In the near future, various types of low-carbon technologies(LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the ex...In the near future, various types of low-carbon technologies(LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage(LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods,where social influence is imposed. Real-life data from an LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood.This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.展开更多
Accurate topological information is crucial in supporting the coordinated operational requirements of source-load-storage in low-voltage distribution networks.Comprehensive coverage of smart meters provides a database...Accurate topological information is crucial in supporting the coordinated operational requirements of source-load-storage in low-voltage distribution networks.Comprehensive coverage of smart meters provides a database for low-voltage topology identification(LVTI).However,because of electricity theft,power line commu-nication crosstalk,and interruption of communication,the measurement data may be distorted.This can seriously affect the performance of LVTI methods.Thus,this paper defines hidden errors and proposes an LVTI method based on layer-by-layer stepwise regression.In the first step,a multi-linear regression model is developed for consumer-branch connectivity identification based on the energy conservation principle.In the second step,a significance factor based on the t-test is proposed to modify the identification results by considering the hidden errors.In the third step,the regression model and significance threshold parameters are iteratively updated layer by layer to improve the recall rate of the final identification results.Finally,simulations of a test system with 63 users are carried out,and the practical application results show that the proposed method can guarantee over 90%precision under the influence of hidden errors.展开更多
基金supported by Scottish and Southern Electricity Networks through the New Thames Valley Vision Project (SSET203 New Thames Valley Vision)funded by the Low Carbon Network Fund established by Ofgem
文摘In the near future, various types of low-carbon technologies(LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage(LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods,where social influence is imposed. Real-life data from an LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood.This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.
基金supported by the National Natural Sci-ence Foundation of China(No.52177085)Science and Technology Planning Project of Guangzhou(No.202102021208).
文摘Accurate topological information is crucial in supporting the coordinated operational requirements of source-load-storage in low-voltage distribution networks.Comprehensive coverage of smart meters provides a database for low-voltage topology identification(LVTI).However,because of electricity theft,power line commu-nication crosstalk,and interruption of communication,the measurement data may be distorted.This can seriously affect the performance of LVTI methods.Thus,this paper defines hidden errors and proposes an LVTI method based on layer-by-layer stepwise regression.In the first step,a multi-linear regression model is developed for consumer-branch connectivity identification based on the energy conservation principle.In the second step,a significance factor based on the t-test is proposed to modify the identification results by considering the hidden errors.In the third step,the regression model and significance threshold parameters are iteratively updated layer by layer to improve the recall rate of the final identification results.Finally,simulations of a test system with 63 users are carried out,and the practical application results show that the proposed method can guarantee over 90%precision under the influence of hidden errors.
