Battery energy storage systems (BESS) represent a strategic advancement in the modernization of distribution networks, yet they introduce significant analytical complexities into expansion planning models.
A recently proposed method addresses this challenge by simplifying BESS modeling, thus expediting simulation processes with minimal compromise on accuracy. This innovation hinges on a price-oriented strategy for determining optimal conditions for BESS deployment across a distribution network, which is particularly beneficial when integrating distributed energy resources such as photovoltaics and wind energy.
A critical component of the model’s efficacy lies in sampling upstream electricity prices, enabling a nuanced prediction of charging and discharging schedules. This predictive capacity reduces the complexity traditionally observed in decision-making models associated with the Distribution Network Expansion Planning (DNEP) framework. When validated against the IEEE 33-bus test system, the model demonstrated a substantial reduction in computational time while maintaining valid investment decisions, a remarkable achievement considering the intricacies of traditional BESS integration methods.
The integration of BESS in distribution networks has been shown to significantly enhance reliability and efficiency by stabilizing voltage levels and providing backup during power outages. Despite these benefits, the conventional methodological approaches often struggle with the intricacies of accurately predicting BESS operation’s impact on the network. By addressing battery behavior prediction through market pricing, the new model emerges as a pragmatic solution that aligns technological advancements with economic realities.
As systems modernize, optimizing the balance between energy demand and supply becomes increasingly critical. Through incorporating DERs, particularly renewables, this method empower networks to mitigate energy transmission losses and champion sustainability. The proposed approach not only augments the flexibility of BESS within the DNEP framework but also circumvents some of the traditional challenges by employing a streamlined, price-sensitive modeling framework.
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