New energy sources, such as wind and solar energy, have been widely adopted; however, their volatility and instability have become the key issues restricting their utilization. To cope with this challenge, hybrid energy storage systems, as flexible regulation schemes, are capable of balancing the supply and demand of the power system according to different timescales and power demands, and enhancing the efficiency and utilization of new energy sources. Therefore, this paper proposes an integrated energy system planning and optimization method based on hybrid energy storage. Firstly, an adaptive noise integration empirical modal decomposition method based on the optimization improvement of the grey wolf algorithm is designed for the power allocation strategy of the hybrid energy storage system; secondly, for the electric–gas system, an energy management strategy for the hybrid electric–gas energy storage system, taking into account the operating characteristics of the alkaline electrolyzer, is proposed in order to strengthen the complementary mechanism between electric energy storage and gas energy storage. Finally, a multi-objective planning and optimization model for a comprehensive energy system based on a hybrid energy storage system is constructed. The combined configuration of long-term and short-term energy equipment can flexibly adjust energy supply and storage strategies according to demand changes on different timescales, achieve optimal resource allocation, and ensure the stability, economy, and reliability of the system. This paper uses a park in Shanxi, China, as a case study to validate the effectiveness of the methodology proposed in this paper. The example shows that the configuration of the electrical–thermal hybrid energy storage system proposed in this paper leads to a significant improvement in the economy, with an increase in annual profit of CNY 3.78 million, or 22.96%. At the same time, environmental protection is significantly enhanced, and total annual carbon emissions are reduced by 7.4 tons, with a reduction of 19.23%.

Urban integrated energy systems (UIESs) are increasingly exposed to uncertainties arising from wind and photovoltaic variability, load fluctuations, and equipment failures, highlighting the need for refined reserve assessment and coordinated operation. This study develops a unified framework that jointly models renewable and load deviations together with a load-dependent failure probability model, using Monte Carlo sampling and K-means scenario reduction to obtain representative system states. A reserve-capacity-oriented optimisation model is formulated to minimise total operating cost—including thermal generation, energy-storage operation, and reserve cost—while satisfying power balance, reserve adequacy, unit operating limits, and state-of-charge constraints. Application to a UIES comprising a 1000 kW load, 800 kW photovoltaic unit, 100 kW wind turbine, five thermal power units (total capacity 1000 kW), and a 250 kW/370 kWh energy storage system shows that reserve requirements fluctuate between −100 kW (downward) and 500 kW (upward) across different scenarios, with uncertainty-driven reserves dominating and failure-related reserves remaining below 100 kW. The optimisation results indicate coordinated operation between thermal units and storage, with storage absorbing surplus renewable output, supporting peak shaving, and providing most upward and all downward reserves. The total operating costs under typical summer and winter scenarios are 2264.02 CNY and 3122.89 CNY, respectively, confirming the method’s ability to improve reserve estimation accuracy and support economical and reliable UIES operation under uncertainty.