Optimal battery schedule for grid-connected photovoltaic-battery systems of office buildings based on a dynamic programming algorithm

The optimal schedule of energy storage systems is an effective way to improve the economy and stability of grid connected photovoltaic-battery energy storage systems (PV-BESS). This study presents an operation strategy considering economic feasibility and photovoltaic self-consumption rate (SCR) for the energy management of office buildings under time-of-use (ToU) electricity price. The strategy aims to optimize FiT revenue streams for the PV-BESS by scheduling the overall energy flow in real time based on a dynamic programming algorithm. The battery control strategy based on a dynamic programming algorithm can control the energy flow in a flexible way to minimize net present value (NPV) in a typical year, while taking such factors as dynamic electricity price, the battery cycling aging, and demand response characteristics into account. An existing PV-BESS used for a middle size office building in Beijing was taken as a case study to evaluate the optimization model. It is shown that the dispatch strategy could achieve superior performance in the cold region in China. Additionally, the indices affecting economic performance were figured out to validate the feasibility of the proposed algorithm. The results show that while the unit cost of energy storage is dropped to about 100$/kWh, the system could gain revenue under different electricity prices. Lastly, it is concluded that electricity price is the most sensitive parameter to the system's economy through sensitivity analysis.

Automated summary

The study introduces an energy management strategy considering economic feasibility and photovoltaic self-consumption rate, optimizing the FiT revenue streams for the PV-BESS through a dynamic programming algorithm. It is shown that the dispatch strategy could achieve superior performance in cold regions of China, while identifying indices affecting economic performance to validate the feasibility of the proposed algorithm.