abstract: The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation. / Dissertation/Thesis / Ph.D. Industrial Engineering 2013

As Redes Elétricas Inteligentes podem ser entendidas como o uso intensivo de tecnologias de informação e comunicação nas redes elétricas, permitindo um fluxo bidirecional de informações e eletricidade pela rede, de forma a obter uma infraestrutura capaz de automaticamente monitorar, proteger e otimizar a operação de seus elementos. A modernização da infraestrutura elétrica no sentido das Redes Elétricas Inteligentes é inevitável e trará profundas mudanças em todos os segmentos do sistema elétrico. Embora a tecnologia necessária para essa modernização já exista a um custo razoável, ainda restam várias questões que devem ser resolvidas. Indefinições em relação aos padrões a serem adotados, regulamentações, segurança, privacidade e vários aspectos tecnológicos dificultam uma implementação coerente, adiando essa modernização. Assim, este trabalho busca apresentar esse cenário em relação às Redes Elétricas Inteligentes, investigando as tendências e situação atual. Dentre essas tendências, há um grande interesse em definir e implementar mecanismos que incentivem uma maior conscientização dos consumidores em relação ao seu uso de energia, além de uma participação ativa dos mesmos no mercado de energia. Isso exigirá a utilização de ferramentas que possibilitem a redução de custos através do uso mais eficiente da energia. Assim, é também proposto um framework para a construção e análise dessas ferramentas que buscam auxiliar os consumidores nesse cenário em formação. O framework proposto foi construído utilizando uma abordagem por Sistemas Multiagentes e possibilita a construção, simulação e análise de diversos sistemas, em diferentes cenários, com variados tipos de equipamentos, tanto reais como virtuais, sob diferentes protocolos de comunicação e com a possibilidade de uso de diversos algoritmos para a operação conjunta dos equipamentos. / Smart Grids can be understood as the intensive use of information and communication technologies over the electricity networks, allowing a bidirectional flow of information and electricity through the network. It is a system that tries to optimize the supply and demand of energy through the integration of distributed generation and renewable energy resources, and through the active participation of consumers as well as an intense trade relationship between all the segments of the electricity sector. The modernization of the electrical infrastructure towards Smart Grids is inevitable and it will bring deep changes in all segments of the electrical system. Although the necessary technology for this modernization already exists at a reasonable cost, there are still several issues to be solved. Uncertainties regarding standards to be adopted, regulations, security, privacy and many technological aspects difficult a consistent implementation and, therefore, delay this modernization. Thus, this study aims to present the Smart Grid scenario, by the investigation of its current situation and tendencies. Among these tendencies, there is a great interest to define and implement mechanisms to encourage consumer to take care about their electrical energy use and to stimulate their active participation in the energy market. This will require tools that will help them to reduce costs through a more efficient use of energy. Therefore, this work proposes also a framework for the development and analysis of these tools that help consumers at this scenario under construction. The proposed framework is built using a Multiagent System approach which allows the construction, simulation and analysis of various systems in different scenarios. In addition, it allows the use of several types of equipments, both real and virtual, under different communication protocols and with the possible use of various algorithms for a joint operation of all Smart Grid equipments.

In this research, carbon emissions and carbon savings in the smart grid are modelled and quantified. Carbon emissions are defined as the product of the activity (energy) and the corresponding carbon factor. The carbon savings are estimated as the difference between the conventional and improved energy usage multiplied by the corresponding carbon factor. An adaptive seasonal model based on the hyperbolic tangent function (HTF) is developed to define seasonal and daily trends of electricity demand and the resultant carbon emissions. A stochastic model describing profiles of energy usage and carbon emissions for groups of consumers is developed. The flexibility of the HTF for modelling cycles of energy consumption is demonstrated and discussed with several case studies. The analytical description to determine electricity grid carbon intensity in the UK is derived, using the available fuel mix data from the Elexon portal. The uncertain realisation of energy data is forecasted and assimilated using the ensemble Kalman filter (EnKF). The numerical optimisation of carbon emissions and savings in the smart grid is further performed using the ensemble-based Closed-loop Production Optimisation Scheme (EnOpt). The EnOpt involves the optimisation of fuel costs and carbon emissions (maximisation of carbon savings) in the smart grid subject to the operational control constraints. The software codes for the based on the application of EnKF and EnOpt are developed, and the optimisation of energy, cost and emissions is performed. The numerical simulation shows the ability of EnKF in forecasting and assimilating the energy data, and the robustness of the EnOpt in optimising costs and carbon savings. The proposed approach addresses the complexity and diversity of the power grid and may be implemented at the level of the transmission operator in collaboration with the operational wholesale electricity market and distribution network operators. The final stage of work includes the quantification of carbon emissions and savings in demand response (DR) programmes. DR programmes such as Short Term Operating Reserve (STOR), Triad, Fast Reserve, Frequency Control by Demand Management (FCDM) and smart meter roll-out are included, with various types of smart interventions. The DR programmes are modelled with appropriate configurations and assumptions in power plants used in the energy industry. This enables the comparison of emissions between the business-as-usual (BAU) and the smart solutions applied, thus deriving the carbon savings. Several case studies involving the modelling and analysing DR programmes are successfully performed. Thus, the thesis represents novel analytical and numerical techniques applied in the fast-growing UK market of smart energy solutions.

Environmental concerns and long term energy security are the key drivers behind most current electric energy policies whose primary aim is to achieve a sustainable, reliable and affordable energy system. In a bid to achieve these aims many changes have been taking place in most power systems such as emergence of new low carbon generation technologies, structural changes of power system and introduction of competition and choice in electricity supply. As a result of these changes, the level of uncertainties is growing especially on generation side where the locations and available capacities of the future generators are not quite clear-cut. The transmission network needs to be flexibly and economically robust against all these uncertainties. The traditional operation of the network under preventive control mode is an inflexible practice which increases the total system cost. Corrective control operation strategy, however, can be alternatively used to boost the flexibility, to expedite the integration of the new generators and to decrease the overall cost. In this thesis, the main focus is on development of new techniques and methodologies that can be used for modelling and solving a transmission planning problem under the assumption that post-contingency corrective actions are plausible. Three different corrective actions, namely substation switching, demand response and generation re-dispatch are investigated in this thesis. An innovative multi-layer procedure deploying a genetic algorithm is proposed to calculate the required transmission capacity while substation switching is deployed correctively to eradicate the post-fault network violations. By using the proposed approach, a numerical study shows that the network investment reduces by 6.36% in the IEEE 24 bus test system. In another original study, generation re-dispatch corrective action is incorporated into the transmission planning problem. The ramp-rate constraints of generators are taken into account so that the network may be overloaded up to its short-term thermal rating while the generation re-dispatch action is undertaken. The results show that the required network investment for the modified IEEE 24 bus test system can be reduced by 23.8% if post-fault generation re-dispatch is deployed. Furthermore, a new recursive algorithm is proposed to study the effect of price responsive demands and peak-shifting on transmission planning. The results of a study case show that 7.8% of total investment can be deferred. In an additional study on demand response, a new probabilistic approach is introduced for transmission planning in a system where direct load curtailment can be used for either balancing mechanism or alleviating the network violations. In addition, the effect of uncertainties such as wind power fluctuation and CO2 emission price volatility are taken into account by using Monte Carlo simulation and Hypercube sampling techniques. Last but not least, a probabilistic model for dynamic thermal ratings of transmission lines is proposed, using past meteorological data. The seasonal correlations between wind power and thermal ratings are also calculated. £26.7 M is the expected annual benefit by using dynamic thermal ratings of part of National Grid's transmission network.

L'effacement des consommations électriques a été identifié comme l'une des solutions pour pallier les problèmes liés aux pics de consommation électrique, à l’intermittence des énergies renouvelables et à la congestion des réseaux. Ces travaux de recherche s’intéressent à l’intégration de la consommation industrielle en tant que ressources effaçables à court terme dans le contexte de la réserve rapide du mécanisme d’ajustement français. Parmi les différents secteurs, le secteur industriel présente un intérêt particulier en raison de l’importance de sa consommation. Afin d'intégrer ce type de consommation dans l’équilibre offre-demande, il est nécessaire de prévoir le comportement de ces consommations à court terme ainsi que d’évaluer la fiabilité de ces prévisions. Ainsi, différentes méthodes de prévision à très court-terme adaptées aux données et au contexte ont été déployées sur différents consommations disponibles à deux niveaux d’agrégation différents : site et usage industriel. Des indicateurs de performance adaptés aux contraintes opérationnelles, appelés "taux de fiabilité", sont proposés et calculés pour évaluer la performance des méthodes de prévision. Ce taux de fiabilité est estimé pour différentes heures de la journée pour les différents sites et usages industriels étudiés. Les taux de fiabilité estimés permettent d'évaluer le risque pour une consommation spécifique (au niveau du site ou au niveau de chaque usage industriel) de ne pas respecter des contraintes opérationnelles imposées à chaque instant de simulation. / Demand response has been identified as one of the solutions to overcome the problems associated with peaks in electricity consumption, intermittency of renewable energy and network congestion. This thesis focuses on the integration of industrial electricity consumptions as short-term demand response resources in the context of a supply-demand balancing mechanism in France. Among the various sectors, industrial electricity consumptions are of particular interest because of their orders of magnitude. In order to integrate these consumptions to the supply-demand balance, it is necessary forecast their behavior in the short term and to evaluate the reliability of these forecasts. Thus, different short-term load forecasting methods adapted to the data and to the operational context are implemented on different sets of industrial consumptions data at two different consumption levels: the industrial site and the end-point equipment consumption. Performance indicators adapted to operational constraints, called "trust factors" are proposed and calculated to evaluate the performance of the forecasting methods. These trust factors are estimated for different hours of the day for all the different studied industrial sites and workshops. The estimated trust are used to assess the risks for a specific consumption to not to respect the operational constraints at a moment a forecast is simulated. Demand response is considered to become one of the elements to be implemented in order to achieve a successful energy transition through a more flexible power system.

Dans le contexte de transition vers un système électrique décentralisé et intelligent, nous abordons le problème de la gestion des flexibilités de consommation électriques. Nous développons différentes méthodes basées sur l'optimisation distribuée et la théorie des jeux.Nous commençons par adopter le point de vue d'un opérateur central en charge de la gestion des flexibilités de plusieurs agents. Nous présentons un algorithme distribué permettant le calcul des profils de consommations des agents optimaux pour l'opérateur.Cet algorithme garantit la confidentialité des agents~: les contraintes individuelles, ainsi que le profil individuel de consommation de chaque agent, ne sont jamais révélés à l'opérateur ni aux autres agents.Ensuite, nous adoptons dans un second modèle une vision plus décentralisée et considérons un cadre de théorie des jeux pour la gestion des flexibilités de consommation.Cette approche nous permet en particulier de modéliser les comportements stratégiques des consommateurs.Dans ce cadre, une classe de jeux adéquate est donnée par les jeux de congestion atomiques fractionnables.Nous obtenons plusieurs résultats théoriques concernant les équilibres de Nash dans cette classe de jeux, et nous quantifions l'efficacité de ces équilibres en établissant des bornes supérieures sur le prix de l'anarchie.Nous traitons la question du calcul décentralisé des équilibres de Nash dans ce contexte en étudiant les conditions et les vitesses de convergence des algorithmes de meilleure réponse et de gradient projeté.En pratique un opérateur peut faire face à un très grand nombre de joueurs, et calculer les équilibres d'un jeu de congestion dans ce cas est difficile.Afin de traiter ce problème, nous établissons des résultats sur l'approximation d'un équilibre dans les jeux de congestion et jeux agrégatifs avec un très grand nombre de joueurs et en présence de contraintes couplantes.Ces résultats, obtenus dans le cadre des inégalités variationnelles et sous certaines hypothèses de monotonie, peuvent être utilisés pour calculer un équilibre approché comme solution d'un problème de petite dimension.Toujours dans la perspective de modéliser un très grand nombre d'agents, nous considérons des jeux de congestion nonatomiques avec contraintes couplantes et avec une infinité de joueurs hétérogènes~: ce type de jeux apparaît lorsque les caractéristiques d'une population sont décrites par une fonction de distribution paramétrique.Sous certaines hypothèses de monotonie, nous prouvons que les équilibres de Wardrop de ces jeux, définis comme solutions d'une inégalité variationnelle de dimension infinie, peuvent être approchés par des équilibres de Wardrop symétriques de jeux annexes, solutions d'inégalités variationnelles de petite dimension.Enfin, nous considérons un modèle de jeu pour l'étude d'échanges d'électricité pair-à-pair au sein d'une communauté de consommateurs possédant des actifs de production électrique renouvelable.Nous étudions les équilibres généralisés du jeu obtenu, qui caractérisent les échanges possibles d'énergie et les consommations individuelles.Nous comparons ces équilibres avec la solution centralisée minimisant le coût social, et nous évaluons l'efficacité des équilibres via la notion de prix de l'anarchie. / In the context of smart grid and in the transition to decentralized electric systems, we address the problem of the management of distributed electric consumption flexibilities. We develop different methods based on distributed optimization and game theory approaches.We start by adopting the point of view of a centralized operator in charge of the management of flexibilities for several agents. We provide a distributed and privacy-preserving algorithm to compute consumption profiles for agents that are optimal for the operator.In the proposed method, the individual constraints as well as the individual consumption profile of each agent are never revealed to the operator or the other agents.Then, in a second model, we adopt a more decentralized vision and consider a game theoretic framework for the management of consumption flexibilities.This approach enables, in particular, to take into account the strategic behavior of consumers.Individual objectives are determined by dynamic billing mechanisms, which is motivated by the modeling of congestion effects occurring on time periods receiving a high electricity load from consumers.A relevant class of games in this framework is given by atomic splittable congestion games.We obtain several theoretical results on Nash equilibria for this class of games, and we quantify the efficiency of those equilibria by providing bounds on the price of anarchy.We address the question of the decentralized computation of equilibria in this context by studying the conditions and rates of convergence of the best response and projected gradients algorithms.In practice an operator may deal with a very large number of players, and evaluating the equilibria in a congestion game in this case will be difficult.To address this issue, we give approximation results on the equilibria in congestion and aggregative games with a very large number of players, in the presence of coupling constraints.These results, obtained in the framework of variational inequalities and under some monotonicity conditions, can be used to compute an approximate equilibrium, solution of a small dimension problem.In line with the idea of modeling large populations, we consider nonatomic congestion games with coupling constraints, with an infinity of heterogeneous players: these games arise when the characteristics of a population are described by a parametric density function.Under monotonicity hypotheses, we prove that Wardrop equilibria of such games, given as solutions of an infinite dimensional variational inequality, can be approximated by symmetric Wardrop equilibria of auxiliary games, solutions of low dimension variational inequalities.Again, those results can be the basis of tractable methods to compute an approximate Wardrop equilibrium in a nonatomic infinite-type congestion game.Last, we consider a game model for the study of decentralized peer-to-peer energy exchanges between a community of consumers with renewable production sources.We study the generalized equilibria in this game, which characterize the possible energy trades and associated individual consumptions.We compare the equilibria with the centralized solution minimizing the social cost, and evaluate the efficiency of equilibria through the price of anarchy.

The global growth in demand for transportation has been phenomenal, owing to an exponential increase in population, industrialization and urbanization. This has led to a corresponding increase in the number of motor vehicles on the roads globally which has made the transport industry one of the main contributors to environmental pollution and energy insecurity. The profile of alternative fuels has been rising as an important component of the solutions to the challenge of energy sustainability. Compressed natural gas is one of the most successful alternative fuels for motor vehicle applications because of its compatibility with the internal combustion engine, reduced engine maintenance costs, reduced criteria air pollutants, low cost, abundance and the existence of renewable sourced natural gas from biomass. The infrastructure for the delivery of compressed natural gas forms part of the primary energy supply network, which has a significant interdependence with the electricity supply network. The compressed natural gas fuelling station is one of the vital nodes of the gas delivery network, that is also reliant on the electricity supply due to the energy intensive compressors that are required to achieve the right pressure conditions for gas transfer to vehicle tanks. At the same time, the increase in human population, industrialization, urbanization and market volatility have threatened the reliability and stability of electricity supply networks. Traditional reliance on supply upgrading to meet rising demand has proven to be unsustainable due to prohibitively high costs and associated environmental impact. As a result, demand side management solutions, where better use of the existing capacity is emphasized have received increasing attention. Demand side management requires that electricity consumers also play a role in the efficient operation of the electricity grid by minimizing their electricity usage as well as shifting their flexible loads away from peak electricity demand periods, so that grid stability is sustained. In order to participate in demand side management initiatives, operators of compressed natural gas stations need technically and economically sound strategies for the operation of station compressors and system components so that energy costs are minimized and gas transfer performance is enhanced. The compressed natural gas fast-fill station, being the most used configuration for commercial fuelling service is the focus of the work carried out in this thesis, with a description of solutions to minimize energy consumption, minimize energy costs and improve gas transfer performance through reduction of filling time. For this purpose, firstly, an optimal control strategy that minimizes energy cost by shifting the compressor load optimally away from the peak electricity pricing period under a time-of-use electricity tariff, while meeting the gas demand is modelled and evaluated. The controller further minimizes the switching frequency of the compressor thereby avoiding an increase in wear and tear which would lead to higher maintenance costs. The results show the effectiveness of the optimal operation model to achieve a huge reduction in electricity cost for the compressed natural gas station, when compressor-on time is shifted to offpeak and standard electricity pricing times. Further strategies for the minimization of switching frequency are compared and the superior approach identified. Secondly, a hierarchical operation optimization model is designed and evaluated. The strategy achieves minimized electricity cost and optimal vehicle filling time by optimally controlling the gas dispenser and priority panel valve function under an optimised schedule of compressor operation. The results show that the proposed approach is effective in achieving a minimum electricity costs in the upper layer optimisation while meeting vehicle gas demand over the control horizon. Further, a reduction in filling time is achieved through a lower layer model predictive control of the pressure-ratio-dependent fuelling process. Thirdly, an evaluation of compressor optimal sizing is carried out to minimize energy consumption and cascade the benefits of optimal operation of the compressed natural gas compressor under the time-of-use tariff. A comparison of the implication of using a variable speed drive or a fixed speed drive which are optimally sized is carried out. Results show that indeed further reduction in electricity costs for the compressed natural gas station is realized when optimally sized compressor drives are used in combination with optimal operation strategies. Additionally, the four line priority panel is evaluated for gas transfer performance and found to further increase the efficiency of vehicle fuelling which is a performance indicator for consumer convenience. The outcomes of this work demonstrate the effectiveness of the approaches proposed as necessary to integrate compressed natural gas stations, which are vital nodes of the gas delivery network, with the demand side management of the electricity grid while at the same time enhancing the gas transfer performance. This increases the economic efficiency of the compressed natural gas as an alternative fuel and also advances the goals of demand side management in electricity grid reliability and stability. / Thesis (PhD)--University of Pretoria, 2019. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

The electric power industry and society are facing the challenges and opportunities of transforming the present power grid into a smart grid. To meet these challenges, new types of control systems are connected over IT infrastructures. While this is done to meet highly set economical and environmental goals, it also introduces new sources of uncertainty in the control loops. In this thesis, we consider control design taking some of these uncertainties into account. In Part I of the thesis, some economical and environmental concerns in smart grids are taken into account, and a scheduling framework for static loads (e.g., smart appliances in residential areas) and dynamic loads (e.g., energy storage systems) in the distribution level is investigated. A robust formulation is proposed taking the user behavior uncertainty into account, so that the optimal scheduling cost is less sensitive to unpredictable changes in user preferences. In addition, a novel distributed algorithm for the studied scheduling framework is proposed, which aims at minimizing the aggregated electricity cost of a network of apartments sharing an energy storage system. We point out that the proposed scheduling framework is applicable to various uncertainty sources, storage technologies, and programmable electrical loads. In Part II of the thesis, we study smart grid uncertainty resulting from possible security threats. Smart grids are one of the most complex cyber-physical systems considered, and are vulnerable to various cyber and physical attacks. The attack scenarios consider cyber adversaries that may corrupt a few measurements and reference signals, which may degrade the system’s reliability and even destabilize the voltage magnitudes. In addition, a practical attack-resilient framework for networked control systems is proposed. This framework includes security information analytics to detect attacks and a resiliency policy to improve the performance of the system running under the attack. Stability and optimal performance of the networked control system under attack and by applying the proposed framework, is proved here. The framework has been applied to an energy management system and its efficiency is demonstrated on a critical attack scenario. / <p>QC 20160830</p>

In the recent years there has been a great deal of attention on the optimal demand and supply side strategy. The increase in renewable energy sources and the expansion in demand response programmes has shown the need for a robust power system. These changes in power system require the control of the uncertain generation and load at the same time. Therefore, it is important to provide an optimal scheduling strategy that can meet an adequate energy mix under demand response without affecting the system reliability and economic performance. This thesis addresses the following four aspects to these changes. First, a renewable obligation model is proposed to maintain an adequate energy mix in the economic dispatch model while minimising the operational costs of the allocated spinning reserves. This method considers a minimum renewable penetration that must be achieved daily in the energy mix. If the renewable quota is not achieved, the generation companies are penalised by the system operator. The uncertainty of renewable energy sources are modelled using the probability density functions and these functions are used for scheduling output power from these generators. The overall problem is formulated as a security constrained economic dispatch problem. Second, a combined economic and demand response optimisation model under a renewable obligation is presented. Real data from a large-scale demand response programme are used in the model. The model finds an optimal power dispatch strategy which takes advantage of demand response to minimise generation cost and maximise renewable penetration. The optimisation model is applied to a South African large-scale demand response programme in which the system operator can directly control the participation of the electrical water heaters at a substation level. Actual load profile before and after demand reduction are used to assist the system operator in making optimal decisions on whether a substation should participate in the demand response programme. The application of these real demand response data avoids traditional approaches which assume arbitrary controllability of flexible loads. Third, a stochastic multi-objective economic dispatch model is presented under a renewable obligation. This approach minimises the total operating costs of generators and spinning reserves under renewable obligation while maximising renewable penetration. The intermittency nature of the renewable energy sources is modelled using dynamic scenarios and the proposed model shows the effectiveness of the renewable obligation policy framework. Due to the computational complexity of all possible scenarios, a scenario reduction method is applied to reduce the number of scenarios and solve the model. A Pareto optimal solution is presented for a renewable obligation and further decision making is conducted to assess the trade-offs associated with the Pareto front. Four, a combined risk constrained stochastic economic dispatch and demand response model is presented under renewable obligation. An incentive based optimal power dispatch strategy is implemented to minimise generation costs and maximise renewable penetration. In addition, a risk-constrained approach is used to control the financial risks of the generation company under demand response programme. The coordination strategy for the generation companies to dispatch power using thermal generators and renewable energy sources while maintaining an adequate spinning reserve is presented. The proposed model is robust and can achieve significant demand reduction while increasing renewable penetration and decreasing the financial risks for generation companies. / Thesis (PhD (Electrical Engineering))--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / PhD (Electrical Engineering) / Unrestricted

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