With respect to the current campus energy systems, the research on energy storage deployment has focused mostly on single users or a single metric, making it difficult to accommodate diverse multiuser needs while efficiently utilizing the available resources. This results in narrow evaluation dimensions and underutilized storage assets. To address this issue, an integrated method for multiuser energy storage, optimal sizing and leasing is proposed in this paper; the method is aimed at improving the economics and utilization of storage. First, we construct a campus energy system architecture that includes an energy storage service provider and develop a storage sizing model that minimizes the average daily total cost, yielding the optimal power ratings and capacities for different users. Second, we construct a comprehensive evaluation framework from both economic and technical perspectives and apply quantitative methods to select the best configuration scheme. On this basis, we propose a multicriteria optimization-based storage leasing mechanism that enables resource sharing among users and maximizes the revenue received by the service provider. Simulation results reveal that across five typical user scenarios, the proposed method outperforms the traditional single-configuration models: the overall storage utilization rate increases by 3.84%, the cost-reduction rates for some users exceed 16%, and the investment payback period decreases by approximately one year. Compared with configuration-only approaches, the proposed integrated configuration–leasing framework simultaneously enhances user-side economics and the profitability of the service provider. The integrated sizing and leasing method not only demonstrates solid economic and technical feasibility but is also applicable to multiuser campuses, shared storage cases, and cloud storage scenarios, providing a reference path for future multidimensional value extraction processes and commercial operations.

To address the challenges of power fluctuations caused by the integration of distributed generation (DG) and the difficulty in simultaneously managing peak-valley load regulation due to diverse user energy demands in a microgrid system, this paper presents a coordinated optimal configuration method for serving a hybrid energy storage system (HESS), which explicitly considers the differentiated requirements from both the supply-side and the demand-side. In the presented method, an improved empirical mode decomposition (EMD) method is first presented to decompose the DG power into high-frequency, medium-frequency, and low-frequency bands. Based on the complementary technical and economic characteristics of different energy storage types, a coordinated regulation strategy for HESS in the multiple time-frequency domains is developed. Second, a coordinated optimal configuration model for HESS is further established. This model integrates key performance indicators, including maximum fluctuation and renewable energy utilization rate on the supply-side and the peak-valley difference reduction rate on the demand-side. Finally, a distributed optimization algorithm based on an improved alternating direction method of multipliers (ADMM) is developed to solve the coordinated configuration model. The experimental results demonstrate that the presented method can effectively smooth the DG power fluctuations and reduce the load peak-valley difference. The renewable energy utilization rate reaches 100%, and the peak-valley difference reduction rate reaches approximately 80%. The presented method successfully achieves the coordinated optimal configuration of HESS on both the supply and demand sides, providing a theoretical underlying infrastructure for the configuration of energy storage in the microgrid system with high penetration of renewable energy.