Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.
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This study used historical data from a Park & Ride facility in Amsterdam to build a validated computer (Python) model to optimize battery and grid connection sizing. The case study modelled is equipped with 8 EV chargers (16 connections), an on-site supplementary battery, and a limited capacity grid connection. This model was then used to optimize the battery energy storage capacity and grid connection capacity for minimal annualized investment, using a future proof monthly load profile. A variety of battery control strategies were simulated using both the optimal system sizing and the current system sizing. The results were compared and a recommended control strategy presented, considering a number of performance metrics.
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This paper investigates smart charging strategies for battery-electric construction machinery (non-road mobile machinery, NRMM) through a case study of a large-scale housing project in The Hague, Netherlands. The study develops a methodology to estimate energy demands and simulate charging profiles during various construction phases. Using a combination of smart charging and temporary battery storage, the paper demonstrates that peak grid loads can be significantly reduced—by up to 46%—compared to conventional charging strategies. Simulations reveal that grid limitations, especially during early construction phases, can be overcome with optimized load management and supplemental battery systems. The findings highlight the importance of smart charging infrastructure and energy planning in enabling the transition to zero-emission construction practices. This research contributes to the practical implementation of electric NRMM in urban construction projects, addressing one of the key bottlenecks in decarbonizing the construction sector.
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