Resilience-oriented Planning and Cost Allocation of Energy Storage Integrated with Soft Open Point Based on Resilience Insurance

TL;DR

The paper tackles resilience of distribution networks under extreme events by pairing energy storage integrated with soft open points (E-SOP) with a resilience-insurance-based cost allocation. It introduces a four-layer stochastic distributionally robust optimization (SDRO) framework to jointly plan E-SOP deployment and price resilience insurance, accounting for uncertainty in extreme-event intensity and in outage/insurance uptake within a carefully defined ambiguity set. Key contributions include a resilience-insurance design that enables cost recovery for E-SOP investments, a probabilistic model for insurance purchases using expected utility theory, and a four-layer SDRO formulation solved via a CC&G algorithm. Case studies on a modified IEEE 33-bus network demonstrate that the approach achieves an IRR of $9.28\%$ and substantial load-loss reductions, confirming the method’s practical viability for improving DN resilience while sharing costs with insured customers.

Abstract

In recent years, frequent extreme events have put forward higher requirements for improving the resilience of distribution networks (DNs). Introducing energy storage integrated with soft open point (E-SOP) is one of the effective ways to improve resilience. However, the widespread application of E-SOP is limited by its high investment cost. Based on this, we propose a cost allocation framework and optimal planning method of E-SOP in resilient DN. Firstly, a cost allocation mechanism for E-SOP based on resilience insurance service is designed; the probability of power users purchasing resilience insurance service is determined based on the expected utility theory. Then, a four-layer stochastic distributionally robust optimization (SDRO) model is developed for E-SOP planning and insurance pricing strategy, where the uncertainty in the intensity of contingent extreme events is addressed by a stochastic optimization approach, while the uncertainty in the occurrence of outages and resilience insurance purchases resulting from a specific extreme event is addressed via a distributionally robust optimization approach. Finally, the effectiveness of the proposed model is verified on the modified IEEE 33-bus DN.

Figures (3)

• Figure 1: Schematic diagram of the proposed resilience insurance.
• Figure 2: Modified IEEE 33-bus system with E-SOP.
• Figure 3: Comparison of load supply with and without E-SOP.