智慧電網中充分利用電池儲能系統儲存多餘電力，減少對傳統電網電力依賴。隨著分佈式可再生能源使用率提升並與物聯網整合而進入了能源物聯網的時代，藉由儲能設備、交易平台使能源可在家戶間傳輸與共享，因此共享能源也成了備受關注的議題。過去多數研究著重於能源互聯網系統優化或是儲能設備決策優化，鮮少結合共享經濟的概念並將其應用在互助社區家戶的能源管理中，因此本研究建立了一個混整數規劃的模型，社區中所有家具有可再生能源和能源存儲設備，並可透過能源交易平台進行交易並共享能源，透過家戶間不同的用電決策模式，目標使社區整體利潤最大化。因混整數規劃是NP-complete問題且提出的模型涉及大量變量及限制式，本論文進一部使用混合的和聲搜尋法和變動鄰域搜尋法簡稱HSVNS求解此問題。將VNS的步驟中使用多種方式來改變解的部分，改採用和聲搜尋法的考量歷史記憶、記憶中微調與隨機選擇的三種型式，而演算法中修正解的設計降低了限制式中的不可行解。實驗中同時考慮來自電力公司數據的電網電力價格及模擬產生的能源交易平台的價格，實驗顯示本研究HSVNS演算法的有效性及穩定性均優於原先的和聲演算法、變動鄰域搜尋法以及基因演算法。實驗表明家戶一天最少可獲得70美分(¢)以上的利潤，此外透過5個互助家戶的實驗可發現電力有效的分配在非峰值或電價較低的時段，整個社區在一天中可節省至少314.15kW的能源浪費並減緩峰值負荷。
Smart grids make full use of battery energy storage systems (BESSs) to store surplus energy and reduce dependence on traditional electrical grids. As the utiliza-tion rate of distributed renewable energy increases and integrates with the Internet of Things(IoT), the era of Internet of Energy(IoE) has entered. Energy can be transmit-ted and be shared among users through energy storage equipment and trading plat-forms. Therefore, sharing energy has received a lot of great attention. In the past, most related works focused on optimizing the IoE systems and the decision-making on energy storage equipment. However, few works combined the concept of sharing economy and the application of the energy management in community. Accordingly, this work establishes a mixed-integer programming (MIP) model. Each house with renewable energy and energy storage equipment can trade and share energy through the energy trading platform. Through different decision-making models among houses in the community, the goal is to maximize the total profit of the whole com-munity. Because the MIP is an NP-complete problem, and the proposed model in-volves a large number of variables and constraints, this work further solves this problem by a hybrid algorithm of harmony search (HS) and variable neighborhood search (VNS). Combine the advantages of HS in the group to find the best and VNS's individual Neighborhood search strategy to solve the problem. Change the solution part in a multiple of ways in the VNS step, and use the three types of HS memory consider, memory adjustment, and random. The design of the repair scheme reduce the infeasible solutions in the constraint. In the experiments, the electricity price of electrical grids based on the data from power company and the market price of the simulated energy trading platform are also considered. Experimental analysis shows that the effectiveness and stability of the proposed algorithm performs in our re-search superior to the original HS, VNS, and genetic algorithm. And experiments show that each house can gain a profit at least 70¢ for one day. In addition, the result displays the electricity are assigned to the periods of off-peak or low electricity pric-es in 5 houses simulation. The entire community can save energy consumption of 314.15kW for one day, and shift the peak load.
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