Aiming at the problems of high energy consumption cost and insufficient demand response potential of commercial buildings cluster, a comprehensive energy management system for commercial buildings cluster considering ...
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ISBN:
(数字)9781728152813
ISBN:
(纸本)9781728152813
Aiming at the problems of high energy consumption cost and insufficient demand response potential of commercial buildings cluster, a comprehensive energy management system for commercial buildings cluster considering distributed generation, energy storage equipment and electrical load is constructed. The optimization model is established with the lowest electricity cost of building operation as the objective function. Meanwhile, through the evaluation and assumption of the demand response potential of various loads in commercial buildings, the demand-price elasticity model under the real-time electricity price mechanism is introduced, and the double-layer optimization algorithm is adopted to optimize the solution of the given model. Through the simulation of the example, the power operating costs of the commercial buildings cluster under three different cases of distributed power generation outputs, as well as whether to participate in demand response are compared. The results show that the energy management scheme and demand response strategy given by the double-layer optimization algorithm can effectively reduce the electricity costs of the commercial buildings cluster and achieve economic operation.
A double-layer optimization algorithm (DLOA) was proposed to solve the minimum time dynamic optimization problem. The first step of DLOA was to discrete time region and control region. The inner optimization is to con...
详细信息
ISBN:
(纸本)9781467313988
A double-layer optimization algorithm (DLOA) was proposed to solve the minimum time dynamic optimization problem. The first step of DLOA was to discrete time region and control region. The inner optimization is to construct optimal control problem with free final states. Differential evolution algorithm is used to find the optimal solution in given terminal time, then the optimization results was compared with the threshold set. In the outer, DLOA calculated the time range of next iteration according to the inner calculation. When applied to typical minimum time dynamic optimization problem, DLOA demonstrated a competitive optimal searching ability and more accurate optimization results. DLOA could solve the optimization problem with local optimum and applied to models without gradient information.
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