An optimal strategy for energy pricing based on equilibrium problem with equilibrium constraints in microgrids by diagonalization algorithm

Efforts achieving 100 % utilization of renewable energy in community microgrids necessitates innovative approaches to energy markets and transactions, especially during periods of energy scarcity. This study contributes to the promising field of Diagonalization Algorithm (DA) energy trading in two primary ways: first, through an examination of a centrally located, welfare-maximizing economic scheduling that defines ideal pricing and allotments, and second, by introducing a novel DA framework to negotiating power transactions that results in physically possible and, at a minimum, weakly Pareto-optimal results. The key findings include: optimal pricing alone does not sufficiently motivate agents with batteries to undertake best actions; a unique DA methodology is proposed to tackle this challenge meanwhile maintaining confidential data; a rigorous demonstration establishes the convergence of this methodology to centrally located resolution in scenario of 2 agents negotiating to a singular timeframe; and computational simulations of DA methodology efficiency involving up to 10 agents and 24 h demonstrate its average convergence to overall welfare inside 0.2 % of collective best in range of 10 to 100 s of repetitions. Convergence time increases with quantity of participants, temporal intervals, and overall storage capability.