Electricity Market Research Articles

Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm

In recent years, distributed energy resources (DERs) have developed greatly, and the total capacity of resources aggregated by virtual power plants (VPPs) has increased. The role VPP plays in the market is changing. It is necessary to explore VPP's bidding strategy in the electricity market based on the impact of VPP on the clearing price. At the same time, the strategy game of different market players will also impact VPP's strategy. The multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm is used to solve the problem of price-maker VPP participation in the day-ahead (DA) market. VPP is required to submit the power and prices of electricity in multi-segments at different times. In the market bidding process, the main market participants are considered as agents. Iterative refinement of each player's strategy is achieved through multi-agent reinforcement learning, with the primary aim of maximizing revenue. For the VPP, the MATD3 algorithm improved the reward by approximately 65 % and increased the convergence speed by 17 % compared to the multi-agent deep deterministic policy gradient and multi-agent proximal policy optimization algorithms. The effects of different influences on VPP's bidding strategy are analyzed, which can provide a decision-making reference for market participants.

The United Kingdom electricity market mechanism: A tool for a battery energy storage system optimal dispatching

In this paper, the role of a Battery Energy Storage System (BESS) in the United Kingdom (UK) electricity market is investigated. Such device is selected since research works related to the topic demonstrate that BESSs help facing challenges caused by renewable energy sources increasing penetration in the power systems’ field. Indeed, in the next future, BESSs will assume a relevant role thanks to their fast response time. In the present paper, an optimization model is implemented to encode the UK market rules and output the optimal set of offers on day-ahead and intraday markets, dynamic frequency response services and imbalance settlement. The market rules are formalised in a mathematical model considering market temporal sequencing and BESS technical limitations. The tool also models the state of energy management, necessary since BESSs are energy limited assets. Objective function and constraints are linearized to define a Mixed Integer Linear Programming problem to reduce the computational burden and to integrate the model in the Energy Management System previously developed at University of Genoa. Specific tests are performed including and excluding ancillary services to evaluate the related advantages. The obtained outcomes reveal that ancillary services are the most convenient options within all the implemented scenarios.

Analysis of Multi-Oligopoly Games in the China’s Electricity Market

Analysis of Multi-Oligopoly Games in the China’s Electricity Market

Directional connectedness between the electricity prices and natural gas prices: evidence from Alberta’s electricity market

Purpose This study aims to assess volatility spillovers and directional connectedness between electricity (EPs) and natural gas prices (GPs) in the Canadian electricity market, based on a hydrothermal power generation market strongly dependent on exogenous variables such as fossil fuel prices and climatology factors. Design/methodology/approach The methodology is divided into two stages. First, a quantile vector autoregression model is used to evaluate the direction and magnitude of the influence between natural gas and electricity prices through different quantiles of their distributions. Second, a cross-quantilogram is estimated to measure the directional predictability between these prices. The data set consists of daily electricity and natural gas prices between January 2015 and December 2023. Findings The main finding shows that electricity prices are pure shock receivers of volatility from natural gas prices for the different quantiles. In this way, natural gas price fluctuations explain 0.20%, 0.98% and 22.72% of electricity price volatility for the 10th, 50th and 90th quantiles, respectively. On the other hand, a significant and positive correlation is observed in the high quantiles of the electricity prices for any natural gas price value. Originality/value The study described the risk to the electricity market caused by nonrenewable source price fluctuations and provided evidence for designing regulatory policies to reduce its exposure in Alberta, Canada. It also allows us to understand the importance of natural gas in the energy transition process and define it as the fundamental determinant of the electricity market dynamic.

Production or transmission investments? A comparative analysis

AbstractThe European electricity market is moving towards an integration process, and to facilitate that integration and the flow of electricity in that market, the European Transmission System Operator proposes different investments in transmission and production capacities. I develop a model of imperfect competition to study those investments, and I identify the economic forces that determine the equilibrium price and the flow of electricity: The size and the competitive effects. I prove formally when those forces operate simultaneously or independently, and how they determine consumers’ surplus and suppliers’ profits. I prove that an investment in transmission capacity outperforms an investment in production capacity by maximizing consumers’ welfare, but the performance in terms of suppliers’ profits is ambiguous.

Electricity GANs: Generative Adversarial Networks for Electricity Price Scenario Generation

The dynamic structure of electricity markets, where uncertainties abound due to, e.g., demand variations and renewable energy intermittency, poses challenges for market participants. We propose generative adversarial networks (GANs) to generate synthetic electricity price data. This approach aims to provide comprehensive data that accurately reflect the complexities of the actual electricity market by capturing its distribution. Consequently, we would like to equip market participants with a versatile tool for successfully dealing with strategy testing, risk model validation, and decision-making enhancement. Access to high-quality synthetic electricity price data is instrumental in cultivating a resilient and adaptive marketplace, ultimately contributing to a more knowledgeable and prepared electricity market community. In order to assess the performance of various types of GANs, we performed a numerical study on Turkey’s intraday electricity market weighted average price (IDM-WAP). As a key finding, we show that GANs can effectively generate realistic synthetic electricity prices. Furthermore, we reveal that the use of complex variants of GAN algorithms does not lead to a significant improvement in synthetic data quality. However, it requires a notable increase in computational costs.

Operation Model Based on Artificial Neural Network and Economic Feasibility Assessment of an EV Fast Charging Hub

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge’s operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil.

Role of power-to-heat and thermal energy storage in decarbonization of district heating

The sector integration of the heat and electricity sectors promotes the flexibility of the energy system. This, together with the already almost carbon dioxide-free electricity supply in Finland, facilitates the environmental transition to a carbon-neutral heating system. In this study, pathways to decarbonize the district heating system in Åland (an autonomous region of Finland) are explored. Especially, the roles of different power-to-heat technologies, e.g., heat pumps and direct electric heating, and thermal energy storage are investigated in the decarbonization of the district heating system. The focus is on studying the impact of varying prices in electricity markets on the operation of climate-neutral district heating systems. For this, three scenarios were modeled over the five years of 2018–2022 with varying demand and electricity market prices by using the modeling software PLEXOS. The study showed that electricity prices, but also reserve market prices, have a very significant impact on the operation cost and profit of a district heating system. Overall, electrification has the strong potential to reduce emissions cost-effectively in a district heating system, but it also makes the system vulnerable to electricity price volatility. It can be concluded that thermal energy storage and reserve market participation are crucial for protecting against cost increases and reducing investment risk.

Calculation Methodology to Determine Electricity Distribution Tariffs Using an Approach Based on Cost Causation

The cost reflectivity of electricity distribution network tariffs has been debated in several countries, and various ways to enhance it have been investigated in recent years. However, the recent academic literature regarding the approach based on cost causation has a clear gap because no case studies show how distribution network tariffs can be determined in practice for large customer groups. This article offers a calculation methodology to determine distribution network tariffs based on cost causation along with a case study where unit prices are determined for the tariff structures still widely used today using the data for two separate network areas being operated by two Finnish distribution system operators (DSOs) in an unbundled electricity market environment. The results of the case study show that the total differences between the target and the realized turnovers in both investigated networks are smaller than 1%, which means almost a full cost recovery. In addition to traditional tariff structures, the proposed calculation methodology can also be modified to design and determine other pricing schemes. The need for systematic calculation processes is growing to improve the cost reflectivity of present tariffs and adapt to the needs of the evolving operating environment, novel tariff structures, and new emerging customer groups.

Reactive power management in distribution networks in the presence of distributed generation sources based on information gap decision theory

The presence of uncertain parameters in power systems has led to many challenges for the designers and operators of these systems. One of these challenges is reactive power management in the presence of distributed renewable generation sources.In this article, the management of reactive power in distribution networks in the electricity market and the presence of distributed renewable generation sources, including wind and solar power plants, is performed considering the uncertainties in the network load, power generation of distributed generation sources, and active and reactive power market prices. Furthermore, reactive power cost modeling of reactive power compensation equipment is carried out.A hybrid stochastic/robust optimization method is employed to model the uncertainties in the problem. Finally, the efficiency of the method is confirmed by numerical examinations using the IEEE 33-bus distribution network and the GAMS optimization software. Simulation results indicate that in the risk-averse strategy, for a certain increase in cost, the radius of uncertainty in the active and reactive power market prices increases. Also, in this strategy, as β increases, the total cost of network operating increases by 81.72 %, while in a risk-seeking strategy, with the increase of β, the total operating cost of the network decreases by 77.78 %.

A hybrid forecasting method considering the long-term dependence of day-ahead electricity price series

Day-ahead electricity market is crucial for ensuring the balance of electricity supply and demand. Its electricity price serves as a key guide for market participants and power dispatching agencies, making accurate short-term price forecasting essential. In this paper, a hybrid short-term electricity price forecasting method considering the long-term dependence of wavelet transform (WT) series is proposed. Different from the existing studies that do not distinguish the characteristics of WT series, this paper uses Hurst exponent and autocorrelation function (ACF) and partial autocorrelation function (PACF) plots to analyze the long-term dependence of subseries, and chooses seasonal autoregressive integrated moving average (SARIMA) or long short-term memory (LSTM) method to construct a hybrid forecasting model according to the characteristics of each subseries. Validation using real electricity prices from the NYISO and PJM markets shows that, compared to methods that do not consider long-term dependencies of subseries, the proposed method can improve prediction accuracy by up to 50.98 % and stability by up to 68.09 %.

Optimal design of greenfield energy hubs in the context of carbon neutral energy supply

This paper proposes a sequential co-optimization (SCO) framework for the design of greenfield energy hubs, aiming to achieve net zero emissions. The SCO framework offers an evolutionary approach to long-term planning and operation, taking into account various evolving factors such as carbon emission policies, technological and economic advancements in energy supply components, changes in land-use, and developments at different stages of the design horizon. The framework ensures that the design of greenfield developments aligns with their evolving characteristics, determining the optimal dispatch flow for firming renewable energy through co-generation and demand flexibility options like smart loads, electric vehicles, and hydrogen-based systems. A sensitivity analysis is conducted to study the impact of emissions, evolving characteristics of greenfield developments, and techno-economic aspects of supply units on the energy hub design. Case studies demonstrate that the proposed design enables large-scale distributed energy systems to meet local energy demand and participate in the electricity market. The SCO framework provides a robust and flexible platform for the correlated optimization of planning and operation of greenfield energy hubs, for advancing towards net zero emissions.

Use of reactive power compensation devices in implementation of distributed generation

In Ukraine, a gradual reduction of electricity production at thermal power plants is planned due to the development of renewable sources of electricity. Small hydraulic power plants are being restored, solar power plants and wind power plants are being built. This will allow solving the existing problems of the domestic energy industry regarding the shortage of fuel resources, energy security and reducing the level of harmful effects on the environment caused by the operation of traditional sources of electricity. There is a trend of transition from a purely centralized electricity supply to a combined one, when the number of local decentralized sources of electricity directly in distribution networks is increasing. In this way, distribution electric networks are gradually transformed into a network with features characteristic of a local electric system, which receives power both from its own distribution electric networks and from a centralized source - the electric power system. Renewable energy has a number of advantages compared to traditional energy, but there are also disadvantages. Among them, the complications of the functioning of electrical networks should be highlighted in the event of an increase in the capacities of renewable sources of electricity installed in them and the instability of generation due to their natural dependence on meteorological conditions, if we talk more specifically about technical shortcomings, then this concerns - sinusoidal of voltages and currents and voltage deviations, quality assurance of electricity, which directly depends on ensuring the balance of active and reactive power in the electrical system. Hence the need for coordinated power supply from renewable sources of electricity and substations of the power system. At the same time, there is a gradual transition from the wholesale electricity market of a single buyer to the balancing market of electricity and electricity supply under bilateral agreements, as well as the introduction of market management methods. This article discusses measures to reduce electrical energy losses, limit voltage deviation, improve the quality of electrical energy, and compensate for the reactive power of local loads due to the introduction of reactive power compensation devices together with renewable sources of electricity and facilitating their integration into the power grid.

The impact of metering methods on collective self-consumption: Insights from multi-dwelling buildings in Finland

Globally, legislative reforms have significantly promoted the adoption of solar photovoltaic systems. In many European countries, updated legislation encourages the formation of energy communities and collective self-consumption in residential buildings. This study investigates the financial viability of solar photovoltaic systems in Finnish multi-dwelling buildings, focusing on the impact of recent legislative enhancements supporting collective self-consumption through virtual net-metering, known as the credit calculation model. By analyzing hourly consumption data from two properties in 2018 and 2020, and incorporating simulated photovoltaic production with electricity market prices, our findings highlight the advantages of virtual net-metering. Self-consumption ratios increased from 15% to 38 %, and annual revenue improved by 10 EUR/kWp to 29 EUR/kWp. Comparing the credit calculation model with behind-the-meter approaches using joint electricity subscriptions revealed self-consumption ratio differences of 5 % to 15 % for identical system sizes. Additionally, electricity purchasing cost savings of 16 % to 19 % were identified with behind-the-meter, increasing further compared to costs from other Finnish distribution system operators. This underscores the economic benefits of such arrangements and highlights potential savings across different regional pricing models. These insights demonstrate the financial benefits of collective self-consumption and joint electricity subscriptions in multi-dwelling contexts, signaling the need for extended research across diverse settings to validate these findings.

A whale optimization algorithm-based multivariate exponential smoothing grey-holt model for electricity price forecasting

Accurately forecasting electricity prices is essential for a variety of stakeholders in the energy sector, including market investors, policymakers, and consumers. However, existing forecasting techniques are often limited by complex parametric estimates and strict restrictions on input variables. This paper proposes a Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model for electricity price forecasting. The proposed WOA-GMHES(1,N) model uses historical data to learn the underlying trends and patterns of electricity prices. The WOA algorithm is used to optimize the model parameters, which are adaptively adjusted to reflect the changing dynamics of the electricity market. The proposed model is evaluated on real high- and low-voltage electricity price data from Cameroon. The results show that the novel WOA-GMHES(1,N) model outperforms competing models, achieving RMSE and SMAPE scores of 0.1359 and 0.61%, respectively. This novel model is also computationally efficient, requiring less than 1.3 s to generate a forecast. The proposed WOA-GMHES(1,N) model is a promising novel approach for electricity price forecasting. The model is accurate, efficient, and flexible, making it a valuable tool for a variety of stakeholders in the energy sector.

Model of a 100 % renewable energy system of self-sufficient wider urban area based on a short-term scale and the integration of the transport and thermal sector

The mutual integration of different energy sectors and their conversion to electricity could promote the integration of renewable energy sources. This paper analyses 100 % renewable electrical power system of the Dubrovnik wider urban area. The Calliope energy system model was developed and compared based on 10-min and hourly data. The energy scenario is modelled up to the year 2050. It includes the replacement of all conventional passenger vehicles with electric vehicles. The vehicle-to-grid model was developed using the Calliope modelling tool. All fossil fuels used in the heating sector are replaced by seawater heat pump systems. Additional storage is added to the system and the system is connected to the adjacent electricity market. The 10-min model led to a reduction in cross-border transmission capacity of around 60 % and a 7-fold reduction in the difference between import and export compared to the hourly model. This led to a more stable system operation, as the import and export capacities were equalised. The vehicle-to-grid technology in the regulated system led to a reduction in import and export transmission capacities of up to 17 % and 42 % respectively compared to the unregulated system, including extremely fast charging and discharging in a short-term step model.

Competitive Analysis at the Cournot-Nash Equilibrium of an Interconnected Network

The electric power network has long been subject to monopolization, encompassing production, transportation, and distribution sectors. However, recent liberalization efforts have introduced competition into the electricity market. To understand and manage this competition, game theory, a prominent tool in economics, is frequently employed. Specifically, competition within the electricity market has been analyzed through various game-theoretical models, including Bertrand's atomicity, Cournot's homogeneity, and Nash's research competition. These models aim to achieve the Cournot-Nash equilibrium, where each participant in the market makes optimal decisions given the strategies of others. To effectively allocate production and ensure a balance between supply and demand, as well as to maintain the stability of the interconnected network, one has adopted a method that combines Load Flow techniques with game theory principles. This hybrid approach enables a strategic distribution of power production, taking into account the competitive dynamics of the market. By integrating these methodologies, one can address the complexities of competition while ensuring efficient and stable operation of the power grid. This innovative approach not only enhances the management of electricity production and distribution but also fosters a more competitive and resilient power network. Moreover, the application of game theory in this context allows for a deeper understanding of strategic interactions among market participants. It helps in predicting behaviors, formulating strategies, and anticipating market changes, thus providing a robust framework for decision-making. This is particularly crucial in a liberalized market where multiple entities vie for market share and profitability. By employing game-theoretical insights, one can simulate various market scenarios, optimize resource allocation, and enhance overall market efficiency. Furthermore, this approach supports the integration of renewable energy sources by ensuring that their variable nature is accommodated within the grid's operational dynamics. In summary, the intersection of game theory and load flow methods offers a comprehensive solution to the challenges posed by a competitive electricity market, paving the way for a sustainable and efficient energy future.

Patterns and correlations in European electricity prices.

Electricity markets are central to the coordination of power generation and demand. The European power system is divided into several bidding zones, each having an individual electricity market price. While individual price time series have been intensively studied in recent years, spatiotemporal aspects have received little attention. This article provides a comprehensive data-centric analysis of the patterns and correlations of the European day-ahead electricity prices between 2019 and 2023, characteristically abnormal due to the energy crisis in Europe. We identify the dominant communities of bidding zones and show that spatial differences can be described with very few principal components. Most bidding zones in Continental Europe were brought together during the energy crisis: Correlations increased, and the number of relevant principal components decreased. Opposite effects occur in the Nordic countries and the Iberian Peninsula where correlations decrease and communities fragment.

MECHANISED COAL EXPLOITATION IN THE "BREZJE" PIT OF THE "DJURDJEVIK" BROWN COAL MINE

In addition to the coal and electricity market and changing geopolitical circumstances, there are other sufficiently rational reasons for opening new underground mining facilities and modernizing existing mining methods.Significant limiting factors and influences, in complex mining-geological conditions, most significantly affect the possibilities of the mechanized way of mining brown coal. The "Brezje" pit is a part of the deposit where the mining-geological factors are complex and determined by the natural conditions of the deposit's genesis. Excavation methods and technologies in complex deposit conditions must be based on great flexibility and adaptability.The complexity of the tectonic structure in the "Đurđevik" pit justified the application of chamber excavation methods with borehole blasting, in various technical modifications and solutions. In all previous solutions, the degree of mechanization of the method was not significantly improved and all modifications of the chamber method were non-mechanized or semi- mechanized with a large participation of manual work, technology based on drilling and blasting, high risk of injuries, low utilization of deposits, unjustified economic effects of work. The aim of this paper is to give an adequate answer to the hypothesis "in the block structure of the deposit with pronounced tectonics, it is possible to reduce the unfavorable influence of the complex mining-geological factors of the deposit on the excavation method by choosing modern mechanization for underground exploitation". The mechanized chamber method of excavation is a conceptual solution for the possible application in complex conditions of the proposed flexible mechanization for work in the "Brezje" pit. The advantage of modern multifunctional mining machines is a high degree of adaptability to the elements of the coal layer with the use of different working mechanisms (tools) that can be changed in a very short time depending on the working phase of the process. Key words: Pit, complexity, factors, chamber method, flexible, multifunctional machine

A Scalable Energy Management Mechanism for Peer-to-Peer Electricity Market

A Scalable Energy Management Mechanism for Peer-to-Peer Electricity Market