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Well-designed multi-energy conversion systems have the potential to reduce energy consumption and carbon emissions. Based on the literature review and the typical energy flow network of multi-energy conversion systems, the necessity of stakeholders' taking part in multi-energy projects have been emphasized. Considering of different stakeholders have different interest demand and theirs' decision-making behaviors interact with each other, both the final results and decision process are important for stakeholders in multi-energy system planning. Game analysis, which is an analysis method for the conveying scheme evolution in different situations, was proposed to descript the complex relationship between stakeholders' profits and technical schemes. A hierarchical game playing scheme and a simplified multi-energy system optimization method were put forward to assist stakeholders to participate in techno-economic analysis process. Annual equivalent full capacity operation time and hourly earnings of each energy conversion technology are used to calculate returns on investment. The total revenues and every stakeholders' economic returns in different situations (energy price) and technical proposals can be acquired and compared. The proposed method is also illustrated and verified along with a case study. Results indicated that all parties can make contingent decisions under multi-partner negotiation with the assist of game analysis method.

Community energy system optimization model has great contribution to formulate community energy planning indexes. But an inappropriate response of uncertainty always makes such “optimal plan” work ended in nothing. It is still a herculean task to solve a hybrid programming model which contains stochastic and fuzzy parameters. In order to acquire more flexible and reliable energy planning indicators in a convenient way, a goal programming based model system (GPMS) is proposed to conduct dynamic variation analysis of community energy flow. GPMS contains general linear programming model, goal programming model and grey relational degree model for results analysis. General linear programming model is used to calculate optimal community energy flow on baseline situation. Deviational variables associated with each independent parameter and total fossil energy consumption (TFEC) are introduced in goal programming model. Many kinds of optimum community secondary energy flow maps can be acquired by adjusting the weight which has been given to TFEC’s deviation variables. The grey correlation degree, a measure of relevancy between two data series, is used to evaluate these optimum community energy flow results. At last, this GPMS for community energy plan is introduced, as well as a case study in Tianjin.