Electrical water heaters(EWHs)are important can-didates to provide demand-response services.The traditional optimization method for EWHs focuses on the optimization of the electricity consumption,without considering t...Electrical water heaters(EWHs)are important can-didates to provide demand-response services.The traditional optimization method for EWHs focuses on the optimization of the electricity consumption,without considering the shifting potential of the wateruse activities.This paper proposes an optimization method for EWHs considering the shifting potentials of both the electricity consumption and wateruse activities.Con-sidering that the wateruse activities could be monolithically shifted,the shifting model of the water-use activities was developed.In addition to the thermodynamic model of the EWH,the optimal scheduling model of the EWH was developed and solved using mixed-integer linear programming.Case studies were performed on a single EWH and aggregate EWHs,demon-strating that the proposed method can shift the water-use activities and therefore increase the load-shifting potential of the EWHs.展开更多
Wind power curtailment is of great importance with the increase of large-scale wind power connected to the grid. A new concept of redundant wind power accommodated by dispatching electric water heaters(EWHs) is develo...Wind power curtailment is of great importance with the increase of large-scale wind power connected to the grid. A new concept of redundant wind power accommodated by dispatching electric water heaters(EWHs) is developed in the paper. Precise predictions of wind power and EWHs load power are the basis for this work. A hybrid multi-kernel prediction approach integrating an adaptive fruit fly optimization algorithm(AFOA)and multi-kernel relevance vector machine(MKRVM) is proposed to deal with the sample distribution of multisource heterogeneous features uncovered by an energy entropy method, where AFOA is used to determine the kernel parameters in MKRVM adaptively and avoid the arbitrariness. For the large computation of the prediction approach, parallel computation based on the Hadoop cluster is used to accelerate the calculation. Then, an economic dispatching model for accommodating wind power is built taking into account the penalty of curtailed wind power and the operation cost of EWHs. The proposedscheme is implemented in an intelligent residential district.The results show that the optimization performance of the hybrid prediction approach is superior to those of four usual optimization algorithms in this case. Regular or orderly scheduling of EWHs enables accommodation of superfluous wind power and reduces dispatch cost.展开更多
Due to the capacity of thermal storage,electric water heater(EWH)is one of the best candidates for demand response programs.However,few attentions are given to the modeling and optimization of EWHs with thermostatical...Due to the capacity of thermal storage,electric water heater(EWH)is one of the best candidates for demand response programs.However,few attentions are given to the modeling and optimization of EWHs with thermostatically-controlled automatic water mixer(TCAWM).In this paper,differential thermodynamic model is established for EWHs with TCAWM and a piecewise linear approximation method is performed for the nonlinear thermodynamic model.The multi-objective optimization model is established by introducing an index reflecting the comfort degree of users,so that the optimal energy usage of the EWH can be obtained by mixed integer linear programming.Testing examples verify the effectiveness of the proposed method.展开更多
In this paper,interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling.First of all,interval numbers are used to de...In this paper,interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling.First of all,interval numbers are used to describe uncertain parameters including hot water demand,ambient temperature,and real-time price of electricity.Moreover,the traditional thermal dynamic model of electric water heater is transformed into an interval number model,based on which,the day-ahead load scheduling problem with uncertain parameters is formulated,and solved by interval number optimization.Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices.Furthermore,the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day.Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand,ambient temperature,and real-time price of electricity,enabling customers to flexibly adjust electric water heater control strategy.展开更多
基金supported in part by the National Natural Science Foundation of China(No.51707099).
文摘Electrical water heaters(EWHs)are important can-didates to provide demand-response services.The traditional optimization method for EWHs focuses on the optimization of the electricity consumption,without considering the shifting potential of the wateruse activities.This paper proposes an optimization method for EWHs considering the shifting potentials of both the electricity consumption and wateruse activities.Con-sidering that the wateruse activities could be monolithically shifted,the shifting model of the water-use activities was developed.In addition to the thermodynamic model of the EWH,the optimal scheduling model of the EWH was developed and solved using mixed-integer linear programming.Case studies were performed on a single EWH and aggregate EWHs,demon-strating that the proposed method can shift the water-use activities and therefore increase the load-shifting potential of the EWHs.
基金supported by National Natural Science Foundation of China (No. 51407077)Fundamental Research Funds for the Central Universities of China (No. 2017MS095)Technology Project of State Grid Corporation of China Headquarter (No. 5204BB16000F)
文摘Wind power curtailment is of great importance with the increase of large-scale wind power connected to the grid. A new concept of redundant wind power accommodated by dispatching electric water heaters(EWHs) is developed in the paper. Precise predictions of wind power and EWHs load power are the basis for this work. A hybrid multi-kernel prediction approach integrating an adaptive fruit fly optimization algorithm(AFOA)and multi-kernel relevance vector machine(MKRVM) is proposed to deal with the sample distribution of multisource heterogeneous features uncovered by an energy entropy method, where AFOA is used to determine the kernel parameters in MKRVM adaptively and avoid the arbitrariness. For the large computation of the prediction approach, parallel computation based on the Hadoop cluster is used to accelerate the calculation. Then, an economic dispatching model for accommodating wind power is built taking into account the penalty of curtailed wind power and the operation cost of EWHs. The proposedscheme is implemented in an intelligent residential district.The results show that the optimization performance of the hybrid prediction approach is superior to those of four usual optimization algorithms in this case. Regular or orderly scheduling of EWHs enables accommodation of superfluous wind power and reduces dispatch cost.
基金supported by National Natural Science Foundation of China(No.51707099)Natural Science Fund for Colleges and Universities of Jiangsu Province(No.16KJB470009)China Postdoctoral Science Foundation(No.2017M611859).
文摘Due to the capacity of thermal storage,electric water heater(EWH)is one of the best candidates for demand response programs.However,few attentions are given to the modeling and optimization of EWHs with thermostatically-controlled automatic water mixer(TCAWM).In this paper,differential thermodynamic model is established for EWHs with TCAWM and a piecewise linear approximation method is performed for the nonlinear thermodynamic model.The multi-objective optimization model is established by introducing an index reflecting the comfort degree of users,so that the optimal energy usage of the EWH can be obtained by mixed integer linear programming.Testing examples verify the effectiveness of the proposed method.
基金This work was supported by the National Natural Science Foundation of China(Grant No.51477111)the National Key Research and Development Program of China(Grant No.2016 YFB-0901102).
文摘In this paper,interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling.First of all,interval numbers are used to describe uncertain parameters including hot water demand,ambient temperature,and real-time price of electricity.Moreover,the traditional thermal dynamic model of electric water heater is transformed into an interval number model,based on which,the day-ahead load scheduling problem with uncertain parameters is formulated,and solved by interval number optimization.Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices.Furthermore,the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day.Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand,ambient temperature,and real-time price of electricity,enabling customers to flexibly adjust electric water heater control strategy.