Load shedding is a major problem in Central Africa, with negative consequences for both society and the economy. However, load profile analysis can help to alleviate this problem by providing valuable information abou...Load shedding is a major problem in Central Africa, with negative consequences for both society and the economy. However, load profile analysis can help to alleviate this problem by providing valuable information about consumer demand. This information can be used by power utilities to forecast and reduce power cuts effectively. In this study, the direct method was used to create load profiles for residential feeders in Kinshasa. The results showed that load shedding on weekends results in significant financial losses and changes in people’s behavior. In November 2022 alone, load shedding was responsible for $ 23,4 08,984 and $ 2 80,9 07,808 for all year in losses. The study also found that the SAIDI index for the southern direction of the Kinshasa distribution network was 122.49 hours per feeder, on average. This means that each feeder experienced an average of 5 days of load shedding in November 2022. The SAIFI index was 20 interruptions per feeder, on average, and the CAIDI index was 6 hours, on average, before power was restored. This study also proposes ten strategies for the reduction of load shedding in the Kinshasa and central Africa power distribution network and for the improvement of its reliability, namely: Improved load forecasting, Improvement of the grid infrastructure, Scheduling of load shedding, Demand management programs, Energy efficiency initiatives, Distributed Generation, Automation and Monitoring of the Grid, Education and engagement of the consumer, Policy and regulatory assistance, and Updated load profile analysis.展开更多
With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising te...With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies.However,effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders.This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring approach.For the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is presented.This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario.In the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes.Based on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification.Moreover,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.展开更多
【目的】针对直流故障后并网点电压大幅度骤升致使风电场面临严重的高电压穿越(high voltage ride-through,HVRT)问题,提出计及保护动作时间协调配合的“有功优先平衡-无功动态补偿”恢复机制。【方法】基于双馈风机的双闭环控制函数,...【目的】针对直流故障后并网点电压大幅度骤升致使风电场面临严重的高电压穿越(high voltage ride-through,HVRT)问题,提出计及保护动作时间协调配合的“有功优先平衡-无功动态补偿”恢复机制。【方法】基于双馈风机的双闭环控制函数,揭示了转子侧变流器无功响应速度优于网侧变流器的动态特性,在减载模式下,提出考虑有功无功协调恢复的风电场HVRT策略。【结果】通过平衡有功功率与无功功率动态补偿,实现了故障期间电压稳定与能量平衡的双重目标。仿真结果表明,该策略可有效抑制暂态过电压风险,提升风电场HVRT能力。【结论】所提策略通过有功-无功协同调控机制,有效破解了高电压穿越过程中系统功率失衡与设备安全运行的矛盾,为含大规模风电的电力系统暂态电压稳定控制提供了新思路。展开更多
文摘Load shedding is a major problem in Central Africa, with negative consequences for both society and the economy. However, load profile analysis can help to alleviate this problem by providing valuable information about consumer demand. This information can be used by power utilities to forecast and reduce power cuts effectively. In this study, the direct method was used to create load profiles for residential feeders in Kinshasa. The results showed that load shedding on weekends results in significant financial losses and changes in people’s behavior. In November 2022 alone, load shedding was responsible for $ 23,4 08,984 and $ 2 80,9 07,808 for all year in losses. The study also found that the SAIDI index for the southern direction of the Kinshasa distribution network was 122.49 hours per feeder, on average. This means that each feeder experienced an average of 5 days of load shedding in November 2022. The SAIFI index was 20 interruptions per feeder, on average, and the CAIDI index was 6 hours, on average, before power was restored. This study also proposes ten strategies for the reduction of load shedding in the Kinshasa and central Africa power distribution network and for the improvement of its reliability, namely: Improved load forecasting, Improvement of the grid infrastructure, Scheduling of load shedding, Demand management programs, Energy efficiency initiatives, Distributed Generation, Automation and Monitoring of the Grid, Education and engagement of the consumer, Policy and regulatory assistance, and Updated load profile analysis.
文摘With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies.However,effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders.This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring approach.For the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is presented.This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario.In the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes.Based on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification.Moreover,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.
文摘【目的】针对直流故障后并网点电压大幅度骤升致使风电场面临严重的高电压穿越(high voltage ride-through,HVRT)问题,提出计及保护动作时间协调配合的“有功优先平衡-无功动态补偿”恢复机制。【方法】基于双馈风机的双闭环控制函数,揭示了转子侧变流器无功响应速度优于网侧变流器的动态特性,在减载模式下,提出考虑有功无功协调恢复的风电场HVRT策略。【结果】通过平衡有功功率与无功功率动态补偿,实现了故障期间电压稳定与能量平衡的双重目标。仿真结果表明,该策略可有效抑制暂态过电压风险,提升风电场HVRT能力。【结论】所提策略通过有功-无功协同调控机制,有效破解了高电压穿越过程中系统功率失衡与设备安全运行的矛盾,为含大规模风电的电力系统暂态电压稳定控制提供了新思路。
