TS low) and high (TSref) temporal resolution.

9Elia is the Belgian transmission system operator.

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A first observation that can be made is that, despite identical targets for renewable electricity generation, adivergence in the amount of investments in VRES can be observed starting from 2040. Installed VRES capacitiesin 2040 and 2050 are markedly higher for the model with the highest temporal resolution. Second, noticeabledifferences in the investments in dispatchable power plants occur starting from 2040. More specifically, investmentsin dispatchable technologies are more diversified in the model operating at hourly resolution. Compared to themodel with time-slice division TS low, CCGTs have gained attractiveness at the expense of OCGTs.

A breakdown of the electric energy generation for the year 2050 is presented in figure 3 for both models.One can observe that, despite the difference in VRES capacity, the VRES electric energy generation shares inboth models are equal. This indicates that the penetration of VRES is overestimated by the model with a lowtemporal resolution. In other words, to reach the imposed target for the share of VRES (expressed in % of electricenergy generation), the model with an hourly resolution needs to make additional investments in VRES comparedto the model with a low temporal resolution. Second, in both models, the remaining demand is predominantlyprovided by nuclear plants. Remarkably, the share of nuclear electricity generation is higher in the model usingTS low, although installed capacities are higher in the model using TS ref. Finally, despite the high investmentsin OCGTs, these are rarely used to provide energy. Investments in OCGTs are almost completely driven by thecapacity requirement in the peaking equation.

(a) TS low

Nuclear PPCCGTOCGTWind OnshorePV

(b) TS ref

Figure 3: Electricity generation shares in 2050. Figure 3a and 3a respectivelydisplay the results from the model with a low (TS low) and high (TS ref) temporalresolution.

The root cause of the differences in model results can be found in the way these models approximate the residualload duration curve. The residual load for each time slice is found by subtracting the potential10 undispatchable(renewable) electricity generation in that time slice from the load corresponding to that time slice. Sorting thisresidual load from high to low gives the residual load curve (RLDC). Figures 4a, 4b and 4c, respectively, show theload and residual load duration curves and their respective approximations for the years 2014, 2030 and 205011

Figure 4d displays the root mean square error (RMSE) of the approximation of the load and residual load durationcurve by using time-slice division TS low. This figure clearly illustrates that, while time-slice division TS lowapproximates the load duration curve with reasonable accuracy, this is not the case for the residual load durationcurve. Furthermore, the RMSE of the approximation of the residual load duration curve increases almost linearlywith the share of VRES electricity generation. After all, the idea behind a time-slice division such as TS low isbased on capturing the significant seasonal and diurnal differences12, on the one hand, and the similarities of datavalues that belong to a specific time slice (e.g. peak periods during winter), on the other hand [19]. As the demandprofile has strong regularities on the seasonal, daily and diurnal level, time-slice divisions such as TS low obtain agood representation of the demand profile. In contrast, due to the lack of regularities in wind-power fluctuations,grouping of wind-turbine electricity generation data belonging to a specific time slice (e.g., all wind data of peakperiods in the entire winter) into a single value for the corresponding time slice causes averaging of wind-turbineoutput. This means that, as the number of time slices is reduced, the wind power profile is increasingly smoothed.On the one hand, this causes a considerable underestimation of the peak residual load as these peaks occur whenVRES generation is very low and demand is high. On the other hand, the residual demand in periods of very highVRES generation and low demand is overestimated.

The differences in investments in both models can be clarified by analyzing the RLDCs. First, due to smoothingof wind data in the model with time-slice division TS low, periods of negative residual demand only occur in the

10We speak of the potential electric energy generation in a specific time slice as opposed to the actual electric energy generationin that time slice to account for the fact that the actual electric energy generation can be lower than the potential electric energygeneration in a specific time slice in case of curtailment.

11The VRES generation profile in each year, needed to create the RLDC, follows from the installed capacities of VRES in that yearin the model with a high temporal resolution.

12As stated above, time-slice division TS low serves here to represent a typical time-slice division used in LT models. Similartime-slice divisions sometimes also consider daily differences (e.g. weekday, Saturday and Sunday).

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0 2000 4000 6000 8000−1

0

1

2x 10

4

Time [h]

(Residual)Load[M

W]

LDCRLDCLDC TS lowRLDC TS low

(a) 2014

0 2000 4000 6000 8000−1

0

1

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(Residual)Load[M

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LDCRLDCLDC TS lowRLDC TS low

(b) 2030

0 2000 4000 6000 8000−1

0

1

2x 10

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Time [h]

(Residual)Load[M

W]

LDCRLDCLDC TS lowRLDC TS low

(c) 2050

2010 2020 2030 2040 20500

2

4

6

Time [h]

RMSE

[GW]

LDCRLDC

(d) RMSE

Figure 4: Approximation of the load duration curve and the residual loadduration curve. Figure 4a, 4b and 4c respectively show the approximations of the(residual) load duration curves for the years 2014,2030 and 2050. Figure 4d displays theevolution of the root mean square error (RMSE) of the load and residual load durationcurves as the share of VRES is increased over time.

model with a high temporal resolution. In these periods, VRES electricity generation exceeds the demand, andexcess energy should be stored or curtailed. As storage opportunities are limited, some curtailment of VRESgeneration is needed. In other words, by using a low temporal resolution the potential uptake of VRES will beoverestimated. The model with a high temporal resolution foresees this curtailment and therefore will invest inadditional VRES capacity to reach the imposed target for the share of VRES electricity generation. In addition,the model attempts to minimize this curtailment and is therefore incentivized to diversify its VRES portfolio13.Second, following the reasoning behind the screening curve methodology [39], a flat RLDC will induce a lessdiversified portfolio of dispatchable power plants. The above analysis clearly shows that data aggregation in a lownumber of time slices is highly undesirable for analyzing electricity systems with a high share of VRES.

5.2 Operational aspects

For both the LT model with a high and low temporal resolution, the dispatch is re-evaluated with a MILP UCmodel. This UC model operates at an hourly resolution and incorporates operational constraints of power plants.For the LT model with low temporal resolution (TS low), differences in dispatch are therefore due to both thedifference in temporal resolution and the inclusion of operational detail, whereas for the TIMES model operatingat hourly resolution, the difference in dispatch can be solely attributed to the inclusion of operational detail.

Figure 5 presents the electric energy generation shares following from the dispatch in the TIMES model andthe corresponding UC model for each milestone year. A first observation is that in the first two periods, there areonly slight differences in dispatch. However, as the share of VRES increases, generation shares from the TIMESmodels start to deviate from the UC model results. This confirms the presumption that the temporal resolutionand the inclusion of operational constraints gain in importance as the share of VRES is increased. The deviationsin dispatch follow two trends. First, the uptake of VRES is systematically overestimated by the TIMES models.In other words, more curtailment of VRES is required/cost-effective than is anticipated by the TIMES models.

13The models used in this work do not consider additional investments in storage technologies, demand response or cross-bordertransmission lines. Increasing the temporal resolution would provide incentives to invest in these options, if included in the model.

10

2014 2020 2030 2040 20500

20

40

60

80

100

Period

GenerationShare[%

]

Nuclear PPCoalCCGTOCGTWind OnshorePV

(a) TS low

2014 2020 2030 2040 20500

20

40

60

80

100

Period

GenerationShare[%

]

Nuclear PPCoalCCGTOCGTWind OnshorePV

(b) TS ref

Figure 5: Electric energy generation shares. Figure 5a and 5b display the generationshares following the dispatch in the TIMES model (left bar) and the UC model (rightbar) in each milestone year.

As discussed in section 5.1, aggregating wind data values in a limited number of time slices causes the model tooverlook periods of excess VRES generation and the corresponding need for curtailment. However, overestimationof the uptake of VRES is also observed for the model TS ref, although to a lesser extent. The motivation forthe additional curtailment of VRES is diverse: curtailment could either be necessary to ensure system balanceor beneficial from an economic perspective. Curtailment as a means to ensure a balanced system can occur inperiods of excess VRES generation or in periods in which VRES generation volatility exceeds the load-followingcapabilities of dispatchable plants. Curtailment for economic reasons typically occurs to prevent start-up costsin situations where a plant would have to be shut down for a short period before starting up again. Figure 6shows the dispatch of different technologies for a two-day period in the year 2050. During the first 6 hours ofthis period, curtailment to prevent excessive start-up costs of nuclear plants is scheduled in the UC model. Asthe TIMES model does not account for start-up costs, no VRES curtailment is scheduled in these hours. A resultof this additional curtailment is that the proposed investments fall short of achieving the imposed target for theshare of VRES in the generation mix. In 2050, the investments following from the TIMES model with time-slicedivision TS low and TS ref respectively obtain a share of VRES equal to 41.9% and 47.13%. This analysis confirmsthe conclusions of Haydt et al. that the potential uptake of renewable energy can be seriously overestimated ifshort-term dynamics are not properly taken into account [25].

Second, also the share of baseload electric energy generation is overestimated by the TIMES model (see againfigure 5 and figure 6). For the model TS low, this can be again partially explained by the flat residual load curve.However, an overestimation of the contribution of baseload plants is also observed in the model TS ref. Again,this can be the result of technical constraints or for economic reasons. Baseload plants are generally less flexiblethan mid or peak load power plants. To deal with the rapid variations in VRES output, mid and peak-load powerplants can be committed to provide the necessary flexibility, even though the baseload plants are operating belowtheir rated capacity. Furthermore, baseload plants can be restricted to start up or shut down due to minimumup and down times. This can be seen for hour 19 and 20, where OCGTs are started up in the UC model tocope with the demand peak. Also, the size of the unit that has to be started up or shut down can play a role.Avoiding the high start-up costs of starting up a large unit when little additional capacity is required can reducethe use of baseload technologies. On the other hand, the high start-up costs of baseload technologies could alsoresult in higher generation shares, e.g., if curtailment of VRES is scheduled to prevent a baseload plant fromshutting down (and starting up some hours later) as can be seen in between hour 1 and 6. Finally, also theimpact of periodic maintenance and outages of discrete units impacts the dispatch. This can be seen from hour32-48 in figure 6, where a large nuclear unit is offline in the UC model. Including operational detail induces aseries of complex interactions which are generally not taken into account in LT planning models. As figure 5bindicates, these interactions can have a significant impact on model results. More specificially, the dispatch directlyimpacts main model outputs such as the primary fuel consumption, GHG emissions and operational costs (figure7b). Furthermore, the complexity of these interactions make it difficult to anticipate the effect of not includingthis operational detail. Based on the presented results, we can conclude that LT models generally tend to favorintermittent renewables, while insufficient flexible technologies might be deployed.

In terms of operational costs14, the impact of increasing the operational detail is less ambiguous. Figure 7

14Operational costs include fuel costs, costs related to emissions of greenhouse gasses, variable O&M costs as well as start-up costs.

11

0 10 20 30 40 500

0.5

1

1.5

2x 10

4

Time [h]

Pow

er[M

W]

TIMES TS ref

NuclearCoalCCGTOCGTWindPVPSLoad

0 10 20 30 40 500

0.5

1

1.5

2x 10

4

Time [h]

Pow

er[M

W]

UC

NuclearCoalCCGTOCGTWindPVPSLoad

Figure 6: Dispatch in the TIMES and UC model for a two-day period in 2050.

shows the breakdown of the operational costs following the dispatch in the TIMES models and the correspondingUC models for each milestone year. For all simulated years, the operational costs according to the UC model arehigher. The impact on operational costs is directly related to the impact on the dispatch decisions. On the onehand, the dispatch in the TIMES models could be technically infeasible. In this case, the dispatch in the UC modelwill schedule additional flexible generation, leading to higher costs. On the other hand, the dispatch proposed bythe TIMES model TS ref could deviate from the cost-effective dispatch when start-up costs are taken into account.Figure 7 shows that the contribution of the start-up costs to the difference in operational costs is rather limited.However, the integration of start-up costs can also lead to increased generation costs to avoid additional start-upsand associated costs.

2014 2020 2030 2040 20500

500

1000

1500

2000

2500

Period

Operational

Costs

[MEur/year]

FuelEmissionsVOMStart-ups

(a) TS low

2014 2020 2030 2040 20500

500

1000

1500

2000

2500

Period

Operational

Costs

[MEur/year]

FuelEmissionsVOMStart-ups

(b) TS ref

Figure 7: Decomposition of operational costs Figure 7a and 7b display the decom-position of operational costs following the dispatch in the TIMES model (left bar) andthe UC model (right bar) in each milestone year.

In figure 8, the relative underestimation of operational costs is related to the share of VRES in the system forboth models TS low and TS ref. In the case presented here, operational costs are shown to be underestimatedby 3.0-57.8% for model TS low (low temporal resolution, no operational detail), depending on the degree ofVRES penetration. For model TS ref (high temporal resolution, no operational detail), operational costs are

12

0 10 20 30 40 500

10

20

30

40

50

60

Share of VRES [%]

Underest.

ofop

erational

costs[%

]TS lowTS ref

Figure 8: Underestimation of operationalcosts for LT models with a low (TS low)and high (TS ref) temporal resolution.

2010 2020 2030 2040 2050 20601.2

1.4

1.6

1.8

2

2.2

2.4x 10

4

Year

Objectivefunction[M

EUR

1990]

TS low TIMESTS low UCTS ref TIMESTS ref UC

Figure 9: Contribution of opera-tional costs to the total discountedsystem cost.

underestimated by 3.0-22.9%. The divergence in operational costs generally increases with the share of VRES inthe system. In other words, the importance of using a high temporal resolution and including operational detailgrows with the share of VRES in the system.

Figure 9 gives an indication of the impact on the total discounted energy-system cost15. In this figure, theenergy-system cost is split up into operational costs and a group containing investment and fixed O&M costs.Yearly costs belonging to this last group are discounted and aggregated to form the initial value of the curves16. Acomparison of the curves for the TIMES models and the corresponding UC correction of operational costs revealsthat the underestimation of operational costs leads to an underestimation of total discounted energy-system costs.Over the entire model horizon (i.e. 2014-2055), the total discounted energy-system cost is underestimated by10.5% for model TS low, and 5.6% for model TS ref. The difference remains limited due to the fact that largestdeviations in operational costs occur in later periods, which contribute less as a result of cost discounting.

Furthermore, if the operational costs are corrected for, the investment decisions from the TIMES model TSref are shown to result in a lower total discounted system cost than the investments from the model TS low. Inaddition, this model also achieves a larger share of VRES. This allows us to conclude that increasing the temporalresolution in LT planning models not only improves the accuracy of the dispatch and the directly related primaryfuel use and emissions, but also leads to a more optimal set of investments. For this reason, the results of our testcase seem to indicate that incorporating technical operational detail into LT energy-system-planning models couldcontribute both to improving the projections of primary fuel consumption and GHG emissions, and to improvingthe cost effectiveness of the proposed set of investments, especially for scenarios analyzing transitions towardselectricity systems with high shares of intermittent renewables.

However, the magnitude of the impact of including this operational detail will be determined by a myriad ofdifferent factors. A non-exhaustive set if factors that could affect the need for incorporating this level of operationaldetail are identified as the share and type of intermittent renewables in the electricity generation mix, the assumedflexibility of different technologies, the amount of storage capacity in the system, the participation of the demandside in balancing supply and demand and interconnections with neighboring electricity system and the lay-out ofneighboring electricity-generation systems.

6 Summary & Conclusions

Long-term energy-system-planning models are frequently used in studies analyzing the transition towards a sus-tainable energy system. In these studies, intermittent renewables are expected to be key contributors in thistransition. However, their highly variable and stochastic nature poses some challenges to long-term models. Inthis regard, closing the gap between short-term operational models and long-term planning models has become anactive field of research. In this work, we focus on analyzing the impacts of using a low temporal resolution and a low

15The yearly energy-system costs consists of yearly investment costs (incl. salvation values) and fixed O&M costs, on the one hand,and operational costs (incl. fuel costs, variable O&M costs and costs related to the emission of greenhouse gases), on the other hand.

16That is, although investments are made over the entire time horizon, the contribution of these discrete investment costs areconverted to a single value to allow visual comparison of the contribution of the dispatch costs in the different models. The rise of thecurves therefore depict the cumulative contribution of operational costs to the total discounted energy system cost.

13

level of operational detail in planning models. In our approach, the results of a planning model using a commonlyused low temporal resolution are contrasted with the results of a planning model operating at hourly resolution.To analyze the importance of including operational characteristics of power plants, the dispatch decisions of theLT models are re-evaluated by soft-linking the LT models to a mixed-integer linear unit-commitment model. Themodels are compared in terms of investments as well as the dispatch decisions and corresponding costs.

Results show that the temporal resolution is of paramount importance for achieving realistic projections ofprimary-fuel consumption and related GHG emissions, as well as for the optimality of the proposed set of invest-ments. Using a low temporal resolution is shown to lead to an erroneous representation of the residual load curve,in turn leading to sub-optimal investments, an over-estimation of the potential uptake of intermittent renewablesand an over-estimation of the use of baseload technologies. Therefore, operational costs are considerably underesti-mated. Moreover, indications are given that, regardless of the temporal resolution used, neglecting the short-termdynamics by not taking into account detailed operational plant characteristics, such as limited ramping rates,minimal up and down times and start-up costs, also contributes to underestimating operational costs. Moreover,due to the complexity of the interactions induced by incorporating operational characteristics of power plants, theimpact on the consumption of primary fuels as well as on GHG emissions can vary with the analyzed system.Finally, both the impact of the temporal resolution and the inclusion of technical detail is shown to increase withthe amount of intermittent renewables in the system.

This work can be expanded in several ways. First, operating reserves requirements could be added to theanalysis. Reserve requirements will increase with the share of intermittent renewables. Therefore, the impacton the dispatch and corresponding costs can be substantial. Second, the impact of the electrical grid could beincorporated in the analysis as grids can have a major impact on the operations of the power system. Third,operational constraints could be directly implemented in planning models to analyze the impact on investments.Finally, the importance of other sources of flexibility such as active demand response and cross-border trade couldbe analyzed.

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16

Appendix

Technology Inve

stm

ent

cost

[kE

UR

/kW

e]

FO

M[E

UR

/(k

We·y

ear)

]

VO

M[E

UR

/M

Wh

]

Lif

eti

me

[yea

rs]

Lea

dti

me

[yea

rs]

2010 2020 2030 2040 2050 2010 2020 2030 2040 2050Nuclear gen III(+) 3.66 3.66 3.66 3.66 3.66 0 0 0 0 0 11.1 60 7Subcrit. coal 1.37 1.37 1.37 - 1.37 27 27 27 - 27 7.7 35 4(Ultra) Supercrit. coal 1.71 1.71 1.71 - 1.71 34 34 34 - 34 6 35 4IGCC coal 2.76 2.49 2.25 - 1.83 55 50 45 - 37 7.5 30 4CCGT 0.86 0.86 0.86 - 0.86 26 21 20 - 20 5 25 2OCGT 0.49 0.49 0.48 - 0.47 12 12 12 - 12 4 15 2Adv. OCGT 0.57 0.57 0.57 - 0.57 17 17 17 - 17 4 15 2Onshore wind turbine 1.40 1.27 1.19 - 1.11 34 27 24 - 21 - 25 1Offshore wind turbine 4.30 3.40 2.70 - 2.10 130 95 75 - 60 - 25 1Solar PV 3.66 1.42 1.14 - 0.78 51 16 13 - 10 - 30 1

Table 2: Overview cost-related parameters. Investment costs and FOM costs aredependent on the timing of the investment.

Technology Effi

cien

cy[%

]

Min

imu

mop

erat

ing

poin

t[%

/Pnom

]

Eff

.lo

ssat

min

imu

mop

.p

oin

t[%

pt]

Max

imu

mra

mp

rate

[%Pnom

/min

]

Ram

pco

st[E

UR

/∆M

W]

Min

imu

mu

pti

me

[hou

rs]

Min

imu

md

own

tim

e[h

ou

rs]

Sta

rt-u

pre

late

dfu

elu

se[M

Whth/∆

MW

e]

Sta

rt-u

pd

epre

ciat

ion

cost

s[E

UR

/∆M

W]

Ava

ilab

ilit

y[%

]

2010 2020 2030 2050 Hot Warm ColdNuclear gen III(+) 10017 100.1 102 104 50 - 0.25 - 24 48 17 17 17 1.7 85Subcrit. coal 37 38 39 41 50 2 0.83 1.3 6 4 3.6 5.7 11.3 5 85(Ultra) Supercrit. coal 45 46 49 49 50 2 0.83 1.3 6 4 3.6 5.7 11.3 5 85IGCC coal 45 46 48 50 50 8 0.83 0.25 4 1 1.7 1.7 5.0 10 85CCGT 58 60 62 64 50 8 0.83 0.25 4 1 1.7 1.7 5.0 10 85OCGT 39 39 40 41 10 21 10 0.66 1 1 0.02 0.02 0.02 10 85Adv. OCGT 42 45 45 45 10 21 10 0.66 1 1 0.02 0.02 0.02 10 85

Table 3: Overview of operational parameters. Efficiencies of power plants aredependent on the timing of the investment.

17 Fuel prices for nuclear plants are expressed in [EUR/MWhe]. For future years, the expected increase in efficiency results inefficiencies above 100%.

17

Fuel Cost [EUR2008/MWhp]2010 2020 2030 2040 2050

Coal 8.81 9.44 9.79 10.33 10.82Natural gas 23.89 24.30 25.12 25.66 26.27Uranium 6.34 6.34 6.34 6.34 6.34

Table 4: Fuel prices.

18