Optimal management of demand response aggregators considering customers' preferences within distribution networks

Talari, Saber; Shafie‐khah, Miadreza; Mahmoudi, Nadali; Siano, Pierluigi; Wei, Wei; Catalão, João P.S.

doi:10.1049/iet-gtd.2020.1047

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…In the proposed DR program, the DSA is only concerned about the aggregate demand reduction of customers when they are asked to provide DR, thus, there is no need for detailed behind-the-meter data sharing. The DR participation in this paper is in line with the approach provided in [42] to preserve the privacy of the customers.…”

Section: Appendixmentioning

confidence: 93%

Modelling the Interdependence of Multiple Electricity Markets in the Distribution System Aggregator Bidding

Afkousi-Paqaleh

Jafarian

Keane

2023

IEEE Trans. on Enrgy Mrkts, Pol and Reg

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The volatility of market prices and the interdependence of multiple markets make it challenging for Distribution System Aggregators (DSAs) to model these prices. In this paper, a novel statistical model based on Gaussian process regression and mutual information screening technique is developed. This model is able to predict different market prices and quantify their uncertainty whilst incorporating the interdependence of different markets. The proposed model is employed to assist DSAs with market price modelling. Price scenarios for various markets generated by this model make it viable to formulate the optimal involvement of DSAs in multiple markets as a stochastic multistep two-stage problem. Other than providing a set of scenarios that efficaciously model multiple electricity market prices, after the clearing of each market, the proposed model leverages market clearing results to improve the accuracy of price prediction of subsequent markets. Extensive simulation results on large price datasets demonstrate that the proposed methodology will result in a considerable increase in the profit of the DSA compared to state-of-the-art price prediction approaches.

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Section: Appendixmentioning

confidence: 93%

Modelling the Interdependence of Multiple Electricity Markets in the Distribution System Aggregator Bidding

Afkousi-Paqaleh

Jafarian

Keane

2023

IEEE Trans. on Enrgy Mrkts, Pol and Reg

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“…This suggests that the decision variables of the other players, that is, customers and microgrids, must also be placed in the model of the EDC. On the other hand, since the game between the EDC, and his aggregators is a cooperative game, we collect the objective functions of aggregators (15) and EDC (17) in a single function of (23) and also inserting the decision variable of customers (22) into collected objective function (23). By applying KKT conditions to this integrated objective function, we ensure optimality and Nash equilibrium:…”

Section: The Tri-level Game Modelmentioning

confidence: 99%

“…They illustrated that their proposed interactive TOU strategy is more effective than its conventional counterpart TOU. Talari et al studied a privacy-based DR trading scheme among end-users and DR aggregators within the retail market framework and by distribution platform optimiser to obtain the optimum DR volume to be exchanged while considering both DRAs' and customers' preferences [17].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the fact that these two studies have adopted tri-level models, like us their models are significantly different from our proposed model as is clarified in the summary Table 1. Observe that researchers in [3,17,20], who have examined actors at three distinct levels, have opted for non-cooperative game models within a singular level. This deliberate choice avoids direct comparison with the tri-level models presented in [22,23], including our own model.…”

Section: Introductionmentioning

confidence: 99%

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A multi‐layer–multi‐player game model in electricity market

Kafshian,

Monfared

2024

IET Generation Trans & Dist

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Here, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs.

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“…A broker aims to increase profits through energy trading while balancing supply and demand in real-time [1]. Traditionally, this problem has been studied using optimization methods [5]. Recently, some studies develop reinforcement learning (RL) algorithms to Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Smart Electricity Markets

Naseri

Talari

Ketter

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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High penetration of intermittent renewable energies in power system portfolios induces high electricity procurement costs during peak times to maintain reliability. Ultimately, aggregators pay these costs in the form of capacity charges. This paper proposes an aggregator model as an autonomous trading agent aiming to maximize profit by developing tariffs using offline and online Reinforcement Learning and market segmentation to consumers. The performance of the proposed aggregator is evaluated using a market simulation platform called Power Trading Agent Competition (Power TAC). We find that designing tariffs with usage-based charges and fixed periodic charges can help the aggregator segment the retail market resulting in lower peak demand and capacity charges. CCS CONCEPTS• Computing methodologies → Multi-agent reinforcement learning.

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Optimal management of demand response aggregators considering customers' preferences within distribution networks

Cited by 10 publications

References 32 publications

Modelling the Interdependence of Multiple Electricity Markets in the Distribution System Aggregator Bidding

Modelling the Interdependence of Multiple Electricity Markets in the Distribution System Aggregator Bidding

A multi‐layer–multi‐player game model in electricity market

Sustainable Smart Electricity Markets

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