Abstract
Peer-to-peer (P2P) energy trading has recently emerged as a promising paradigm for integrating renewable and distributed energy resources into local energy grids with the presence of active prosumers. However, prosumers often have different preferences on energy trading price and amount. Therefore, in decentralized P2P energy markets, a negotiation between prosumers is needed to obtain a commonly satisfactory set of preferences, i.e., a market-clearing solution. To achieve that, this paper proposes a novel approach in which a decentralized inverse optimization problem is solved by prosumers to cooperatively learn to set their objective function parameters, given their preferential intervals of energy prices and amounts. As such, prosumers’ parameters can be determined in specific intervals computed analytically from the lower and upper bounds of their preferential intervals, if a certain learning condition is satisfied. Next, the structural robustness of prosumer’s cooperative learning against the malicious and Byzantine models of cyberattacks is studied with the weighted-mean-subsequence-reduced (WMSR) resilient consensus algorithm. A novel sufficient robustness condition is then derived. Finally, case studies are conducted on the IEEE European Low Voltage Test Feeder system to validate the effectiveness of the proposed theoretical results.
Highlights
For clarity of illustrating the resilience of P2P electricity trading with the WMSR algorithm, we assume that two prosumers are suffered from a fault-data injection attack (FDIA), where constant values are added to the states of selling prosumer 1 and buying prosumer 1
An analytical cooperative learning approach is proposed in which prosumers can locally and randomly select their cost function parameters in certain intervals to achieve successful energy transactions with desired price and amounts
The issue is formulated as a decentralized inverse optimization problem for which interval analysis and multi-agent consensus are employed to solve
Summary
The wide adoption of renewable and DERs all over the world, as an effort to reduce carbon emissions, poses many challenges to the operation and management of energy systems, and brings opportunities to develop novel. Two fundamental assumptions have been commonly employed in the literature of P2P energy systems research, one is the successful trading of all prosumers, while the other assumption is the right selection of cost function parameters by each prosumer to obtain expected energy transactions. To cope with the challenge on relaxing the abovementioned assumptions, a heuristic approach for selecting parameters of prosumers’ cost functions in P2P energy systems composing of multiple selling and buying prosumers has been introduced in [18]. An analytical cooperative learning approach for setting parameters of prosumers’ cost functions to guarantee their energy preferences, in decentralized P2P energy systems consisting of multiple buying and multiple selling prosumers. A(t) is a symmetric and doubly-stochastic matrix (i.e. row sums and column sums of A(t) are all equal to 1)
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