Intelligent Bidding Strategies in Local Electricity Markets: A Simulation-based Analysis

Okwuibe, Godwin C.; Wadhwa, Mukund; Brenner, Thomas; Tzscheutschler, Peter; Hamacher, Thomas

doi:10.1109/epec48502.2020.9320067

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…The agent's action is intelligent and makes bidding decisions based on reinforcement learning. [36,[48][49][50][51][52][53][54][55][56][57] Parallel Multidimensional willingness Multidimensional variables such as the historical records of trading data and counter behavior are modeled to mimic the microgrid fluctuation during bidding processes.…”

Section: Intelligently Bidding Agentsmentioning

confidence: 99%

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Business Model of Peer-to-Peer Energy Trading: A Review of Literature

et al. 2022

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Peer-to-peer (P2P) energy trading is a promising energy trading mechanism due to the deployment of distributed energy resources in recent years. Trading energy between prosumers and consumers in the local energy market is undergoing massive research and development, paying significant attention to the business model of the energy market. In this paper, an extensive review was conducted on the current research in P2P energy trading to understand the business layer of the energy market concerning business model dimensions: bidding strategies and the market-clearing approach. Different types of game theoretical-based and auction-based market-clearing mechanisms are investigated, including a detailed classification of auctions. This study considers the possibility of employing the P2P technique in developing countries and reviewing existing business models and trading policies. The business layer of the P2P structure plays a vital role in developing an effective trading mechanism based on interactive energy markets.

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Section: Intelligently Bidding Agentsmentioning

confidence: 99%

“…This figure is built based on the included papers, and they have been considered as the core of literature classification. [1,[7][8][9]12,18,23,25,26,[29][30][31][32]37,40,[43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][98][99][100][101][102][103][104][105][107][108][109][110][111][112][113].…”

Section: Future Workmentioning

confidence: 99%

Business Model of Peer-to-Peer Energy Trading: A Review of Literature

et al. 2022

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“…[22], a deep learning based on data-driven approach was developed and used to model the transaction behaviour of prosumers and consumers based on public information in a two-stage P2P local electricity market. A Q-learning based intelligent bidding strategy was proposed by [23] for prosumers in a competitive two-sided pay-as-bid LEM. To further integrate electric vehicle trading in an LEM, [24] proposed a data analytics and deep reinforcement learning based bidding strategy for electric vehicle aggregators in an LEM.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Eq. (23) shows that if a consumer's trade rate is lower than the community's median rate p t , the consumer's agent receives a positive reward since he/she (the consumer) paid less than the other members of the community and outperformed them. When a consumer's trade rate exceeds the market's median rate, the consumer's agent earns a negative reward.…”

Section: ) Reward Functionmentioning

confidence: 99%

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

Okwuibe

Bhalodia²,

Gazafroudi³

et al. 2022

IEEE Access

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Local energy markets (LEMs) are proposed in recent years as a way to enable local prosumers and community to trade their electricity and have control over their electrical related resources by ensuring that electricity is traded closer to where it is produced. However, literature is still scarce with the most optimal and effective trading strategies for LEM design. In this work, we propose two reinforcement learning based intelligent bidding strategies for prosumers and consumers trading within an LEM. Our proposed models were evaluated of their performance by testing them in a German real case scenario. The simulation results show that intelligent bidding strategies create additional self sufficiency and market savings to the local community compared to the baseline strategy where the agents make their trading decision randomly without an intelligent agent. Moreover, modelling the intelligent agents to perform towards a common goal creates more share of individual savings for the prosumers and consumers compared to the classical intelligent bidding strategies employed in this work.INDEX TERMS Bidding strategy, energy community, local energy markets, Markov decision process, peerto-peer, reinforcement learning.

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“…In [7], a bi-level optimization problem of the electricity-heat integrated system is proposed, where customer aggregators are introduced to supply downstream demand in the most economical way. In [8], the PV-generated electricity from households is integrated into the energy system and a bidding strategy in the local electricity market is proposed. In [9], a bilevel programming approach for the collaborative management of active distribution networks by designing comprehensive prices is proposed.…”

Section: Nomenclaturementioning

confidence: 99%

Bi-Level Programming for Integrating Flexible Demand in Combined Smart Energy System

Hamacher

Perić

et al. 2021

2021 IEEE International Smart Cities Conference (ISC2)

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In this paper, a gas-electricity-heat integrated energy system with smart buildings is described. The special focus is flexible energy demand including electricity and heat integrated into the smart buildings, where customers have multiple options to satisfy their energy demand. Considering energy prices in the market, the aggregator is introduced to manage these smart buildings in the most economical way. This paper proposes a bi-level programming approach for the integration of flexible demand in the combined smart energy system (CoSES). The integrated system operator (ISO) aims at maximizing social welfare according to the operation strategy of the aggregator, which minimizes the energy purchase cost of downstream demand. Then, the linearization method and Karush-Kuhn-Tucker (KKT) conditions are used to transform the bi-level optimization problem into an easily handled singlelevel problem. The proposed approach is illustrated in a modified test system.

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Intelligent Bidding Strategies in Local Electricity Markets: A Simulation-based Analysis

Cited by 8 publications

References 11 publications

Business Model of Peer-to-Peer Energy Trading: A Review of Literature

Business Model of Peer-to-Peer Energy Trading: A Review of Literature

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

Bi-Level Programming for Integrating Flexible Demand in Combined Smart Energy System

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