Optimal Engagement of Residential Battery Storage to Alleviate Grid Upgrades Caused by EVs and Solar Systems
🤖 AI Summary
This study addresses the heightened voltage fluctuations and power losses in distribution networks caused by high penetration of distributed photovoltaic (PV) generation and electric truck charging, which threaten to necessitate costly grid upgrades. To mitigate these challenges, the authors propose a dynamically adjustable dispatchable proportion (PP) mechanism for residential energy storage systems. Leveraging a particle swarm optimization (PSO) algorithm, the method optimizes storage charge–discharge schedules within an IEEE 33-node test system by jointly considering real-time PV output and anticipated electric truck charging demand. Simulation results demonstrate that the proposed approach effectively reduces voltage deviations and network losses, substantially enhancing the grid’s hosting capacity. Furthermore, it provides quantitative insights to support coordinated planning between PV deployments with and without co-located storage.
Application Category
📝 Abstract
The integration of distributed energy resources has ushered in a host of complex challenges, significantly impacting power quality in distribution networks. This work studies these challenges, exploring issues such as voltage fluctuations and escalating power losses caused by the integration of solar systems and electric vehicle (EV) chargers. We present a robust methodology focused on mitigating voltage deviations and power losses, emphasizing the allocation of a Permitted Percentage (PP) of battery-based solar systems within residential areas endowed with storage capabilities. A key facet of this research lies in its adaptability to the changing landscape of electric transportation. With the rapid increase of electric trucks on the horizon, our proposed model gains relevance. By tactically deploying PP to oversee the charging and discharging of batteries within residential solar systems, utilities are poised not only to assist with grid resilience but also to cater to the upcoming demands spurred by the advent of new EVs, notably trucks. To validate the efficacy of our proposed model, rigorous simulations were conducted using the IEEE 33-bus distribution network as a designed testbed. Leveraging advanced Particle Swarm Optimization techniques, we have deciphered the optimal charging and discharging commands issued by utilities to energy storage systems. The outcomes of these simulations help us understand the transformative potential of various PP allocations, shedding light on the balance between non-battery-based and battery-based solar residences. This research underscores the need for carefully crafted approaches in navigating the complexities of modern grid dynamics amid the anticipated increase in electric vehicles.
Problem
Research questions and friction points this paper is trying to address.
voltage fluctuations
power losses
grid upgrades
distributed energy resources
electric vehicles
Innovation
Methods, ideas, or system contributions that make the work stand out.
Permitted Percentage
residential battery storage
voltage regulation
Particle Swarm Optimization
distribution network resilience
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