Novel optimisation integrates battery swapping with wind and PV systems for smarter grid planning

Distributed generation (DG) planning in modern power systems is primarily driven by the need to reduce losses, improve reliability, and enhance voltage profiles. Among DG options, wind farms have gained prominence due to their increasing economic viability. However, beyond the geographical siting of these wind farms, determining their optimal size and placement is critical to balancing profitability with operational safety. This process is complicated by the inherent variability of wind speeds, which introduces significant uncertainty in power output. Compounding these challenges, the rise in plug-in electric vehicles (PEVs) adds another layer of unpredictability to load demand, as charging behaviours vary widely depending on owner habits and are often concentrated during peak residential hours—straining the grid further. As a result, power systems must adopt more sophisticated, probabilistic approaches to planning embedded renewable generation to mitigate energy losses, minimise voltage fluctuations, and control investment costs.

A novel methodology has been proposed that simultaneously co-allocates battery swapping stations alongside wind and photovoltaic (PV) systems within radial distribution networks. This approach is distinct from previous research, which largely focused on either charging infrastructure or renewable sources in isolation. By integrating these elements using data clustering techniques—specifically the K-means algorithm combined with the elbow method to optimise clustering of price, energy demand, and renewable generation profiles—the methodology aims to optimise overall system performance. The Particle Swarm Optimization (PSO) algorithm is employed to refine the placement and sizing of this hybrid infrastructure, seeking to maximise net profits by balancing investment and loss costs against energy sales revenue. For multi-criteria evaluation, the approach utilises the TOPSIS method, measuring success across minimum voltage, profitability, energy loss, and renewable energy contribution. Simulations reveal robust validation of the approach, marking significant progress in holistic DG planning for EV load management coupled with renewables.

The importance of effective DG placement is reinforced by complementary studies exploring optimisation in distribution networks. For instance, hybrid methods that combine network restructuring with algorithms like the harmony search have demonstrated notable success in reducing real and reactive power losses while improving voltage stability, with return on investments seen within four years. Particle Swarm Optimization emerges repeatedly as a powerful tool, facilitating the optimal placement and sizing of multiple DGs, significantly lowering losses and elevating voltage profiles, especially when combined with network reconfiguration strategies. Furthermore, hybrid algorithms merging Grey Wolf Optimizer with PSO have been leveraged specifically for PV distributed generation units, optimising simultaneous objectives of loss minimisation and voltage enhancement better than conventional methods. Advances in biogeography-based, teaching-learning, and dolphin echolocation optimisation algorithms also underscore the critical role of sophisticated computational techniques in managing DG performance in complex network settings.

With the dynamic integration of renewables and increasing EV penetration, traditional deterministic power flow methods fall short under the variable and stochastic conditions posed by wind and solar energy sources. Probabilistic methods offer greater accuracy by embracing uncertainty, particularly when supported by data reduction techniques like clustering to manage large datasets efficiently. Battery swapping stations, offering rapid exchange of depleted EV batteries for charged ones, add further complexity and opportunity by potentially fostering distinct EV charging ecosystems. These stations are particularly advantageous in contexts demanding quick turnaround times, such as urban electric taxi fleets, and may alleviate grid stress by smoothing load demand patterns.

Previous operational models targeting battery swapping stations have optimised profitability and minimised grid impacts, accounting for fleet characteristics and battery leasing. Scenario-based algorithms have integrated photovoltaic resources to offset vehicular load impacts, employing multivariate stochastic modelling to capture load demand uncertainties. Queue network modelling has provided frameworks for infrastructure design of swapping stations, offering insights into customer demand management and local charging capacity. Additionally, real-time energy management strategies have been proposed within smart community microgrids using Lyapunov optimisation to streamline scheduling and enhance the economic and renewable integration benefits of swapping stations. Mathematical models employing mixed-integer linear programming factor in battery degradation and demand shifting to reduce operational costs, highlighting the ongoing evolution of battery swapping infrastructure management.

Taken together, these advancements point towards an integrated future where the optimal co-deployment of renewable energy sources, advanced optimisation algorithms, and innovative EV infrastructure can collectively enhance power system resilience, economic viability, and environmental sustainability. While challenges remain in addressing the uncertainties and complexities inherent in such systems, the convergence of probabilistic modelling, metaheuristic optimisation, and system-level integration represents a promising pathway for meeting the evolving energy demands ushered in by widespread EV adoption and renewable energy deployment.

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Source: Noah Wire Services