As an intermediator between the wholesale electricity market and retail market, a typical load aggregator submits an optimal bid to the system operator to meet the expected demands of its customers. In this regard, the provision of an effective optimal bidding strategy is very crucial for a load aggregator to increase its profit. Within this context, this paper proposes a two-stage artificial neural network based adaptive bidding strategy procedure for an LA by revealing, modelling, and predicting the aggregative behaviour of the competitors in an hourly electricity market. To this end, we develop the concept of decentralized equivalent rival whose behaviour in the electricity market reflects the aggregation of behaviours of all individual competitors. Also, an equivalent market which its outcomes are approximately equal to those of the real market is modelled. The equivalent market's participants are the load aggregator and its corresponding DER. The proposed approach is capable enough to consider transmission constraints. The performance of the proposed approach has been examined on an illustrative example and the IEEE 30-bus test system by considering transmission network constraints. The proposed artificial neural network-based adaptive bidding strategy has compared with a Q -learning-based bidding approach and the results are analysed.
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North Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R ChinaNorth Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R China
You, Jiangwei
Jia, Jianfang
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North Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R ChinaNorth Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R China
Jia, Jianfang
Pang, Xiaoqiong
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North Univ China, Sch Comp Sci & Technol, Taiyuan 030051, Peoples R ChinaNorth Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R China
Pang, Xiaoqiong
Wen, Jie
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North Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R ChinaNorth Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R China
Wen, Jie
Shi, Yuanhao
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North Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R ChinaNorth Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R China
Shi, Yuanhao
Zeng, Jianchao
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North Univ China, Sch Comp Sci & Technol, Taiyuan 030051, Peoples R ChinaNorth Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R China
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Univ Putra Malaysia, Dept Civil Engn, Fac Engn, Serdang 43400, Selangor, MalaysiaUniv Putra Malaysia, Dept Civil Engn, Fac Engn, Serdang 43400, Selangor, Malaysia
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Hong Kong Polytech Univ, Dept Hlth Technol & Informat, Kowloon, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Dept Hlth Technol & Informat, Kowloon, Hong Kong, Peoples R China
Tang, Fuk-hay
Ip, H. S. Horace
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City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Dept Hlth Technol & Informat, Kowloon, Hong Kong, Peoples R China