The economics of renewable energy portfolio management in solar based microgrids: A comparative study of smart strategies in the market

Abstract

In this study, an energy-management approach based on digital twin is developed to manage a microgrid (MG) based on the energy generated by renewable sources of solar and wind, diesel generators, energy storage, and a variety of loads consumers in the market. The MG is assumed to participate in the pool market and schedule its controllable sources accordingly as a means of maximizing profits. This paper proposes a risk-limited scenario-driven stochastic programming strategy that uses the conditional value at risk approach for handling different types of uncertainty. Double stochastic optimization levels are used to solve the proposed scheme. The day-ahead market can be optimized at one level by submitting the best bids at hourly intervals based on forecasts. As a second level of optimization, scenario-driven stochastic information has been used for determining the best schedule. Also, shark optimization algorithm is proposed to solve the optimization problem. Aside from being advantageous for MGs and their users, the suggested energy management system likewise takes advantage of the MG's aggregators and non-real power plants. This model is shown to be valid based on the outcomes and would be beneficial for the market owners.