Regulated peer-to-peer energy markets for harnessing decentralized demand flexibility

Lee, Zachary E.; Zhang, K. Max

doi:10.1016/j.apenergy.2023.120672

Cited by 21 publications

(6 citation statements)

References 49 publications

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“…This market evolution is aided by the massive penetration of IoT appliances, smart appliances, and meters which allow consumers to have greater control over their energy consumption [3]. The main objec-tive of LEM is to solve grid problems at a local level by empowering prosumers via the provision of local flexibility and increased deployment of DERs [4], [5]. LEM encourages trading by offering energy services to consumers under better terms than the existing remuneration mechanisms and monetary rewards without intermediaries.…”

Section: Related Workmentioning

confidence: 99%

“…In [9], a comprehensive framework for energy sharing in smart buildings is proposed, while authors in [10] employ a data-driven distributionally robust optimization technique for P2P energy trading, resulting in better performance compared to other stochastic optimization methods. In [5] authors introduce a regulated peer-topeer market structure for residential prosumers, where energy demand and supply curves are bid into a market that can be directly used by the utility to dispatch demand flexibility. The paper emphasizes the mitigation of negative impacts arising from unregulated price-reactive agents, proposing a utilityregulated structure to maintain balance and efficiency in the energy market.…”

Section: Related Workmentioning

confidence: 99%

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Design of a Prosumer-Centric Local Energy Market: An Approach Based on Prospect Theory

Andriopoulos,

Plakas,

Birbas

et al. 2024

IEEE Access

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The energy sector is undergoing a transformative shift, driven by advancements in Distributed Energy Resources (DERs), the digitization of the energy supply chain and decarbonization policy objectives across the world. This paradigm shift has led to the emergence of Local Energy Markets (LEMs), which enable small-scale prosumers to actively participate in the energy market, trade power, and leverage their flexible resources. To ensure the success and acceptance of LEMs, this paper proposes a cooperative game-theoretic approach that fosters prosumer engagement and fair profit allocation. We utilize prospect theory from behavioural economics to examine the decision-making process of prosumers and incorporate their preferences for changes in wealth status. By adopting a cooperative game structure, prosumers can pool their resources, reduce transaction costs, and enhance data utilization. The paper introduces a novel pricing algorithm inspired by prospect theory that incentivizes prosumer participation and accounts for the uncertainty involved in LEM operations. Additionally, a computationally efficient method for profit allocation based on the variation of the Shapley value is proposed to ensure scheme stability. A use case evaluation is conducted on a real-world low-voltage network, demonstrating the effectiveness of the proposed approach in terms of economic efficiency and market characteristics. The results highlight the benefits of the consumer-centric LEM, including improved local trading dynamics, fair profit distribution, and enhanced grid stability. Overall, this research contributes to the design and development of LEMs that prioritize prosumer engagement, community cooperation, financial inclusion and democratization of the energy market.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Design of a Prosumer-Centric Local Energy Market: An Approach Based on Prospect Theory

Andriopoulos,

Plakas,

Birbas

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To increase the accuracy of day-ahead forecasting, a unique dense dropout attention-based deep learning model is developed in (Li et al, 2022;Annamalai et al, 2022a) developed an adaptive machine learning-based SOC estimate (OML-SOCE) model for HEVs in TEM, ensuring dependable operation and effective battery balancing. A regulated peer-to-peer market structure is proposed allowing energy curves to be submitted by prosumers in transactive energy systems and achieving significant reductions in peak load and ramp rates while promoting local electricity generation (Lee and Zhang, 2023;Mehdin et al, 2022) proposed a decentralized energy market for small-scale prosumers, facilitating P2P trading and addressing uncertainty through robust optimization and the FADMM approach. Distributed TE management for P2P energy trading considers network constraints, utilizes the AC OPF model, and employs decentralized transaction clearing (Zhou et al, 2023;Nguyen, 2023) explores stochastic market clearing solutions in P2P energy markets, deriving explicit formulas for probability density functions and validating theoretical findings through simulations.…”

Section: Literature Reviewmentioning

confidence: 99%

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

Safari,

Gharehbagh,

Nazari-Heris

et al. 2023

Front. Energy Res.

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Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they properly estimate supply and demand variations and optimize energy distribution, and the other featured values, for participants in decentralized energy marketplaces. Consequently, DeepResTrade is a research work that presents an advanced model for predicting prices in a given traditional energy market. This model includes numerous fundamental components, including the concept of P2P trading systems, long-term and short-term memory (LSTM) networks, decision trees (DT), and Blockchain. DeepResTrade utilized a dataset with 70,084 data points, which included maximum/minimum capacities, as well as renewable generation, and price utilized of the communities. The developed model obtains a significant predictive performance of 0.000636% Mean Absolute Percentage Error (MAPE) and 0.000975% Root Mean Square Percentage Error (RMSPE). DeepResTrade’s performance is demonstrated by its RMSE of 0.016079 and MAE of 0.009125, indicating its capacity to reduce the difference between anticipated and actual prices. The model performs admirably in describing actual price variations in, as shown by a considerable R2 score of 0.999998. Furthermore, F1/recall scores of [1, 1, 1] with a precision of 1, all imply its accuracy.

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“…A prosumer is an individual household that consumes and produces renewable electricity and may inject the surplus into the grid and withdraw electricity when the self-production is not sufficient [4]. Prosumers may participate in the management of energy communities, by trading energy and providing services like demand flexibility and decentralized energy storage [5]. In this context, peer-to-peer (P2P) energy trading enables direct transactions between prosumers, fostering a sense of community by allowing neighbors to share their energy resources.…”

Section: Introductionmentioning

confidence: 99%

Social Factors in P2P Energy Trading Using Hedonic Games

Mitrea,

Rozina Chifu,

Cioara

et al. 2024

IEEE Access

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Within energy communities, the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable technological and organizational mechanisms, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for peer-to-peer (P2P) energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. A heuristic is defined to optimize the prosumers' coalitions, considering their social and price preferences while balancing the energy demand and supply within the community. The proposed hedonic game model was integrated into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results demonstrate the effectiveness of considering social factors when creating coalitions, resulting in a 5% increase in the total amount of energy transacted in a market session compared to other game theory-based solutions and a degree of satisfaction varying between 91-96%. The results underscore the importance of the social dimensions of P2P energy trading, the positive social dynamics within the energy community leading to an even greater increase in energy transacted around 30%, potentially generating daily savings between $34 and $47 and additional carbon savings of approximately 15.4 kgCO2eq.INDEX TERMS hedonic games, peer-to-peer energy trading, social factors, social dynamic, prosumers, energy community.

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Regulated peer-to-peer energy markets for harnessing decentralized demand flexibility

Cited by 21 publications

References 49 publications

Design of a Prosumer-Centric Local Energy Market: An Approach Based on Prospect Theory

Design of a Prosumer-Centric Local Energy Market: An Approach Based on Prospect Theory

DeepResTrade: a peer-to-peer LSTM-decision tree-based price prediction and blockchain-enhanced trading system for renewable energy decentralized markets

Social Factors in P2P Energy Trading Using Hedonic Games

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