In the context of the current sharing economy, the application of shared energy storage (SES) among local integrated energy systems (LIESs) is underexplored. There is an urgent need for developing appropriate modeling and solution methods so as to facilitate the application of SES among LIESs. To this end, this paper proposes a cooperative-game-based day-ahead scheduling model of LIESs with SES. The proposed model can be transformed into a sequential two-step optimization problem for alleviating the computational complexity. More specifically, the subproblem of cooperative alliance profit maximization is established in the first step to obtain the optimal scheduling strategies of each LIES, while the subproblem of SES bargaining is constructed in the second step to obtain the optimal pricing information. Besides, a distributed solution algorithm based on the alternating direction method of multipliers with a warm start and dual update accelerated iteration strategies (ADMM-W-D) is proposed to effectively preserve the privacy of each LIES and improve the convergence performance of traditional ADMM. Case studies show that the proposed model can achieve lower operating costs and higher renewable energy consumption levels of LIESs after considering SES. It is also demonstrated that the proposed algorithm based on ADMM-W-D has stronger convergence and better scalability than traditional algorithms.

When power systems encounter outages and large-scale blackouts, system restoration is critical and should be carried out with dedicated schemes. In this past, most studies divided the power system restoration into three stages and deal with them separately. After that, few studies considering the three stages together were emerging while the support of multiple flexible resources, i.e., renewable energy source (RES), electric vehicle system (EVS) and energy storage system (ESS), were not considered comprehensively. Therefore, a bi-level coordinated power system restoration (BiCPSR) model is proposed in this work considering the support of multiple flexible resources. In the upper level, skeleton-network indexes and restoration characteristics of generators and loads are utilized for optimizing the start-up sequence of generators and network reconfiguration. In the lower level, the uncertainties of RES and EVS are considered by various scenarios and the support of multiple flexible resources is utilized cooperatively for accelerating the restoration process and maximizing the restorable load. Case studies on the revised IEEE-39, WECC-179 and the actual Zhejiang power systems are performed to illustrate the basic features of the proposed model and its availability in bulk power systems. The comparisons between the proposed model and other models are also performed to illustrate the strengths of the proposed model

Inspired by economies of scale, the joint planning of community-shared energy storage (CSES) among prosumers provides a new solution to the issues of high investment costs associated with independent construction of individual energy storage. However, the cooperation among large-scale prosumers is rarely investigated and there is an urgent need to formulate an appropriate strategy. Hence, a multi-step clustering and generalized Nash bargaining (GNB)-based CSES joint planning strategy for large-scale prosumers is proposed. First, a CSES planning-oriented multi-step clustering algorithm based on nonparametric kernel density estimation (NPKDE) and a modified fuzzy C-means (FCM) algorithm is proposed to reduce the input data dimensionality of the planning model. Then, taking the optimal construction time sequence into consideration, a bi-level CSES joint planning model is established to minimize the total costs of prosumers. On this basis, a contribution rate index is designed to evaluate prosumers' bargaining power, and a GNB-based allocation strategy is formulated for realizing fair cost allocation. Finally, case studies show that the proposed strategy can help large-scale prosumers effectively deal with the joint CSES planning problem and achieve lower investment and operation costs. It also demonstrates that the proposed cost allocation approach is more efficient and practical than other cooperative game-based methods.

Under the background of carbon neutrality and large‐scale penetration of renewable energy, the integrated local energy system (ILES) has been extensively recognized as an effective way to reduce carbon emissions and improve renewable energy consumption level due to its high energy utilization and multiple energy complementarity. Hence, an optimal low‐carbon scheduling model of ILES considering oxygen‐enriched combustion plant (OECP) and generalized energy storages (GESs) is proposed in this paper to reduce the carbon emissions, improve the renewable energy consumption level and reduce the operating costs of ILES. More specifically, the cooperation mechanism between OECP and power to gas (P2G) is established to maintain a high carbon capture level during peak load periods; the GESs under the background of ILES is modelled, which can make full use of the multiple energy complementarity to improve the renewable energy consumption level of ILES. Case studies are performed on an IES that consists of an IEEE 33‐bus distribution network, a 44‐node district heating network and a 20‐node natural gas network to verify the effectiveness and advantages of the proposed model.