Carbon Footprint and Cost Minimization for Grid Systems Through Day-ahead Order and Battery Size Optimization

We modeled the problem of peak hours day-ahead order for smart grid companies integrating renewable energy and power storage systems. This results in optimizing day-ahead order, battery storage size, and consequently lowering the use of fossil fuels and emissions. The utility-scale power storage can balance the difference between the day-ahead forecasts and real-time consumer demand through energy arbitrage and transmission deferral for peaking capacity. We define system parameters and their associated costs and run a suggested algorithm to minimize the grid operating cost by optimizing day-ahead order amount and battery storage capacity. The model is designed to prioritize and take power resources depending on their availability and associated costs in real-time. The resources include day-ahead reserve, wind power, utility-scale storage system, and two-stage real-time power, which can be adjusted by users based on their available resources. Multiple comparisons on a finite feasible set of discrete decision variables through simulation optimization provide us with the optimal day-ahead order and battery size. Furthermore, the model will be tested and validated based on data provided by the U.S Energy Information Association. The model can be used by grid operators to evaluate their potential savings, can be used by energy regulatory agencies for simulating and examining their rules and policies, and can be used by state and local air pollution control agencies to evaluate the impact of different energy resources such as batteries on power generation.