Peer-to-peer (P2P) energy trading is a decentralized energy market where local energy prosumers act as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling, assume users’ sustained active participation, and full compliance in the decision-making process. To overcome these unrealistic assumptions, and their deleterious consequences, in this paper we propose an automated P2P energy trading framework that specifically considers the users’ perception by exploiting prospect theory . We formalize an optimization problem that maximizes the buyers’ perceived utility while matching energy production and demand. We prove that the problem is NP-hard and we propose a Differential Evolution-based Algorithm for Trading Energy ( DEbATE ) heuristic. Additionally, we propose two automated pricing solutions to improve the sellers’ profit based on reinforcement learning. The first solution, named Pricing mechanism with Q-learning and Risk-sensitivity ( PQR ), is based on Q-learning. Additionally, the given scalability issues of PQR , we propose a Deep Q-Network-based algorithm called ProDQN that exploits deep learning and a novel loss function rooted in prospect theory. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approaches achieve \(26\% \) higher perceived value for buyers and generate \(7\% \) more reward for sellers, compared to recent state-of-the-art approaches.
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Real-time Rolling Horizon Energy Management for the Energy-Hub-Coordinated Prosumer Community from a Cooperative Perspective
The concept of Energy Hub has been proposed to facilitate the synergies among different forms of energy carriers. Under the new electricity market environment, it is of great significance to build a win-win situation for prosumers and Hub manager (HM) at the level of community without bringing extra burden to the utility grid. This paper proposes a cooperative trading mode for a community-level energy system (CES), which consists of Energy Hub (EH) and PV prosumers with the automatic demand response (DR) capability. In the cooperative trading framework, a real-time rolling horizon energy management model is proposed based on cooperative game theory considering the stochastic characteristics of PV prosumers and the conditional value at risk (CVaR). The validity of the proposed model is analyzed through the optimality proof of the grand coalition. A contribution-based profit distribution scheme and its stability proof are also provided. Moreover, in order to solve the optimization model, it is further transformed into a more easily resolved mixed integer linear programming (MILP) model by adding auxiliary variables. Finally, via a practical example, the effectiveness of the model is verified in terms of promoting local consumption of PV energy, increasing HM's profits, and reducing prosumers' costs, etc.
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- NSF-PAR ID:
- 10082512
- Date Published:
- Journal Name:
- IEEE Transactions on Power Systems
- ISSN:
- 0885-8950
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TPWRS.2018.2877236
