关键词: 多元储能, 综合能源规划, 电能替代, 能源优化, 改进粒子群算法

Abstract: The proposal of the “30·60” dual carbon target and construction of a new power system with new energy as the main body has promoted the rapid development of renewable energy in China. However, the grid connected regulation of renewable energy, mainly based on wind and light, remains the main limit factor for its development, which has had a significant impact on winter heating in the “Three North” regions of China. Configuring energy storage batteries can to some extent alleviate the problem of grid connected consumption of new energy. Electric energy storage equipment has a fast response speed and can effectively cope with power fluctuations generated by renewable energy output, which to some extent solves the problem of renewable energy output fluctuations. But its high cost increases the cost of energy consumption. The unit cost of thermal storage equipment is low, which can better absorb electricity and respond to peak and valley electricity prices, reducing energy usage costs. Coupling electric energy storage with thermal energy storage can fully utilize the advantages of electric and thermal energy storage to achieve optimal system configuration. With the continuous promotion of the “30·60” dual carbon target, solving the problem of renewable energy consumption from the perspective of comprehensive energy systems has become a hot research topic. The integrated energy system couples different types of energy together to achieve collaborative and comprehensive utilization of different types of energy, thereby reducing the impact of new energy grid connection on the power grid.
In view of this, in response to the problem with a high cost of traditional electrochemical energy storage leading to difficulties in the consumption of renewable energy in China, this article proposes an optimization planning method for comprehensive energy systems from the perspective of integrated energy systems, considering the synergy between electricity and thermal energy storage. Firstly, the physical characteristics of lead-acid batteries and thermal storage tanks are analyzed, and their state characterization models are established. Then, a collaborative operation strategy is designed for the integration of the electric thermal energy storage system to suppress fluctuations in renewable energy output, ensure supply-demand balance, and peak valley arbitrage. A planning and optimization strategy considering electric thermal synergy is established from the perspectives of the power subsystem and thermal subsystem. Based on optimization theory, with the minimum annual total cost as the optimization objective, combined with energy balance constraints, equipment output constraints, planning constraints, investment constraints, etc., a comprehensive energy system planning and optimization model considering electricity thermal storage synergy is proposed, and the model is solved using an improved particle swarm optimization algorithm.
In order to verify the effectiveness of the model, the article conducts simulation research using actual data from a commercial complex in Northeast China as an example. The calculation sets two simulation scenarios: “only configuring lead-acid batteries” and “configuring lead-acid batteries and thermal storage equipment”. The calculation results show that after configuring the thermal energy storage device, the annual maintenance cost is reduced by about 8.54%, the annual energy purchase cost is reduced by about 11.5%, and the total annual cost is reduced by about 8.32%. The daily consumption of renewable energy has increased from 1164.38 kW to 1693.52 kW. Compared to genetic algorithms, simulated annealing algorithms, and other algorithms, improved particle swarm optimization algorithms are less prone to getting stuck in local optima and have high accuracy in solving problems. The comprehensive energy system planning and optimization model proposed in this article considering the synergy of electricity and thermal energy storage can increase the installed capacity of renewable energy, effectively improve the proportion of renewable energy consumption in the energy system, and reduce the impact of distributed energy systems on the power grid under the premise of economic optimization.
In addition, the article also analyzes the impact of energy storage costs and energy prices on the optimization results. The results show that as the cost of energy storage continues to decrease, the installed capacity of renewable energy will also continue to increase. The energy price increases or decreases in the same direction as the annual total cost.

Key words: multiple energy storage, integrated energy planning, electric energy substitution, energy optimization, improved particle swarm optimization