Energy Management of Combined Cooling, Heating and Power Micro Energy Grid Based on Leader-Follower Game Theory

Kaijun Lin, Junyong Wu, Di Liu, Dezhi Li, Taorong Gong
2018 Energies  
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
more » ... 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. Generation Market", with the purpose of enhancing the multi-energy complementary community and further enlarging the energy market. Consequently, MEG is facing a two-sided challenge due to these technological innovations and government guidelines. Firstly, the increase of energy types and technological advancements has enhanced the structural complexity of MEG, making equipment control and management more complicated. Secondly, the gradual opening-up of energy market and the increase of multi-type participants cause higher variability in the relationship between MEG and users, which leads to difficulties in operating and managing. With all of the above said, the modeling and energy management of MEG are the current focus of the academic community. In the modeling aspect, the partial load performance of CCHP and the performance of ice-storage air-conditioner has been modulated, and the cooling and electricity coordinated microgrid day-ahead scheduling model is established [6] . Ref. [7] presents a highly integrated and reconfigurable microgrid testbed containing various distributed generation units and diverse energy storage systems, which can provide energy both electrically and thermally. In [8, 9] , to augment the flexibility of energy management, the authors discuss the energy hub structure (including cooling, heating, and power hubs) and the corresponding optimal dispatch strategies for multiple energy system on the whole MEG level. In [10] , based on a model predictive control approach, a novel framework of energy management system in microgrid is proposed, which yields optimal dispatch decisions of generators, energy storage system, and peak demand for controllable loads. Though laying down the cornerstones of the MEG framework, most of the above-mentioned works only paid attention to the operating costs of the microgrid, without discussing the pros of MEG pricing to the market. In the energy management aspect, the relevant researches addressing the optimization methods adopted for CCHP or microgrid are shown, as follows. An approach-EABOT (energetic analysis as a basis for robust optimization of tri-generation systems by linear programming) is proposed in [11] to perform CCHP optimization. Ref. [12] addresses the optimization problem of an Organic Rankine Cycles system, and uses a multi-objective approach to optimize the electric efficiency and overall area of the heat exchangers simultaneously. In [13], a multi-period artificial bee colony optimization algorithm is implemented for economic dispatch to combine generation, storage, and responsive load offers in microgrids. In order to improve the operational efficiency, the improved particle swarm optimization (PSO) algorithm is proposed in [14, 15] to solve the multi-period optimization problem in microgrid energy management system. Ref. [16, 17] propose an effective approach to identify the most stable modular cogeneration plant solutions through a multi-objective robust design optimization, and highlight how the optimized combined heat and power plants can be characterized by reasonable levels of energetic and economic sensitivity. A hybrid grey wolf optimizer-PSO algorithm is introduced to solve the multi-objective energy management problem in [18] . The above works use traditional multi-objective optimization algorithms for energy management optimization to obtain Pareto non-inferior solution front. Yet, usually, the non-inferior solution front produces a set multiple optimal solutions for managers to choose from. The final decisions, without doubt, are heavily influenced by the personal biases of the managers. Unlike the traditional multi-objective optimization, the game-theoretical optimization method can obtain the unique optimal strategy for rational participants. The resulting game equilibrium can serve to guide the formulation of energy price and microgrid planning and operation. In the microgrid related game optimization, Ref. [19] contemplates on a distributed energy management algorithm by taking the interactions and interconnections among utility companies, microgrids, and customers into consideration. In [20, 21] , a Stackelberg game-theoretical framework is used to study the interactions between production units and microgrids. In [22] , a data-based Stackelberg market strategy for a distribution market operator is proposed to coordinate power dispatch among different virtual power plants, e.g., the demand response aggregators. In [23, 24] , a novel game model based on the hierarchical Stackelberg game for analyzing the multiple energies trading problem is discussed in integrated energy systems. Ref. [25] proposes a novel incentive-based demand response model from the view of a grid operator to enable system-level dispatch of demand response resources. Ref. [26] proposes Energies 2018, 11, 647 3 of 21 a distributed demand-side energy management scheme in residential smart grids based on ordinal state-based potential game with various kinds of household electrical appliances. Ref. [27] designs an incentive contract menu to achieve long-term stability for electricity prices in a day-ahead electricity market, together with a bi-level Stackelberg game model to search for the optimal incentive mechanism. Nevertheless, most of the above game models with energy trading focus only on the electrical energy trading. The researches on various energy trading and the impact of user's individual motivation are not integrated enough into these researches. By taking consideration of the coordination between different decision-making entities, and with the intention of treating the user in energy market in an unbiased way, this paper introduces an analytical method based on leader-follower game theory, which is aiming to solve multi-energy trading problems between the CCHP integrated MEG and the user. The main contributions of this work are as follows:
doi:10.3390/en11030647 fatcat:vk3w4age7fhudhawbwyv2riwva