Optimal Energy Dispatch Engine for PV-DG-ESS Hybrid Power Plants Considering Battery Degradation and Carbon Emissions

Uncertainties in load and solar power forecasting, complex energy storage system (ESS) constraints, and feedback correction pose challenges for very short-term and short-term hybrid power plant scheduling. This paper proposes a two-stage mixed-integer linear programming (MILP)-based energy dispatch engine (EDE). The proposed model ensures optimized scheduling through accurate load and power forecasting, a feedback correction loop, and a set of constraints governing the state of charge (SOC) and state of health (SOH) of the ESS. Such an EDE aims to reduce the plant's operating costs and the usage of diesel generators (DGs), and minimize the cost of carbon emissions. To test the performance of the developed model, real-time load and photovoltaic (PV) data were used in conjunction with a PV-DG-ESS hybrid plant. The system was evaluated against a heuristic control model and a multistage stochastic control model, with the daily overall electricity and carbon emission costs as evaluation metrics. The test results revealed a 9.2% and 3.5% decrease in daily costs compared to the heuristic and stochastic methods, respectively, and a 29.4% decrease in carbon emission costs.

Automated summary

This paper proposes a two-stage mixed-integer linear programming-based energy dispatch engine that aims to reduce the operating costs and the usage of diesel generators, and minimize the cost of carbon emissions. The experiment results show a decrease of 9.2% and 3.5% in daily costs compared to the heuristic and stochastic methods, respectively, and a 29.4% decrease in carbon emission costs.