Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

A novel strategy is proposed to tackle an optimal dispatch of a microgrid in response todynamic conditions, utilizing a water wave optimization (WWO) algorithm and considering a day-aheadload forecasting. Amongst meta-heuristic algorithms, the WWO algorithm stands out in terms of populationsize, parameter tuning, exploitation and exploration, convergence speed, as well as optimization mechanism.It leverages its ability to efficiently explore solution spaces and adapt to changing conditions. It is appliedto the dynamic optimal dispatch of a microgrid with the uncertainty of load power considered and solvedby day-ahead load forecasting. It dynamically adjusts the microgrid operation in response to these inputs,ensuring optimal decision-making in the face of varying load scenarios. With the competition of various day-ahead load forecasting techniques in the microgrid, a multi-variate linear regression (MLR) model shows itsadvantage features, being more transparent, more effective, and more robust than other techniques, especiallytransparent explainability, as well as simple and fast in model training. These are requirements to achievethe result of day-ahead load forecasting. Thus, the MLR model is proposed to forecast day-ahead load inthe microgrid in this paper. The simulation results show that the percentage error (PE) between the MLRmodel-based forecasted and actual load powers is always less than 4.42%, the mean absolute percentageerror (MAPE) of the forecasting result is 3.33%, and the execution time is 49 (s). These achievementsmeet the accurate and fast requirements. They are completely competitive with the results of using othertechniques such as convolutional neural networks (CNN) and long short-term memory (LSTM), especiallyin the execution time. This has contributed to improving the efficiency of the dynamic optimal dispatch inthe microgrid. Then, the diesel generation, battery energy storage, and total microgrid generation costs are68.76 ($), 5.09 ($), and 73.85 ($) respectively by using the WWO algorithm which are better than thoseby using a genetic algorithm (GA), a non-dominated sorting genetic algorithm-II (NSGA-II), a particleswarm optimization (PSO) algorithm, and a transient search optimization (TSO) algorithm in the microgrid.The findings offer valuable insights for microgrid operators, energy planners, and policymakers seekingsustainable and cost-effective solutions for distributed energy resource management.