Outline

  • Abstract
  • Highlights
  • Keywords
  • 1. Introduction
  • 2. Demand Response and Types
  • 2.1. Incentive-Based Programs
  • 2.2. Time-Based Programs
  • 3. Modeling of Demand Response – Price Elasticity Matrix
  • 4. Consumer Rationality Assumptions
  • 4.1. Long Range (lr) or Optimizing Consumers
  • 4.2. Real World (rw)-Postponing Consumer
  • 4.3. Real World-Advancing Consumers
  • 4.4. Real World-Mixed Consumers
  • 4.5. Short Range (sr) Consumers
  • 5. Test System
  • 6. Results
  • 6.1. Voltage Analysis of Dr Integrated Test System
  • 6.2. Loss Analysis of Dr Integrated Test System
  • 7. Conclusions
  • Acknowledgment
  • References

رئوس مطالب

  • چکیده
  • 1. مقدمه
  • 2. پاسخگویی بار و انواع آن
  • 2.1 : برنامه های مبتنی بر تشویق
  • 2.2 : برنامه های مبتنی بر زمان
  • 3 - مدل سازی پاسخگویی بار- ماتریس الاستیسیته قیمت
  • جدول 1 روز ساعتی در امتداد ضرایب خود کشش RTP
  • 4. مفروضات عقلانیت مصرف کننده
  • 4.1 : مصرف کنندگان بلند مدت (LR) یا بهینه ساز
  • 4.2 : مصرف کننده تعویقی- جهان واقعی (RW)
  • 4.3 : مصرف کنندگان پیشرونده جهان واقعی
  • 4.4 : مصرف کنندگان جهان واقعی
  • 4.5 : مصرف کنندگان کوتاه مدت (SR)
  • 5. سیستم آزمایشی
  • شکل 1. بخش PEM مصرف کننده LR
  • شکل 2. قسمتی از PEM مصرف كننده RW-delaying
  • شکل 3. بخش PEM مصرف کننده RW-مخلوط
  • شکل 4. فیدر تست گره IEEE 8500 نشان دهنده بخش با بار DR
  • 6 - نتایج
  • 6.1 : تجزیه و تحلیل ولتاژ سیستم آزمایشی یکپارچه DR
  • 6.2 : تجزیه و تحلیل سیستم آزمایشی یکپارچه DR
  • جدول 2 تلفات بیست و چهار ساعت برای فیدر و بخش تجزیه و تحلیل شده برای سناریوهای مختلف
  • 7 - نتیجه گیری

Abstract

This paper develops a model for Demand Response (DR) by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These price elasticity matrices (PEMs) are utilized to calculate the level of Demand Response for a given consumer considering a day-ahead real time pricing scenario. DR models are applied to the IEEE 8500-node test feeder which is a real world large radial distribution network. A comprehensive analysis has been performed on the effects of demand reduction and redistribution on system voltages and losses. Results show that considerable DR can boost in system voltage due for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).

Highlights ► We model electricity consumption patterns of residential consumers using price elasticity matrices. ► Demand Response model is developed for different consumer types from price elasticity matrices. ► Demand Response is integrated into 24 h time series distribution power flow. ► Demand Response boosts system voltage during peak pricing hours providing room for further Volt/Var control. ► Considerable loss reduction resulted during peak hours due to Demand Response

Keywords: - - - - -

Conclusions

This paper has developed a model for residential Demand Response by developing price elasticity matrices for different types of consumers. Comprehensive price elasticity matrices have been developed for each consumer type based on their rationality assumptions. Further, the impact of Demand Response on system voltage and losses has been evaluated on a large IEEE test feeder. Results indicate that DR impacts the distribution network in 3 positive ways:

  1. Voltage profile improvement
  2. Loss minimization
  3. Valley filling

Voltage analysis results indicate that DR has a great potential to boost the distribution system voltage at most of the critical nodes. Until recently, DR was only viewed as a means of curtailing demand side load during peak hours. However, with advancement in smart grid technologies and advanced metering infrastructure, there is an excellent scope for integrating DR with demand side Volt/Var control. This coordination can yield huge profits to utilities and consumers if applied appropriately during peak hours.

Maximum benefits of peak demand curtailment can be achieved through integration of DR with Volt/Var control algorithm. Future work will be directed towards implementing a Volt/Var control algorithm that utilizes DR model and distribution operation model in real time to demonstrate their combined effect on peak load shaving.

دانلود ترجمه تخصصی این مقاله دانلود رایگان فایل pdf انگلیسی