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Securing profits from energy, reserve capacity and balancing markets is critical to ensure the profitability of battery energy systems (BES). However, the intimate connection between offers on these trading floors combined with the limited energy storage capacity of BES renders its scheduling very complex. In this paper, we develop a bilevel optimization problem for strategic participation of a BES in the day- ahead energy-reserve and balancing markets, improving the state-of-the-art by (i) considering the conditional-value-at-risk ; (ii) ensuring the real-time feasibility of the obtained day-ahead schedule ; (iii) addressing the operational underperformance risk stemming from inaccurate battery modeling. In a case study, we illustrate how the proposed model allows risk-averse BES owners to hedge their day-ahead position without jeopardizing their expected profit, while ensuring the feasibility of their day-ahead schedule.

Energy storage systems (ESS) may provide the required flexibility to cost-effectively integrate weather-dependent renewable generation, in particular by offering operating reserves. However, since the real-time deployment of these services is uncertain, ensuring their availability requires merchant ESS to fully reserve the associated energy capacity in their day-ahead schedule. To improve such conservative policies, we propose a data-driven probabilistic characterization of the real-time balancing stage to inform the day-ahead scheduling problem of an ESS owner. This distributional information is used to enforce a tailored probabilistic guarantee on the availability of the scheduled reserve capacity via chance constrained programming, which allows a profit-maximizing participation in energy, reserve and balancing markets. The merit order-based competition with rival resources in reserve capacity and balancing markets is captured via a bi-level model, which is reformulated as a computationally efficient mixed-integer linear problem. Results show that a merchant ESS owner may leverage the competition effect to avoid violations of its energy capacity limits, and that the proposed risk-aware method allows sourcing more reserve capacity, and thus more value, from storage, without jeopardizing the real-time reliability of the power system.

This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent.To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics.

In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporate complex market dynamics in the optimal bidding strategy. This method uses input-convex neural networks (ICNNs) to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., to replace the lower-level problem by a neural network. In a comparative analysis, the optimal bidding problem of a load agent is formulated via the proposed MPNNC and via the classical bi-level programming method, and compared against each other.

To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day- ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators.

This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent.To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics.

Current model predictive control (MPC) applications for residential space heating typically rely upon accurate building models, obtained via extensive data acquisition and/or experts’ knowledge. However, in the context of older residential buildings, one needs to rely upon sparse, publicly available data. Therefore, the aim of this paper is to come up with an estimate of the parametric uncertainty of building controller models in case neither detailed information about the building thermal properties nor experts’ knowledge is available. In addition, the impact of this uncertainty on the optimal space heating strategy is investigated. The results show that the considered approach gives rise to rather large parametric uncertainty. The obtained variation in model parameters is shown to markedly affect the optimal space heating control, both in terms of dynamic effects (i.e., peak demand and timing) and yearly energy use, thereby indicating the need for improved data acquisition and/or dedicated control strategies that operate robustly under uncertainty. ispartof: Proceedings of Building Simulation 2021: 17th International Conference of the International Building Performance Simulation Association ispartof: Building Simulation 2021 Conference location:Bruges date:1 Sep - 3 Sep 2021 status: published

This paper presents new risk-based constraints for the participation of an energy community in day-ahead and real-time energy markets. Forming communities offers indeed an effective way to manage the risk of the overall portfolio by pooling individual resources and associated uncertainties. However, the diversity of flexible resources and the related user-specific comfort constraints make it difficult to properly represent flexibility requirements and to monetize constraint violations.To address these issues, we propose a new risk-aware probabilistic enforcement of flexibility constraints using the conditional-value-at-risk (CVaR). Next, an extended version of the model is introduced to mitigate the distributional ambiguity faced by the community manager when new sites with limited information are embedded in the portfolio. This is achieved by defining the worst-case CVaR based-constraint (WCVaR-BC) that differentiates the CVaR value among different sub-clusters of clients.Both reformulations are linear, thus allowing to tackle large-scale stochastic problems. The proposed risk-based constraints are then trained and evaluated on real data collected from several industrial sites. Our findings indicate that using the WCVaR-BC leads to systematically higher out-of-sample reliability, while decreasing the exposure to extreme outcomes.

Trading electricity across market zones furthers competitive power prices, security of supply and the integration of renewable energy. In the European Union, flow-based market coupling is the target model to compute correct trading capacities between markets, while approximating physical grid constraints. The methodology relies on predictive, carefully designed parameters to maximize trading opportunities, while keeping congestion management at acceptable levels. This paper explains the fundamentals of flow-based market coupling. It provides an open-access model based on a test network and its data. By providing a guide to the theory and conducting several case studies, the functioning and effects of the most influential parameters are demonstrated. Innovative aspects of this paper are its illustrative nature and its answer to the following research questions: (1) What is the effect of (a) generation shift keys and (b) threshold choice on the selection of critical network elements? (2) What is the effect of (a) generation shift keys, (b) selected critical network elements and (c) security margins on market coupling and congestion management results? It also addresses the effect of higher shares of renewable energy and minimum trading capacities on flow-based market coupling. This analysis shows that (1) the effects of flow reliability margins and selected critical network elements dominate the effect of generation shift keys on flow-based market coupling domains, (2) power systems with an increasing share of renewable energy may necessitate re-adjusted parameters and (3) minimum trading capacities can be guaranteed by redispatch or altering the base case.

Capacity credits, i.e., metrics that describe the contribution of different technologies in meeting the load during peak periods, are widely used in the context of long-term energy-system optimization models to ensure a pre-defined level of firm capacity. In the same vain, such capacity credits may be used in capacity markets to reflect the availability of an asset during periods of peak load. For storage technologies it seems that there is a discrepancy between the capacity credit that correctly captures the capacity contribution to the capacity target, and the capacity credit that correctly values the storage capacity. This is illustrated in a case study, which shows the differences in planning model outcomes when different capacity credit interpretations are used. Our results indicate that defining the capacity credit according to the contribution to the capacity target overvalues storage technologies, causing overinvestments. On the contrary, defining the capacity credit to reflect the value of the storage capacity, triggers the correct amount of storage investments, but underestimates the true peak reduction potential, which results in overinvestments in other firm capacity providers. In this regard, a novel iterative approach is introduced that leverages both capacity credit interpretations simultaneously to remove any bias in the solution.

ispartof: pages:811-814 ispartof: CIRED - Open Access Proceedings Journal vol:2020 issue:1 pages:811-814 ispartof: CIRED 2020 Berlin Workshop Online location:Berlin (online) status: published

Because of the EU ETS' cancellation policy, its fixed cap or waterbed is punctured, meaning that shocks and overlapping policies can change cumulative carbon emissions, i.e., waterbed leakage. This paper explains the mechanisms behind waterbed leakage and quantifies the effect of COVID-19, the European Green Deal, and the recovery stimulus package on cumulative EU ETS emissions and allowance prices. We find that the negative demand shock of the pandemic has limited effect on the EU ETS price and is almost completely translated into lower carbon emissions, because of high waterbed leakage. Increasing the 2030 reduction target to -55% increases the price of allowances to 67 euro/ton CO2 today and decreases carbon emissions in the period 2020-2050 by around 16.3 GtCO2 or 42% of the cumulative cap under current policies. These results are robust to significant changes in allowance demand triggered by overlapping policies in the period 2021-2031.

Energy system optimisation (ESOM) and generation expansion planning (GEP) models are often used to study energy transition pathways. These typically entail an increased penetration of variable renewable energy sources (VRES), which can lead to increased operating reserve requirements due to their associated forecast uncertainty. Representing this effect has previously been tackled using either stochastic programming techniques or deterministic GEPs which use heuristics to size reserves while ignoring their activation cost. In this paper, we propose a novel GEP formulation which determines operating reserve requirements using a second order cone (SOC) constraint. This formulation approximates the solution of a stochastic GEP by accounting for reserve activation costs without resorting to scenario based methods. A case study on the Belgian system indicates possible cost savings of 70 MAC(0.9%) and less bias towards installing peaking technologies to satisfy reserve requirements compared to a deterministic GEP. The sensitivity of the results to the assumption of normality of forecast errors and temporal detail is also investigated. Two final case studies on the value of emergency measures and improving forecast uncertainties illustrate the benefits of accounting for reserve activation costs and appropriate reserve sizing.

To provide a strong price signal for greenhouse gas emissions abatement, Europe decided to strengthen the European Union Emissions Trading System (EU ETS) by implementing a market stability reserve (MSR) that includes a cancellation policy and to increase the linear reduction factor from 1.74% to 2.2% after 2020. Results of a detailed long-term investment model, formulated as a large-scale mixed complementary problem, show that this strengthened EU ETS may quadruple EUA prices and may decrease cumulative CO2 emissions with 21.3 GtCO2 compared to the cumulative cap before the strengthening (52.2 GtCO2). Around 40% of this decrease (8.3 GtCO2) is due to the increased linear reduction factor and 60% due to the cancellation policy (13 GtCO2). Without the increased linear reduction factor, the MSR's cancellation policy would decrease emissions by only 4.1 GtCO2, indicating their complementarity. A sensitivity analysis on key model assumptions and parameters reveals that the impact of the MSR is, however, strongly dependent on other policies (e.g., renewable energy targets, nuclear, lignite and coal phase-outs) and cost evolutions of abatement options (e.g., investment cost reductions for wind and solar power). This renders the effective CO2 emissions cap highly uncertain. In our simulation results, cancellation volumes range between 5.6 and 17.8 GtCO2, which is to be compared with our central estimate of 13 GtCO2. We calculate the required linear reduction factors to achieve these CO2 emission reductions without an MSR, which would remove all uncertainty on the cumulative CO2 emissions and interference with other complementary climate or energy policies.

To cope with the variability and uncertainty introduced by, i.a., intermittent renewable energy sources, the flexible planning and operation of generation units is crucial. Their reaction time is constrained by the lead times on the start-up decisions, whereas the demand for flexibility and operating reserves depends on the market clearing frequency. The start-up lead times are limited by the operator’s tolerance for increased maintenance on the asset, which should be reflected in short-term scheduling models. To study this interaction between the market clearing frequency and the start-up capabilities of combined-cycle gas turbines, we develop a unit commitment model. The model considers multiple start-up trajectories and the scheduling decisions in joint energy-operating reserve and balancing markets. The uncertainty on wind power forecasts is presented via wind power forecast updates generated by a dedicated data-driven tool. Leveraging this model, we investigate the interaction between (i) the frequency of wind power forecast updates, linked to the market clearing frequency, (ii) cost-optimal operating reserve volumes and (iii) combined-cycle gas turbine start-up decisions. Results show that, in general, higher market clearing frequencies lead to lower operating costs, driven by decreasing volumes of operating reserves and facilitated by the fast start-up capabilities of combined-cycle gas turbines.

European electricity markets are zonal markets, a set-up that naturally entails an imperfect representation of intra-zonal congestion patterns that may limit cross-border trade. The method of flow-based market coupling aims to reflect limitations to cross-border trade by incorporating intra- and interzonal grid elements within the setting of zonal pricing through monitoring the flows on these critical network elements caused by inter-zonal trade. A major challenge for grid operators is the selection of critical network elements, essentially deciding which grid elements send congestion signals and trade limitations to the markets. Our main research question is: Can insights on hypothetically re-configured market zones help to improve the selection of critical network elements and lead to cost reductions without effectively changing the market zone setting? Using a flow-based market coupling optimization model based on a 3-zone test network, we propose a hypothetical nodal price-based market zone re-configuration to identify congestion signals and derive an improved set of critical network elements. We find that around 90% of the cost reductions from this market zone re-configuration can be maintained when the critical network elements, obtained from the re-configured market zones, are used in the original 3-zone setting. This is a strong indication that, both in reality as well as model-based research of flow-based market coupling, the selection of critical network elements should be based on expected congestion patterns. The proposed approach can constitute a helpful addition to static and assumption-based selection criteria for critical network elements that are currently used by European grid operators.

An electricity generation system adequacy assessment aims to generate statistically significant adequacy indicators given projected developments in, i.a., renewable and conventional generation, demand, demand response and energy storage availability. Deterministic unit commitment (DUC) models with exogenous reserve requirements, as often used in today's adequacy studies to represent day-to-day power system operations, do not account for the contribution of operating reserves to the adequacy of the system. Hence, the adequacy metrics obtained from such an analysis represent a worst-case estimate and should be interpreted with care. In this paper, we propose to use a DUC model with a set of state-of-the-art probabilistic reserve constraints (DUC-PR). The performance of the DUC-PR model in the context of adequacy assessments is studied in a numerical case study. The Expected Energy Not Served (EENS) volume obtained with the DUC model is shown to be a poor estimate of the true EENS volume. In contrast, the DUC-PR methodology yields an accurate estimate of the EENS volume without significantly increasing the computational burden. Policy makers should encourage adopting novel operational power system models, such as the DUC-PR model, to accurately estimate the contribution of operating reserves to system adequacy. ispartof: ENERGY POLICY vol:121 pages:112-123 status: published

© 2017 Elsevier Ltd Cross-border reserve markets—the procurement and activation of reserves in one control area to maintain system balance in another control area—can lead to increased cost-efficiency and reliability. However, network constraints impose limits on cross-border reserve coordination. Transmission capacity allocation in the reserve market is a complex problem, as it happens under uncertainty and interferes with transmission capacity allocation in energy markets. This paper studies network constraints in the reserve procurement phase, by means of a simulation model and scenario analysis. Three different approaches are proposed and evaluated based on a case study of the Central Western European electricity system. Towards this aim, a dedicated model is developed to simulate the day-ahead energy market, the day-ahead reserve procurement and the real-time reserve activation. In a case study of the Central Western European power system, we show that the best reserve market outcome—weighing cost-efficiency and system reliability—is obtained when reserve activation scenarios are considered in the procurement phase. Policy makers should design, in close cooperation with regulators and system operators, efficient and robust transmission capacity allocation procedures for cross-border reserve markets. This paper can help them to do so as it demonstrates the impact of transmission capacity allocation on cross-border reserve markets. ispartof: Energy Policy vol:113 pages:193-205 status: published

© 2017 IEEE. Controllable loads can modify their electricity consumption in response to signals from a system operator, providing some of the flexibility needed to compensate for the stochasticity of electricity generated from renewable energy sources (RES) and other loads. However, unlike traditional flexibility providers, e.g., conventional generators and energy storage systems, demand response (DR) resources are not fully controlled by the system operator and their availability is limited by user-defined comfort constraints. This paper describes a deterministic unit commitment model with probabilistic reserve constraints that optimizes day-ahead power plant scheduling in the presence of stochastic RES-based electricity generation and DR resources that are only partially controllable, in this case residential electric heating systems. This model is used to evaluate the operating cost savings that can be attained with these DR resources on amodel inspired by the Belgian power system. ispartof: IEEE Transactions on Sustainable Energy vol:9 issue:99 pages:1-10 status: published

The resource adequacy of the interconnected Central Western Europe (CWE) electricity system is assessed considering the cross-border exchange capacities defined through the Flow-Based (FB) domains. Integration of FB domains into adequacy assessments poses several challenges since the FB domains depend on factors which are not known over the horizon of adequacy study. Computing hourly FB domains for each generated scenario of adequacy study, firstly, requires adopting assumptions on those unknown parameters (that may not fully match with the reality). Secondly, it noticeably increases the computational complexity of the study. The above challenges can, however, be circumvented by the data-driven alternatives. This paper presents a novel clustering technique for FB domains, which is specifically tailored for adequacy assessments. In contrast to the classical approach employed by the CWE Transmission System Operators (TSOs), which clusters the FB domains based on their overall geometrical resemblance, the proposed technique relies on the maximum and minimum zonal balances allowed by the FB domains, which are decisive factors in the CWE resource adequacy assessments. Indeed, during scarcity moments, the zonal net positions (balances) tend to reach their extreme values to reduce the costs of energy not served. The proposed goal-oriented clustering technique is examined against the classical clustering methodology employed by the CWE TSOs. The conducted simulations demonstrate that the proposed technique considerably (by a factor of over 5.5) improves the accuracy of the CWE adequacy assessments while being scalable with the future evolution of the Flow-based Market Coupling (FBMC). As such, it has direct implications for the adequacy assessment considering the FB domains.

An important aspect of long-term power system planning models is their adequacy awareness, i.e., their ability to ensure generation adequacy in the final solution, which in turn strongly depends on the level of temporal detail included in the model structure. To maintain computational tractability, the temporal detail included in long-term planning models is, however, often limited, which decreases their adequacy awareness resulting in inadequate capacity mixes. To compensate for this, several adequacy-improving measures can be taken. The aim of this paper is to investigate the performance of three of these measures, namely (i) adding a traditional static planning reserve margin (PRM) constraint, (ii) adding a dynamic PRM constraint and (iii) a novel approach that increases the level of temporal detail with which critical peak periods are modeled. To this end, we compare each of these three adequacy methods in a long-term planning exercise based on (i) total system costs, (ii) adequacy of the resulting capacity mix and (iii) technology choices. Our results suggest that the novel approach more effectively increases the adequacy of the obtained capacity mix, resulting in lower total system costs and less technology biases. Also the dynamic PRM approach performs well, although occasional inadequacies can occur in individual milestone years due to exogenously imposed capacity credits.

To reduce the computation time of Energy System Optimization Models and Generation Expansion Planning Models operational detail is typically limited to several hours, days, or weeks in a year selected using Time Series Aggregation methods. We compare time series aggregation methods and generation expansion planning models which aim to capture the value of long-term storage for the first time in the literature. Time Series Aggregations methods were compared by varying the number of representative periods and then running a full year generation expansion planning model on novel synthetic time series. Generation Expansion Planning Models were run on selections and ordering of representative periods in order to compare them. Our results suggest that approximating the full-year time series does not necessarily translate to approximating the full-year generation expansion planning solution and that selecting hours or days is a greater determinant of performance than the time series aggregation method itself. Two of the generation expansion planning models considered, Enhanced Representative Days and Chronological Time Period Clustering, could capture the value of long-term storage, though over or underinvestment in long-term storage by more than a factor of 2 was also possible and the latter formulation exhibited a clear bias towards long-term storage. Based on these results we formulate recommendations for modelers seeking to include long-term storage in generation expansion planning models.

Due to the increased penetration of intermittent renewables, operating reserves are becoming increasingly important in electricity markets. Coordinating the sizing, allocation and activation of reserves among market zones can decrease operational costs and enhance system reliability. However, network limitations constrain reserve coordination among zones. This paper investigates the value of interzonal coordination of reserve sizing, allocation and activation. A series of three models that simulate unit commitment and dispatch decisions within network-constrained markets simulate the impact of intermarket coordination of each of these sets of decisions. A case study for the Central Western European electricity system indicates that such coordination can lower operational costs and increase system reliability. However, the best performing strategy for the considered case study turns out to be a strategy with coordinated activation but uncoordinated sizing and allocation of reserves due to suboptimal coordination of sizing and allocation with activation. In particular, because transmission constraints are simplified when sizing and allocating reserves, reserves might not actually be deliverable to where renewable output is different from forecast.

Abandoned underground quarries or mines may be rehabilitated as natural reservoirs for underground pumped hydro energy storage (UPHES). In addition to the inherent modeling inaccuracies of the traditional PHES that arise from, e.g., approximating the nonlinear pump/turbine head-dependent performance curves, the optimal operation of these underground plants is also affected by endogenous model uncertainties. The latter typically arise from a limited knowledge of the physical characteristics of the system such as the geometry and hydraulic properties of the underground cavity. In this paper, chance-constrained programming is leveraged to immunize the day-ahead scheduling of an UPHES owner against both these model uncertainties and the modeling approximations. The proposed method is tested on a fictitious UPHES system using an existing underground quarry as lower reservoir. Results demonstrate that the methodology allows finding a compromise between conservativeness and economic performance, while being computationally efficient. This model may thus be integrated in the daily scheduling routine of UPHES owners, or may help regulators and system operators to better estimate the available flexibility of such resources.

This paper describes a step-by-step procedure that converts a physical model of a building into a Markov Process that characterizes energy consumption of this and other similar buildings. Relative to existing thermo-physics-based building models, the proposed procedure reduces model complexity and depends on fewer parameters, while also maintaining accuracy and feasibility sufficient for system-level analyses. Furthermore, the proposed Markov Process approach makes it possible to leverage real-time data streams available from intelligent data acquisition systems, which are readily available in smart buildings, and merge it with physics-based and statistical models. Construction of the Markov Process naturally leads to a Markov Decision Process formulation, which describes optimal probabilistic control of a collection of similar buildings. The approach is illustrated using validated building data from Belgium.

Active demand response (ADR) is a powerful instrument among electric demand side management strategies to influence the customers’ load shape. Assessing the real potential of ADR programmes in improving the performance of the electric power system is a complex task, due to the strict interaction between supply and demand for electricity, which requires integrated modelling tools. In this paper an analysis is performed aimed at evaluating the benefits of ADR programmes in terms of electricity consumption and operational costs, both from the final user’s and the overall system’s perspective. The demand side technologies considered are electric heating systems (i.e. heat pumps and electric resistance heaters) coupled with thermal energy storage (i.e. the thermal mass of the building envelope and the domestic hot water tank). In particular, the effect of the penetration rate of ADR programmes among consumers with electric heating systems is studied. Results clearly show that increasing the number of participating consumers increases the flexibility of the system and, therefore, reduces the overall operational costs. On the other hand, the benefit per individual participant decreases in the presence of more ADR-adherent consumers since a reduced effort from each consumer is needed. Total cost saving ranges at most between about 400 € and 200 € per participant per year for a 5% and 100% ADR penetration rate respectively.

The possibly increasing volatile gas off-take from gas-fired power plants to accommodate volatile renewable generation in combination with the integration of power-to-gas (P2G) warrants further study into the operation of a coupled electrical power and natural gas system. Therefore, this paper presents and validates a novel operational model comprising both the electrical power and gas systems. Model improvements include (i) the use of zonal gas loads in addition to nodal loads, (ii) ramp rates for conventional gas production facilities and, (iii) an improved detailed technological model of P2G units, which all increase the realism of the obtained results. Results of several small-scale case studies illustrate the relevance of these model additions. In addition, a case study inspired by the Belgian electrical power and gas systems shows that the Belgian gas network has abundant capacity to integrate a possibly volatile injection of synthetic methane from P2G. This model may be used by electricity and gas transmission system operators to study the interaction between their systems and inform policy makers and regulators.

The large scale introduction of variable and limitedly predictable renewables requires flexible power system operation, enabled by, i.a., dynamic power plant operation, storage, demand response and enhanced interconnections. The fast start-up capabilities of combined-cycle gas turbines (CCGTs) are crucial in this regard. However, these non-standard operating conditions significantly reduce the lifetime of critical turbine components, as reflected in long-term service agreements (LTSAs). This should also be reflected in short-term scheduling models. In light of this challenge, we apply a unit commitment model that allows multiple start-up loading modes while accounting for the corresponding turbine maintenance costs based on LTSAs. Leveraging this model, we investigated the need for fast start-up capabilities of a set of CCGTs as part of a small scale test system considering various shares of renewables and dynamic reserve requirements. We have found that fast starts are often cost-optimal despite their greater turbine maintenance costs and a cost reduction of around 1 % is obtained when considering more costly fast start-up modes when scheduling. Furthermore, cost-optimal reserve sizing is a function of the planning frequency and is reduced by fast starting capabilities. We conclude that taking advantage of fast start-up capabilities benefits the electricity generation system and yields a significant cost reduction. ispartof: Ieee Transactions On Sustainable Energy vol:9 issue:3 pages:1468-1476 status: published

European legislation encourages prosumers to generate, consume and sell self-generated electricity in local and large-scale electricity markets. Since the capacity of residential prosumers is typically insufficient to take part in wholesale markets, aggregators act as mediators. This allows prosumers to monetize the flexibility of their loads through demand response (DR). This paper analyzes the strategic behavior of a DR aggregator, responsible for the optimization of its DR providers' bids in a wholesale market. The DR resource consists of an aggregation of residential electric heating systems, governed by a physical load model and user-specified comfort constraints. The interaction between the aggregator and the DR providers is modelled via Nash Bargaining Theory. Possible deviations from the scheduled heating patterns are accounted for via chance constraints. Results show that limitedly controllable DR resources and a risk-averse aggregator may significantly reduce the monetary benefits of DR.

© 2018 IEEE. The distribution tariff design, which is currently being overhauled in multiple European countries, is a significant part of a residential consumer's electricity bill. This paper studies how the tariff design influences the behavior of a strategic aggregator, of residential consumers with photovoltaics (PV) and energy storage systems (ESS), on a wholesale market. The aggregator-wholesale market interaction is formulated as a Stackelberg game. We show that the distribution tariff design impacts the strategic operation of the aggregator's PV and ESS and the accompanying cost savings the aggregator attains compared to a retailer of active consumers. Electronic ISBN: 978-1-5386-1488-4 ispartof: pages:604-608 ispartof: 2018 15th International Conference on the European Energy Market (EEM) vol:2018-June pages:604-608 ispartof: 15th International Conference on the European Energy Market (EEM) location:Lodz, Poland date:27 Jun - 29 Jun 2018 status: published

Heat pumps are widely recognized as a key technology to reduce CO2 emissions in the residential building sector, especially when the electricity-generation system is to decarbonize by means of large-scale introduction of renewable electric power generation sources. If heat pumps would be installed in large numbers in the future, the question arises whether all building types show equal benefits and thus should be given the same priority for deployment. This paper aims at answering this question by determining the CO2-abatement cost of installing a heat pump instead of a condensing gas boiler for residential space heating and domestic hot-water production. The electricity system, as well as the building types, are based on a possible future Belgian setting in 2030 with high RES penetration at the electricity-generation side. The added value of this work compared to the current scientific literature lies in the integrated approach, taking both the electricity-generation system and a bottom up building stock model into account. Furthermore, this paper analyzes the possible benefits of active demand response in this framework. The results show that the main drivers for determining the CO2-abatement cost are the renovation level of the building and the type of heat pump installed. For thoroughly insulated buildings, an air-coupled heat pump combined with floor heating is the most economic heating system in terms of CO2-abatement cost. Finally, performing active demand response shows clear benefits in reducing costs. Substantial peak shaving can be achieved, making peak capacity at the electricity generation side superfluous, hence lowering the overall CO2-abatement cost.