Multi-agent energy management optimization for integrated energy systems under the energy and carbon co-trading market

Sun, Qingkai; Wang, Xiaojun; Liu, Zhao; Mirsaeidi, Sohrab; He, Jinghan; Pei, Wei

doi:10.1016/j.apenergy.2022.119646

Cited by 36 publications

(14 citation statements)

References 32 publications

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“…The work has identified a range of specific and broad challenges including resource allocation in wireless sensor networks with multiple UAVs (Seid et al, 2021;, governance of AI in power-related general-purpose technologies (Niet et al, 2021;Przhedetsky, 2021;Nitzberg and Zysman, 2022), fault detection, fault diagnosis, and anomaly detection in smart energy systems (Sun et al, 2021;Badr et al, 2022), edge computing for detecting power demand attacks (Alagumalai et al, 2022;Haseeb et al, 2022;Zhu et al, 2022), blockchain-based reliability and security (Al-Abri et al, 2022;Jose et al, 2022), governance of energy markets and energy pipeline systems (Belinsky and Afanasev, 2021;Serna Torre and Hidalgo-Gonzalez, 2022;Sun et al, 2022), forecasting short-term energy demand (Xie et al, 2021;Gürses-Tran et al, 2022), energy trading using federated learning in smart cities (Bracco et al, 2022), energysaving edge AI applications (Khosrojerdi et al, 2022), performance optimization and stability of smart grid operations and nuclear power systems (Luo et al, 2021;Volodin and Tolokonskij, 2022), and others. All these areas are candidates for future research.…”

Section: Discussionmentioning

confidence: 99%

“…It captures various dimensions of "Energy Markets and Management," including using the IML method for the management of decentralized optimal power flow (Serna Torre and Hidalgo-Gonzalez, 2022), designing an interpretable Deep reinforcement learning (DRL) approach for transmission network expansion in wind power , and power distribution systems' reliability, interpretability, and security (Gao and Yu, 2021). Further dimensions include optimal multi-agent energy management for interconnected energy systems in the context of a co-trading market to promote fair commerce and to maintain the privacy of entities (Sun et al, 2022), management of energy pipeline infrastructure (Belinsky and Afanasev, 2021), and applying IML and collaborative game theory for market regression analysis and its use in energy forecasting (Pinson et al, 2021).…”

Section: Energy Markets and Managementmentioning

confidence: 99%

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AI explainability and governance in smart energy systems: A review

2023

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Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on “deep journalism,” our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.

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Section: Discussionmentioning

confidence: 99%

Section: Energy Markets and Managementmentioning

confidence: 99%

AI explainability and governance in smart energy systems: A review

2023

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“…• The input of EH The types of input energy for EH in this energy hub are electric energy and natural gas. Therefore, its column matrix [I N EH ( t )] is [16] : (4) where, among them, P EH in (t) and G EH in (t) are the electrical power input of the EH and the natural gas power.…”

Section: Ehmentioning

confidence: 99%

“…In [3], an autonomous scheduling model designed for decentralized market participants at the user level is proposed along with a peer-to-peer transaction mechanism that takes into account a multiagent noncooperative game. In [4], an IES joint trading market using an improved Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm is proposed to optimize the operating cost of IESs. In [5], an integrated energy market system is developed by utilizing a multi-market equilibrium approach that considers both the IES and market aspects.…”

Section: Introductionmentioning

confidence: 99%

Research on energy purchase combination strategies for multiple energy commercial buildings considering multistage market uncertainties

Xu,

Tang,

Huang

et al. 2024

J. Phys.: Conf. Ser.

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With the gradual development of the power markets, various types of uncertainty are gradually increasing. The overlapping effects of various uncertainties have a significant impact on the operational economy of multiple energy commercial buildings (MECB) relying on various energy sources. MECB can purchase electricity from multiple stages of the market, such as spot from medium to long term and the market electricity price risk varies at different stages. Therefore, this paper investigates the energy purchasing strategies of MECB in multistage markets, taking into account various uncertainties. Considering the actual operational characteristics of MECB, this paper proposes a MECB low-carbon economic optimization model and establishes an energy purchase strategy model for MECB under various risk scenarios based on random optimization theory. Research has found that in scenarios with higher risks, MECB will participate more conservatively in the market. By simultaneously participating in the medium to long-term market and spot market, MECB can effectively avoid the cost risks brought about by various uncertainties.

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“…However, this model requires a comprehensive understanding of the market, a large amount of data, and higher prediction accuracy of new energy output. It is not possible to fully trial the distributed energy system, and the composition of many distributed energy systems is more complex, making multiagent collaborative decision-making more important (Fang et al, 2021;Sun et al, 2022;Wang et al, 2022a;Wang. et al, 2022a;Zhu et al, 2022).…”

Section: Open Accessmentioning

confidence: 99%

Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors

et al. 2023

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In recent years, the power market and regional distributed energy systems (RDES) in China have experienced considerable growth. However, the critical issue of how multi-stakeholder parties within the distributed energy system evaluate risk preferences in order to develop scientifically sound trading strategies remains unclear. To address this problem, this study constructs a multi-agent assisted decision-making model that incorporates the critical features of a regional distributed energy system. By simulating various calculation scenarios using this model, the study aims to provide a better understanding of the system’s multi-agent interactions and decision-making processes. First, different types of stakeholders and risk preferences in RDES are delineated. Second, supply and demand fluctuations in RDRS are treated and the impact of wholesale market price volatility risk on distributed energy system aggregators (DERA) decisions is fully considered. Meanwhile, a multi-stakeholders DERA transaction decision-making model in the day-ahead market considering risk preference behaviors is constructed based on information gap decision theory (IGDT) and solved by the Opposition Learning Grey Wolf Optimizer (OLGWO). The mathematical analysis conducted in this study indicates that the approach proposed could provide an effective trading scheme and operational strategy for multi-interest entities participating in the market of RDES. Therefore, incorporating the proposed approach would be beneficial in enhancing the performance and effectiveness of such systems.

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Multi-agent energy management optimization for integrated energy systems under the energy and carbon co-trading market

Cited by 36 publications

References 32 publications

AI explainability and governance in smart energy systems: A review

AI explainability and governance in smart energy systems: A review

Research on energy purchase combination strategies for multiple energy commercial buildings considering multistage market uncertainties

Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors

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