Optimal Generation Scheduling of Grid-Connected Microgrid Considering Uncertainty in Renewable Energy and Emission Costs

This paper proposes an advanced optimization framework for scheduling distributed energy resources (DERs) and energy storage systems (ESS) in a grid-connected microgrid, incorporating emission costs and uncertainties in renewable energy generation. The microgrid consists of controllable generators, including a microturbine (MT), fuel cell (FC), photovoltaic (PV), wind turbine (WT), and an ESS. The objective is to minimize the overall operational cost, which comprises generation costs, grid power exchange costs, start-up and shut-down costs, as well as emission costs associated with pollutants such as NOx, CO2, and SO2. The scheduling problem accounts for the uncertainty in renewable energy outputs by applying monthly variability coefficients to PV and WT generation. A nonlinear optimization approach is employed to determine the optimal power generation from each resource, the charge and discharge schedule for the ESS, and the grid interaction strategy over a 24-hour period. Technical constraints, including generator power limits, grid exchange limits, energy balance, and the state of charge (SOC) of the ESS, are included to ensure system reliability. The results demonstrate that the proposed model effectively reduces operational costs while lowering emissions, even under variable renewable energy generation conditions. The inclusion of emission costs highlights the importance of environmentally conscious energy management. This study provides a viable approach for optimizing the operation of grid-connected microgrids, balancing cost-efficiency, environmental impact, and renewable energy uncertainty, and can serve as a guideline for future energy management strategies in distributed systems.