Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-Side of the Smart Grid

TLDR

The electric energy distribution infrastructure is undergoing a technological evolution with the smart grid concept, enabling greater interaction between supply and demand and unlocking significant optimization potential. The paper focuses on a smart grid with demand‑side users owning distributed energy sources or storage, and addresses grid optimization from both user‑oriented and holistic perspectives using a general pricing model. A day‑ahead demand‑side management mechanism regulated by an independent central unit is employed, with users optimizing either individually via a noncooperative game grounded in variational‑inequality theory or jointly through cooperative system‑wide optimization. We devise distributed, iterative algorithms that yield optimal production/storage strategies with proven convergence, preserve user privacy, and require minimal signaling to the central unit.

Abstract

The electric energy distribution infrastructure is undergoing a startling technological evolution with the development of the smart grid concept, which allows more interaction between the supply- and the demand-side of the network and results in a great optimization potential. In this paper, we focus on a smart grid in which the demand-side comprises traditional users as well as users owning some kind of distributed energy source and/or energy storage device. By means of a day-ahead demand-side management mechanism regulated through an independent central unit, the latter users are interested in reducing their monetary expense by producing or storing energy rather than just purchasing their energy needs from the grid. Using a general energy pricing model, we tackle the grid optimization design from two different perspectives: a user-oriented optimization and an holistic-based design. In the former case, we optimize each user individually by formulating the grid optimization problem as a noncooperative game, whose solution analysis is addressed building on the theory of variational inequalities. In the latter case, we focus instead on the joint optimization of the whole system, allowing some cooperation among the users. For both formulations, we devise distributed and iterative algorithms providing the optimal production/storage strategies of the users, along with their convergence properties. Among all, the proposed algorithms preserve the users' privacy and require very limited signaling with the central unit.