The regional energy system (RES) is a system that consumes multiple forms of energy in the region and achieves coordinated and efficient utilization of energy resources. The RES is composed of multiple micro energy systems (MESs); however, due to the mismatch of energy resources and different energy consumption within each MES, a large amount of clean energy is wasted, and each MES has to acquire extra energy. This significantly increases operation costs and contributes to environmental pollution. One of the promising ways to solve this problem is to deploy an energy storage system in the RES, which can make use of its advantages to transfer energy in space-time and fulfill the demand for loads in different periods, and conduct unified energy management for each MES in the RES. Nevertheless, a large number of users are deterred by the high investment in energy storage devices. A shared energy storage system (SESS) can allow multi-MESs to share one energy storage system, and meet the energy storage needs of different systems, to reduce the capital investment of energy storage systems and realize efficient consumption of clean energy. Taking multiple MESs as the object, this paper proposes a model and collaborative optimal strategy of energy management for the RES to accomplish high utilization of clean energy, environmental friendliness, and economy. First, the paper analyzes the internal energy supply characteristics of the RES and develops a model of the RES with an SESS. Then, the paper poses the management concept of load integration and unified energy distribution by using the operational information of each subsystem. An optimal operation strategy is established to minimize daily operation costs and achieve economic, environmentally friendly, and efficient operation of the RES. Third, by setting up scenarios such as no energy storage system and an independent energy storage system (IESS) of each MES and SESS, a case of a science and education park in Guangzhou, China, is illustrated for experiments. Numerical experiment results show that with an SESS built by the investor in the RES and applying the mentioned energy management strategy, the utilization of clean energy can be 100%, the operation costs can be reduced by up to 9.78%, the pollutant emission can be reduced by 3.92%, and the peak-to-valley difference can be decreased by 20.03%. Finally, the influence of energy storage service fees and electricity tariffs on daily operation costs is discussed, and the operation suggestions of the SESS are proposed. It validates the effectiveness of the proposed strategy.

Under the global decarbonization trend, combined cooling, heating, and power (CCHP) systems are critical for improving regional energy efficiency. However, the integration of high-penetration variable renewable energy (RE) sources introduces significant volatility and multi-dimensional uncertainties, challenging conventional operation strategies designed for stable energy inputs. This review systematically examines recent advances in CCHP optimization under high-RE scenarios, with a focus on flexibility-enabled operation mechanisms and uncertainty-aware optimization strategies. It first analyzes the evolving architecture of variable RE-driven CCHP systems and core challenges arising from RE intermittency, demand volatility, and multi-energy coupling. Subsequently, it categorizes key flexibility resources and clarifies their roles in mitigating uncertainties. The review further elaborates on optimization methodologies tailored to high-RE contexts, along with their comparative analysis and selection criteria. Additionally, it details the formulation of optimization models, model formulation, and solution techniques. Key findings include the following: Generalized energy storage, which integrates physical and virtual storage, increases renewable energy utilization by 12–18% and reduces costs by 45%. Hybrid optimization strategies that combine robust optimization and deep reinforcement learning lower operational costs by 15–20% while strengthening system robustness against renewable energy volatility by 30–40%. Multi-energy synergy and exergy-efficient flexibility resources collectively improve system efficiency by 8–15% and reduce carbon emissions by 12–18%. Overall, this work provides a comprehensive technical pathway for enhancing the efficiency, stability, and low-carbon performance of CCHP systems in high-RE environments, supporting their scalable contribution to global decarbonization efforts.