A Novel Algorithm for Optimal Electricity Pricing in a Smart Microgrid Network

TL;DR

This paper introduces a new polynomial-time algorithm for optimal electricity pricing and energy trading in smart microgrids, leveraging a divide-and-conquer approach to improve market efficiency without altering generation costs.

Contribution

The paper presents a novel algorithm that efficiently solves the nonlinear, non-convex power allocation problem in microgrids by decomposing it into manageable subproblems.

Findings

The proposed algorithm reduces electricity prices compared to traditional methods.

Simulation results show improved energy trading efficiency in microgrid scenarios.

The method handles various marginal cost functions effectively.

Abstract

The evolution of smart microgrid and its demand-response characteristics not only will change the paradigms of the century-old electric grid but also will shape the electricity market. In this new market scenario, once always energy consumers, now may act as sellers due to the excess of energy generated from newly deployed distributed generators (DG). The smart microgrid will use the existing electrical transmission network and a pay per use transportation cost without implementing new transmission lines which involve a massive capital investment. In this paper, we propose a novel algorithm to minimize the electricity price with the optimal trading of energy between sellers and buyers of the smart microgrid network. The algorithm is capable of solving the optimal power allocation problem (with optimal transmission cost) for a microgrid network in a polynomial time without modifying the actual marginal costs of power generation. We mathematically formulate the problem as a nonlinear non-convex and decompose the problem to separate the optimal marginal cost model from the electricity allocation model. Then, we develop a divide-and-conquer method to minimize the electricity price by jointly solving the optimal marginal cost model and electricity allocation problems. To evaluate the performance of the solution method, we develop and simulate the model with different marginal cost functions and compare it with a first come first serve electricity allocation method.