Modeling Optimal Energy Exchange Operation of Microgrids Considering Renewable Energy Resources, Risk-based Strategies, and Reliability Aspect Using Multi-objective Adolescent Identity Search Algorithm

The inherent volatility and unpredictable nature of renewable resources and load demand have posed significant challenges to the optimal energy exchange of microgrids (MGs). Therefore, this study presents a novel risk-based multi-objective energy exchange optimization for the networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. For this purpose, three different riskbased strategies are distinguished using the conditional value at risk approach. The proposed model is specified as an objective function in two distinct ways. The first function minimizes operation and maintenance costs, the cost of exchanging power between the upstream grid and MGs, and the cost of power loss, whereas the second function minimizes the amount of energy that is not supplied. Moreover, a stochastic scenario-based approach is incorporated into this approach to control uncertainties. Also, the scenario reduction method is implemented to reduce the computational burden. Finally, a developed multi-objective adolescent identity search algorithm (AISA) is proposed. Pareto theory and non-dominated sorting are employed to develop the proposed model. In the proposed data development model, it has been tried to improve the local and global search in a better way in order to solve local points and early convergence. The fuzzy model is used to select the optimal solution from the set of solutions. The modified IEEE 33-bus distribution system is used to implement the suggested model and assess its efficacy. The obtained results show that the proposed risk-based model can control the risk of the EMS problem due to the stochastic behavior of the input parameters. In this paper, for risk assessment high consequences and low probability events are considered extreme events. In the risk aversion strategy, the cost of the event is significantly reduced compared to the risk strategy, while the additional cost is much less than this is.

Automated summary

This study presents a novel risk-based multi-objective energy exchange optimization for microgrids, considering the inherent volatility and unpredictable nature of renewable resources and load demand uncertainties. Three different risk-based strategies are distinguished using the conditional value at risk approach. The proposed model minimizes operation and maintenance costs, power exchange costs, power loss costs, and the amount of energy not supplied. A stochastic scenario-based approach and scenario reduction method are incorporated to control uncertainties. The proposed algorithm effectively improves local and global search and uses a fuzzy model for selecting the optimal solution.