In the past 10 years, the automobile market of Taiwan has been about 400,000 automobiles per year, and the average output per automaker in Taiwan has been in the range from 30,000 to 50,000 automobiles per year. According to the relevant studies, to reach the goal of economic size of production, 100,000 automobiles should be produced per model per year and the total output of an automaker should be more than 400,000 automobiles. There are 10 automakers in Taiwan. What are the economic and trade policies that have brought about so many automakers in Taiwan? Is this an example of extreme loosening of the control of the automobile industry or if this has something to do with the big profit margin in the industry? Taiwan has entered the WTO; the economic and trade policies of Taiwan should be so set that they can further the development of the automobile industry of Taiwan in the Chinese market and other parts of the world by utilizing the trade advantages of Taiwan. In this study, we look into the changes of the economic and trade policies of Taiwan in terms of their impacts on the automobile industry of Taiwan; we also try to assess the causes of such changes. What are the policies that have caused the evolution of the automobile industry of Taiwan? Why can¡¦t the automakers in Taiwan develop their own brand like the manufacturers in the microelectronic industry, motorcycle industry and bicycle industry in Taiwan? In this study, from the economic perspective and the perspective of the relevant policies, we examine the consumer market and buyers¡¦ preferences on the demand side; while, on the supply side, we look into the competition strategies adopted by the automakers and their competitiveness through the R & D, production technologies and competitiveness of these automakers. We also examine the impacts of the relevant government policies on the automobile industry and the challenges posed by Taiwan¡¦s entry into the WTO. Our aim is to understand the problems and difficulties found in the government policies and the evolution of the automobile industry of Taiwan so that we infer how the government policies will be evolved into and the crucial factors that have caused the changes in the policies. In this study, we look into how the crucial factors have caused the changes in the automobile industry. Through the previously described approaches and the assessment of the policies relating to the automobile industry, we can identify the successful and failure experience in the past and infer the competitive advantages and the policies in the future. Also, through the simulation of the optimal development direction of the policies relating to the automobile industry, we bring forth the future development direction of the automobile industry for the reference of the relevant government agencies so as to create an environment that allows the manufacturers of the industry to establish production facilities in China and to enter other markets of the world and so that the goals of autonomous product development and the sustainable development of the automobile industry of Taiwan may be reached and the products of these manufacturers may be more competitive in terms of the global market.

Electric power systems are experiencing dramatic changes with respect to structure, operation and regulation and are facing increasing pressure due to environmental and societal constraints. Bulk electric system reliability is an important consideration in power system planning, design and operation particularly in the new competitive environment. A wide range of methods have been developed to perform bulk electric system reliability evaluation. Theoretically, sequential Monte Carlo simulation can include all aspects and contingencies in a power system and can be used to produce an informative set of reliability indices. It has become a practical and viable tool for large system reliability assessment technique due to the development of computing power and is used in the studies described in this thesis. The well-being approach used in this research provides the opportunity to integrate an accepted deterministic criterion into a probabilistic framework. This research work includes the investigation of important factors that impact bulk electric system adequacy evaluation and security constrained adequacy assessment using the well-being analysis framework.<p> Load forecast uncertainty is an important consideration in an electrical power system. This research includes load forecast uncertainty considerations in bulk electric system reliability assessment and the effects on system, load point and well-being indices and reliability index probability distributions are examined. There has been increasing worldwide interest in the utilization of wind power as a renewable energy source over the last two decades due to enhanced public awareness of the environment. Increasing penetration of wind power has significant impacts on power system reliability, and security analyses become more uncertain due to the unpredictable nature of wind power. The effects of wind power additions in generating and bulk electric system reliability assessment considering site wind speed correlations and the interactive effects of wind power and load forecast uncertainty on system reliability are examined. The concept of the security cost associated with operating in the marginal state in the well-being framework is incorporated in the economic analyses associated with system expansion planning including wind power and load forecast uncertainty. Overall reliability cost/worth analyses including security cost concepts are applied to select an optimal wind power injection strategy in a bulk electric system. The effects of the various demand side management measures on system reliability are illustrated using the system, load point, and well-being indices, and the reliability index probability distributions. The reliability effects of demand side management procedures in a bulk electric system including wind power and load forecast uncertainty considerations are also investigated. The system reliability effects due to specific demand side management programs are quantified and examined in terms of their reliability benefits.

This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable candidates is then selected accordingly, which may explain price behavior in this market. In the process of selecting the explanatory variable candidates, some important issues, such as direct or indirect effects of the variables on price behavior, availability of the variables before real-time, choice of appropriate forecasting horizon and market time-line, are taken into account. Price and demand in three neighboring electricity markets, namely, the New York, New England, and PJM electricity markets, are also considered among the explanatory variable candidates. Electricity market clearing prices in Ontario are calculated every five minutes. However, the hourly average of these 5-minute prices, referred to as the Hourly Ontario Energy Price (HOEP), applies to most Ontario market participants for financial settlements. Therefore, this thesis concentrates on forecasting the HOEP by employing various linear and non-linear modeling approaches. The multivariate Transfer Function (TF), the multivariate Dynamic Regression (DR), and the univariate Auto Regressive Integrated Moving Average (ARIMA) are the linear time series models examined. The non-linear approaches comprise the Multivariate Adaptive Regression Splines (MARS), and the Multi-Layer Perceptron (MLP) neural networks. Multivariate HOEP models are developed considering two forecasting horizons, i.e. 3 hours and 24 hours, taking into account the case market time-line and the ability of market participants to react to the generated forecasts. Univariate ARIMA models are also developed for day-ahead market prices in the three neighboring electricity markets. The developed models are used to generate price forecasts for low-demand, summer peak-demand, and winter peak-demand periods. The HOEP forecasts generated in this work are significantly more accurate than any other available forecast. However, the accuracy of the generated HOEP forecasts is relatively lower than those of the price forecasts for Ontario's neighboring electricity markets. The low accuracy of the HOEP forecasts is explained by conducting a price volatility analysis across the studied electricity markets. This volatility analysis reveals that the Ontario electricity market has the most volatile prices compared to the neighboring electricity markets. The high price volatility of the Ontario electricity market is argued to be the direct result of the real-time nature of this market. It is further observed that the inclusion of the just-in-time publicly available data in multivariate HOEP models does not improve the HOEP forecast accuracy significantly. This lack of significant improvement is attributed to the information content of the market data which are available just-in-time. The generated HOEP forecasts are used to plan the short-term operation of two typical demand-side case-study BEMCs. The first case-study BEMC is a process industry load with access to on-site generation facilities, and the second one is a municipal water plant with controllable electric demand. Optimization models are developed for the next-day operation of these BEMCs in order to minimize their total energy costs. The optimization problems are solved when considering market price forecasts as the expected future prices for electricity. The economic impact of price forecast inaccuracy on both the case study is analyzed by introducing the novel Forecast Inaccuracy Economic Impact (FIEI) index. The findings of this analysis show that electricity market price forecasts can effectively be used for short-term scheduling of demand-side BEMCs. However, sensitivity to price forecast inaccuracy significantly varies across market participants. In other words, a set of price forecasts may be considered ``accurate enough'' for a customer, while leading to significant economic losses for another.

No description available.

In this thesis, a novel framework for planning and investment studies pertaining to the implementation of system-wide conservation voltage reduction (CVR) is presented. In the CVR paradigm, optimal voltage profiles at the load buses are determined so as to yield load reductions and hence energy conservation. The system modifications required for CVR is known to be capital intensive; therefore, the proposed model determines the system savings and the appropriate price incentives to offer industries such that a minimum acceptable rate-of-return (MARR) is accrued. In this model, the industrial facilities are represented by a combination of constant impedance, constant current, and constant power loads. A detailed case study for Ontario, Canada, is carried out considering that industrial loads are investing in CVR implementation to reduce their energy costs. The optimal incentives that need be offered by the system planner, over a long-term horizon and across various zones of Ontario, are determined using the presented mathematical model. Furthermore, a comprehensive risk analysis, comprising sensitivity studies and Monte Carlo simulations, is carried out considering the variations in the most uncertain model parameters. In this work, it is shown that savings from CVR are enough so that incentives are not required in Ontario. Sensitivity analysis shows that electricity price and project cost have the highest impact on the incentives, and that electricity price and industrial demand have the most effect on system savings. Monte Carlo simulations show that the expected energy cost savings result in expected incentive rates to be relatively low compared to the average electricity price in Ontario. CVR is shown in this thesis to be a low cost Demand Side Management program to implement from the perspective of the power system planner, and a worthwhile investment for the industrial load.

This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable candidates is then selected accordingly, which may explain price behavior in this market. In the process of selecting the explanatory variable candidates, some important issues, such as direct or indirect effects of the variables on price behavior, availability of the variables before real-time, choice of appropriate forecasting horizon and market time-line, are taken into account. Price and demand in three neighboring electricity markets, namely, the New York, New England, and PJM electricity markets, are also considered among the explanatory variable candidates. Electricity market clearing prices in Ontario are calculated every five minutes. However, the hourly average of these 5-minute prices, referred to as the Hourly Ontario Energy Price (HOEP), applies to most Ontario market participants for financial settlements. Therefore, this thesis concentrates on forecasting the HOEP by employing various linear and non-linear modeling approaches. The multivariate Transfer Function (TF), the multivariate Dynamic Regression (DR), and the univariate Auto Regressive Integrated Moving Average (ARIMA) are the linear time series models examined. The non-linear approaches comprise the Multivariate Adaptive Regression Splines (MARS), and the Multi-Layer Perceptron (MLP) neural networks. Multivariate HOEP models are developed considering two forecasting horizons, i.e. 3 hours and 24 hours, taking into account the case market time-line and the ability of market participants to react to the generated forecasts. Univariate ARIMA models are also developed for day-ahead market prices in the three neighboring electricity markets. The developed models are used to generate price forecasts for low-demand, summer peak-demand, and winter peak-demand periods. The HOEP forecasts generated in this work are significantly more accurate than any other available forecast. However, the accuracy of the generated HOEP forecasts is relatively lower than those of the price forecasts for Ontario's neighboring electricity markets. The low accuracy of the HOEP forecasts is explained by conducting a price volatility analysis across the studied electricity markets. This volatility analysis reveals that the Ontario electricity market has the most volatile prices compared to the neighboring electricity markets. The high price volatility of the Ontario electricity market is argued to be the direct result of the real-time nature of this market. It is further observed that the inclusion of the just-in-time publicly available data in multivariate HOEP models does not improve the HOEP forecast accuracy significantly. This lack of significant improvement is attributed to the information content of the market data which are available just-in-time. The generated HOEP forecasts are used to plan the short-term operation of two typical demand-side case-study BEMCs. The first case-study BEMC is a process industry load with access to on-site generation facilities, and the second one is a municipal water plant with controllable electric demand. Optimization models are developed for the next-day operation of these BEMCs in order to minimize their total energy costs. The optimization problems are solved when considering market price forecasts as the expected future prices for electricity. The economic impact of price forecast inaccuracy on both the case study is analyzed by introducing the novel Forecast Inaccuracy Economic Impact (FIEI) index. The findings of this analysis show that electricity market price forecasts can effectively be used for short-term scheduling of demand-side BEMCs. However, sensitivity to price forecast inaccuracy significantly varies across market participants. In other words, a set of price forecasts may be considered ``accurate enough'' for a customer, while leading to significant economic losses for another.

Electric power systems are experiencing dramatic changes with respect to structure, operation and regulation and are facing increasing pressure due to environmental and societal constraints. Bulk electric system reliability is an important consideration in power system planning, design and operation particularly in the new competitive environment. A wide range of methods have been developed to perform bulk electric system reliability evaluation. Theoretically, sequential Monte Carlo simulation can include all aspects and contingencies in a power system and can be used to produce an informative set of reliability indices. It has become a practical and viable tool for large system reliability assessment technique due to the development of computing power and is used in the studies described in this thesis. The well-being approach used in this research provides the opportunity to integrate an accepted deterministic criterion into a probabilistic framework. This research work includes the investigation of important factors that impact bulk electric system adequacy evaluation and security constrained adequacy assessment using the well-being analysis framework.<p> Load forecast uncertainty is an important consideration in an electrical power system. This research includes load forecast uncertainty considerations in bulk electric system reliability assessment and the effects on system, load point and well-being indices and reliability index probability distributions are examined. There has been increasing worldwide interest in the utilization of wind power as a renewable energy source over the last two decades due to enhanced public awareness of the environment. Increasing penetration of wind power has significant impacts on power system reliability, and security analyses become more uncertain due to the unpredictable nature of wind power. The effects of wind power additions in generating and bulk electric system reliability assessment considering site wind speed correlations and the interactive effects of wind power and load forecast uncertainty on system reliability are examined. The concept of the security cost associated with operating in the marginal state in the well-being framework is incorporated in the economic analyses associated with system expansion planning including wind power and load forecast uncertainty. Overall reliability cost/worth analyses including security cost concepts are applied to select an optimal wind power injection strategy in a bulk electric system. The effects of the various demand side management measures on system reliability are illustrated using the system, load point, and well-being indices, and the reliability index probability distributions. The reliability effects of demand side management procedures in a bulk electric system including wind power and load forecast uncertainty considerations are also investigated. The system reliability effects due to specific demand side management programs are quantified and examined in terms of their reliability benefits.

Contemporary business world is now facing a challenge, a shift from traditional innovation to eco-innovation. Organizations need to recognize the importance of environment in any aspect of innovation. This paper aims to deduce the drivers of eco-innovation from the overview of existing literature and empirical study to provide an understanding of the organization aiming towards eco-innovation. The aim of this thesis is to identify the drivers of eco-innovation and objectives being able to understand and review the contribution of innovation and eco-innovation as separate entities.This research is limited to the investigation of drivers of eco-innovation in one single organization i.e. Tekniska Verken, Linköping. Conceptual model of drivers of eco-innovation is created from previous research and verified through empirical study. The model of this research is to outline the three categories of drivers within the limit and scope of this analysis. However modification of the model on the basis of additional drivers has been duly appreciated and elucidated to reflect reality of the research.

Abstract This thesis proposes a PC based electric energy management system as well as load control strategies for demand side management in high voltage customer. Besides, this thesis proposes a sequential search method for the decision of optimal demand contract. By the proposed approach, We expect to decrease the basic demand charge and the total electrical cost. The load survey and load characteristics of selected high voltage customers are first fulfilled to derive the load composition and statistic data for large air conditioner. Furthermore,digital power meters are installed at each substation and they are connected in star configuration with telephone network to form automatic meter reading system. Power parameters such as V, I, P, Q, P.F. etc. are periodically collected via telephone network. By inspecting the trend of peak load as well as the load composition, the specification and structure of electric energy management system and their application functions are difined. The proposed PC based electric energy management system is integrated programmable logic controller (PLC) with power meters to form basic Supervisory Control And Data Acquisition ¡]SCADA¡^functions. Besides, advance functions such as demand monitoring/load shedding, periodical load control, clock load control, direct load control, alarm, and real time/historical trending are embedded to enhance the capability of proposed system. By the Visual Function Block in Diamond Control View, automatic meter reading system can be simulated and demonstrated. The academic power system in National Sun Yat-Sen University(NSYSU) are selected for testing to demonstrate the effectiveness of proposed system. Finally,the effect of peak load cutting will not only save energy consumption of the customer but also increase the power capacity of substations for Taiwan power system.

This report asks how Austin Energy can optimally operate residential energy efficiency and demand side management programs including demand response measures. Efficient energy use is the act of using less energy to provide the same level of service. Demand side management encompasses utility initiatives that modify the level and pattern of electrical use by customers, without adjusting consumer behavior. Demand side management is required when a utility must respond to increasing energy needs, or demand, by its customers. In order to achieve the 20% carbon emissions and 800 MW peak demand reductions mandate of the Generation, Resource and Climate Plan, AE must aggressively pursue an increase in customer participation by expanding education and technical services, enlist the full functionality of a smart grid and subsequently reduce energy consumption, peak demand, and greenhouse gas emissions. Energy efficiency is in fact the cheapest source of energy that Austin Energy has at its disposal between 2010 and 2020. But this service threatens Austin Energy’s revenues. With the ascent of onsite renewable energy generation and advanced demand side management, utilities must address the ways they generate revenues. As greenhouse gas emissions regulations lurk on the horizon, the century-old business model of “spinning meters” will be fundamentally challenged nationally in the coming years. Austin Energy can develop robust analytical methods to determine its most cost-effective energy efficiency options, while creating a clear policy direction of promoting energy efficiency while addressing the three-fold challenges of peak demand, greenhouse gas emissions and total energy savings. This report concludes by providing market-transforming recommendations for Austin Energy. / text