An optimal real-time pricing strategy for aggregating distributed generation and battery storage systems in energy communities: A stochastic bilevel optimization approach

Sarfarazi, S, Mohammadi, S, Khastieva, D, Hesamzadeh, M R, Bertsch, V and Bunn, D W (2022) An optimal real-time pricing strategy for aggregating distributed generation and battery storage systems in energy communities: A stochastic bilevel optimization approach. International Journal of Electrical Power and Energy Systems, 147. ISSN 0142-0615 (In Press)

Abstract

The expansion of distributed electricity generation and increasing capacity of installed battery storage systems on the community level has posed challenges to efficient technical and economic operation of the power system. With advances in smart-grid infrastructure many innovative demand response business models aim to tackle these challenges, while creating financial benefits for the participating actors. In this context, we propose an optimal real-time pricing (ORTP) approach for the aggregation of distributed energy resources within energy communities. We formulate the interaction between a profit-maximizing community-owned aggregator and the users (prosumagers and electric vehicles) as a stochastic bilevel disjunctive program. To solve the problem efficiently, we offer a novel solution algorithm, which applies a linear quasi-relaxation approach and an innovative dynamic partitioning technique. We introduce benchmark tariffs and solution algorithms and assess the performance the proposed pricing strategy and solution algorithm in four case studies: Our results show that the ORTP strategy increases community welfare compared to benchmark electricity, while providing useful grid services. Furthermore, our findings reveal superior computational efficiency of our proposed solution algorithm in comparison to benchmark algorithms.

More Details

Item Type:	Article
Subject Areas:	Management Science and Operations
Additional Information:	This research is financed by the German Aerospace Center (DLR) basic-funding project SoGuR. This work was financially supported by the Swedish Energy Agency (Energimyndigheten) under Grant 3233. The required computation is performed by computing resources from the Swedish National Infrastructure for Computing (SNIC) at PDC center for high performance computing at KTH Royal Institute of Technology which was supported by the Swedish Research Council under Grant 2018-05973.
Date Deposited:	13 Dec 2022 15:14
Date of first compliant deposit:	05 Dec 2022
Subjects:	Electricity supply industry
Last Modified:	20 Dec 2024 01:50
URI:	https://lbsresearch.london.edu/id/eprint/2706

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