Economic Dispatch Optimization for Grid-Connected Microgrid Models with Battery Storage Systems

With the increasing integration of renewable energy sources, grid-connected microgrid models incorporating battery storage have become crucial for enhancing energy utilization efficiency. The objective of economic dispatch optimization is to minimize total system operating costs while meeting load demand, achieved through coordinated management of distributed energy resources (such as photovoltaic systems and wind turbines), battery storage systems, and power exchanges with the main grid. In code implementation, this typically involves formulating an optimization problem with constraints for power balance, generator capabilities, and battery operating limits.

Battery storage plays a vital role in microgrid operations. It not only mitigates renewable energy fluctuations but also reduces electricity costs by charging during low-price periods and discharging during peak hours. Optimization dispatch must account for battery charging/discharging efficiency, cycle life constraints, and maintaining state-of-charge (SOC) within safe operating ranges to prevent overcharging or deep discharging that could degrade battery lifespan. Algorithm implementation requires SOC tracking functions and efficiency penalty models in the cost function.

In grid-connected mode, microgrids engage in energy exchanges with the main grid. The core of economic dispatch lies in dynamic trade-offs: when renewable generation is sufficient and batteries are fully charged, excess power can be sold back to the grid for revenue; conversely, during power shortages or high electricity price periods, priority is given to utilizing battery storage or purchasing power from the grid, while balancing grid purchase costs against battery degradation costs. Programming solutions often employ conditional statements and real-time price monitoring to implement these decision rules.

To achieve optimal dispatch, mixed-integer linear programming (MILP) models are typically established, performing rolling optimization on hourly or minute-level timescales. The objective function incorporates generation costs, battery degradation costs, and grid interaction costs. Sensitivity analysis can further reveal how electricity price fluctuations and load variations impact dispatch strategies, providing decision support for long-term microgrid economic planning. Implementation typically requires optimization solvers like CPLEX or Gurobi, with MATLAB/Python code structures handling constraint matrices and variable definitions.