臺灣博碩士論文加值系統

傳統電網中，發電廠端至客戶端的電力供應通常為單向傳輸，稱為集中式電力系統。而集中式電力傳輸系統有個嚴重的缺點，即無法應付電廠發生緊急狀況時，對於用戶端造成不穩定供電時所帶來之缺電影響，故智慧電網已被提出做為上述問題的可行解決方案，並降低此問題所帶來的衝擊。在智慧電網中，電力公司與微電網之間的發(用)電資訊可以彼此分享流通，藉此提高用戶端的用電穩定度並可同時降低發電廠發電的成本。一般而言，智慧電網中包含許多個微電網，各微電網中包含著許多不同類型的小型可再生能源發電機組與用電負載。可再生能源發電機組包含太陽能發電機組、風力發電機組與燃料電池等。而用電負載可區分為住宅區、商業區與工業區等不同形式。然而可再生能源發電機組多受天氣因素的影響而存在著供電穩定性的隱憂，造成電力資源的浪費進而影響供電的品質。因此，能源儲存系統(Energy Storage Systems, ESS) 扮演著解決可再生發電機組供電品質不穩定的重要角色。然而，智慧電網設計始終尚未成熟有兩個重要的原因：(1) 電力基礎設施間缺乏完善的通訊與適應變化的能力 (2) 難以有效地掌握電力市場上的經濟議題。因此，本論文基於服務導向架構(Service-oriented Architecture)之可擴充性的概念，提出智慧電網即服務(Smart Grid as a Service, SGaaS)的新穎架構，此架構不但可以結合傳統電網的供電模式，也同時允許使用者根據不同的用電需求來提出特定的電力服務申請。基於本論文所提出SGaaS之架構，本文進一步探討智慧電網中三個非常重要的研究議題，即如何利用ESS排程來降低用電成本、如何優化電力的交易成本與如何確保各微電網間電力交易的公平性。首先，本文針對微電網中ESS排程提出基於模型預測控制(Model-Predictive Control, MPC)的排程方法，透過準確的用電預測可有效地選擇對於ESS最佳的充放電時機與充放電量。其次，本文針對智慧電網中的先進配電管理系統(Advanced Distribution Management System, ADMS)提出基於模型預測最佳化(Model-Predictive Optimization, MPO)的配電優化策略。透過自回歸整合移動平均模型(Autoregressive Integrated Moving Average Model, ARIMA)預測未來的電力情況，可以協助用電方制定未來在電力市場中的交易策略，進而透過粒子群優化演算法(Particle Swarm Optimization, PSO)來找到市場中最佳的交易組合。最後，為了確保微電網在電力交易中的公平性，本文提出一套公平電力資源分配(Fair Energy Resource Allocation, FERA)的交易機制，此方法不但能確保微電網在長期交易的公平性，同時也能減少整體電力交易的成本。

實驗結果中充分顯示本文所提出的三個方法的優勢。與傳統的ESS排程策略比較，本文所提出之基於MPC之ESS排程可節省高達3.4%的用電成本。而基於MPO的配電優化策略搭配基於MPC之ESS排程方法，並利用ARIMA方法預測未來四個時間點的用電量時，可節省高達19.38%的整體用電成本。運用本文所提出之FERA方法於30個微電網間，在最差情況下至少能維持96.22%的交易公平性，然而當前最常被運用於公平電力分配的輪替式(Round Robin-based)方法與優先權式(Priority-based)方法則分別只有57.8%與11.29%的交易公平性。此外，在長期交易中(一萬次交易)，若運用FERA方法與上述兩方法相比則可提供高達51.48%節省成本的機會，然而輪替式與優先權式方法分別僅有14.07%與34.44%。

Power lines in traditional electrical power grids are generally one-way from power plants to consumers, thus resulting in a centralized power system. A fatal problem in such centralized power systems is when some accidents occur, a large number of consumers are affected. Smart grid has been proposed as a feasible solution to mitigate the effects of this problem, it introduces a two-way communication, where electricity and information can be exchanged between the utility company and micro-grids. Given a smart grid with multiple micro-grids, where each micro grid contains different types of small scale generators and power loads, this Dissertation tries to propose a novel architecture for smart grids. Generators include renewable energy resources such as solar cells, wind turbines, and fuel cells. Loads could be residential, industrial, and/or commercial. In general, there exists intermittency of distributed renewable generators caused by unstable weather conditions resulting in reduced power quality. Energy storage systems (ESS) are a viable means to address this issue. Smart grid design is still far from being mature because of two reasons: (1) the lack of a basic infrastructure for communication and adaptation, and (2) effective ways to handle market economy issues. In this Dissertation, we try to leverage on the popular service-oriented architecture (SOA) for constructing smart grid infrastructure, because of its high reliability, flexibility, and scalability. A Smart Grid as a Service (SGaaS) architecture is proposed, which not only allows a smart grid to be composed of basic services, but also allows power users to choose between different services based on their own requirements.

Based on the proposed SGaaS architecture, we further discuss three important design issues in smart grids, namely, ESS scheduling so as to reduce cost, optimization of electricity trading cost in a smart grid, and guarantee of fairness in trading among micro-grids. First, we propose a Model-Predictive Control (MPC)-based scheduling method for ESS in a micro-grid. By using a high accuracy load prediction model, we can effectively choose the time and the amount of energy to charge/discharge ESS. Second, we propose a Model Predictive Optimization (MPO) method for advanced distribution management system (ADMS) in smart grids. We can predict future electricity situations based on the Autoregressive Integrated Moving Average (ARIMA) model. The predicted electricity surplus and deficit helps users to determine the bidding attitude for their bidding strategy. Optimal trading pairs for saving trading cost are found through a Particle Swarm Optimization (PSO)-based method. Third, in order to address the fairness issue in trading among micro-grids, we propose a Fair Energy Resource Allocation (FERA) method, which not only guarantees long term fair energy allocation among micro-grids, but also reduces the overall power cost for actual smart grid.

Experimental results demonstrate the benefits of our proposed methods for the above issues. Compared to other ESS strategies, the MPC-based scheduling method for ESS gives the highest cost reduction of 3.4%. The proposed MPO method combined with MPC-based ESS scheduling method for ADMS saves totally 19.38% of cost if four time slots are used for the ARIMA-based prediction. The proposed FERA method for trading among 30 micro grids, results in a high fairness index of 96.22% even in the worst case, while the state-of-the-art schemes including round robin scheme and priority-based scheme result in a worst-case fairness index of only 57.8% and 11.29%, respectively. For long term trading (10,000 rounds), the proposed FERA method results in a cost saving opportunity (CSO) of 51.48%, while round robin scheme and priority-based scheme only result in 14.07% and 34.44%, respectively. Therefore, the CSO of proposed FERA method is much higher than that by the other two methods.

Table of Contents
Abstract i
Table of Contents iv
1 Introduction 1
1.1 Traditional Smart Grid Design . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1 Motivation and Objective . . . . . . . . . . . . . . . . . . . . 7
1.1.2 Dissertation Organization . . . . . . . . . . . . . . . . . . . . 9
2 Related Work 10
2.1 Research On Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Research On Service-Oriented Architecture . . . . . . . . . . . . . . . 14
2.3 Research On Model Predictive Control . . . . . . . . . . . . . . . . . 18
2.4 Research On Prediction Methods . . . . . . . . . . . . . . . . . . . . 20
2.5 Research On Optimization Methods . . . . . . . . . . . . . . . . . . . 22
2.6 Research On Energy Storage System Management . . . . . . . . . . 24
3 Preliminaries 27
3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.1 Architecture Assumptions . . . . . . . . . . . . . . . . . . . . 34
3.4.2 Electricity Assumptions . . . . . . . . . . . . . . . . . . . . . 35
3.4.3 Transaction Assumptions . . . . . . . . . . . . . . . . . . . . . 36
3.5 Parameter Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.5.1 Parameters for Prediction Model . . . . . . . . . . . . . . . . 37
3.5.2 Parameters for Optimizer . . . . . . . . . . . . . . . . . . . . 40
4 Advanced Distribution Management System Design 41
4.1 The proposed SGaaS Architecture . . . . . . . . . . . . . . . . . . . . 42
4.1.1 Micro-Grid Level . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1.2 Coordination Control Level . . . . . . . . . . . . . . . . . . . 45
4.1.3 Smart Grid Management Level . . . . . . . . . . . . . . . . . 46
4.2 Model Predictive Optimization . . . . . . . . . . . . . . . . . . . . . 48
4.3 Prediction with ARIMA model . . . . . . . . . . . . . . . . . . . . . 50
4.4 Model Predictive Control Scheduling on ESS . . . . . . . . . . . . . . 52
4.5 Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . 65
4.5.1 Fair Energy Resource Allocation . . . . . . . . . . . . . . . . . 71
5 Experiment Results 76
5.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.1.1 Demand Load Data, Generation Data, ESS Specification, and
Dynamic Utility Electricity Price . . . . . . . . . . . . . . . . 77
5.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2.1 Evaluation of ARIMA Model Prediction . . . . . . . . . . . . 80
5.2.2 ESS Scheduling Experiment . . . . . . . . . . . . . . . . . . . 84
5.2.3 Estimation of Fairness and Average Cost in Micro Grid . . . . 88
5.2.4 Optimization Efficiency . . . . . . . . . . . . . . . . . . . . . . 96
5.2.5 Cost Savings on the MPO-based ADMS . . . . . . . . . . . . 97
6 Conclusions 104
Bibliography 106

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