This paper aims to optimize total energy costs in an operational model of a novel energy hub(EH) in a residential area. The optimization problem is set up based on daily load demand(such as electricity, heat, and cool...This paper aims to optimize total energy costs in an operational model of a novel energy hub(EH) in a residential area. The optimization problem is set up based on daily load demand(such as electricity, heat, and cooling) and time-of-use(TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic(PV) generation, solar heat exchanger(SHE), and a battery energy storage system(BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price,capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational efficiency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads.展开更多
The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances.Though the dynamic pricing environment results in several benefits to the utilities and consumers,it also po...The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances.Though the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some challenges.The crowding among residential customers is one of such challenges.The scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed limits.In order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy communities.In the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority factor.The non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding phenomena.Since the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global optimum.The proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.展开更多
基金supported by National Natural Science Foundation of China(No.51377060)
文摘This paper aims to optimize total energy costs in an operational model of a novel energy hub(EH) in a residential area. The optimization problem is set up based on daily load demand(such as electricity, heat, and cooling) and time-of-use(TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic(PV) generation, solar heat exchanger(SHE), and a battery energy storage system(BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price,capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational efficiency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads.
基金supported by the Project entitled“Indo-Danish Collaboration for Data-driven Control and Optimization for a Highly Efficient Distribution Grid (ID-EDGe)”funded by Department of Science and Technology (DST),India (No.DST-1390-EED)。
文摘The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances.Though the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some challenges.The crowding among residential customers is one of such challenges.The scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed limits.In order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy communities.In the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority factor.The non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding phenomena.Since the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global optimum.The proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.
