In the quest to achieve home comfort with the highest possible efficiency, there is an increasing interest in combined cooling, heating and power (CCHP) systems. These can be fuelled by natural gas and potentially by hydrogen enriched natural gas and ultimately by hydrogen. The optimised designs largely depend on the energy dispatch algorithms that take into account aspects of economic and environmental impacts as well as system efficiency. This paper details a new algorithm to optimise the operation of a novel hybrid solid oxide fuel cell (SOFC)-solar hybrid CCHP residential system. The proposed algorithm is based on the multi-objective particle swarm optimization (MOPSO) and the grey relationship analysis (GRA) (named as MOPSO-GRA) with the capability of resolving the objective energy dispatch issues within the system by effectively avoiding local optimum problem. More specifically, the grey incidences are first integrated into MOPSO to analyse the degree of closeness between non-ideal solutions and the ideal solution. Then, the relationship of degree is introduced to give objective energy dispatch in order to maximise the efficiency of energy utilization while minimising the system capital cost, operating costs, maintenance costs, fuel costs and emissions. Finally, the new algorithm is validated on the proposed new design of CCHP system. Ten cases are studied to evaluate the technical, economic and environmental performance of the MOPSO-GRA algorithm when being applied to an SOFC-based CCHP system under both the grid-connected and the island modes. A comparison is made with the conventional MOPSO method and system structure, and the impact of a plug-in EV is also evaluated. Based on a detailed cost and emissions analysis, the results indicate the environmental and economic advantages, in addition to the higher efficiency of the proposed methodology and system structure. The impact of these results and observations leads to the promotion of intelligent FC-based multi-energy system technologies for residential use.

为了以尽可能高的效率实现家居舒适度，人们对冷热电联产 (CCHP) 系统越来越感兴趣。这些可以由天然气和可能由富氢天然气和最终由氢气提供燃料。优化设计在很大程度上取决于能源调度算法，这些算法考虑了经济和环境影响以及系统效率等方面。本文详细介绍了一种优化新型混合固体氧化物燃料电池 (SOFC)-太阳能混合 CCHP 住宅系统运行的新算法。该算法基于多目标粒子群优化（MOPSO）和灰色关联分析（GRA）（命名为MOPSO-GRA），能够有效避免局部最优问题，解决系统内的客观能量调度问题。 . 更具体地说，首先将灰色关联度整合到 MOPSO 中，以分析非理想解与理想解之间的接近程度。然后，引入度的关系，给出客观的能源调度，以最大限度地提高能源利用效率，同时最小化系统资金成本、运行成本、维护成本、燃料成本和排放。最后，新算法在提出的 CCHP 系统新设计上得到验证。研究了十个案例来评估技术，MOPSO-GRA 算法在并网模式和孤岛模式下应用于基于 SOFC 的 CCHP 系统时的经济和环境性能。与传统的 MOPSO 方法和系统结构进行了比较，并评估了插电式电动汽车的影响。基于详细的成本和排放分析，结果表明除了所提出的方法和系统结构的更高效率之外，还具有环境和经济优势。这些结果和观察结果的影响导致了基于智能 FC 的多能源系统技术在住宅中的推广。并且还评估了插电式 EV 的影响。基于详细的成本和排放分析，结果表明除了所提出的方法和系统结构的更高效率之外，还具有环境和经济优势。这些结果和观察结果的影响导致了基于智能 FC 的多能源系统技术在住宅中的推广。并且还评估了插电式 EV 的影响。基于详细的成本和排放分析，结果表明除了所提出的方法和系统结构的更高效率之外，还具有环境和经济优势。这些结果和观察结果的影响导致了基于智能 FC 的多能源系统技术在住宅中的推广。