Earticle

초록

영어

In this paper, a Price learning based Load Distribution Strategy (PLDS) is proposed at first. In PLDS model, Smart Power Service, Utility Company and History Load Curves are included, and by considering both the average electricity consumption cost and the average electricity consumption habit, we proposed a convex optimization model to solve the model. In order to accelerate the convergence of PLDS, a price learning mechanism is proposed, which learns a price curve according to the history price data, and predicts price as a learned price for the next iteration. The optimization cycle of PLDS is one day or 24 hours, and in order to further improve the peak shaving performance, an extended version of PLDS named PLRS (Price learning based Load Redistribution Strategy) is proposed, whose optimization cycle length is 1 hour. The optimization models of PLDS and PLRS are the same, and the differences between them are the optimization cycle and the constraint conditions. In the simulation, we compared the convergence performance, peaking shaving performance and total cost among PLDS, PLRS and other strategy ODC in reference [11], and we found that the convergence performances of PLDS and PLRS are both better than that of ODC. The peak shaving performance of PLRS is better than that of ODC in the long term, and the total cost of PLRS is very close to that of ODC.

목차

Abstract
 1. Introduction
 2. Load Distribution Strategy PLDS
  2.1. Model Definition
  2.2. Load Distribution Module
  2.3. Habit Load Calculating Module
  2.4. Price Learning Module
 3. Extended Load Distribution Strategy PLRS
 4. Simulation
  4.1. Data and Parameters Setting
  4.2. Convergence Performance
  4.3. Peak Shaving Performance
  4.4. Electricity Consumption Cost
 5. Conclusion and Future Work
 References

키워드

저자

• Qiang Tang [ Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, China / School of Computer and Communication Engineering Changsha University of Science and Technology, China ]
• Ming-zhong Xie [ Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, China / School of Computer and Communication Engineering Changsha University of Science and Technology, China ]
• Kun Yang [ School of Computer Science and Electronic Engineering University of Essex, Colchester, United Kingdom ]
• Yuan-sheng Luo [ Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, China / School of Computer and Communication Engineering Changsha University of Science and Technology, China ]
• Ping Li [ Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, China / School of Computer and Communication Engineering Changsha University of Science and Technology, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605