A global research team led by scientists from China’s Tianjin Renai College has developed a novel stochastic optimization technique for enhanced dispatching and operational efficiency in PV-powered electric vehicle (EV) charging stations equipped with energy storage systems (ESS).
The proposed frameworks integrates a day-ahead optimization and a real-time optimization to handle forecast uncertainties, minimizing operational costs by dynamically scheduling the energy storage system.
“The mounting EV population brings forth a significant concern: their arbitrary grid access through charging units could worsen load fluctuations,” the academics said. “This paper proposes a multi-timescale stochastic dispatch strategy that integrates both day-ahead scheduling and intraday rolling optimization. While PV–storage integration and dispatch models have been studied individually, few works have coordinated day-ahead and real-time scheduling in a unified framework under uncertainty. This study fills that gap by introducing a scenario-based optimization strategy that dynamically adjusts dispatch decisions based on updated forecasts.”
The novel technique uses the autoregressive moving average (ARMA) to predict the day-ahead PV output and EV charging load. Then, it uses Latin hypercube sampling (LHS) to create many usage scenarios, later clustering them into a few, to be computationally more efficient. It uses this data to optimize the system. In the intraday level, the system uses a rolling optimization strategy on a 15-minute cycle, while dynamically optimizing the system.
To demonstrate the capabilities of the technique, the scientists simulated three PV charging stations, each with 60 charging units of 32 kW. The PV infrastructure generates 300 kW, using 600 kW inverters with 97% efficiency and an ESS with a capacity of 800 kWh. The PV levelized cost of electricity (LCOE) was estimated at CNY 0.6041 ($0.085) /kWh, while the ESS costs CNY 0.3 million/kWh. The EVs have a battery capacity of 70 kWh and a charging power of 7 kW.
Under those conditions, four cases were tested; the first was a deterministic scenario with no storage, and the second was a scenario with 800 kWh of storage. Case three was stochastic, with nine scenarios generated and 800 kWh of storage, and case four was stochastic as well, with 800 kWh and 25 scenarios.
Using only the day-ahead technique, case four had a run cost of CNY 9,716.96, case three of CNY 9,692.65, case two of CNY 9,663.15, and case one of CNY 10,916.04. Using the intrada optimization, prices decreased to CNY 9,283.63, CNY 9,279.53, CNY 9,274.98, and CNY 10,516.68, respectively.
“Unlike deterministic models, which fail to account for forecast errors, and single-layer stochastic approaches that lack real-time responsiveness, the proposed method integrates both day-ahead and intraday optimization using a scenario-based approach, enhancing flexibility and accuracy,” the team concluded. “The cost savings and resilience improvements observed in our simulations demonstrate superior adaptability under forecast uncertainty, underscoring the advantages of our multi-timescale stochastic optimization framework.”
The proposed methodology was presented in “Multi-timescale stochastic optimization for enhanced dispatching and operational efficiency of electric vehicle photovoltaic charging stations,” published in the International Journal of Electrical Power & Energy Systems. Scientists from China’s Tianjin Renai College, Spain’s Polytechnic University of Catalonia, and Denmark’s Aalborg University have participated in the study.
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