The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.

Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

No abstract available. / Thesis (M.U.R.P.)-University of Natal, Durban, 2002.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Thesis (M.S.)--Ohio University, June, 2008. / Title from PDF t.p. Includes bibliographical references.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1975. / Vita. / Bibliography: leaves 460-462. / by Raymond Eng. / Ph.D.

In recent years, due to the increasing efficiency and availability of information technologies for collecting massive amounts of data (e.g., smart meters and sensors), a variety of advanced technologies and decision-making strategies in the civil engineering sector have shifted in leaps and bounds to a data-driven manner. While there is still no consensus in industry and academia on the latest advances, challenges, and trends in some innovative data-driven methods related to, e.g., deep learning and neural networks, it is undeniable that these techniques have been proven to be considerably effective in helping our academics and engineers solve many real-life tasks related to the smart city framework. This dissertation systematically presents the investigation and development of the cutting-edge data-driven methods related to two specific areas of civil engineering, namely, Residential Electricity Modeling (REM) and Structural Health Monitoring (SHM). For both components, the presentation of this dissertation starts with a brief review of classical data-driven methods used in particular problems, gradually progresses to an exploration of the related state-of-the-art technologies, and eventually lands on our proposed novel data-driven strategies and algorithms. In addition to the classical and state-of-the-art modeling techniques focused on these two areas, this dissertation also put great emphasis on the proposed effective feature extraction and selection approaches. These approaches are aimed to optimize model performance and to save computational resources, for achieving the ideal characterization of the information embedded in the collected raw data that is most relevant to the problem objectives, especially for the case of modeling deep neural networks. For the problems on REM, the proposed methods are validated with real recorded data from multi-family residential buildings, while for SHM, the algorithms are validated with data from numerically simulated systems as well as real bridge structures.

The thesis aims to introduce an efficient resource development strategy in electric utility long term planning under uncertainty considerations. In recent years, electric utilities have recognized the concepts of robustness, flexibility, and risk exposure, to be considered in their resource development strategy. The concept of robustness means to develop resource plans that can perform well for most, if not all futures, while flexibility is to allow inexpensive changes to be made if the future conditions deviate from the base assumptions. A risk exposure concept is used to quantify the risk hazards in planning alternatives for different kinds of future conditions. This study focuses on two technical issues identified to be important to the process of efficient resource development: decision-making analysis considering robustness and flexibility, and decision-making analysis considering risk exposure. The technique combines probabilistic methods and tradeoff analysis, thereby producing a decision set analysis concept to determine robustness that includes flexibility measures. In addition, risk impact analysis is incorporated to identify the risk exposure in planning alternatives. Contributions of the work are summarized as follows. First, an efficient resource development framework for planning under uncertainty is developed that combines features of utility function, tradeoff analysis, and the analytical hierarchy process, incorporating a performance evaluation approach. Second, the multi-attribute risk-impact analysis method is investigated to handle the risk hazards exposed in power system resource planning. Third, the penetration levels of wind and photovoltaic generation technologies into the total generation system mix, with their constraints, are determined using the decision-making model. The results from two case studies show the benefits of the proposed framework by offering the decision makers various options for lower cost, lower emission, better reliability, and higher efficiency plans. / Ph. D.

Master of Arts / Department of Economics / Tracy M. Turner / Economic growth requires growth of energy consumption. In the second half of the twentieth century energy consumption began to outgrow its production and the United States. Consequently, we observe growing dependence of the U.S. economy on energy imports which is causing political and economic insecurity; increasing pollution and depletion of natural resources. One way to alleviate these problems is to encourage renewable electricity production. Because the electric power industry is the largest consumer of energy sources, including renewable energy, it has become one of the most frequent subjects of the regulatory policies and financial incentives aiming to stimulate renewable electricity production. One of the most promoted renewable energy policies in this industry is a renewable portfolio standard (RPS), which requires electric utilities and other retail electric providers to supply a specified amount of electricity sales from renewable energy sources. Currently 29 states and District of Columbia have the RPSs, while 7 states have goals; but only about two third of those with the RPS have certain targets to meet. To my best knowledge, there are no studies analyzing compliance with the RPSs targets or the role of penalty mechanism in the RPS design on meeting its goal. In my Master Thesis I estimate which states are in compliance with their individual RPSs goals and analyze which factors affect the probability of compliance, with the focus on the role of penalty size, and controlling for complimentary policies promoting renewable energy production. I use a fixed effects linear probability model and state level data. Results indicate that including a penalty in the RPS design significantly increases the probability that states will comply with their goals.