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Smart city article details
| Title | Bidding Strategy For Two-Sided Electricity Markets: A Reinforcement Learning Based Framework |
|---|---|
| ID_Doc | 11968 |
| Authors | Pedasingu B.S.; Subramanian E.; Bichpuriya Y.; Sarangan V.; Mahilong N. |
| Year | 2020 |
| Published | BuildSys 2020 - Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation |
| DOI | http://dx.doi.org/10.1145/3408308.3427976 |
| Abstract | We aim to increase the revenue or reduce the purchase cost of a given market participant in a double-sided, day-ahead, wholesale electricity market serving a smart city. Using an operations research based market clearing mechanism and attention based time series forecaster as sub-modules, we build a holistic interactive system. Through this system, we discover better bidding strategies for a market participant using reinforcement learning (RL). We relax several assumptions made in existing literature in order to make the problem setting more relevant to real life. Our Markov Decision Process (MDP) formulation enables us to tackle action space explosion and also compute optimal actions across time-steps in parallel. Our RL framework is generic enough to be used by either a generator or a consumer participating in the electricity market. We study the efficacy of the proposed RL based bidding framework from the perspective of a generator as well as a buyer on real world day-ahead electricity market data obtained from the European Power Exchange (EPEX). We compare the performance of our RL based bidding framework against three baselines: (a) an ideal but un-realizable bidding strategy; (b) a realizable approximate version of the ideal strategy and (c) historical performance as found from the logs. Under both perspectives, we find that our RL based framework is more closer to the ideal strategy than other baselines. Further, the RL based framework improves the average daily revenue of the generator by nearly €7,200 (€2.64 M per year) and €9,000 (€3.28 M per year) over the realizable ideal and historical strategies respectively. When used on behalf of a buyer, it reduces average daily procurement cost by nearly €2,700 (€0.97 M per year) and €57,200 (€52.63 M per year) over the realizable ideal and historical strategies respectively. We also observe that our RL based framework automatically adapts its actions to changes in the market power of the participant. © 2020 ACM. |
| Author Keywords | Bidding; Electricity Markets; Forecasting; Optimization; Reinforcement Learning |
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