In this paper,a market price-based combined heat–power dynamic dispatch model for a microgrid is presented.The microgrid comprises cogeneration units and wind and solar power-generation units.A battery and a heat sto...In this paper,a market price-based combined heat–power dynamic dispatch model for a microgrid is presented.The microgrid comprises cogeneration units and wind and solar power-generation units.A battery and a heat storage tank are incorporated to optimally balance variations in heat-and-power load demands.The proposed model explores the impact of market prices of electricity,heat supply and load variability on the optimal schedule such that profit maximizes and emission,loss and waste heat are minimized.The Weibull probability distribution function is applied to characterize the uncertain renewable power variable in the model and to find the over-and under-scheduling costs.The problem is solved using an improved differential evolution algorithm in which a fuzzy membership module is appended to obtain a solution having the highest attainment for the selected multiple objectives.The results show that the proposed model can handle uncertain heat–power demand and price scenarios to produce feasible and optimal schedules with owner profits,heat utilization and renewable share varying between 10.55–115.97%,72.51–90.39%and 26.82–38.05%,respectively.展开更多
文摘In this paper,a market price-based combined heat–power dynamic dispatch model for a microgrid is presented.The microgrid comprises cogeneration units and wind and solar power-generation units.A battery and a heat storage tank are incorporated to optimally balance variations in heat-and-power load demands.The proposed model explores the impact of market prices of electricity,heat supply and load variability on the optimal schedule such that profit maximizes and emission,loss and waste heat are minimized.The Weibull probability distribution function is applied to characterize the uncertain renewable power variable in the model and to find the over-and under-scheduling costs.The problem is solved using an improved differential evolution algorithm in which a fuzzy membership module is appended to obtain a solution having the highest attainment for the selected multiple objectives.The results show that the proposed model can handle uncertain heat–power demand and price scenarios to produce feasible and optimal schedules with owner profits,heat utilization and renewable share varying between 10.55–115.97%,72.51–90.39%and 26.82–38.05%,respectively.
