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19 pages, 2234 KiB

Open AccessArticle

Coordinated Optimization of Multi-Regional Integrated Energy Service Providers with Flexible Reserve Resources

by Xueting Wang, Hao Zhong, Xianqiu Zou, Qiujie Wang and Lanfang Li

Energies 2025, 18(2), 284; https://doi.org/10.3390/en18020284 - 10 Jan 2025

Viewed by 557

Abstract

Aiming at solving the problem of new energy and load uncertainty leading to a steep increase in the demand for flexible reserve resources by integrated energy service providers (IESPs), a coordinated and optimized scheduling method for multi-region integrated energy service providers considering flexible [...] Read more.

Aiming at solving the problem of new energy and load uncertainty leading to a steep increase in the demand for flexible reserve resources by integrated energy service providers (IESPs), a coordinated and optimized scheduling method for multi-region integrated energy service providers considering flexible reserve resources is proposed. First, for the uncertainty of new energy and load, Latin hypercube sampling is used to generate scenarios, and the scenarios are reduced by a K-means clustering algorithm. Second, based on the interaction relationship between the active distribution network (ADN) and multi-region IESPs, a mixed game model of the ADN and IESP alliance is established. ADN guides IESPs to optimize their operation by setting prices for electricity and reserves, and IESPs fully tap their own flexible reserve resources according to the prices set by ADN and achieve power interoperability through the interaction of IESPs in multiple regions to synergistically cope with the uncertainties of new energy and load. Finally, the example results show that the model proposed in this paper is able to realize the allocation of flexibility resources in a wider range, reduce the reserve pressure on the superior grid, and improve the profitability of IESPs. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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9 pages, 831 KiB

Open AccessArticle

Modeling of Collusion Behavior in the Electrical Market Based on Deep Deterministic Policy Gradient

by Yifeng Liu, Jingpin Chen, Meng Chen, Zhongshi He, Ye Guo and Chenghan Li

Energies 2024, 17(22), 5807; https://doi.org/10.3390/en17225807 - 20 Nov 2024

Viewed by 706

Abstract

The evolution of the electricity market has brought the issues of market equilibrium and collusion to the forefront of attention. This paper introduces the Deep Deterministic Policy Gradient (DDPG) algorithm on the IEEE three-bus electrical market model. Specifically, it simulates the behavior of [...] Read more.

The evolution of the electricity market has brought the issues of market equilibrium and collusion to the forefront of attention. This paper introduces the Deep Deterministic Policy Gradient (DDPG) algorithm on the IEEE three-bus electrical market model. Specifically, it simulates the behavior of market participants through reinforcement learning (DDPG), and Nash equilibrium and the collusive equilibrium of the power market are simulated by setting different reward functions. The results show that, compared with the Nash equilibrium, collusion equilibrium can increase the price of nodal marginal electricity and reduce total social welfare. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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19 pages, 2254 KiB

Open AccessFeature PaperArticle

An Off-Site Power Purchase Agreement (PPA) as a Tool to Protect against Electricity Price Spikes: Developing a Framework for Risk Assessment and Mitigation

by Karolina Kapral, Kobe Soetaert and Rui Castro

Energies 2024, 17(9), 2161; https://doi.org/10.3390/en17092161 - 30 Apr 2024

Cited by 2 | Viewed by 3053

Abstract

Significant price spikes occurred as early as 2021, initially driven by low gas storage levels, a post-pandemic economic rebound and then exacerbated by the Russian invasion of Ukraine. The situation had a range of wide-ranging consequences, from rising inflation, increasing energy poverty, food [...] Read more.

Significant price spikes occurred as early as 2021, initially driven by low gas storage levels, a post-pandemic economic rebound and then exacerbated by the Russian invasion of Ukraine. The situation had a range of wide-ranging consequences, from rising inflation, increasing energy poverty, food insecurity, business bankruptcies and recession. A well-known tool to protect energy consumers from energy price spikes, while at the same time contributing to the development of sustainable technologies, is Power Purchase Agreements. PPAs are long-term bilateral contracts for the purchase and sale of a certain amount of electricity, usually generated from renewable sources. The primary goal of this paper is to assess how the risk associated with PPAs has evolved between 2020 and 2023. It aims to examine whether, after the events in 2022, PPAs remain a robust solution that protects the off-taker from energy price spikes, ensures greater energy budget stability and enables savings. To achieve this, the probability of PPA prices being higher than market prices is evaluated, considering the changing market landscape. Furthermore, this paper intends to gain a thorough understanding of each risk related to PPAs and the best strategies for mitigating it, to maximize the protection of the off-taker. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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13 pages, 746 KiB

Open AccessArticle

Online Distribution Network Scheduling via Provably Robust Learning Approach

by Naixiao Wang, Xinlei Cai, Linwei Sang, Tingxiang Zhang, Zhongkai Yi and Ying Xu

Energies 2024, 17(6), 1361; https://doi.org/10.3390/en17061361 - 12 Mar 2024

Viewed by 1015

Abstract

Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, [...] Read more.

Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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17 pages, 3053 KiB

Open AccessArticle

Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources

by Jacek Caban, Arkadiusz Małek and Branislav Šarkan

Energies 2024, 17(5), 1264; https://doi.org/10.3390/en17051264 - 6 Mar 2024

Cited by 9 | Viewed by 2014

Abstract

The ever-growing number of electric vehicles requires increasing amounts of energy to charge their traction batteries. Electric vehicles are the most ecological when the energy for charging them comes from renewable energy sources. Obtaining electricity from renewable sources such as photovoltaic systems is [...] Read more.

The ever-growing number of electric vehicles requires increasing amounts of energy to charge their traction batteries. Electric vehicles are the most ecological when the energy for charging them comes from renewable energy sources. Obtaining electricity from renewable sources such as photovoltaic systems is also a way to reduce the operating costs of an electric vehicle. However, to produce cheap electricity from renewable energy sources, you first need to invest in the construction of a photovoltaic system. The article presents a strategic model for charging a fleet of electric vehicles with energy from photovoltaic systems. The model is useful for sizing a planned photovoltaic system to the energy needs of a vehicle fleet. It uses the Metalog family of probability distributions to determine the probability of producing a given amount of energy needed to power electric vehicle chargers. Using the model, it is possible to determine the percentage of energy from photovoltaic systems in the total energy needed to charge a vehicle fleet. The research was carried out on real data from an operating photovoltaic system with a peak power of 50 kWp. The approach presented in the strategic model takes into account the geographical and climatic context related to the location of the photovoltaic system. The model can be used for various renewable energy sources and different sizes of vehicle fleets with different electricity demands to charge their batteries. The presented model can be used to manage the energy produced both at the design stage of the photovoltaic system and during its operation. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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23 pages, 2642 KiB

Open AccessArticle

The Impact of Deep Decarbonization Policy on the Level of Greenhouse Gas Emissions in the European Union

by Rafał Nagaj, Bożena Gajdzik, Radosław Wolniak and Wieslaw Wes Grebski

Energies 2024, 17(5), 1245; https://doi.org/10.3390/en17051245 - 5 Mar 2024

Cited by 22 | Viewed by 2928

Abstract

The Green Deal, a cornerstone of the European Union’s climate goals, sets out to achieve a substantial 55% reduction in greenhouse gas emissions by 2030 compared to 1990 levels. The EU’s decarbonization strategies revolve around three pivotal avenues. First, there is a focus [...] Read more.

The Green Deal, a cornerstone of the European Union’s climate goals, sets out to achieve a substantial 55% reduction in greenhouse gas emissions by 2030 compared to 1990 levels. The EU’s decarbonization strategies revolve around three pivotal avenues. First, there is a focus on enhancing energy efficiency and decreasing the energy intensity of economies. Second, concerted efforts are made to diminish the reliance on fossil fuels, particularly within industrial sectors. Lastly, there is a deliberate push to augment the share of renewable energy sources in the final energy consumption mix. These measures collectively aim to propel the decarbonization of EU economies, establishing EU member countries as global leaders in implementing these transformative processes. This manuscript seeks to evaluate the efficacy of three primary decarbonization strategies adopted by EU economies, namely the enhancement in energy efficiency, the promotion of renewable energy consumption and the reduction in fossil fuel consumption. The objective is to discern which strategies wield a decisive influence in achieving decarbonization goals across EU countries. The analysis encompasses all 27 member states of the European Union, spanning from 1990 to 2022, with data sourced from reputable outlets, including Eurostat, Our World in Data and the Energy Institute. Research findings underscore that, in the realm of decarbonization policies, statistically significant impacts on carbon dioxide emission reduction are attributable to the strategies of improving energy efficiency and augmenting the share of renewables in energy consumption across almost all EU countries. Conversely, the strategy with the least impact, embraced by a minority of EU member states, revolves around diminishing the share of fossil fuels in primary energy consumption. This approach, while statistically less impactful, is intricately linked with transitioning the economies toward renewable energy sources, thus playing a contributory role in the broader decarbonization landscape. The uniqueness of this research lies not only in its discernment of overarching trends but also in its fervent advocacy for a comprehensive and adaptive approach to EU decarbonization policy. It underscores the enduring significance of prioritizing energy efficiency, endorsing the integration of renewable energy and acknowledging the distinctive dynamics inherent in diverse regions. The study accentuates the necessity for nuanced, region-specific strategies, challenging the conventional wisdom of a uniform approach to decarbonization. In doing so, it accentuates the critical importance of tailoring policies to the varied energy landscapes and transition strategies evident in different EU member states. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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11 pages, 2166 KiB

Open AccessArticle

Day-Ahead Dynamic Assessment of Consumption Service Reserve Based on Morphological Filter

by Xinlei Cai, Naixiao Wang, Qinqin Cai, Hengzhen Wang, Zhangying Cheng, Zhijun Wang, Tingxiang Zhang and Ying Xu

Energies 2023, 16(16), 5979; https://doi.org/10.3390/en16165979 - 15 Aug 2023

Cited by 2 | Viewed by 1133

Abstract

With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the [...] Read more.

With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the historical actual power into typical scenarios. In addition, the online matching between the typical scenario and the day-ahead forecast power is conducted. In order to realize the hierarchical quantification of reserve demand, the reserve resources in the whole power system are classified according to their response time. Furthermore, the mathematical morphology filter based on the structural elements that are consistent with the response time of the hierarchical reserve resources is initially applied to decompose the historical forecast error of the matched scenarios. The simulation results verify that the proposed dynamic assessment effectively reduces the reserve cost on the basis of being able to cope with multi-time-scale power fluctuations. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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Review

Jump to: Research

46 pages, 487 KiB

Open AccessFeature PaperReview

Stochastic Approaches to Energy Markets: From Stochastic Differential Equations to Mean Field Games and Neural Network Modeling

by Luca Di Persio, Mohammed Alruqimi and Matteo Garbelli

Energies 2024, 17(23), 6106; https://doi.org/10.3390/en17236106 - 4 Dec 2024

Viewed by 1302

Abstract

This review paper examines the current landscape of electricity market modelling, specifically focusing on stochastic approaches, transitioning from Mean Field Games (MFGs) to Neural Network (NN) modelling. The central objective is to scrutinize and synthesize evolving modelling strategies within power systems, facilitating technological [...] Read more.

This review paper examines the current landscape of electricity market modelling, specifically focusing on stochastic approaches, transitioning from Mean Field Games (MFGs) to Neural Network (NN) modelling. The central objective is to scrutinize and synthesize evolving modelling strategies within power systems, facilitating technological advancements in the contemporary electricity market. This paper emphasizes the assessment of model efficacy, particularly in the context of MFG and NN applications. Our findings shed light on the diversity of models, offering practical insights into their strengths and limitations, thereby providing a valuable resource for researchers, policy makers, and industry practitioners. The review guides navigating and leveraging the latest stochastic modelling techniques for enhanced decision making and improved market operations. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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18 pages, 943 KiB

Open AccessReview

China and Italy’s Energy Development Trajectories: Current Landscapes and Future Cooperation Potential

by Chunhong Liu, Shisong Jiang, Hanfei Zhang, Ziyi Lu and Umberto Desideri

Energies 2024, 17(4), 897; https://doi.org/10.3390/en17040897 - 14 Feb 2024

Cited by 2 | Viewed by 1689

Abstract

In order to achieve the ambitious goal of “carbon neutrality”, countries around the world are striving to develop clean energy. Against this background, this paper takes China and Italy as representatives of developing and developed countries to summarize the energy structure composition and [...] Read more.

In order to achieve the ambitious goal of “carbon neutrality”, countries around the world are striving to develop clean energy. Against this background, this paper takes China and Italy as representatives of developing and developed countries to summarize the energy structure composition and development overview of the two countries. The paper analyzes the serious challenges facing the future energy development of both countries and investigates the possibilities of energy cooperation between the two countries, taking into account their respective advantages in energy development. By comparing the policies issued by the two governments to encourage clean energy development, this paper analyzes the severe challenges faced by the two countries’ energy development in the future and combines their respective energy development advantages to look forward to the possibility of energy cooperation between the two countries in the future. This lays the foundation for China and Italy to build an “Energy Road” after the “Silk Road”. Full article

(This article belongs to the Special Issue Electricity Market Modeling Trends in Power Systems)

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