Credibility theory-based available transfer capability assessment

Since the development of large scale power grid interconnections and power markets, research on available transfer capability (ATC) has attracted great attention. The challenges for accurate assessment of ATC originate from the numerous uncertainties in electricity generation, transmission, distribution and utilization sectors. Power system uncertainties can be mainly described as two types: randomness and fuzziness. However, the traditional transmission reliability margin (TRM) approach only considers randomness. Based on credibility theory, this paper firstly built models of generators, transmission lines and loads according to their features of both randomness and fuzziness. Then a random fuzzy simulation is applied, along with a novel method proposed for ATC assessment, in which both randomness and fuzziness are considered. The bootstrap method and multi-core parallel computing technique are introduced to enhance the processing speed. By implementing simulation for the IEEE-30-bus system and a real-life system located in Northwest China, the viability of the models and the proposed method is verified

Quantification of information flow in cyber physical systems

In Cyber Physical Systems (CPSs), traditional security mechanisms such as cryptography and access control are not enough to ensure the security of the system since complex interactions between the cyber portion and physical portion happen frequently. In particular, the physical infrastructure is inherently observable; aggregated physical observations can lead to unintended cyber information leakage. Information flow analysis, which aims to control the way information flows among different entities, is better suited for CPSs than the access control security mechanism. However, quantifying information leakage in CPSs can be challenging due to the flow of implicit information between the cyber portion, the physical portion, and the outside world. Within algorithmic theory, the online problem considers inputs that arrive one by one and deals with extracting the algorithmic solution through an advice tape without knowing some parts of the input. This dissertation focuses on statistical methods to quantify information leakage in CPSs due to algorithmic leakages, especially CPSs that allocate constrained resources. The proposed framework is based on the advice tape concept of algorithmically quantifying information leakage and statistical analysis. With aggregated physical observations, the amount of information leakage of the constrained resource due to the cyber algorithm can be quantified through the proposed algorithms. An electric smart grid has been used as an example to develop confidence intervals of information leakage within a real CPS. The characteristic of the physical system, which is represented as an invariant, is also considered and influences the information quantification results. The impact of this work is that it allows the user to express an observer\u27s uncertainty about a secret as a function of the revealed part. Thus, it can be used as an algorithmic design in a CPS to allocate resources while maximizing the uncertainty of the information flow to an observer --Abstract, page iii

Data-driven power system operation: Exploring the balance between cost and risk

Supervised machine learning has been successfully used in the past to infer a system's security boundary by training classifiers (also referred to as security rules) on a large number of simulated operating conditions. Although significant research has been carried out on using classifiers for the detection of critical operating points, using classifiers for the subsequent identification of suitable preventive/corrective control actions remains underdeveloped. This paper focuses on addressing the challenges that arise when utilizing security rules for control purposes. The inherent trade-off between operating cost and security risk is explored in detail. To optimally navigate this trade-off, a novel approach is proposed that uses an ensemble learning method (AdaBoost) to infer a probabilistic description of a system's security boundary and Platt Calibration to correct the introduced bias. Subsequently, a general-purpose framework for building probabilistic and disjunctive security rules of a system's secure operating domain is developed that can be embedded within classic operation formulations. Through case studies on the IEEE 39-bus system, it is showcased how security rules can be efficiently utilized to optimally operate the system under multiple uncertainties while respecting a user-defined cost-risk balance. This is a fundamental step towards embedding data-driven models within classic optimisation approaches

Investigation on electricity market designs enabling demand response and wind generation

Demand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a volatilidade das energias renováveis, em particular da energia eólica, acarretam vários desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios. Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável (RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As incertezas associadas à geração eólica são consideradas através de um modelo de programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE modificado para demonstrar o efeito da DR na redução do custo de operação

Risk-based security-constrained optimal power flow: Mathematical fundamentals, computational strategies, validation, and use within electricity markets

This dissertation contributes to develop the mathematical fundamentals and computational strategies of risk-based security-constrained optimal power flow (RB-SCOPF) and validate its application in electricity markets. The RB-SCOPF enforces three types of flow-related constraints: normal state deterministic flow limits, contingency state deterministic flow limits (the N-1 criteria), and contingency state system risk, which depends only on contingency states but not the normal state. Each constraint group is scaled by a single parameter setting allowing tradeoffs between deterministic constraints and system risk. Relative to the security-constrained optimal power flow (SCOPF) used in industry today, the RB-SCOPF finds operating conditions that are more secure and more economic. It does this by obtaining solutions that achieve better balance between post-contingency flows on individual circuits and overall system risk. The method exploits the fact that, in a SCOPF solution, some post-contingency circuit flows which exceed their limits impose little risk while other post-contingency circuit flows which are within their limits impose significant risk. The RB-SCOPF softens constraints for the former and hardens constraints for the latter, thus achieving simultaneous improvement in both security and economy. Although the RB-SCOPF is more time-intensive to solve than SCOPF, we have developed efficient algorithms that allow RB-SCOPF to solve in sufficient time for use in real-time electricity markets. In contrast to SCOPF, which motivates market behavior to offload circuit flows exceeding rated flows, the use of RB-SCOPF provides price signals that motivate market behavior to offload circuit flows and to enhance system-wide security levels. Voltage stability testing has demonstrated that the dispatch result based on RB-SCOPF has higher reactive margins at normal state and after a contingency happens, thus has better static voltage stability performance

UNCERTAINTY MODELING IN DISTRIBUTED POWER SYSTEMS

Ph.DDOCTOR OF PHILOSOPH

Development of robust building energy demand-side control strategy under uncertainty

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.Ph.D.Committee Chair: Augenbroe, Gofried; Committee Member: Brown, Jason; Committee Member: Jeter, Sheldon; Committee Member: Paredis,Christiaan; Committee Member: Sastry, Chellur

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.A tecnologia atual na monitorização inteligente, incluindo a Internet of Things (IoT), permite que a rede elétrica ao nível da transporte e distribuição faça uso de programas de demand response (DR) para garantir a operação segura e económica dos sistemas de energia. A liberalização e a reestruturação da indústria dos sistemas de energia elétrica também promovem a gestão do lado da procura de forma otimizada. Os impactes da implementação de DR no mercado elétrico podem ser expressos pelo conceito de agregadores de DR (DRAs), sendo a interface entre o lado da oferta e o lado da procura de energia elétrica. Vários mercados, como os mercados diário e em tempo real, são estudados visando a gestão otimizada do ponto de vista do Independent System Operator (ISO) ou do Distribution System Operator (DSO). Para atingir os objetivos propostos, modelos de otimização em um ou dois níveis podem ser desenvolvidos. O comportamento das fontes de energia renováveis dependentes do clima, como a produção de energia eólica e fotovoltaica que acarretam incerteza, é modelado pelo método de simulação de Monte Carlo. Ainda, two-stage stochastic programming é aplicada para minimizar o custo de operação. Os resultados deste estudo demonstram a importância de considerar todos os participantes efetivos no mercado, como DRAs e clientes finais, no custo de operação. Ainda, considerando a incerteza no modelo beneficia os operadores da rede na redução de custos, capacitando a resiliência e fiabilidade da rede

Benchmarking renewable energy sources carbon savings and economic effectiveness

Over the last decade, the levelised cost of energy (LCOE) of many renewable technologies has sharply declined. As a result, direct cost comparisons of LCOE figures have made renewables to be perceived as economically very competitive options to decarbonise energy systems when compared to other low-carbon technologies such as Nuclear and Carbon Capture and Storage. We identify several theoretical shortcomings in relation to using LCOE or similar life-cycle economic metrics to make inferences about the relative economic effectiveness of using renewable technologies to decarbonise energy systems. We outline several circumstances in which the sole reliance on these metrics can lead to suboptimal or misguided investment and policymaking decisions. The thesis proposes a new theoretical framework to measure and benchmark the cost- effectiveness of decarbonising electric systems using renewables. The new framework is generic, technology-neutral, and enables consolidation of the results of decarbonisation studies that consider various renewable technologies and low carbon technologies. It also enables measuring and tracking the cost-effectiveness of the renewable decarbonisation process at a country or a system level. As a result, it also allows the direct comparison of the economic implications of different decarbonisation scenarios and various policy proposals in a very intuitive graphical way. In addition, the thesis proposes a new, unit-free metric, tentatively called Carbon Economic Effectiveness Credit (CEEC), to benchmark the relative cost-effectiveness of using different renewable technologies to achieve long-term carbon emission savings. Theoretically, CEEC represents the elasticity of the system total cost with respect to the carbon reduction savings attributable to renewables. In contrast to stand-alone, life-cycle metrics such as the LCOE, the proposed metric considers the economic and technical parameters of the renewable technologies and characteristic of the system under study. It also allows expressing the cost- effectiveness of the renewable decarbonisation process as a function of the system-wide decarbonisation level. Using historical load profiles, high-resolution solar radiation data and long-term meteorological data for a relatively small Gulf country, we investigate the deep decarbonisation of the electric system through the large-scale deployment of different renewables technologies. In particular, we use two well-established optimisation methodologies that have been used extensively in the literature to study the decarbonisation of power systems, namely: the screening curve (SC) method and the unit commitment (UC) method. In analysing the results of the two methodologies, we find that the choice of the modelling methodology, in some cases, can greatly influence the perceived carbon cost- effectiveness of renewables and subsequently their carbon abatement cost estimates. In particular, our results suggest that under deep decarbonisation scenarios, the estimate of the long-term carbon savings of renewables is strongly influenced by (1) the choice of the modelling method and (2) the technical specifications of the simulation models. Our results suggest that under deep decarbonisation scenarios, using simpler optimisation models may change the perceived economic effectiveness of renewables to decarbonise some electric systems. More importantly, our research sheds light on potential shortcomings in the current modelling practices and help identify patterns of possible inaccuracies or biases in renewable decarbonisation results. Moreover, our research suggests that the variations in the technical characteristics of renewable technologies can have a large influence on the economics of the decarbonisation process. We show that not all renewable technology types can have a suppressing effect on the variable costs of the systems due to their “zero marginal costs.” In particular, we identify certain technologies and circumstances in which an increase in renewable penetration can significantly inflate the variable energy costs of the system. More specifically, we find that under deep decarbonisation scenarios, renewable technologies with a highly volatile production profiles can act as an amplifier for the variable cost of the systems through (1) reducing the effectiveness of thermal generation units due the increased start-up and shutting downing activities, and (2) increasing the energy output levels from more flexible and yet more expensive thermal technologies. In addition, we identify circumstances in which an increased renewable penetration can materially affect the capacity adequacy of electric systems, leading to an increase in capacity investment in thermal flexibility assets. Perhaps more importantly, we find that these additional flexibility assets will not be commercially viable on an energy-output basis. We believe that this might have specific implications for the energy-only markets. Finally, we discuss the policy implications of our findings and propose several important recommendations. Altogether, we hope that our work will advance the understanding of the economics of climate change and integrating renewables into energy systems.Open Acces