Synergistic emission-aware energy management of a multi-energy microgrid with electric mobility integration incorporating a new composite operational flexibility index

Abstract

Due to increasing integration of renewable energy sources along with electric vehicles, new uncertainties related to solar radiation, wind velocity, load consumption, and EV charging demand have emerged. Together with contradictory economic, environmental, and operational targets such as the requirement of flexibility, these uncertainties make it hard for traditional deterministic optimization methods to solve the problem. In this paper, a new paradigm is suggested for carbon-aware transactive energy management in multi-source microgrids, including photovoltaic (PV) panels, wind turbines (WT), microturbines (MT), fuel cells (FC), battery energy storage systems (BESS), and electric vehicle/e-bike charging stations with vehicle-to-grid (V2G) and battery-to-grid (B2G) capabilities. The developed synergistic paradigm minimizes operating costs, reduces pollutant emissions, and increases a new composite operational flexibility index (COFI) over 24 h through a transactive energy coordination mechanism that enables efficient interaction between peers and the grid. In order to find a solution to this multi-objective optimization problem, the multi-objective Sinh Cosh Optimizer (MOSCHO) algorithm is proposed, using properties of hyperbolic functions in order to provide an effective combination of exploration and exploitation. Stochastic modeling is carried out by means of a cloud theory-based approach, where initially 1000 scenarios are generated with an incremental cloud generator and then reduced to 50 scenarios with the K-means algorithm. A fuzzy decision-making procedure is used to pick the best compromise solution from the found Pareto front. Simulations on a test microgrid showed that using the BESS allows decreasing the cost of operation by 10.7% and increasing COFI based-flexibility by 161%. Further, the introduction of V2G/B2G charging stations decreases the cost of operation and increases flexibility by 43.7%. The stochastic model ensures higher reliability of the results under uncertainties. Validation of the results obtained with the stochastic model with the Monte Carlo method proved high accuracy and a 17% reduction in computation time. Comparing with MOPSO, it is found that MOSCHO is better in quality of the Pareto front, convergence, and diversity of solutions. The proposed scheme can be regarded as an effective approach for energy management in microgrids under uncertain conditions.