Sustainable distribution network planning considering multi-energy systems and plug-in electric vehicles parking lots

Entre todos os recursos associados à evolução das redes elétricas para o conceito de smart grid, os sistemas de multi-energia e os veículos eléctricos do tipo plug-in (PEV) são dois dos principais tópicos de investigação hoje em dia. Embora estes recursos possam acarretar uma maior incerteza para o sistema de energia, as suas capacidades de demanda/armazenamento flexível de energia podem melhorar a operacionalidade do sistema como um todo. Quando o conceito de sistemas de multi-energia e os parques de estacionamento com estações de carregamento para os PEVs são combinados no sistema de distribuição, a demanda pode variar significativamente. Sendo a demanda de energia uma importante informação no processo de planeamento, é essencial estimar de precisa essa demanda. Deste modo, três níveis padrão de carga podem ser extraídos tendo em conta a substituição da procura entre carriers de energia, a demanda associada ao carregamento dos PEVs, e presença de parques de estacionamento com estações de carregamento no sistema. A presença de PEVs num sistema multi-energia obriga a outros requisitos (por exemplo, um sistema de alimentação) que devem ser fornecidos pelo sistema, incluindo as estações de carregamento. A componente elétrica dos PEVs dificulta a tarefa ao operador do sistema na tentativa de encontrar a melhor solução para fornecer os serviços necessários e utilizar o potencial dos PEVs num sistema multi-energia. Contudo, o comportamento sociotécnico dos utilizadores de PEVs torna difícil ao operador do sistema a potencial gestão das fontes de energia associada às baterias. Desta forma, este estudo visa providenciar uma solução para os novos problemas que irão ocorrer no planeamento do sistema. Nesta tese, vários aspetos da integração de PEVs num sistema multi-energia são estudados. Primeiro, um programa de resposta à demanda é proposto para o sistema multi-energia com tecnologias do lado da procura que possibilitem alternar entre fornecedores de serviços. Em seguida, é realizado um estudo abrangente sobre as questões relativas à modelação dos PEVs no sistema, incluindo a modelação das incertezas, as preferências dos proprietários dos veículos, o nível de carregamento dos PEV e a sua interação com a rede. Posteriormente é proposta a melhor estratégia para a participação no mercado de energia e reserva. A alocação na rede e os possíveis efeitos subjacentes são também estudados nesta tese, incluindo o modelo dos PEVs e dos parques de estacionamento com estações de carregamento nesse sistema de multi-energia.Among all resources introduced by the evolution of smart grid, multi-energy systems and plugin electric vehicles are the two main challenges in research topics. Although, these resources bring new levels of uncertainties to the system, their capabilities as flexible demand or stochastic generation can enhance the operability of system. When the concept of multienergy systems and plug-in electric vehicles (PEV) parking lots are merged in a distribution system, the demand estimation may vary significantly. As the main feed of planning process, it is critical to estimate the most accurate amount of required demand. Therefore, three stages of load pattern should be extracted taking into account the demand substitution between energy carriers, demand affected by home-charging PEVs, and parking lot presence in system. The presence of PEVs in a multi-energy system oblige other requirements (i.e. fueling system) that should be provided in the system, including charging stations. However, the electric base of PEVs adds to the responsibilities of the system operator to think about the best solution to provide the required services for PEVs and utilize their potentials in a multi-energy concept. However, the socio-technical behavior of PEV users makes it difficult for the system operator to be able to manage the potential sources of PEV batteries. As a result, this study tries to raise the solution to new problems that will occur for the system planners and operators by the future components of the system. In this thesis, various aspects of integrating PEVs in a multi-energy system is studied.Firstly, a carrier-based demand response program is proposed for the multi-energy system with the technologies on the demand side to switch between the carriers for providing their services. Then, a comprehensive study on the issues regarding the modeling of the PEVs in the system are conducted including modeling their uncertain traffic behavior, modeling the preferences of vehicle owners on the required charging, modeling the PEV parking lot behavior and its interactions with the network. After that the best strategy and framework for participating the PEVs energy in the energy and reserve market is proposed. The allocation of the parking lot in the network and the possible effects it will have on the network constraints is studied. Finally, the derived model of the PEVs and the parking lot is added to the multi-energy system model with multi-energy demand

Nonlinear Interval Parameter Programming Combined with Cooperative Games: a Tool for Addressing Uncertainty in Water Allocation Using Water Diplomacy Framework

This paper shows the utility of a new interval cooperative game theory as an effective water diplomacy tool to resolve competing and conflicting needs of water users from different sectors including agriculture, domestic, industry and environment. Interval parameter programming is applied in combination with cooperative game theoretic concepts such as Shapley values and the Nucleolus to provide mutually beneficial solutions for water allocation problems under uncertainty. The allocation problem consists of two steps: water resources are initially allocated to water users based on the Nash bargaining model and the achieved nonlinear interval parameter model is solved by transforming it into a problem with a deterministic weighted objective function. Water amounts and net benefits are reallocated to achieve efficient water usage through net benefit transfers. The net benefit reallocation is done by the application of different cooperative game theoretical methods. Then, the optimization problem is solved by linear interval programming and by converting it into a problem with two deterministic objective functions. The suggested model is then applied to the Zarrinehrud sub-basin, within Urmia Lake basin in Northwestern Iran. Findings suggest that a reframing of the problem using cooperative strategies within the context of water diplomacy framework - creating flexibility in water allocation using mutual gains approach - provides better outcomes for all competing users of water

Reliability Evaluation and Defense Strategy Development for Cyber-physical Power Systems

With the smart grid initiatives in recent years, the electric power grid is rapidly evolving into a complicated and interconnected cyber-physical system. Unfortunately, the wide deployment of cutting-edge communication, control and computer technologies in the power system, as well as the increasing terrorism activities, make the power system at great risk of attacks from both cyber and physical domains. It is pressing and meaningful to investigate the plausible attack scenarios and develop efficient methods for defending the power system against them. To defend the power grid, it is critical to first study how the attacks could happen and affect the power system, which are the basis for the defense strategy development. Thus, this dissertation quantifies the influence of several typical attacks on power system reliability. Specifically, three representative attack are considered, i.e., intrusion against substations, regional LR attack, and coordinated attacks. For the intrusion against substations, the occurrence frequency of the attack events is modeled based on statistical data and human dynamics; game-theoretical approaches are adopted to model induvial and consecutive attack cases; Monte Carlo simulation is deployed to obtain the desired reliability indices, which incorporates both the attacks and the random failures. For the false data injection attack, a practical regional load redistribution (LR) attack strategy is proposed; the man-in-the-middle (MITM) intrusion process is modeled with a semi-Markov process method; the reliability indices are obtained based on the regional LR attack strategy and the MITM intrusion process using Monte Carlo simulation. For the coordinated attacks, a few typical coordination strategies are proposed considering attacking the current-carrying elements as well as attacking the measurements; a bilevel optimization method is applied to develop the optimal coordination strategy. Further, efficient and effective defense strategies are proposed from the perspectives of power system operation strategy and identification of critical elements. Specially, a robustness-oriented power grid operation strategy is proposed considering the element random failures and the risk of man-made attacks. Using this operation strategy, the power system operation is robust, and can minimize the load loss in case of malicious man-made attacks. Also, a multiple-attack-scenario (MAS) defender-attack-defender model is proposed to identify the critical branches that should be defended when an attack is anticipated but the defender has uncertainty about the capability of the attacker. If those identified critical branches are protected, the expected load loss will be minimal

Multi-Echelon Inventory Optimization and Demand-Side Management: Models and Algorithms

Inventory management is a fudamental problem in supply chain management. It is widely used in practice, but it is also intrinsically hard to optimize, even for relatively simple inventory system structures. This challenge has also been heightened under the threat of supply disruptions. Whenever a supply source is disrupted, the inventory system is paralyzed, and tremenduous costs can occur as a consequence. Designing a reliable and robust inventory system that can withstand supply disruptions is vital for an inventory system\u27s performance.First we consider a basic type of inventory network, an assembly system, which produces a single end product from one or several components. A property called long-run balance allows an assembly system to be reduced to a serial system when disruptions are not present. We show that a modified version is still true under disruption risk. Based on this property, we propose a method for reducing the system into a serial system with extra inventory at certain stages that face supply disruptions. We also propose a heuristic for solving the reduced system. A numerical study shows that this heuristic performs very well, yielding significant cost savings when compared with the best-known algorithm.Next we study another basic inventory network structure, a distribution system. We study continuous-review, multi-echelon distribution systems subject to supply disruptions, with Poisson customer demands under a first-come, first-served allocation policy. We develop a recursive optimization heuristic, which applies a bottom-up approach that sequentially approximates the base-stock levels of all the locations. Our numerical study shows that it performs very well.Finally we consider a problem related to smart grids, an area where supply and demand are still decisive factors. Instead of matching supply with demand, as in the first two parts of the dissertation, now we concentrate on the interaction between supply and demand. We consider an electricity service provider that wishes to set prices for a large customer (user or aggregator) with flexible loads so that the resulting load profile matches a predetermined profile as closely as possible. We model the deterministic demand case as a bilevel problem in which the service provider sets price coefficients and the customer responds by shifting loads forward in time. We derive optimality conditions for the lower-level problem to obtain a single-level problem that can be solved efficiently. For the stochastic-demand case, we approximate the consumer\u27s best response function and use this approximation to calculate the service provider\u27s optimal strategy. Our numerical study shows the tractability of the new models for both the deterministic and stochastic cases, and that our pricing scheme is very effective for the service provider to shape consumer demand

Efficient solution approach to nonlinear optimal control problems and application to autonomous driving

Diese Arbeit beschäftigt sich mit der numerischen Lösung von dynamischen nichtlinearen Optimierungsaufgaben und der Entwicklung neuer Methoden für deren Analyse, um die Effizienz der Berechnungen zu erhöhen. Der Betrieb vieler natürlicher und technischer Prozesse kann als nichtlineares Optimierungsproblem mit Beschränkungen formuliert werden. Aufgrund der steigenden Komplexität wird die Lösung eines solchen Problems zu einer Herausforderung, insbesondere wenn das Problem in Echtzeit gelöst werden muss. Der Ansatz des kombinierten Mehrfachschießverfahren mit Kollokation ist effizient, um solche Probleme zu lösen, auch wenn sie eine schnelle Dynamik aufweisen. So ist das erste Ziel dieser Arbeit die weitere Verbesserung der Rechenleistung durch die Bereitstellung einer analytischen Hesse-Matrix und die Realisierung eines Parallelberechnungs-Schemas. Zunächst wurden die Formeln zur Berechnung der Sensitivitäten zweiter Ordnung für den kombinierten Ansatz abgeleitet. Mit Hilfe des Mehrfachschießverfahrens können die Lösungen von Modellgleichungen und Auswertungen von Sensitivitäten erster und zweiter Ordnung für jedes Zeitintervall unabhängig voneinander berechnet werden. Der zweite Beitrag widmet sich daher der Realisierung eines parallelen Rechenschemas. Dadurch wird ein hoher Beschleunigungsfaktor durch Parallelisierung erreicht, der zu einer Reduzierung des Rechenaufwands führt. Als dritter Beitrag wurde eine neuartige Korrelationsanalyse der Steuergrößen eingeführt, die auf die Notwendigkeit hinweist, die analytische Hesse-Matrix anstelle seiner Approximation einzusetzen, um ein Optimierungsproblem effizient zu lösen. Die numerische Leistung dieser drei Beiträge wurde mit Hilfe von herausfordernden dynamischen Optimierungsproblemen einschließlich der optimalen Steuerung eines großen Problems mit mehr als tausend dynamischen Variablen demonstriert. Die kombinierte Methode wandelt das Problem der kontinuierlichen dynamischen Optimierung in ein nichtlineares Programmierungsproblem mit einer vorgegebenen Anzahl der Zeitintervalle um. Es gibt jedoch keine umfassenden Regeln, um diese Anzahl der Zeitintervalle passend zu wählen. Daher widmet sich das vierte Ziel dieser Arbeit der Analyse der zugrunde liegenden Optimierungsprobleme mit dem besonderen Fokus auf der Anzahl der diskreten Zeitintervalle. Aus Anwendungssicht sollte die Anzahl der Zeitintervalle so gewählt werden, dass gleichzeitig die Bilanz zwischen der numerischen Genauigkeit und der Rechenlast zur Lösung des diskreten Optimierungsproblems erreicht wird. Darüber hinaus ist es unerlässlich, die Mindestanzahl an Zeitintervallen zu finden, um diese Genauigkeit zu gewährleisten. So wurde im Rahmen der Kollokation auf finiten Elementen ein neuartiger Bilevel-Ansatz vorgeschlagen, bei dem die äußere Schleife für die Ermittlung der minimalen Anzahl von Zeitintervallen zuständig ist und die innere Schleife eine obere Grenze des Approximationsfehlers auswertet, indem sie ein Fehlermaximierungsproblem durch Manipulation der Steuergrößen löst. Auf diese Weise kann eine Mindestanzahl von Zeitintervallen festgelegt werden, die eine benutzerdefinierte Fehlertoleranz gewährleistet. Außerdem wird der Einfluss der Anfangsbedingungen auf den maximalen Approximationsfehler berücksichtigt, so dass die ermittelte Anzahl von Intervallen für unterschiedliche Anfangsbedingungen gilt und somit für die nichtlineare modellprädiktive Regelung (engl.: nonlinear model predictive control (NMPC)) angewendet werden kann. Mehrere Fallstudien wurden verwendet, um die Wirksamkeit des vorgeschlagenen Ansatzes zu demonstrieren. Sowohl die theoretisch entwickelten Methoden als auch der kombinierte Ansatz wurden mit Hilfe von Open-Source-Software als allgemeines Framework für Testzwecke implementiert. Schließlich wurden die entwickelten Methoden auf das autonome Fahren im NMPC-Framework angewendet. Autonomes Fahren ist der aktuelle Trend in der Automobilindustrie mit dem Ziel, vollautomatisierte oder selbstfahrende Fahrzeuge zu entwickeln und zu produzieren. Reglerentwurf und -betrieb von autonomen Fahrzeugen stellen mehrere Herausforderungen dar, weshalb umfangreiche und intensive Forschungsarbeiten notwendig sind, um den wachsenden industriellen Bedarf abzudecken. Die Fahrzeugbewegung wurde als ein dynamisches Optimierungsproblem dargestellt, das online effizient gelöst wird. Der erfolgreiche Test der NMPC mit zwei Modellfahrzeugen (im Maßstab 1:5 und 1:8 im Vergleich zum realen Fahrzeug) zeigte die Effizienz des entwickelten Ansatzes.This thesis deals with the numerical solution of dynamic nonlinear optimization problems and the development of new methods for their analysis in order to increase the efficiency of calculations. The operation of many natural and technical processes can be formulated as a nonlinear optimal control problem with constraints. Because of the increasing complexity, the solution of such a problem becomes challenging, in particular if it has to be obtained in real-time. The approach of combined multiple-shooting with collocation is efficient for solving such problems even if they contain fast dynamics. Thus, the first target of this work is to further improve its computational performance by providing an analytical Hessian and realizing a parallel-computing scheme. First, the formulas for computing the second-order sensitivities for the combined approach were derived. Using multiple-shooting, the solutions of model equations and evaluations of both first-order and second-order sensitivities can be provided independently for each time interval. Therefore, the second contribution is dedicated to the realization of a parallel computing scheme. As a result, a high speedup factor is attained through parallelization leading to reduction of computational expenses. As a third contribution, a novel control-variable correlation analysis was introduced, which indicates the necessity of employing the analytical Hessian instead of its approximation to efficiently solve an optimization problem. The numerical performance of these three contributions was demonstrated through challenging dynamic optimization problems including optimal control of a large-scale problem containing more than one thousand dynamic variables. The combined method converts the continuous dynamic optimization problem into a nonlinear programming problem using a given number of time intervals. However, there have been no comprehensive rules to properly choose this number. Therefore, the fourth target of this work is devoted to the analysis of the underlying optimization problem with the special focus on the number of discrete time intervals. From the application point of view, the number of time intervals should be selected to simultaneously achieve the balance between the numerical accuracy and the computation load for solving the discretized optimization problem. Moreover, it is imperative to find the minimum number of time intervals to guarantee this balance. Thus, in the context of collocation on finite elements, a novel bilevel approach was proposed, where the outer loop is responsible for finding the minimum number of time intervals and the inner loop evaluates an upper limit of the approximation error by solving an error maximization problem by manipulating the control variables. In this way, a minimum number of time intervals can be determined guaranteeing a user defined error tolerance. Moreover, the impact of the initial conditions on the maximum approximation error is taken into account so that the determined number of intervals is valid for varying initial conditions and thus can be applied to nonlinear model predictive control (NMPC). Several case studies were conducted to demonstrate the efficacy of the proposed approach. Both theoretically developed methods as well as the combined approach were implemented using open-source software as a generalized framework for testing purposes. Finally, the developed methods were applied to autonomous driving in the NMPC framework. Autonomous driving is the current trend in the automotive industry with the aim of designing and producing fully automated or self-driving vehicles. Control design and field operation of autonomous vehicles impose several challenges and thus extensive as well as intensive research studies need to be made to cover the growing industrial demand. In this work, the vehicle motion was modeled as a dynamic optimization problem which is efficiently solved on-line. The successful test of the NMPC with two model vehicles (with scale of 1:5 and 1:8 to real vehicles) demonstrated the effectiveness of the developed approach

SOLVING TWO-LEVEL OPTIMIZATION PROBLEMS WITH APPLICATIONS TO ROBUST DESIGN AND ENERGY MARKETS

This dissertation provides efficient techniques to solve two-level optimization problems. Three specific types of problems are considered. The first problem is robust optimization, which has direct applications to engineering design. Traditionally robust optimization problems have been solved using an inner-outer structure, which can be computationally expensive. This dissertation provides a method to decompose and solve this two-level structure using a modified Benders decomposition. This gradient-based technique is applicable to robust optimization problems with quasiconvex constraints and provides approximate solutions to problems with nonlinear constraints. The second types of two-level problems considered are mathematical and equilibrium programs with equilibrium constraints. Their two-level structure is simplified using Schur's decomposition and reformulation schemes for absolute value functions. The resulting formulations are applicable to game theory problems in operations research and economics. The third type of two-level problem studied is discretely-constrained mixed linear complementarity problems. These are first formulated into a two-level mathematical program with equilibrium constraints and then solved using the aforementioned technique for mathematical and equilibrium programs with equilibrium constraints. The techniques for all three problems help simplify the two-level structure into one level, which helps gain numerical and application insights. The computational effort for solving these problems is greatly reduced using the techniques in this dissertation. Finally, a host of numerical examples are presented to verify the approaches. Diverse applications to economics, operations research, and engineering design motivate the relevance of the novel methods developed in this dissertation

Power Market Cybersecurity and Profit-targeting Cyberattacks

The COVID-19 pandemic has forced many companies and business to operate through remote platforms, which has made everyday life and everyone more digitally connected than ever before. The cybersecurity has become a bigger priority in all aspects of life. A few real-world cases have demonstrated the current capability of cyberattacks as in [1], [2], and [3]. These cases invalidate the traditional belief that cyberattacks are unable to penetrate real-world industrial systems. Beyond the physical damage, some attackers target financial arbitrage advantages brought by false data injection attacks (FDIAs) [4]. Malicious breaches into power market operations could induce catastrophic consequences on fair financial settlements and reliable transmission services. In this dissertation, an in-depth study is conducted to investigate power market cybersecurity and profit-targeting cyberattacks. In the first work, we demonstrate the importance of market-level behavior in defending cyberattacks and designing cyberattacks. A market-level defense analysis is developed to help operators identify cyberattacks, and an LMP-disguising attack strategy is developed to disguise the abnormal LMPs, which can bypass both the bad data detection and market-level detection. In the second work, we propose a comprehensive CVA model for delivering a detailed analysis of four aspects of vulnerability: highly probable cyberattack targets, devastating attack targets, risky load levels, and mitigation ability under different degrees of defense. In the third work, we identify that revenue adequacy, a fundamental power market operation criterion, has not been analyzed under the context of cybersecurity, and we explore the impact of FDIAs targeting real-time (RT) market operations on ISO revenue adequacy analytically and numerically. In the last work, we extend the power system cybersecurity analysis to multi-energy system (MES) framework. An optimally coordinated (OC-FDIA) targeting MES is proposed. Then, we show that the OC-FDIA cause much more severe damages than single-system FDIA and uncoordinated FDIAs. Further, an effective countermeasure is developed against the proposed OCFDIA based on deep learning technique (DL)