Efforts to efficiently promote the participation of distributed energy resources in community microgrids require new approaches to energy markets and transactions in power systems. In this paper, we contribute to the promising approach of peer-to-peer (P2P) energy trading. We first formalize a centralized welfare maximization model of an economic dispatch with perfect information based on the value of consumption with zero marginal-cost energy. We characterize the optimal solution and corresponding price to serve as a reference for P2P approaches and show that the profit-maximizing strategy for individuals with storage in response to an optimal price is not unique. Second, we develop a novel P2P algorithm for negotiating energy trades based on iterative price and quantity offers that yields physically feasible and at least weakly Pareto-optimal outcomes. We prove that the P2P algorithm converges to the centralized solution in the case of two agents negotiating for a single period, demonstrate convergence for the multi-agent, multi-period case through a large set of random simulations, and analyze the effects of storage penetration on the solution.
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