[HTML][HTML] Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning

N Harder, R Qussous, A Weidlich - Energy and AI, 2023 - Elsevier
Electricity markets need to continuously evolve to address the growing complexity of a
predominantly renewable energy-driven, highly interconnected, and sector-integrated …

Modeling participation of storage units in electricity markets using multi-agent deep reinforcement learning

N Harder, A Weidlich, P Staudt - Proceedings of the 14th ACM …, 2023 - dl.acm.org
Modeling electricity markets realistically plays a crucial role for understanding complex
emerging market dynamics and guiding policy making. In systems with a high share of …

Deep reinforcement learning-aided bidding strategies for transactive energy market

A Taghizadeh, M Montazeri… - IEEE Systems Journal, 2022 - ieeexplore.ieee.org
The concept of transactive energy market (TEM) has been introduced to efficiently balance
supply and demand across the electrical networks in a distributed manner. TEM allows …

Machine learning applications for electricity market agent-based models: A systematic literature review

AJM Kell, S McGough, M Forshaw - arXiv preprint arXiv:2206.02196, 2022 - arxiv.org
The electricity market has a vital role to play in the decarbonisation of the energy system.
However, the electricity market is made up of many different variables and data inputs …

A reinforcement learning approach to autonomous decision-making in smart electricity markets

M Peters, W Ketter, M Saar-Tsechansky, J Collins - Machine learning, 2013 - Springer
The vision of a Smart Electric Grid relies critically on substantial advances in intelligent
decentralized control mechanisms. We propose a novel class of autonomous broker agents …

Exploring market power using deep reinforcement learning for intelligent bidding strategies

AJM Kell, M Forshaw… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Decentralized electricity markets are often dominated by a small set of generator companies
who control the majority of the capacity. In this paper, we explore the effect of the total …

Multi-period and multi-spatial equilibrium analysis in imperfect electricity markets: A novel multi-agent deep reinforcement learning approach

Y Ye, D Qiu, J Li, G Strbac - IEEE access, 2019 - ieeexplore.ieee.org
Previously works on analysing imperfect electricity markets have employed conventional
game-theoretic approaches. However, such approaches necessitate that each strategic …

Approximating Nash equilibrium in day-ahead electricity market bidding with multi-agent deep reinforcement learning

Y Du, F Li, H Zandi, Y Xue - Journal of modern power systems …, 2021 - ieeexplore.ieee.org
In this paper, a day-ahead electricity market bidding problem with multiple strategic
generation company (GEN-CO) bidders is studied. The problem is formulated as a Markov …

Intelligent agent strategies for residential customers in local electricity markets

E Mengelkamp, J Gärttner, C Weinhardt - Proceedings of the ninth …, 2018 - dl.acm.org
The energy transition from a formerly centralized, fossil-fuel based system towards a
sustainable system based on a large share of renewable generation calls for a …

Data-driven decision-making strategies for electricity retailers: A deep reinforcement learning approach

Y Liu, D Zhang, HB Gooi - CSEE Journal of Power and Energy …, 2020 - ieeexplore.ieee.org
With the continuous development of the electricity market, the electricity retailers, as the
intermediaries between producers and consumers, have emerged in some of the liberalized …