A Non-Cooperative Framework for Coordinating a Neighborhood of Distributed Prosumers

This paper introduces a scalable framework to coordinate the net load scheduling, sharing, and matching in a neighborhood of residential prosumers connected to the grid. As the prosumers are equipped with smart appliances, photovoltaic panels, and battery energy storage systems, they take advantage of their consumption, generation, and storage flexibilities to exchange energy with neighboring prosumers through negotiating on the amount of energy and its price with an aggregator. The proposed framework comprises two separate multi-objective mixed integer nonlinear programming optimization models for prosumers and the aggregator. Prosumers' objective is to maximize the comfort level and minimize the electricity cost at each instant of time, while aggregator intends to maximize its profit and minimize the grid burden by matching prosumers' supply and demand. The evolutionary nondominated sorting genetic algorithm-III (NSGA-III) is employed to generate a set of feasible nondominated solutions to the optimization problem of each individual prosumer and the aggregator. As a bilateral negotiation between each prosumer and the aggregator results in significant computational and communication overhead, a virtual power plant is introduced as an intermediator on behalf of all prosumers to proceed the negotiation with the aggregator in a privacy-preserving noncooperative environment, where no private information is shared. Hence, an automated negotiation approach is embedded in the framework, which enables the negotiators to reactively negotiate on concurrent power and price using private utility functions and preferences. To converge to an acceptable agreement, the negotiation approach follows an alternating-offer production protocol and a reactive utility value concession strategy. The effectiveness of the framework is evaluated by several economic and environmental assessment metrics through a variety of numerical simulations.