Electricity Market Simulation Research Articles

A new energy-economy-environment modeling framework: Insights from decarbonization of the Turkish power Sector towards net-zero Emission targets

The power sector plays a crucial role towards decarbonization for many economies, especially in line with the net-zero targets to limit global warming to 1.5 °C. Technical constraints intrinsic to the sector, penetration of new technologies, investment and operational costs, and its connections with the rest of the economy make the power sector a complex system to analyze. Although there are numerous studies to integrate bottom-up power sector technology models with top-down macroeconomic models, this study is the first attempt to link the three separate and interrelated models within a single framework: an electricity market simulation model, a generation expansion planning model, and an applied general equilibrium model. The proposed framework is implemented to analyze a feasible decarbonization scenario for Türkiye, with a particular focus on the power sector. The results suggest that, given the existing capacity and potential for renewables, Türkiye can achieve a coal-phase out by early 2030s, alongside a trajectory towards a full-fledged fossil fuel phase-out in power generation. The results also indicate that while installed capacity and generation of coal-fired power plants are reduced, real GDP and electricity demand can be maintained and the carbon dioxide emissions from the power sector could be reduced by as much as 50% in 2030 compared to 2018 levels.

Investigating the role of nuclear power and battery storage in Hungary's energy transition using hourly resolution electricity market simulations

Electricity supply in European countries faces a number of challenges, such as achieving carbon neutrality, tackling rising prices, reducing dependence on fossil fuels, including fossil fuel imports. To achieve these goals, the electricity systems of all European countries will have to undergo major changes, while taking into account technical, environmental, economic and social objectives. Our simulations provide essential data for this transition by analyzing different power plant portfolios and electricity consumption scenarios. The analyses focus on the cooperation of nuclear power and weather-dependent renewables, and on the possible role that battery-based electricity storage can play in the Hungarian electricity system.In this paper, we present the experience gained in setting up an electricity market model and the results of running the model on the electricity systems of Hungary and its six neighboring countries (Slovakia, Romania, Serbia, Croatia, Slovenia and Austria), taking into account the constraints of the cross-border capacities. The results of the sensitivity analysis for the 2030 power plant portfolios, battery capacities and renewables analyzed in this paper cover Hungary's import/export position, the energy source structure of its electricity generation, battery operation, CO2 emissions from electricity generation, expected prices in the system and the utilization parameters of nuclear power plants.

Pricing and Simulating Energy Transactions in Energy Communities

Extensive literature is available for modeling and simulating local electricity markets, often called P2P electricity markets, and for pricing local energy transactions in energy communities. Market models and pricing mechanisms provide simulation tools to better understand how these new markets behave, helping to design their main rules for real applications, and assessing the financial compensations of the internal energy transactions. As such, pricing mechanisms are often needed in energy management systems when centralized management approaches are preferred to market-based ones. First, this paper highlights the links between local electricity markets, pricing mechanisms for local electricity transactions, and other approaches to sharing the collective benefits of participating in transactive energy communities. Then, a standard nomenclature is defined to review some of the main pricing mechanisms for local energy transactions, an innovative pricing mechanism based on the economic principles of a post-delivery pool market is proposed, and other relevant approaches for local electricity market simulation such as Nash equilibrium or agent-based simulation are also revisited. The revision was based on systematic searches in common research databases and on the authors’ experience in European and national projects, including local industrial applications for the past five years. A qualitative assessment of the reviewed methods is also provided, and the research challenges are highlighted. This review is intended to serve as a practical guide to pricing mechanisms and market simulation procedures for practical designs of internal financial compensation to share the collective benefits of energy communities.

Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning

The increasing penetration of weather-dependent renewable energy generation calls for high-resolution modeling of the possible future energy mixes to support the energy strategy and policy decisions. Simulations relying on the data of only a few years, however, are not only unreliable but also unable to quantify the uncertainty resulting from the year-to-year variability of the weather conditions. This paper presents a new method based on artificial neural networks that map the relationship between the weather data from atmospheric reanalysis and the photovoltaic and wind power generation and the electric load. The regression models are trained based on the data of the last 3 to 6 years, and then they are used to generate synthetic hourly renewable power production and load profiles for 42 years as an ensemble representation of possible outcomes in the future. The modeled profiles are post-processed by a novel variance-correction method that ensures the statistical similarity of the modeled and real data and thus the reliability of the simulation based on these profiles.The probabilistic modeling enabled by the proposed approach is demonstrated in two practical applications for the Hungarian electricity system. First, the so-called Dunkelflaute (dark doldrum) events, are analyzed and categorized. The results reveal that Dunkelflaute events most frequently happen on summer nights, and their typical duration is less than 12 h, even though events ranging through multiple days are also possible. Second, the renewable energy supply is modeled for different photovoltaic and wind turbine installed capacities. Based on our calculations, the share of the annual power consumption that weather-dependent renewable generation can directly cover is up to 60% in Hungary, even with very high installed capacities and overproduction, and higher carbon-free electricity share targets can only be achieved with an energy mix containing nuclear power and renewable sources. The proposed method can easily be extended to other countries and used in more detailed electricity market simulations in the future.

Electricity market modeling considering a high penetration of flexible heating systems and electric vehicles

The electrification of the heating and transportation sectors is a major building block in Germany’s effort to reduce carbon dioxide emissions. The additional loads and flexibilities resulting from the anticipated large-scale integration of the heating and transportation sectors necessitate an adequate representation in electricity market modeling. First, the paper presents a comprehensive modeling approach of the heating sector in a pan-European electricity market simulation considering both small-scale building heat pumps and large-scale conversion units with thermal energy storages in district heating networks. The operation of small-scale building heat pumps is emulated using an electro-thermal 1R1C building model integrated into the economic dispatch formulation while the operation of units in district heating networks is restricted by additional constraints resulting from the combined heat and power output, local heat load coverage and possible heat power transfer between heating networks. The transportation sector is mapped using previously published models by the authors including both controlled and fast charging processes. Subsequently, the load shift potentials resulting from the market-integration of the heating and transportation sectors and their impact on the pan-European electricity market with focus on Germany based on the three target years 2030, 2040 and 2045 are examined. Furthermore, by applying a Monte Carlo simulation to map possible future primary energy source prices a major uncertainty dimension is addressed. Considering the current German electricity market design, the integration of the two sectors lead to a moderate increase in future annual electricity demands and a substantial increase in market-based peak load. The resulting annual peak load from power-to-heat devices account to 22 GW (2030), 53 GW (2040) and 102 GW (2045) and from electric vehicles to 12 GW (2030), 36 GW (2040) and 96 GW (2045) for the target years. Further results demonstrate the larger load-shift potentials of electric vehicles compared to building heat pumps by comparing the weighted annual average market price of the flexible loads. The primary fuel price uncertainty is examined based on the 0.25, 0.50 and 0.75 quantiles for the target years which result in annual average electricity market price spreads of over 2.16 (2030), 2.97 (2040) and 3.44 (2045) between the 0.25 and 0.75 quantile scenarios.

Policy implications of spatially differentiated renewable energy promotion: A multi-level scenario analysis of onshore wind auctioning in Germany

Auctions have been introduced in many countries as a useful alternative for renewable energy support. However, they often lead to a high concentration of renewable energy power plants at productive sites, at the expense of other, less favorable sites. This paper studies the impact of alternative renewable energy auction schemes on the promotion of renewable energy in Germany, using a novel multi-level approach—analyzing auctioning on the national, north/south, and federal states level, respectively. First, using a GIS tool, the onshore wind potential is studied at a high regional and temporal resolution. The results of this analysis show a considerable untapped onshore wind potential in the southern federal states of Germany. Second, an onshore wind power auction model is developed by using a system dynamics approach. Finally, the policy support payments are compared for different auction designs by using an electricity market simulation model. The findings suggest that more bidders from the southern federal states could win in the regional auctions. Detailed spatial analysis reveals a trade-off between balanced diversity of bidders and the average auction price. We conclude that regional auctioning can indeed lead to significant support payment savings, and should thus be considered in renewable energy support policy design.

A review of current analytical methods, modelling tools and development frameworks applicable for future retail electricity market design

Retail electricity markets require development to become transparently functioning, operate in near real-time, permit the integration of smart technology, improve customer engagement, better reflect wholesale prices, be driven by optimal sustainability metrics and mitigate against unexpected price spikes, so that society transitions to net zero in a just manner. This study postulates that it is essential that future retail electricity markets are designed by cross-linking current analytical approaches and integrating proprietary and open-source modelling tools, by adopting the latest development frameworks across data science, software development, cloud computing and Internet of Things interconnectivity. Current analytical methods applicable for retail electricity market design are reviewed and multi-criteria decision analyses are applied to assess modelling tool and development framework effectiveness. A key finding of this study is that current analytical methods applied in the research domain are typically segregated or siloed. As current analytical methods tend to focus on specific clusters, such as electricity customer profiling, demand response and dynamic pricing, peer-to-peer electricity trading, interconnectivity of Internet of Things devices and distribution-level power dynamics, rather than the integration of these clusters and how each analytical method interacts with each other. Similarly, this study finds that modelling tools are also typically segregated or siloed, focusing on energy system planning, electricity market simulation, power system simulation and energy consumption analysis. To provide the necessary tools to design future retail electricity markets, this study suggests that robust modelling tool integration is required.

Scarcity events analysis in adequacy studies using CN2 rule mining

Dealing with scarcity events is nowadays gaining relevance in electricity market studies, as traditionally predictable generation and consumption patterns are fading. Policymakers and system planners use therefore adequacy studies to a) understand if the current market design will attract sufficient generation capacity to meet electricity demand in the future and b) to comprehend what drives system inadequacy or resource scarcity when future scenarios lack adequate capacity. This work addressed the latter and showcases a first in-its-kind rule-based methodology that filters scarcity events from a large set of electricity market simulations. In this proof-of-concept, a rule-mining algorithm is applied to outputs from ENTSO-E's Pan-European electricity market model, which is run for 700 model scenarios, each covering 8760 time steps. The developed methodology shows how to unveil potential reasons behind scarcity events in an automated, interpretable, and scalable manner.

Comparative study of pricing mechanisms and settlement methods in electricity spot energy market based on multi-agent simulation

With the advancement of electricity market reform, eight pilot spot provinces in China have started electricity spot market trading. In the settlement rules of spot markets in each region, some areas apply nodal weighted average price to the settlement’s customer and supply sides. The economic incentive effect of the spot market pricing mechanism on market participants affects their bidding strategies. Therefore, based on the current research on electric spot market pricing mechanisms, this paper investigates the effects of bidding procedures and fairness of generator and consumption sides under three settlement mechanisms: locational marginal pricing, zonal pricing, and average system pricing. Through multi-agent-based electricity market simulation, the paper establishes a spot market clearing and settlement model to quantitatively analyze the impact of zonal settlement mechanisms on the interests of each market player. By validating analysis results, the paper makes suggestions for selecting electricity price mechanisms in China’s electric spot market environment.

Diffusion and system impact of residential battery storage under different regulatory settings

Cost reductions of rooftop photovoltaics and battery storage, increasing retail electricity prices as well as falling feed-in remuneration provide strong incentives for many German households to engage in self-consumption. These developments may also affect the electricity system as a whole. Against this background, we jointly apply a prosumer simulation and an agent-based electricity market simulation in order to investigate the long-term impacts of a residential battery storage diffusion on the electricity market. We analyze different regulatory frameworks and find significant effects on the household level, yet only moderate system impacts. In the long run, the diffusion of residential battery storage seems difficult to govern, even under a restrictive regulation. In contrast, the way the batteries are operated may be easier to regulate. Policymakers and regulators should focus on this aspect, since a system-friendly battery operation supports the system integration of residential photovoltaics while having little impact on the households’ self-sufficiency.

Evolution of Fundamental Price Determination within Electricity Market Simulations

Electricity prices are the key instrument for coordinating electricity markets. For long-term market analyses, price determination based on fundamental unit commitment simulations is required. Within the European wholesale market, electricity prices result from a market clearing, which finds a welfare-optimal price–quantity tuple considering a coupling of multiple market areas with limited transmission capacity. With increasing exchange capacities in Europe, the precise modeling of the market coupling is required. Many market simulation models use multi-stage approaches with a separation of market coupling and price determination. In this paper, we analyze a new single-stage approach that combines both steps and theoretically and empirically demonstrate its precision by a backtest. For this purpose, we compare a simulated versus a historical electricity price distribution. Moreover, we explain the necessary adjustments for future regulatory developments of the European electricity market regarding flow-based market coupling and propose a concept for the application of future regulatory developments. We demonstrate further developments using a future scenario.

Investigating the market effects of increased RES penetration with BSAM: A wholesale electricity market simulator

Currently electricity markets worldwide encounter a transition phase into cleaner energy. This is specially the case in Greece, where not only the market structure changes to a harmonized EU target model, but also ‘green electricity’ is projected to reach 50% share by 2030 according to the National Energy and Climate Plan of Greece. This paper presents the extension of the Business Strategy Assessment Model (BSAM) into a computationally efficient central-dispatch wholesale market model. BSAM integrates agent-based profit-maximizing policy making with unit commitment scheduling, into one novel tool that can be used to explore the effects of mid-term market design policies in wholesale electricity markets. BSAM is subsequently utilized to model the Greek market’s transitional phase and explore the effects of even higher RES penetration scenarios. Results indicate that the planned short-to-mid-term “de-lignification” and transition of the Greek power system is feasible and will not lead to an excessive RES curtailment situation, but would yield significant CO2 emissions’ reductions and small reductions in the system marginal price, with even more ambitious RES shares also being possible.

Ontologies to Enable Interoperability of Multi-Agent Electricity Markets Simulation and Decision Support

This paper presents the AiD-EM Ontology, which provides a semantic representation of the concepts required to enable the interoperability between multi-agent-based decision support systems, namely AiD-EM, and the market agents that participate in electricity market simulations. Electricity markets’ constant changes, brought about by the increasing necessity for adequate integration of renewable energy sources, make them complex and dynamic environments with very particular characteristics. Several modeling tools directed at the study and decision support in the scope of the restructured wholesale electricity markets have emerged. However, a common limitation is identified: the lack of interoperability between the various systems. This gap makes it impossible to exchange information and knowledge between them, test different market models, enable players from heterogeneous systems to interact in common market environments, and take full advantage of decision support tools. To overcome this gap, this paper presents the AiD-EM Ontology, which includes the necessary concepts related to the AiD-EM multi-agent decision support system, to enable interoperability with easier cooperation and adequate communication between AiD-EM and simulated market agents wishing to take advantage of this decision support tool.

Data mining for energy systems: Review and prospect

AbstractAn in‐depth study on big data mining is urgently needed for the next‐generation energy systems, which are characterized by a deep integration of cyber, physical, and social components. This paper presents an initial discussion on big data mining and its applications in intelligent energy systems. New progress in big data mining, such as deep learning, transfer learning, randomized learning, granular computing, and multisource data fusion, is introduced first. Some applications of data mining in energy systems, such as load forecasting and modeling, integrated power and transportation system, and electricity market forecasting and simulation, are discussed then. Moreover, some research problems in energy system data mining, such as cyber–physical–social system modeling and super‐resolution perception for smart meter data, which require further attention in the future, are also discussed.This article is categorized under: Application Areas > Business and Industry

Advanced price forecasting in agent-based electricity market simulation

Machine learning and agent-based modeling are two popular tools in energy research. In this article, we propose an innovative methodology that combines these methods. For this purpose, we develop an electricity price forecasting technique using artificial neural networks and integrate the novel approach into the established agent-based electricity market simulation model PowerACE. In a case study covering ten interconnected European countries and a time horizon from 2020 until 2050 at hourly resolution, we benchmark the new forecasting approach against a simpler linear regression model as well as a naive forecast. Contrary to most of the related literature, we also evaluate the statistical significance of the superiority of one approach over another by conducting Diebold–Mariano hypothesis tests. Our major results can be summarized as follows. Firstly, in contrast to real-world electricity price forecasts, we find the naive approach to perform very poorly when deployed model-endogenously (mean absolute percentage error 0.40–0.53). Secondly, although the linear regression performs reasonably well (mean absolute percentage error 0.17–0.32), it is outperformed by the neural network approach (mean absolute percentage error 0.17–0.21). Thirdly, the use of an additional classifier for outlier handling substantially improves the forecasting accuracy, particularly for the linear regression approach. Finally, the choice of the model-endogenous forecasting method has a clear impact on simulated electricity prices. This latter finding is particularly crucial since these prices are a major results of electricity market models.

Robust nonlinear mathematical transmission expansion planning based on German electricity market simulation

Requirements on electrical energy transmission and the work of system operators are changing due to the shift from centralized towards decentralized generation. This leads to an increased need of transport capacities in grids. In this paper, a robust method is presented to solve the resulting transmission expansion planning (TEP) problem using a nonlinear mathematical optimization. In terms of TEP optimization successive measures are considered in the used mathematical formulation such as grid optimization, grid enhancement and grid extension. The mathematical optimization is based on a DC power flow formulation using unreduced grid models. The concept of the TEP model design and the implementation of different measures are given in detail in the paper. The applicability of the developed approach is shown by the application to a model of the German transmission grid based on a German electricity market simulation for a yearly simulation calculating an expansion demand of 3047 GWkm.

Mix-generation optimization for electricity market simulation

Owning several types of generating units requires an optimized schedule to cover the negotiated bilateral contracts. This approach will lead to a better electricity market strategy and benefits for an electricity producer. In this paper, we will simulate the operation of five different generators including generators based on Renewable Energy Sources (such as wind turbines and photovoltaic panels) that belong to an electricity producer. The five generators are modelled considering the specificity of their type and primary energy source. For instance, for renewable energy sources, we will consider the 24-hour generation forecast. The objective function of the optimization process is to obtain an optimal loading of generators, while the constraints are related to the capacity and performance of the generators. The output consisting in a generating unit optimized operation schedule will be further used for day-ahead or balancing market bidding process. Hence, the producer will be able to adequately bid on the future electricity markets knowing the commitment of generators for negotiated bilateral contracts market. The simulations are tested for more than five generators considering the connection to a relational database where more data for generators is stored.

Adjacent Markets Influence Over Electricity Trading—Iberian Benchmark Study

This paper presents a study on the impact of adjacent markets on the electricity market, realizing the advantages of acting in several different markets. The increased use of renewable primary sources to generate electricity and new usages of electricity such as electric mobility are contributing to a better and more rational way of living. The investment in renewable technologies for the distributed generation has been creating new opportunities for owners of such technologies. Besides the selling of electricity and related services (ancillary services) in energy markets, players can participate and negotiate in other markets, such as the carbon/CO2 market, the guarantees of origin market, or provide district heating services selling of steam and hot water among others. These market mechanisms are related to the energy market, originating a wide market strategy improving the benefits of using distributed generators. This paper describes several adjacent markets and how do they complement the electricity market. The paper also shows how the simulation of electricity and adjacent markets can be performed, using an electricity market simulator, and demonstrates, based on market simulations using real data from the Iberian market, that the participation in various complementary markets can enable power producers to obtain extra profits that are essential to cover the production costs and facilities maintenance. The findings of this paper enhance the advantages for investment on energy production based renewable sources and more efficient technologies of energy conversion.

An integrated model of coupled heat and power sectors for large-scale energy system analyses

In Europe and other regions of the world, heat and power sectors are highly coupled. Thus, both sectors are interdependent which poses a challenge when modeling these systems. This paper presents a novel and integrated model framework to analyze coupled heat and power sectors. Said framework is designed to assess large-scale/multi-national systems such as the European continent’s one. The integral model character derives from a model sequence that first models heat demand at individual district heating network level, then determines the heat supply schedules of individual heat generation units in these networks and finally uses an extended version of a pre-existing large-scale market model, the WILMAR Joint Market Model, for electricity market simulations. All models are embedded in a common database environment. In order to validate the model framework, a historical year is chosen, and model outputs are compared to historical market outcomes. For comparison, two alternative, frequently used modeling approaches are equally tested. Results show that the developed model framework is far superior to the alternative modeling approaches. This statement concerns the reproduction of heat demands, power plant dispatch, electricity prices and net electricity exports. The results also suggest that the developed model can yield improved intra-zonal exchange schedules, which are one major driver for congested transmission network elements.

Operation of the Hybrid Photovoltaic-Battery System on the Electricity Market—Simulation, Real-Time Tests and Cost Analysis

This paper presents research on a hybrid photovoltaic-battery energy storage system, declaring its hourly production levels as a member of a balancing group submitting common scheduling unit to the day-ahead market. It also discusses the variability of photovoltaic system generation and energy storage response. The major research questions were whether the operation of a hybrid photovoltaic-battery energy storage system is viable from the technical and economic viewpoint and how to size battery energy storage for that purpose. The DIgSILENT PowerFactory environment was used to develop the simulation model of postulated hybrid system. Then, tests were conducted on real devices installed in the LINTE^2 laboratory at Gdańsk University of Technology, Poland. Firstly, power generation in the photovoltaic system was modeled using hardware in the loop technique and tested in cooperation with emulated photovoltaic and real battery energy storage system (lithium-ion battery, 25 kWh). Secondly, a real photovoltaic power plant (33 kW) and real battery energy storage were applied. The results obtained from laboratory experiments showed that market operation of hybrid photovoltaic-battery energy storage system is feasible. However, developing a control strategy constitutes a great challenge, as the operator is forced to intervene more frequently than the simulation models indicate in order to keep the parameters of battery storage within accepted ranges, especially in view of a sudden weather breakdown. Levelized cost of electricity from photovoltaic-battery energy storage system varied from 314 to 455 $/MWh, which has proven to be from two to three times higher than the current annual average day-ahead market price in Poland.