The work presents a demand side response（DSR） model,which assists electricity consumers to proactively mitigate peak demand on electrical networks in Eastern and Southern Australia. A low-cost technical arrangement,...The work presents a demand side response（DSR） model,which assists electricity consumers to proactively mitigate peak demand on electrical networks in Eastern and Southern Australia. A low-cost technical arrangement,which is made of Internet relay,a router,solid state switches,and the suitable software,is used to control electricity demand at user＇s premises. The model allows shifting loads from peak to off-peak periods in order to reduce peaks,which helps to moderate the national electrical demand. The model can be concurrently used to accommodate the utilization of renewable energy sources and the introduction of electric vehicles. The results present possible savings on the electrical energy consumption in the designated regions.展开更多
Renewable energy,such as wind and photovoltaic(PV),produces intermittent and variable power output.When superimposed on the load curve,it transforms the load curve into a‘load belt’,i.e.a range.Furthermore,the large...Renewable energy,such as wind and photovoltaic(PV),produces intermittent and variable power output.When superimposed on the load curve,it transforms the load curve into a‘load belt’,i.e.a range.Furthermore,the large scale development of electric vehicle(EV)will also have a significant impact on power grid in general and load characteristics in particular.This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs.The probabilistic model of wind and PV power outputs is developed.Based on the probabilistic model,the method of assessing the peak-valley difference of the stochastic load curve is put forward,and a two-stage peak-valley price model is built for controlled EV charging.On this basis,an optimization model is built,in which genetic algorithms are used to determine the start and end time of the valley price,as well as the peak-valley price.Finally,the effectiveness and rationality of the method are proved by the calculation result of the example.展开更多
In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(D...In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(DSR).Due to high volatility and uncertainty of residential load,it is significantly challenging to forecast it precisely.Thus,this paper presents a short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering,which is capable of effectively extracting the similarity of residential load and performing residential load forecasting accurately at the individual level.It first makes full use of k-means clustering to extract similarity among residential load and then employs deep learning to extract complicated patterns of residential load.The presented method is tested and validated on a real-life Irish residential load dataset,and the experimental results suggest that it can achieve a much higher prediction accuracy,in comparison with a published benchmark method.展开更多
Deep exploration of user-side flexibility resources is crucial for large-scale renewable energy consumption.This paper proposed a typical integrated energy system(IES)that comprehensively includes wind power,photovolt...Deep exploration of user-side flexibility resources is crucial for large-scale renewable energy consumption.This paper proposed a typical integrated energy system(IES)that comprehensively includes wind power,photovoltaic,thermal power,combined heat and power,hybrid energy storage,and flexible load and constructed the system’s unified power flow model based on the heat current method.On this basis,the regulation capabilities of different typical industrial and residential flexible loads were considered the symmetrical source-type load,which can transfer load and align user demand with the peaks and valleys of renewable energy generation,thus achieving power-energy decoupling.This contributes effectively to renewable energy accommodation capacity when the total electrical energy consumption remains constant.In both typical industrial and residential load scenarios,flexible load reduces integrated costs,increases renewable energy consumption,lowers peak thermal power generation,and decreases the requirement for a battery energy storage system(BESS).Besides,on typical industrial and residential load days,smoothing thermal power generation necessitates 12%and 18%flexible load,respectively,while replacing BESS requires 18%and 23%flexible load,respectively.Therefore,we can obtain the feasible operation ranges of symmetrical source-type load and provide suggestions for configuration capacity design of demand response in integrated energy systems.展开更多
基金supported by Directorate General of Higher Education Department of National Education, the Indonesian Government and the State Polytechnic of Ujung Pandang, Makassar, Indonesia, and the Australia Power Institute (API)
文摘The work presents a demand side response（DSR） model,which assists electricity consumers to proactively mitigate peak demand on electrical networks in Eastern and Southern Australia. A low-cost technical arrangement,which is made of Internet relay,a router,solid state switches,and the suitable software,is used to control electricity demand at user＇s premises. The model allows shifting loads from peak to off-peak periods in order to reduce peaks,which helps to moderate the national electrical demand. The model can be concurrently used to accommodate the utilization of renewable energy sources and the introduction of electric vehicles. The results present possible savings on the electrical energy consumption in the designated regions.
基金This work is supported by National Natural Science Foundation of China(No.51477116)the Special Founding for"Thousands Plan"of State Grid Corporation of China(No.XT71-12-028).
文摘Renewable energy,such as wind and photovoltaic(PV),produces intermittent and variable power output.When superimposed on the load curve,it transforms the load curve into a‘load belt’,i.e.a range.Furthermore,the large scale development of electric vehicle(EV)will also have a significant impact on power grid in general and load characteristics in particular.This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs.The probabilistic model of wind and PV power outputs is developed.Based on the probabilistic model,the method of assessing the peak-valley difference of the stochastic load curve is put forward,and a two-stage peak-valley price model is built for controlled EV charging.On this basis,an optimization model is built,in which genetic algorithms are used to determine the start and end time of the valley price,as well as the peak-valley price.Finally,the effectiveness and rationality of the method are proved by the calculation result of the example.
基金supported by the Science and Technology Program of State Grid Corporation of China(Data Mining Technology of Potential High-Value Industrial Users for Data Operations,No.5700-202055267A-0-0-00)。
文摘In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(DSR).Due to high volatility and uncertainty of residential load,it is significantly challenging to forecast it precisely.Thus,this paper presents a short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering,which is capable of effectively extracting the similarity of residential load and performing residential load forecasting accurately at the individual level.It first makes full use of k-means clustering to extract similarity among residential load and then employs deep learning to extract complicated patterns of residential load.The presented method is tested and validated on a real-life Irish residential load dataset,and the experimental results suggest that it can achieve a much higher prediction accuracy,in comparison with a published benchmark method.
基金the National Natural Science Foundation of China(Grant No.52176068)the National key research and development program Intergovernmental projects(2022YFE0129400).
文摘Deep exploration of user-side flexibility resources is crucial for large-scale renewable energy consumption.This paper proposed a typical integrated energy system(IES)that comprehensively includes wind power,photovoltaic,thermal power,combined heat and power,hybrid energy storage,and flexible load and constructed the system’s unified power flow model based on the heat current method.On this basis,the regulation capabilities of different typical industrial and residential flexible loads were considered the symmetrical source-type load,which can transfer load and align user demand with the peaks and valleys of renewable energy generation,thus achieving power-energy decoupling.This contributes effectively to renewable energy accommodation capacity when the total electrical energy consumption remains constant.In both typical industrial and residential load scenarios,flexible load reduces integrated costs,increases renewable energy consumption,lowers peak thermal power generation,and decreases the requirement for a battery energy storage system(BESS).Besides,on typical industrial and residential load days,smoothing thermal power generation necessitates 12%and 18%flexible load,respectively,while replacing BESS requires 18%and 23%flexible load,respectively.Therefore,we can obtain the feasible operation ranges of symmetrical source-type load and provide suggestions for configuration capacity design of demand response in integrated energy systems.
