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Abstract:In this paper, we consider a general model and solution algorithm for the energy management of combined cooling, heating, and power micro energy grid (MEG) under the game theory framework. An innovative dynamic leader-follower game strategy is proposed in this paper to balance the interactions between MEG and user. We show that such game between MEG and user has a unique Nash equilibrium (NE), and in order to quantify the user's expenditure and dissatisfaction, we model them and adopt the fuzzy bi-objective algorithm. For more details in the proposed game model, the MEG leads the game by deciding energy sales prices and optimizing the power, cooling and heating outputs according to the user's load plan to maximize its own profit. With the prices being released by MEG, user's adjustment of energy consumption follows and is again fed to MEG. In practice, we initialize simulations with daily loads of a typical community. As the numerical results demonstrate, MEG is proficient in consumption capacity of renewable energy and energy optimization. It also shows that the user achieves his economic optimum with experience of energy usage taken into account.

The increasing complexities of multi energy-type micro energy grid (MEG) integrated with distributed renewable energy resources require more effective planning method. This paper presents an improved Kriging model for the planning of MEG to satisfy user's demands in cooling, heating, and electrical energy. First, a generic MEG model containing energy supply devices (combined cooling, heating, and power system, and energy storage systems) and energy supply networks is established. Second, the improved Kriging model combined with the Latin hypercube sampling method is proposed for searching the MEG optimal configuration to minimize the total annual cost. Third, for the sake of completeness and practicality, the sample points are updated by a novel mixed infill-sampling criterion comprised of minimum surrogatemodel point criterion, trust region criterion, and mean square error criterion. The optimal configuration and operation schemes are obtained simultaneously in the case study. Eventually, the numerical results indicate that the proposed method could efficiently solve the optimal planning problem in contradistinction to three other scenarios regarding the Kriging model. INDEX TERMS Micro energy grid, planning, Kriging model, trust region, optimal configuration.

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