Optimal Siting and Sizing of EV Charging Station Using Stochastic Power Flow Analysis for Voltage Stability

Abstract

Existing literature on planning for electric vehicle charging stations (EVCS) fails to consider uncertain factors in power systems, such as load fluctuations and the impact of EV integration. Consequently, using deterministic power flow (DPF) algorithms for EVCS planning is unreliable. To address this, we propose a probabilistic model for EV charging loads and introduce a novel dynamic system voltage stability (DSVS) index. We then present an effective optimization model for EVCS site and size planning using stochastic power flow (SPF). Our model aims to maximize capital gains on investment costs of EVCS, minimize yearly EV users' average wait time and distance to charge costs and minimize the DSVS index. To simplify the problem, we use the super-efficiency data envelopment analysis (SEDEA) method to determine objective weights and transform the multi-objective optimization problem into a single-objective one. Finally, we jointly solve the model using the voronoi diagram and adaptive differential evolution optimization algorithm (ADEOA). We verify the effectiveness of our proposed method using a case study with the IEEE 33-node distribution network topology diagram and a planning area diagram.

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

“…The advancements in optimization techniques for dynamic EV charging demand prediction and voltage stability are explored in [85,115,116], showcasing the sophistication of current methods in managing demand, ensuring system stability, and maintaining cost efficiency and emphasizing digital technologies' importance in EV infrastructure planning.…”

The Reliability and Economic Evaluation Approach for Various Configurations of EV Charging Stations

“…Conducting routine voltage stability analysis can effectively mitigate voltage instability and optimize the performance of EV charging stations. The IEEE-33 bus test system demonstrates reliable performance in the context of charging station ports inside the power distribution system [49]- [50].…”

Optimal Siting and Sizing of Electric Vehicle Energy Supplement Infrastructure in Highway Networks

“…From another aspect, Ma et al proposed a model which minimized both user cost and station cost, and a model was then developed by applying game theory [11]. Meanwhile, some studies consider uncertainty in charging station siting and sizing problems, and the Monte Carlo method and adaptive differential evolution optimization algorithm are applied to determine the result [12,13]. Further, some studies consider optimal charging station planning in a coupled power and transportation network.…”

“…Constraints (11) and (12) ensure the balance of full batteries and empty batteries. Note that T i.j is the minimal integer larger than or equal to T i.j .…”

“…Constraint (11) shows that the number of full batteries in one time interval equals the number in the last time interval plus the number of batteries which have finished charging and the number of batteries being transported to this station, and minus the number of full batteries being swapped in the past interval and the number being transported away. Constraint (12) is constructed in a similar way. Note that Constraints (11) and (12) are developed based on the assumption that all batteries being swapped from EVs are empty.…”