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- Goal Programming Approach for Energy Management of Smart BuildingPublication . Foroozandeh, Zahra; Ramos, Sérgio; Soares, João; Vale, ZitaIn this paper, a collective residential building is considered in which the following points are taken into consideration: (i) a flexibility value of Contract Power (CP) is considered for each consumer; (ii) it is assumed a single CP for the entire building; (iii) an energy resource manager entity is considered to manage the energy resources in the residential building, such as Electric Vehicles (EVs), Photovoltaic (PV) generation system, and the Battery Energy Storage System (BESS). Taking into consideration the previous assumptions, the major goal of this work is to minimize the electricity consumption costs of the residential building by using a Multi-Objective Mixed-Binary Linear Programming (MOMBLP) formulation. The objective function of the MOMBLP model minimizes the electricity cost consumption of each apartment. Then, a Goal Programming (GP) strategy is applied to find the most appropriate solutions for the proposed MOMBLP model. Finally, the performance of the suggested model is evaluated by comparing the obtained results from a Single-Objective Mixed-Binary Linear Programming (SOMBLP) approach in which the whole building consumption cost is minimized. The results show that using the GP strategy a reduction of 7.5% in the total annual energy consumption is verified in comparison with SOMBLP. Moreover, the GP approach leads to fair benefit among building consumers, by finding a solution with less distance from the desired level.
- Energy Management in Smart Building by a Multi-Objective Optimization Model and Pascoletti-Serafini Scalarization ApproachPublication . Foroozandeh, Zahra; Ramos, Sérgio Filipe Carvalho; Soares, João; Vale, ZitaGenerally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these objectives are desirable in a smart building, however, in most of the related works, just one of these mentioned goals is considered and investigated. In this work, authors aim to consider two goals via a multi-objective framework. In this regard, a multi-objective mixed-binary linear programming is presented to minimize the total energy consumption cost and peak load in collective residential buildings, considering the scheduling of the charging/discharging process for electric vehicles and battery energy storage system. Then, the Pascoletti-Serafini scalarization approach is used to obtain the Pareto front solutions of the presented multi-objective model. In the final, the performance of the proposed model is analyzed and reported by simulating the model under two different scenarios. The results show that the total consumption cost of the residential building has been reduced 35.56% and the peak load has a 45.52% reduction