||Simulation for the Demand Response program in Smart Grid
||Institute of Computer & Communication
電力公司透過需求回應程式(Demand Response Program)告知價格及金錢獎勵的資訊給用戶端的裝置，降低尖峰時段用電，並減少電力公司在尖峰時段發電成本。其中用戶端的裝置，包括負荷控制裝置、智能恆溫器以及家庭能源控制台。
Demand Response (DR) program allows utilities to inform customers about the information through the devices from customer side. For utilities, DR program reduces the cost of generation electricity during peak time by offering the profit to customers. Such devices include load control devices, smart thermostats and home energy consoles. DR program provides pricing information and monetary incentives.
Expect to create a Win-Win situation for customers and utilities. We devise a deferred mechanism in SG (Smart Grid) which can shift demand to non-peak time. Then, we develop a simulation model to evaluate the mechanism. Finally, we use simulation experiments to discuss the performances in detail of each figure. In this thesis, with this mechanism reduces generation cost of utilities during peak time and also customers can spend less price to use electricity.
List of Figures v
List of Tables viii
Chapter 1 Introduction 1
Chapter 2 Related Works 9
Chapter 3 Demand Response program 11
3.1 What is Demand Response? 11
3.2 DR Program Model 12
3.3 DR Benefits 15
Chapter 4 Simulation Design for Demand Response program 18
4.1 Simulation Process 20
4.2 Deferred mechanism illustration 23
4.3 Counting process of calculating successful or failure proportion 27
Chapter 5 Performance Evaluation 30
5.1 Effect of variation of probability of a deferred job 30
5.1.1 Case I: set the value of the job deadline to 360 30
5.1.2 Case II: set the value of the job deadline mean to 420 35
5.2 Effect of variation of average holding time of jobs 39
5.2.1 Case I: set the value of the job deadline mean to 360 39
5.2.2 Case II: set the value of the job deadline mean to 420 45
5.3 Effect of variation of job arrival rate in peak time 50
5.3.1 Case I: set the value of the job deadline mean to 360 50
5.3.2 Case II: set the value of the job deadline mean to 420 55
5.4 Effect of variation of DR deployment time 60
5.4.1 Case I: set the value of the job deadline mean to 360 60
5.4.2 Case II: set the value of the job deadline mean to 420 64
Chapter 6 Conclusions 69
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