Abstract:

Under the vision of high-proportion renewable energy development, in order to effectively shorten the energy storage payback period, enhance renewable energy accommodation, and reduce distribution network carbon emissions, this paper proposes a multi-objective bilevel planning model for energy storage systems (ESSs) in distribution networks that considers refined charging/discharging strategies and carbon benefits. Firstly, typical photovoltaic scenarios are generated using an improved Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and the K-medoids clustering algorithm. Secondly, a refined charging/discharging model for ESS is established, and a carbon benefit model is constructed based on both the carbon emission reduction enabled by ESS and its lifecycle carbon emissions. And then, a bilevel ESS planning and operation model for distribution network is constructed considering the refined charging/discharging strategies and carbon benefits. The upper-level model aims to minimize the total daily cost for optimal energy storage configuration, while the lower-level model pursues the minimization of operational costs and voltage deviation, as well as the maximization of carbon benefits from energy storage, to achieve optimized distribution network operation. Subsequently, the bilevel model is transformed into a single-layer multi-objective model by modeling the inter-layer coupling variables. The multi-objective model is then solved using the normalized normal constraint (NNC) method, and the optimal compromise solution is selected via the entropy-weighted TOPSIS method. Finally, the effectiveness of the proposed model is verified through numerical case studies based on the IEEE 33-node system.

Key words: photovoltaic-storage, carbon benefits, improved generative adversarial networks, refined charging and discharging, peak shaving and valley filling auxiliary services, distribution network