WP EN2014-20

Impact of Temporal andOperational Detail in

Energy-System Planning Models

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

D’haeseleer

TME Working Paper - Energy and Environment

Last update: March 2015

Preprint submitted to Applied EnergyAn electronic version of the paper may be downloaded from the TME website:

http://www.mech.kuleuven.be/tme/research/

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Impact of Temporal and Operational Detail in

Energy-System Planning Models

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

%University of Leuven (KU Leuven), Energy Institute, Celestijnenlaan 300, B-3001 Leuven, Belgium+EnergyVille, Thor Park, B-3600 Genk, Belgium

#VITO, Boeretang 200, B-2400 Mol, Belgium*Corresponding author: +32 16 32 25 10, william.dhaeseleer@kuleuven.be

Abstract

To limit the computational cost, bottom-up long-term (LT) energy-system planning modelstypically have a stylized temporal representation and do not consider techno-economic oper-ational constraints of power plants. The aim of this work is to analyze the impact of thesemodel simplifications in the context of an increasing penetration of intermittent renewableenergy sources (IRES), and identify opportunities for model improvements. While the focus inthe literature lies primarily on the impact of the temporal resolution, this work more fundamen-tally considers and assesses the different approaches of dealing with the temporal dimension.We find that the temporal representation typically used in large-scale energy-system planningmodels does not sufficiently account for the variability of IRES, resulting in a technology biastowards intermittent and inflexible technologies. Depending on the computational resourcesavailable, suggestions are made for alternative temporal representations which better accountfor the variability of IRES generation. Furthermore, the impact of the temporal representa-tion and the level of techno-economic operational detail are quantified simultaneously, allowingto make the trade-off between improving planning models by extending the operational timedimension, and/or aiming for a better technical representation. As such, this paper presentsnew findings and goes beyond the existing literature. We show that the gains obtained byimproving the temporal representation outweigh the gains obtained by incorporating detailedtechno-economic constraints. Moreover, a temporal representation that preserves chronologyand has a sufficiently high resolution is a prerequisite for detailed modeling of techno-economicoperational constraints. For these reasons, we suggest to prioritize improving the temporalrepresentation.

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1 Introduction

Bottom-up, long-term (LT) energy system planning models are frequently used to analyze pathways for the transi-tion of the energy system and to deduce policy advice. In this category of models, popular examples are, amongstothers, MARKAL/TIMES, PRIMES, EnergyPLAN, IKARUS and PERSEUS [6]. These types of models are used ina variety of studies to analyze (aspects of) the evolution towards low-carbon energy systems [10, 28, 30, 21, 23, 11, 5].

To decarbonize the energy system, the share of renewable generation is expected to increase significantly,especially in the electricity sector [19]. Some of these renewable energy sources, such as wind energy and solarphotovoltaic (PV) energy, have an intermittent character, i.e., they are (highly) variable and have a limitedpredictability. Due to these characteristics, a large penetration of intermittent renewable energy sources (IRES)can have a significant impact on the operation of the electric power system. First, the variability of IRES generationincreases the need for cycling of dispatchable power plants (i.e. changing the electric power output by rampingup/down or by switching on/off) [16, 34, 33]. Second, sufficient back-up capacity is needed to deal with periods inwhich IRES output is low. Third, the limited predictability of IRES generation leads to an increased demand foroperating reserves [22, 4].

Long-term energy system planning models typically use a stylized temporal representation (i.e., a year istypically represented by 1-12 so-called ”time slices”). Furthermore, these models typically operate at a technology-type level, rather than considering individual power plants and corresponding technical load-following constraintsand cycling costs. This level of detail has so far been reserved for operational models (i.e., unit-commitmentmodels). Historically, this level of detail could be ignored in planning models without a major impact on thequality of the results [25, 26]. However, recently it is becoming more and more clear that the traditional approachused in LT planning models falls short of accurately representing the unit commitment and dispatch for electricitysystems with high shares of IRES. In this regard, a myriad of approaches attempting to bridge the gap betweenplanning models and operational models is currently being developed [28, 8, 25, 26, 20, 15].

The aim of this work is to analyze the impact of these model simplifications in the context of an increasingpenetration of IRES, and to identify opportunities for model improvements. A comprehensive analysis is providedon the fundamental priciples of different methods of accounting for the temporal dimension. Furthermore, quanti-tative results of the impact of the temporal representation and the level of techno-economic operational detail areprovided for systems with an increasing share of IRES generation (a planning model is set up and deployed in thisregard). This allows to balance the impact of the level of temporal and techno-economic operational detail.

Different authors have recently investigated the impact of the temporal resolution on the model results. Afirst group analyzes the effect of increasing the temporal resolution on balancing electricity demand and supply(i.e., the dispatch, no investment decisions are considered) [8, 12]. Using a low temporal resolution is shown tolead to an overstestimation of the uptake of IRES. A second group analyzes the impact of the temporal resolutionon investment decisions [20, 14, 27]. Main results are that by using a low temporal resolution, the optimal levelof investments in less flexible technologies and IRES is overestimated, while the optimal level of investments inflexible generation technologies is underestimated.

Regarding the impact of the level of techno-economic operational detail, Palmintier [25] shows that neglectingoperational constraints results in a sub-optimal capacity mix, in turn leading to higher operating costs and carbonemissions. Nweke et al. [24], show in a case study of the South Australian power system that integration ofoperational constraints in planning models has a significant impact on the investment decisions. Finally, Welschet al. [35] demonstrate in a case study of Ireland that neglecting flexibility requirements strongly impacts thegeneration portfolio.

This paper contributes to the existing literature in two ways. First, while the existing literature primarilyfocusses on the impact of the temporal resolution (i.e. the number of (diurnal) time slices), the work presented inthis paper takes a broader perspective for the analysis of different approaches to represent the temporal dimension,thereby providing fundamental insights into how this choice affects main model results. Second, while the literaturetypically focusses either on the impact of the temporal aspect, or on the impact of the techno-economic aspect,this work addresses both aspects and the interactions between the two. This allows making the trade-off betweenimproving planning models by extending the temporal detail and/or aiming for a better technical representation.

The remainder of this paper is organized as follows: Section 2 provides a methodological analysis of differentapproaches to represent the temporal dimension. Subsequently, Section 3 presents an overview of the methodology,the assumptions and the data used in the quantitative analysis. The results of this analysis are discussed in Section4. Finally a conclusion and some good practice guidelines are presented in Section 5.

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2 Temporal representation

Bottom-up long-term planning models typically divide the time horizon into a set of periods, each representedby a single year (so-called ”milestone year”). In turn, these milestone years can be divided into a set of (user-defined) time slices, serving to represent intra-annual variations in demand and supply. Within each time slice, allparameters are fixed, i.e. the load and the availability of IRES generation are assigned a single value within eachtime slice. Figure 1 gives a schematic overview of the temporal structure in the TIMES model generator. WithinTIMES, the number of time slices within each level of the time slice tree and the duration of each time slice canbe specified freely by the user. Due to computational restrictions, the number of time slices used in large-scaleenergy-system planning models is generally restricted to 1-12.

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Figure 1: An example of the temporal structure in TIMES models [18]. In this example, a year is disaggregatedinto four seasons, which are in turn disaggregated into weekdays and weekends periods. Finally, diurnal variations areintroduced via a day and a night time slice. Text on the right corresponds to the terminology used in TIMES for thedifferent time-slice levels.

As discussed in Section 1, much attention has recently gone to the temporal resolution used in planning models.However, it is not merely the resolution (i.e., the number of (diurnal) time slices) which is of importance. Lessdiscussed in the scientific literature, though equally important, is how the set of time slices is chosen (i.e., thegeneral structure of the time-slice tree) and the way values are assigned to each time slice. Following Haydt etal. [12], we distinguish between two methods of representing the temporal dimension. Both methods use a set ofintra-annual time slices which can be represented in a time-slice tree similar to the one displayed in Figure 1. Thefundamental difference between both methods lies in the way values are assigned to these time slices.

The first method, referred to as the ”integral” method, typically uses a low number of time slices (4-12)1 tocapture the main seasonal, daily and diurnal variations in demand (and supply). In this approach, the valueassigned to each time slice is taken as the average value of the specific profile belonging to this time slice2. Thismethod is generally used in large-scale energy-system planning models. Recent applications of this approach canbe found in [10, 30]. A second method, labeled the ”semi-dynamic” method, is based on selecting typical days(or weeks) to represent an entire year. Each selected day/week represents a part of the year (e.g. a season or amonth). These selected days/weeks can in turn be divided into a number of diurnal time slices. As such, the valueassigned to each time slice is not the result of taking an average over multiple days, as is the case for the integralmethod. Depending on the number of days/weeks selected and the diurnal resolution, the total number of timeslices used in this approach is typically somewhat higher (16-288).

To illustrate the difference between the integral and the semi-dynamic method, consider a model with 4 seasonaltime slices and 3 diurnal time slices (day, night and peak). Figure 2 visualizes the process of assigning a valueto the time slice representing peak periods during the winter, both in the integral and the semi-dynamic method.Visualizing the processes of assigning a value to a specific time slice highlights some of the main advantages anddrawbacks of each method. The main drawback of the integral method is that the range of IRES generationlevels occurring in reality is not retained, as the average value of all samples belonging to this time slice is taken.Therefore, this method does not capture periods with very high or very low IRES generation. In contrast, inthe semi-dynamic method, the IRES generation can be anywhere within the range actually observed. A secondadvantage of the semi-dynamic method is that, intra-daily, chronology of observed values is maintained, making thisapproach more suited for integrating dynamic operational constraints of power plants. However, this approach does

1The structure of the time-slice tree, and therefore the number of time slices used can be determined freely by the user. The rangespresented here merely serve to indicate common practice. Recent applications with a higher resolution are i.a. [10, 20]

2Here, it is assumed that data are available at a finer resolution (e.g., hourly or quarter-hourly data). If data are not available at afiner resolution, data can be disaggregated into values for each time slice, following a logical rule.

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not necessarily capture events of a longer duration (e.g., longer periods without wind), which may require differenttechnological solutions. Moreover, another issue with the semi-dynamic method is the difficulty of selecting a setof periods which is representative for an entire year or multiple years. Figure 2 illustrates the sensitivity of thismethod to the selected representative days/weeks. Nonetheless, de Sisternes and Webster remark that there is noconsistent criterion to select days/weeks or to assess the validity of the assumption [7].

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Figure 2: Process of assigning values to time slices in the integral and the semi-dynamic method. Thegraphs on the left hand side show profiles of the load, the residual load and onshore wind generation during a winterweek for an exemplary system with a high penetration of wind power. Every day, samples are taken during the peakhours (lasting 2 hours in each day). The graphs on the right hand side display all the samples taken during the entirewinter season. The blue lines indicate the values assigned to the winter peak time slice in the integral method, i.e. theaverage of all samples. Following the semi-dynamic method, the value assigned to the winter peak time slice correspondsto the value of a single selected sample. Hence, the value can lie anywhere in the shaded intervals, depending on theselected day. The red line indicates the value assigned to this time slice if the sample displayed in red would be selected.

In this section, we illustrate how the temporal representation impacts main model results by analyzing how theresidual load duration curve (RLDC) is approximated. Different authors have highlighted the significance of theRLDC for the investment planning problem [32, 13, 31]. The residual load for each time slice is found by subtractingthe potential3 undispatchable (renewable) electricity generation in that time slice from the load corresponding tothat time slice. Sorting this residual load from high to low gives the RLDC. This RLDC offers information ondifferent aspects related to the integration of IRES, such as the capacity credit of IRES, the reduction of operatinghours of thermal power plants, and the amount of excess energy (possibly leading to curtailment) [32]. Moreover,

3We 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. (Note that we consider electrical energy, being the time integral of theinstantaneous electrical power, over the duration of a time slice - i.e., interval. This electrical energy is also a measure of the averageelectrical power considered over the time slice.)

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combining the RLDC with screening curves of dispatchable technologies allows for a visual interpretation of theoptimal thermal capacity and generation mix (see Figure 3). However, it must be noted that, as chronology is notretained, the RLDC can give no information about the impact of dynamic inter-temporal load-following constraintsand costs on the optimal capacity and generation mix. Similarly, the optimal level of investments in energy storagefacilities and the impact of these investments on the optimal thermal capacity mix cannot be determined usingthis approach.

Figure 3b illustrates how the RLDC would be approximated in a LT energy-system planning model with atypical temporal representation, disaggregating a year into 4 seasons and 3 diurnal time slices (day, night andpeak) following the integral method (referred to as TS low - see Table 1). This figure clearly illustrates that, whilethe load duration curve is approximated with reasonable accuracy, this is not the case for the RLDC. After all,the idea behind a time-slice division such as TS low is based on capturing the significant seasonal and diurnaldifferences, on the one hand, and the similarities of data values that belong to a specific time slice (e.g. peakperiods during winter), on the other hand [27]. As the load profile has strong regularities on the seasonal, dailyand diurnal level, time-slice divisions such as TS low obtain a good representation of the load profile. In contrast,due to the lack of regularities in wind-power fluctuations, grouping of wind-turbine electricity generation databelonging to a specific time slice (e.g., all wind data of periods of peak demand during the entire winter) into asingle value for the corresponding time slice causes averaging of wind-turbine output. This means that, as thenumber of time slices is reduced, the wind power profile is increasingly smoothed.

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Figure 3: Impact of the approximation of the RLDC. Figure3b indicates how the LDC and the RLDC areapproximated for a stylized temporal representation (TS low). Using the screening curves of a set of technologies(bottom graph), the cost-optimal capacity and generation mix corresponding to a RLDC can be determined graphically.This is visualized for both the original (3a) and the approximation (3b) of the RLDC. The significant differences in thecapacity and energy mix highlight the importance of a good approximation of the RLDC.

On the one hand, this causes a considerable underestimation of the peak residual load as these peaks occurwhen IRES power generation is very low and the load is high. In this regard, the role of the so-called ”peakingequation” (demand for firm capacity) is crucial to obtain generation portfolios which can achieve a reasonablesecurity of supply. On the other hand, the residual load in periods of very high IRES generation and low load isoverestimated. As a result, periods of negative residual load (excess energy) are overlooked. In these periods, IRESelectricity generation exceeds the load, and excess energy should be stored or curtailed. Storage opportunities aregenerally limited or expensive, making some curtailment of IRES generation necessary or cost-optimal. In otherwords, by using a simplified temporal representation, the potential uptake of IRES will be overestimated, thereby

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giving insufficient incentives for flexible technologies. Furthermore, Figure 3 also demonstrates the impact on thedispatchable capacity and generation mix. In this regard, the potential for baseload technologies can be severelyoverestimated, while electricity generation by mid-merit and peak-load technologies is underestimated. The aboveanalysis clearly shows that data aggregation (i.e., averaging) in a low number of time slices solely based on capturingload patterns is highly undesirable for analyzing electricity systems with a high share of IRES.

To improve the approximation of the RLDC, different alternative temporal representations can be thought of,either by adapting the structure of the time-slice tree, the way values are assigned to each time slice, or both.In the further analysis, three alternative temporal representations are considered. A first alternative temporalrepresentation (”Integral TS high”) is based on increasing the resolution of the time-slice tree in the integralapproach. A second alternative devotes a separate level in the time slice tree to explicitly distinguish periods ofhigh and low resource availability (”Integral TS alt”). This approach also consists of taking the average value ofIRES profiles over multiple days. However, here the average is only taken over a subset of samples with similarresource availabilities. Finally, one could use a set of representative days, following the semi-dynamic approach(”Semi-dynamic TS high”). The details of each considered temporal representation are presented in Table 1.

Temporal Number of time slicesrepresentation Seasonal Daily Diurnal IRES TotalIntegral TS low 4 - 3 (day, night, peak) - 12Integral TS high 4 3 (Weekday, Sat, Sun) 24 - 288Integral TS alt 4 - 3 (day, night, peak) 3 (high, medium, low) 36Semi-dynamic TS high 4 3 (Weekday, Sat, Sun) 24 - 288Reference (TS ref) 52 7 24 - 8736

Table 1: Structure of different temporal representations considered. This table indicates the number of levelsand branches in the time slice tree. The terms ”Integral” and ”Semi-dynamic” refer to the methodology used for assigningvalues to each time slice.

Figure 4 displays for each considered temporal representation, the accuracy of the approximation of the RLDCas a function of the wind power penetration. Here, the root mean square error (RMSE) relative to the peak loadis used as a metric for the quality of the approximation. This figure shows that, relative to Integral TS low,increasing the resolution in the integral approach (Integral TS high) yields some benefits, predominantly by abetter approximation of the load. However, the gain in accuracy compared to the low resolution case (IntegralTS low) remains almost constant regardless of the wind power penetration. This indicates that increasing theresolution (integral approach) brings about no benefits related to grasping IRES variability. The opposite holdstrue for temporal representation ”Integral TS alt”. Adding a time slice level to explicitly account for variationsin IRES availability brings about no advantage for approximating load variations, but, as the share of IRES isincreased, drastically improves the approximation of the RLDC. Thus, by a different choice of the structure of thetime-slice tree, results can be significantly improved, with only a limited increase in the number of time slices.However, this approach has one major limitation: chronology is not retained, resulting in a loss of all informationregarding the dynamics of IRES generation and load variations. Therefore, this approach (Integral TS alt) is notdirectly suited to include inter-temporal technical load-following constraints or to analyze the potential of storagetechnologies. Finally, using the semi-dynamic approach (Semi-dynamic TS high) is likely to improve the accuracyof the RLDC approximation. However, the large spread in the accuracy obtained using this approach (see Figure4) highlights the importance of a good selection of representative periods. Furthermore, it must be noted that notonly a good approximation of the RLDC is of importance, but also the resource availability profiles (wind, sun,etc.) should be approximated with reasonable accuracy to avoid a technology bias. In this regard, some of thecombinations of days obtaining the lowest RMSE for the RLDC, might not be applicable.

3 Methodology, data and assumptions

3.1 Methodology

In a first instance, we aim to quantify how, given a set of power plants, the operational decisions (i.e., the dispatch)and associated costs are affected by the temporal representation and the level of techno-economic operational detail.When deciding on the amount of capacity to be built of each technology, investment planning models based onoptimization simultaneously minimize investment and operational costs over the specified time horizon. Therefore,a different level of operational detail not only impacts operational decisions, but also impacts investment decisions.In a second step, we demonstrate to what extent the level of operational detail affects the investment decisionsand total system costs.

The methodology applied in this work is presented in Figure 5. Two LT investment planning models are

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Figure 4: RMSE of the approximation of the RLDC for different temporal representations and a varyingwind power penetration. Blue lines indicate the RMSE of the approximation of the RLDC for the different timeslice division following the integral method. The RMSE of the RLDC approximation following the semi-dynamic methoddepends on which days are selected. The boxplots presented indicate the median (+), the 25th and 75th percentile(rectangle), the 1st and 99th percentiles (whiskers) as well as the outliers (circles). The presented values reflect 1000random selections of 1 week day, 1 Saterday and 1 Sunday per season (each day is appropriately weighted).

set-up, differing only in their temporal representation. The temporal representation of the first planning modelcorresponds to a temporal representation typically used in LT energy system planning models and aggregates datainto 12 time slices following the integral method (Integral TS low - see Table 1)4. The second planning modeluses hourly data for the entire year and serves as a benchmark in terms of temporal resolution (TS ref). In afirst step, the generation portfolios stemming from the LT models are used as input in more detailed operationalmodels. The focus in this step lies on the differences in the dispatch (i.e., the electric energy generation mix andoperational costs) between the planning model and the operational model. To distinguish between the impact ofthe level of temporal detail and the level of techno-economic detail, the dispatch of the LT model TS low is firstre-evaluated using a model operating at hourly resolution, but without detailed technical constraints (merit-order(MO) dispatch model). This step is omitted for the LT model TS ref, as this model performs a MO dispatchimplicitly. Subsequently, the dispatch is re-evaluated using a UC model. This model operates at hourly resolutionand includes detailed techno-economic operational constraints of power plants. The level of IRES penetration isvaried over the time horizon of the LT model to analyze the relation between this share of IRES and the impactof the level of temporal detail and techno-economic operational detail. In the second step, the impact of using atemporal representation on investment decisions and total system costs is evaluated by comparing the results ofthe two planning models.

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Figure 5: Schematic overview of the methodology. To analyze the impact of the temporal and techno-economicoperational detail, the dispatch of the LT planning models are re-evaluated using more detailed operational models. Todistinguish between the impact of the level of temporal and techno-economic operational detail, the dispatch is firstre-evaluated using a merit-order dispatch model (hourly resolution for the entire year) for TS low. Finally, the dispatchis re-evaluated using a unit commitment model (high level of temporal and techno-economic operational detail). Finally,to analyze the impact of the model simplifications on investment decisions, the results of two planning models differingonly in their temporal representation are contrasted.

The LT models used in this work are generated in the TIMES environment. TIMES (an acronym for The

4As 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.

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Integrated MARKAL-EFOM System) is an economic model generator for local, national or multi-regional energysystems [18]. TIMES aims to maximize the total discounted surplus of a user-specified reference energy systemby simultaneously making investment and operational decisions over a specified time horizon. TIMES models aretherefore bottom-up (technology explicit), dynamic partial equilibrium models of energy markets [18], typicallyformulated as LP problems. A full description of the mathematical structure in the TIMES model generator ispresented in [17]. The UC model used in this work is based on the MILP formulation of the unit-commitmentproblem as described by Delarue and D’haeseleer [9].

3.2 Data and assumptions

The models used in this work are based on the Belgian electricity system, studied as a representative case study. Itis assumed here that the Belgium electricity system is representative for other thermally-dominated systems withlow potentials for reservoir hydro generation.

To achieve a varying penetration of IRES throughout the model horizon, a linearly increasing target for theshare of annual electric energy generated by IRES is imposed (0% in 2010, 50% in 2050). Besides the demand forelectric power, a demand for firm capacity, exceeding the annual peak load by 5%, is imposed to ensure generationadequacy (peaking equation). In the investment models, a constant discount rate of 5% is applied. The assumedfuel and emission allowance prices are given in Table A1. In all presented models, grids and cross-border trade aredisregarded (i.e., island operation with a single node). Moreover, demand is assumed to be inelastic. Finally, therequirement for operating reserves is disregarded (fully deterministic setting).

The set of technologies considered and their respective economic and operational characteristics are presentedin Tables A2-A3. Next to the thermal and renewable technologies, existing pumped storage plants are includedin the models. However, given the limited geographical potential for the considered case study, no additionalinvestments in new pumped-storage plants are allowed.

With the exception of the nuclear plants, data on investment costs, fixed operations and maintenance (FOM)costs, life times and efficiencies are taken from [30]. Data on nuclear plants and lead times are taken from[1]. Variable operations and maintenance (VOM) costs and technical characteristics of different technologies areadopted from [29]. Regarding IRES, generation profiles for onshore and offshore wind turbines and solar PV panelsare taken from measured output in 2013, as provided by Elia5 [3]. This generation profile is scaled to the installedcapacity in future years. The generation system in the base year (2014), documented by Elia [3], is taken as thecurrent Belgian electricity generation system. The age of existing power plants is assumed to be equally distributedbetween 0 year (new) and the respective technology’s lifetime. Similar to the IRES generation profiles, the profileof future electricity demand is considered to be identical to the one observed in 2013 [3]. This profile is scaledusing a constant electricity demand growth rate of 1% per year. Fuel prices in the first period are adopted from[1], while fuel price evolutions are derived from [2].

4 Results and Discussion

4.1 Impact on operational decisions

4.1.1 Generation shares

Figure 6 presents, for each milestone year, the electric energy generation shares following from the dispatch in theTIMES models and the corresponding operational models. Furthermore, Table 2 displays the generation mix errorfor the different models. The most detailed model (UC) serves as a reference for comparison. For the LT modelwith temporal representation TS low, differences in dispatch with the UC model are due to both the differencein temporal and technical detail, whereas for the TIMES model operating at hourly resolution (TS ref) and theMO dispatch model, the difference in dispatch with the UC model can be solely attributed to the level of techno-economic operational detail. At this point, it must be stressed that the aim of this work is not to present scenariosfor the evolution of the Belgian electricity system, but rather to analyze the impact of modeling assumptionstypically used in LT planning models. In this regard, our interest lies in the difference in results between thedifferent models, and not in the model results as such.

A first observation is that in the first two periods, there are only slight differences in dispatch. However,as the share of IRES increases, generation shares from the TIMES models start to deviate from the results ofthe operational models. This confirms the presumption that the importance of the temporal resolution and theinclusion of operational constraints grows with the level of IRES penetration. However, whereas the impact of thetemporal aspect on the generation shares is solely dependent on the amount and type of IRES installed, the impact

5Elia is the Belgian transmission system operator.

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of the techno-economic operational detail also depends on the flexibility of the thermal generation fleet. This canbe clearly seen from Table 2, where the impact of the level of temporal detail is shown to increase strongly withthe level of IRES penetration, whereas the impact of the techno-economic operational detail is shown to be relatedalso to the installed capacity of less flexible nuclear plants.

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Figure 6: Electric energy generation shares. Figures 6a and 6b display the generation shares following the dispatchin the TIMES model (left bar) and the UC model (right bar) in each milestone year. To distinguish the impact ofthe temporal and technical detail, the merit-order dispatch (high level of temporal detail, no technical constraints) ispresented in the middle bar for TS low.

TS low TS ref2014 2020 2030 2040 2050 2014 2020 2030 2040 2050

Generation mix error TIMES [%] 0.4 0.4 6.5 10.2 12.8 0.4 0.4 3.1 2.8 2.8Generation mix error MO [%] 0.4 0.4 3.5 3.1 2.4 - - - - -∆ generation mix error temporal detail [%] 0.0 0.0 3.0 7.1 10.4 - - - - -∆ generation mix error techno-economic detail [%] 0.4 0.4 3.5 3.1 2.4 0.4 0.4 3.1 2.8 2.8Installed cap IRES/Peak load [-] 0.30 0.42 0.77 1.12 1.49 0.30 0.42 0.77 1.17 1.90Installed cap Nuclear/Peak load [-] 0.45 0.52 0.63 0.52 0.42 0.45 0.53 0.64 0.52 0.52

Table 2: Generation mix error6in the different models. The generation mix from the TIMES model and themerit-order (MO) model are contrasted to the generation mix of the unit commitment (UC) model (highest level ofdetail), which serves as a reference. The installed capacity of intermittent renewable energy sources (IRES) and Nuclearplants are presented as explanatory variables.

Two patterns can be observed in the deviations in dispatch (see Figure 6). First, the uptake of IRES issystematically overestimated by the TIMES models. In other words, more curtailment of IRES is required/cost-effective than is anticipated by the TIMES models. A result of this additional curtailment is that the proposedportfolio falls short of achieving the imposed target for the share of IRES in the generation mix. An overview ofcurtailment of IRES and the share of IRES in the generation mix in the different models is presented in Table 3.As discussed in Section 2, aggregating wind data values in a limited number of time slices causes the model tooverlook periods of excess IRES generation (potential IRES generation exceeds the load) and the correspondingneed for curtailment. However, some overestimation of the uptake of IRES also occurs due to the limited technicaldetail. The motivation for this additional curtailment of IRES in the UC model is diverse: curtailment could benecessary to ensure system balance or beneficial from an economic perspective. Curtailment as a means to ensurea balanced system occurs in periods in which IRES generation volatility exceeds the load-following capabilitiesof dispatchable plants. Curtailment for economic reasons typically occurs to prevent start-up costs in situationswhere a plant would have to be shut down for a short period before having to start up again.

Second, also the share of baseload electric energy generation tends to be overestimated by the TIMES models.The impact of the temporal representation can again be explained by analyzing the approximation of the RLDC.As can be seen in Figure 3b, the model with temporal representation TS low will yield an approximation of theRLDC which is too flat, resulting in an overestimation of the number of full load hours of baseload technologies.Neglecting detailed techno-economic operational constraints is also shown to contribute to overestimating theamount of baseload generation. Again, this can be the result of technical constraints or for economic reasons.

6The generation mix error is defined as: GenMixError =∑

i

| ˜generation sharei−generation shareUCi |

2, where index i runs over all

technologies and the generation shares are expressed as a percentage.

10

TS low TS ref2014 2020 2030 2040 2050 2014 2020 2030 2040 2050

Excess energy [%] 0 0 0.1 4.5 11.7 0 0 0.1 5.6 10.9Curtailment TIMES [%] 0 0 0 0 0 0 0 0 4.3 9.3Curtailment MO [%] 0 0 0 3.8 10.9 - - - - -Curtailment UC [%] 0 0 1.9 11.5 15.4 0 0 2.2 10.3 15.9Impact temporal representation [%pt] 0 0 0 3.8 10.9 - - - - -Impact techno-economic detail [%pt] 0 0 1.9 7.7 4.5 0 0 2.2 6.0 6.6Share IRES TIMES [%] 7.7 12.5 25 37.5 50 7.7 12.5 25 37.5 50Share IRES UC [%] 7.7 12.5 24.5 33.1 42.3 7.7 12.5 24.5 35.7 47.2

Table 3: Curtailment of intermittent renewable energy sources (IRES) and shares of IRES generation inthe energy mix. The amount of curtailment in the TIMES model and the merit-order (MO) model are contrastedto the amount of curtailment in the unit commitment (UC) model (highest level of detail), which serves as a reference.Excess energy and curtailment are expressed as a percentage of the maximal IRES generation (no curtailment). Theshare of renewable electricity generation is expressed as a percentage of total consumed electric energy.

Baseload plants are generally less flexible than mid-merit or peak-load power plants. To deal with the rapidvariations in IRES output, mid-merit and peak-load power plants can be committed to provide the requiredflexibility, even though the baseload plants are operating below their rated capacity. Also, the size of the unit thathas to be started up or shut down can play a role. Avoiding the high start-up costs of starting up a large unitwhen little additional online capacity is required can reduce the use of baseload technologies. On the other hand,the high start-up costs of baseload technologies could also result in higher generation shares, e.g., if curtailment ofIRES is scheduled to prevent a baseload plant from shutting down (and having to start up some hours later). Thiscan be observed in the year 2050 of the TS ref case (see Figure 6). Finally, also periodic maintenance requirementsand outages of discrete units impact the dispatch. These complex interactions make it difficult to generalize theimpact of the level of techno-economic operational detail on the generation mix.

4.1.2 Operational costs

Table 4 presents the operational costs7 for the different models. This table clearly indicates that both a low level oftemporal and techno-economic detail contribute to underestimating costs. An unrealistic temporal representationcauses an underestimation of the electricity generation by mid-merit and peak-load technologies, thereby leadingto an underestimation of operational costs. Furthermore, both the level of technical and economic operationaldetail influences the operational costs. On the one hand, the dispatch in the TIMES models and the merit-orderdispatch model could be technically infeasible. In this case, the dispatch in the UC model will schedule additionalflexible generation (as can be observed in Figure 6), leading to higher generation costs. On the other hand, thedispatch proposed by the models with less detail could deviate from the cost-effective dispatch when start-up andramping costs are taken into account.

In the case presented here, in comparison to the ”most correct” model (UC), the operational costs are shownto be underestimated by 3-53% for model TS low (low temporal resolution, no operational detail), depending onthe degree of IRES penetration. For model TS ref (high temporal resolution, no operational detail), operationalcosts are underestimated by 3-13%. The divergence in operational costs generally increases with the share of IRESin the system, although the flexibility of the thermal generation fleet also plays a role.

TS low TS ref2014 2020 2030 2040 2050 2014 2020 2030 2040 2050

Operational cost TIMES [EUR/MWh] 29.9 25.2 13.1 10.9 9.4 29.9 24.7 14.2 13.8 10.8Operational cost MO [EUR/MWh] 30.0 25.3 14.5 15.5 17.5 - - - - -Operational cost UC [EUR/MWh] 30.7 26.0 16.8 18.6 20.1 30.7 25.5 16.2 15.9 12.2Impact temporal representation [EUR/MWh] 0.1 0.1 1.4 4.6 8.1 - - - - -Impact techno-economic detail [EUR/MWh] 0.7 0.7 2.3 3.1 2.6 0.7 0.8 2.0 2.1 1.4

Table 4: Operational costs in the different models. The operational costs in the TIMES model and the merit-order(MO) model are contrasted to the operational costs in the (most-detailed) unit commitment (UC) model, which servesas a reference. All costs are expressed relative to the total consumed electric energy.

7Operational costs include fuel costs, costs related to emissions of greenhouse gasses, VOM costs as well as start-up costs.

11

2014 2020 2030 2040 20500

10

20

30

40

50

60

Period

Installed

Capacity

[GW

]Existing CapacityNuclear PPCCGTOCGTWind OnshorePV

Figure 7: Breakdown of the installed capacity foreach milestone year. The left and right bar show theresults of the models with temporal representationTS low and TS ref respectively.

49%

1%

50%

TS low

46%

3%

43%

7%

TS ref

Figure 8: Electric energy generation shares in 2050.The left and right chart display the electric energygeneration shares for the model with temporal rep-resentation TS low and TS ref respectively.

4.1.3 Temporal versus techno-economic operational detail

Qualitatively, the level of temporal and techno-economic operational detail are shown to have a similar impact onoperational decisions. Quantitatively, the above results indicate that the impact of the temporal representationgrows more strongly with the level of IRES penetration in the electricity generation mix than the level of techno-economic operational detail. One reason for this is that a growing IRES penetration reduces the number of full-loadhours of baseload technologies, leading to a lower fraction of less flexible baseload capacitiy. For a high penetrationof IRES (35-50%), the impact of the temporal dimension is significantly higher than the impact of the level oftechno-economic detail (Tables 2-4). Furthermore, a temporal representation which preserves chronology and hasa sufficiently high resolution is a prerequisite for detailed modeling of techno-economic operational constraints. Forthese reasons, we advice to prioritize addressing the temporal representation. In case of stringent computationalrestrictions, one could consider using an alternative structure of the time slice tree (similar to TS alt, see Section2). As this approach does not maintain chronology, detailed modeling of techno-economic operational constraintsis not possible. On the other hand, if sufficient computational resources are available, using a well-chosen set ofrepresentative days is recommended.

4.2 Impact on investment decisions

The effect of the temporal representation on the investments is shown in Figure 7, which displays the compositionof the total installed capacity in each milestone year, for both the model with time slice division TS low and TSref. A breakdown of the electric energy generation (dispatch) for the year 2050 is presented in Figure 8 for bothmodels.

A first observation that can be made is that, despite identical targets for annual renewable electric energygeneration, a divergence in investments in IRES can be observed starting from 2040. Installed IRES capacities in2040 and 2050 are markedly higher for the model using hourly profiles. This is due to the fact that the model withtemporal representation TS low does not account for periods of excess energy, and associated need for curtailment ofrenewable energy (see Section 2 and Table 3). The model with a high temporal resolution foresees this curtailmentand therefore will invest in additional IRES capacity to reach the imposed target for the share of IRES electricitygeneration. In addition, the model attempts to reduce this curtailment and is therefore incentivized to diversifyits IRES portfolio (thereby reducing the excess energy, see year 2050 in Table 3).

Second, noticeable differences in the investments in dispatchable power plants occur starting from 2040. Morespecifically, investments in dispatchable technologies are more diversified in the model operating at hourly resolu-tion. Again, the answer lies in the RLDC. As can be seen in Figure 3b, the model with temporal representationTS low will yield an approximation of the RLDC which is too flat. Combining this flat RLDC with the screeningcurves will result in a less diversified capacity mix. In terms of electric energy generation, a flat RLDC will resultin an overestimation of the number of full-load hours that can be obtained by baseload technologies. This can beclearly seen for the year 2050, in which the share of nuclear electricity generation is higher in the model using TSlow, although installed capacities are higher in the model using TS ref.

Table 5 gives an indication of the impact on the total discounted energy-system cost8. This table reveals that, in

8The yearly energy-system costs consists of yearly investment costs (incl. salvation values) and FOM costs, on the one hand, and

12

Discounted cost TS low TS ref[GEUR2014] Fixed Operational Total Fixed Operational TotalTIMES 37.43 30.77 68.20 39.75 32.00 71.75UC 37.43 37.27 74.70 39.75 34.34 74.10

Table 5: Breakdown of the total discounted system cost. Total costs consist of operational costs (incl. fuel costs,emission costs and variable operations and maintenance costs (OM)) and fixed costs (incl. investment costs and fixedOM costs) of the generation side of the electricity system. Costs over the entire model horizon (2014-2055) are discountedto the year 2014 using a constant discount rate of 5%.

comparison to the most detailed model (UC), the underestimation of operational costs in the TIMES model, leadsto an underestimation of total discounted energy-system costs in the TIMES model of 10.5% for model TS low,and 5.6% for model TS ref. These differences remain limited due to the fact that largest deviations in operationalcosts occur in later periods (with higher shares of IRES), which contribute less as a result of cost discounting.

Finally, this re-evaluating of operational costs reveals that having a more accurate temporal representationincreases the optimality of the solution. That is, the model with a high temporal resolution has a slightly lowerre-evaluated total discounted system cost, while at the same time achieving higher shares of IRES. Incorporatinghigh levels of temporal and techno-economic operational detail in planning models will therefore not only improvethe accuracy of the dispatch and directly related quantities (e.g., primary fuel consumption and GHG emissions),but also leads to a more optimal set of investments.

5 Summary and 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 renewable energy sources (IRES) are expected to be keycontributors to this transition. However, their highly variable and stochastic nature poses challenges to long-termenergy-system planning models, as these models typically use a low level of temporal and techno-economic op-erational detail. In this regard, closing the gap between short-term operational models and long-term planningmodels has become an active field of research.

This paper provides a comprehensive analysis of how the operational dimension of the electric power sectorcan be included in system planning models. Different approaches for accounting for the temporal aspect can bedistinguished from the literature. By carefully analyzing these approaches and identifying the causes for differentoutcomes, this paper extends the existing literature. Furthermore, not every approach is suited to incorporatedetailed techno-economic operational constraints. To balance the impact of this techno-economic operationaldetail to the impact of the temporal representation, a detailed modeling analysis is set-up to quantify the impactof both the temporal and techno-economic representation for an increasing penetration of IRES. This allows makingthe trade-off between improving planning models by extending the operational time dimension, and/or aiming fora better technical representation.

Different temporal representations are analyzed using the residual load duration curve (RLDC) as a tool toassess how and to what extent the temporal representation drives model results. We find that the temporalrepresentation typically used in large-scale energy-system planning models does not sufficiently account for thevariability of IRES generation. This results in an inaccurate approximation of the RLDC, underestimating periodsof peak residual load and periods of excess energy. This in turn provides a technology bias towards IRES andless-flexible baseload technologies, while flexible technologies are not valued sufficiently. Alternative temporalrepresentations are presented. In this regard, not only the structure of the time-slice tree is shown to be ofimportance, but the method of assigning values to each time slice is shown to be equally important. Using atime-slice level to explicitly account for levels of IRES penetration improves the approximation of the RLDCsignificantly, without drastically increasing the number of time slices. On the other hand, a set of representativedays can also result in a drastic increase in accuracy, although this strongly depends on the choice of representativedays.

The impact of the temporal representation and the level of techno-economic operational details has been quan-tified by re-evaluating the dispatch decisions of the long-term planning models using a merit-order dispatch (hourlyresolution, low level of techno-economic operational detail) and a unit-commitment model (hourly resolution, highlevel of techno-economic operational detail). Both the temporal and techno-economic operational representationare shown to have the same qualitative impact on the results, i.e. a low level of detail results in an overestimation of

operational costs (incl. fuel costs, VOM costs and costs related to the emission of greenhouse gases), on the other hand.

13

the potential uptake of IRES, an overestimation of the generation by baseload technologies and an underestimationof operational costs. The impact of the temporal representation is shown to increase more strongly with the shareof IRES in the system than the impact of the techno-economic operational detail. The presented results indicatethat for a high share of IRES electric energy generation (35-50%), the accuracy of the results is dominated by thetemporal representation.

To conclude, we present some guidelines that can be considered when performing a scenario analysis using abottom-up long-term energy system planning model:

• When setting up the temporal representation of the model, evaluate how this representation might drivethe results of the model by analyzing how the RLDC is approximated. Be aware of the limitations of thisapproach. First, due to the loss of chronology, the suggested temporal representation might approximate theRLDC with high accuracy, while the dynamics of the variations of the residual load might not be grasped.Second, the RLDC alone does not contain information about the resource availability profiles (e.g., wind, sun).To prevent a technology bias, consider additionally evaluating how the temporal representation approximatesthe resource availability duration curves.

• Document not only the temporal structure of the time slice tree, but also how values are assigned to thesetime slices. This information is often lacking, but is proved to be essential for assessing and interpreting theresults obtained by these models.

• When increasing the level of operational detail of the long-term planning model, prioritize improving thetemporal representation to increasing the level of techno-economic operational detail.

• When computational resources are limited, consider deviating from the typical structure of the time slice tree(i.e., based on a time slice levels to account for seasonal, daily and diurnal variations) by using a time slicelevel that explicitly accounts for IRES availability for analyzing energy systems with high shares of IRES.

• Using a set of well-chosen representative days is an alternative when computational restrictions are lessstringent. As, intra-daily, chronology is maintained in this approach, it allows for detailed modeling oftechno-economic operational constraints.

Acknowledgements

The research of Kris Poncelet is supported by a PhD grant provided by VITO. Erik Delarue is a post-doctoralresearch fellow of the Research Foundation Flanders (FWO).

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Appendix A

Price 2010 2020 2030 2040 2050[EUR2008/MWhp]

Coal 8.81 9.44 9.79 10.33 10.82Natural gas 23.89 24.30 25.12 25.66 26.27Uranium9 6.34 6.34 6.34 6.34 6.34

[EUR2008/tonCOeq2 ]

GHG emissions 0 12.5 25 37.5 50

Table A1: Fuel and GHG emission prices. Fuel pricesin the baseyear are taken from [1], the evolution of fuelprices is derived from [2].

Technology Inve

stm

ent

cost

[kE

UR

/kW

e]

FO

M[E

UR

/(kW

e·y

ear)

]

VO

M[E

UR

/M

Wh

]

Lif

eti

me

[yea

rs]

Lea

dti

me

[yea

rs]

2010 2020 2030 2050 2010 2020 2030 2050Nuclear gen III(+) 3.66 3.66 3.66 3.66 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 A2: Overview cost-related parameters. Investment costs and FOM costs are dependent on the timing of theinvestment.

9 Fuel prices for nuclear plants are expressed in [EUR/MWhe] and include front-end and back-end costs of the nuclear fuel cycle.

17

Technology Effi

cien

cy[%

]

Min

imu

mop

erati

ng

poi

nt

[%/P

nom

]

Eff

.lo

ssat

min

imu

mop

.p

oint

[%p

t]

Max

imu

mra

mp

rate

[%Pnom

/min

]

Ram

pco

st[E

UR

/∆M

W]

Min

imu

mup

tim

e[h

ou

rs]

Min

imu

md

own

tim

e[h

ou

rs]

Sta

rtu

pre

late

dfu

elu

se[M

Whth/∆M

We]

Sta

rt-u

pd

epre

ciat

ion

cost

s[E

UR

/∆M

W]

Ava

ilab

ilit

y[%

]

2010 2020 2030 2050Nuclear gen III(+) 10010 100.1 102 104 50 - 5 - 24 48 17 1.7 85Subcrit. coal 37 38 39 41 40 2 3 1.3 6 4 5.7 5 85(Ultra) Supercrit. coal 45 46 49 49 50 2 4 1.3 6 4 5.7 5 85IGCC coal 45 46 48 50 50 8 4 0.25 4 1 1.7 10 85CCGT 58 60 62 64 50 8 7 0.25 4 1 1.7 10 85OCGT 39 39 40 41 10 21 17.5 0.66 1 1 0.02 10 85Adv. OCGT 42 45 45 45 10 21 17.5 0.66 1 1 0.02 10 85

Table A3: Overview of operational characteristics. Efficiencies of power plants are dependent on the timing of theinvestment.

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

18