Modeling a Local Electricity Market for Transactive Energy Trading of Multi-Aggregators

Haghifam, Sara; Laaksonen, Hannu; Shafie‐khah, Miadreza

doi:10.1109/access.2022.3186309

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…One approach involved a distributed energy management system that promoted collaboration between energy suppliers and users, aiming to enhance societal wellbeing [15]. Haghifam et al introduced a Stackelberg gamebased approach for optimizing the participation of multiple stakeholders in the day-ahead energy market [16]. In another study, Bhattacharya et al conducted an in-depth analysis of the impact on unit energy prices when participants exhibit enhanced flexibility [17].…”

Section: B Relevant Literature Reviewmentioning

confidence: 99%

Credit Rating-Based Transactive Energy System With Uncertainties in Energy Behavior

Anil,

Arun

2023

IEEE Access

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In recent years, the evolution of demand-side management techniques has ushered in substantial improvements for distribution systems. Among these advancements, the Transactive Energy System (TES) emerges as a promising innovation, empowering end-users by facilitating surplus generation/demand trading within local energy markets. However, in contrast to the wholesale electricity markets of conventional grids, TES integrates a significant share of small-scale prosumers and highly variable intermittent Distributed Energy Resources (DERs), introducing elevated market uncertainties. In this conditions the economic stability of TES hinges on active and credible participant engagement. The central challenge lies in addressing uncertainties surrounding the energy requirements of community participants and incentivizing their involvement. To address these challenges, this paper introduces a novel market clearing strategy based on credit ratings. This strategy aims to mitigate uncertainties while motivating credible participants through substantial savings on their electricity expenses. Further this paper presents final demand response consideration, among a group of participants in a TES using an auction-theoretic approach which increases monetary gains. The effectiveness of this proposed methodology is validated through comprehensive case studies involving multiple households within a community. The results are highly promising, with participating households realizing monthly savings of 17.18%, 16.11%, 20.7%, and 23.22% on their electricity bills when compared to the conventional transactive energy exchange method. These tangible outcomes underscore the significant positive impact achievable through the implementation of our proposed market clearing approach and demonstrate the substantial increase in monthly savings for participants across various scenarios.

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Section: B Relevant Literature Reviewmentioning

confidence: 99%

Credit Rating-Based Transactive Energy System With Uncertainties in Energy Behavior

Anil,

Arun

2023

IEEE Access

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An Interval-based privacy – Aware optimization framework for electricity price setting in isolated microgrid clusters

et al. 2023

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Multi‐agent reinforcement learning in a new transactive energy mechanism

Mohsenzadeh‐Yazdi,

Kebriaei,

Aminifar

2024

IET Generation Trans & Dist

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Thanks to reinforcement learning (RL), decision‐making is more convenient and more economical in different situations with high uncertainty. In line with the same fact, it is proposed that prosumers can apply RL to earn more profit in the transactive energy market (TEM). In this article, an environment that represents a novel framework of TEM is designed, where all participants send their bids to this framework and receive their profit from it. Also, new state‐action spaces are designed for sellers and buyers so that they can apply the Soft Actor‐Critic (SAC) algorithm to converge to the best policy. A brief of this algorithm, which is for continuous state‐action space, is described. First, this algorithm is implemented for a single agent (a seller and a buyer). Then we consider all players including sellers and buyers who can apply this algorithm as Multi‐Agent. In this situation, there is a comprehensive game between participants that is investigated, and it is analyzed whether the players converge to the Nash equilibrium (NE) in this game. Finally, numerical results for the IEEE 33‐bus distribution power system illustrate the effectiveness of the new framework for TEM, increasing sellers' and buyers' profits by applying SAC with the new state‐action spaces. SAC is implemented as a Multi‐Agent, demonstrating that players converge to a singular or one of the multiple NEs in this game. The results demonstrate that buyers converge to their optimal policies within 80 days, while sellers achieve optimality after 150 days in the games created between all participants.

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Modeling a Local Electricity Market for Transactive Energy Trading of Multi-Aggregators

Cited by 7 publications

References 33 publications

Credit Rating-Based Transactive Energy System With Uncertainties in Energy Behavior

Credit Rating-Based Transactive Energy System With Uncertainties in Energy Behavior

An Interval-based privacy – Aware optimization framework for electricity price setting in isolated microgrid clusters

Multi‐agent reinforcement learning in a new transactive energy mechanism

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