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One-day ahead predictive management of building hybrid power system improving energy cost and batteries lifetime

机译:建筑物混合动力系统的提前一天预测管理,可降低能源成本和电池寿命

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In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.
机译:近年来,建筑物设施消耗的能量变得可观。高效的本地能源管理对于应对建筑用电需求的处罚至关重要。当混合动力系统包括在电力系统中时,该操作变得复杂。这项研究提出了通过控制PV / BESS逆变器在光伏(PV)系统,电池储能系统(BESS)和建筑物中的电网之间进行新的能源管理。该策略基于显式模型预测控制(MPC),可以为建筑物提前一天寻找最佳功率流。该控制算法基于简单的潮流方程和天气预报。然后,使用基于遗传算法的求解器来制定和优化成本函数。目的是减少从电网导入的能量,防止BESS的饱和和空化。包括能源价格动态和BESS降级在内的其他控制策略目标,MPC可以极大地优化全球建筑电力系统的效率。该策略已使用MATLAB / SimPowerSystems软件成功实施和测试,与传统的磁滞管理相比,MPC的能源成本经济性提高了10%,BESS寿命提高了25%。

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