Developing an Artificial Hummingbird Algorithm for Probabilistic Energy Management of Microgrids Considering Demand Response - Alamir, Nehmedo; Kamel, Salah; Megahed, Tamer F.; Hori, Maiya; Abdelkader, Sobhy M. (Frontiers in Energy Research, 2022)
This paper proposes Artificial Hummingbird Algorithm (AHA) for Energy Management (EM) for optimal operation of Microgrid (MG), including Conventional Sources and Renewable Energy Sources (RES), with Incentive-based demand response (DR). Due to the stochastic nature of solar and wind output power and uncertainty of prices and load, a probabilistic EM with hybrid AHA and Point Estimation Method (PEM) is proposed to model this uncertainty by utilizing the normal and Weibull distribution functions. The PEM method is considered a good tool to handle stochastic EM problems. It achieves good results by using the same procedures used with the deterministic problems while maintaining low computational efforts. The proposed Artificial hummingbird Algorithm technique is used firstly deterministic to solve an incentive Demand Response Program, the optimum operation with the goal of reducing the overall cost, which includes the cost of conventional generator fuel and the cost of power transaction with the main grid while taking into account the load demand; Two different case studies are The tested; The simulation results of the proposed AHA is compared with the results of well-known meta-heuristic algorithms, to demonstrate its efficacy. And according to AHA’s results, a total reduction of energy consumption by 104 KWh for first case study, and 2677 MWh for the second case study reduced by is achieved , while achieving lowest achieving lowest overall operating cost. The results demonstrate that the AHA is adequate for tackling the EM problem. Then to examine the effect of uncertainty on MG state a probabilistic EM is solved using AHA-PEM.