Power System Stability Assessment and Enhancement using Computational Intelligence

The main objective of the dissertation is to develop a fast and robust tool for assessment of power system stability and design a framework for enhancing system stability. The proposed framework is - based on the investigation of the dynamic behavior of the system - a market based rescheduling strategy that increases the stability margin. The dissertation specifically puts emphasis on the following approached: Power System Stability Evaluation: System stability is investigated by simulating a set of critical contingencies to determine whether the disturbances will result in any unsafe operating conditions and extract the necessary information to classify system states. The classification is based on the computation of the critical fault clearing time (CCT) for transient stability assessment (TSA) and the minimum damping of oscillation (MDO) for power system oscillatory stability assessment (OSA). The customary method of power system transient stability analysis including time-domain simulation (TDS) is used to compute the CCT at each critical contingency and Prony analysis as an efficient identification technique to estimate the mode parameters from the actual time response. The use of Prony analysis is to account for the effects of the change in location of the small disturbances as well as the increase in system nonlinearity on oscillating modes. Fast Power System Stability Assessment Tool: An artificial neural network (ANN) is designed to serve as accurate and fast tool for dynamic stability assessment (DSA). Fast response of ANN allows system operators to take suitable control actions to enhance the system stability and to forestall any possible impending breakup of the system. Two offline trained ANN are designed to map the dynamic behavior by relating the selected input features and the calculated CCT (as indicator for transient stability) and MDO (as indicator for oscillatory stability). Input features of ANN are selected to characterize the following: Changes in system topology and power distributions due to outage of major equipment such as transmission line, generation unit or large load Change in fault location and the severity of the fault Variation in loading levels and load allocation among market participants The features are generated for a wide range of loading at each expected system topology. Initial feature sets are pre-selected by engineering judgment based on experience in power system operation. In order to improve the accuracy of ANN to map the power system dynamic behavior, final selection is performed in the following three steps. In the first step, the generators terminal voltage drops immediately after fault are selected features to characterize the severity of the contingency with respect to the generators and to detect the fault location. In the second step, new features based on the inertia constant and the generated power in each area are calculated to characterize the changes in system topology and power flow pattern during normal and abnormal operation. In the third step, a systematic clustering feature selection technique is used to select the most important features that characterize the load levels and the power flow through lines from the mathematical viewpoint. The results prove the suitability of ANN in DSA with a reasonable degree of accuracy. Dynamic Stability Enhancement: To achieve online dynamic stability enhancement an online market based rescheduling strategy is proposed in the deregulated power systems. In case of power system operation by a centralized pool in vertically integrated electric utilities, generation rescheduling based sensitivity analysis is proposed. In the proposed market for deregulated power systems, the transactions among suppliers and consumers participating in the market are reallocated based on optional power bids to enhance system stability in case the available control actions are insufficient to enhance system stability. All participants are allowed to submit voluntary power bids to increase or decrease their scheduled level with equal chance. These bids represent the offered power quantity and the corresponding price. The goal of the framework is to enhance system stability with minimum additional and opportunity costs arising from the rescheduling. In case of vertically integrated electric utility, generation rescheduling based sensitivity analysis is used to enhance the system stability. The sensitivity analysis is based on the generators response following the most probable contingency. The generators are split into critical machines with positive sensitivity and non-critical machines with negative sensitivity. The change of the generation level among critical and non-critical machines provides the trajectories for stabilization procedure. The re-allocation of power among generators in each group is calculated based on the generator capacities and inertia constant, which simplifies the optimization procedure and speeds up the iterative to find a feasible solution. The objective is to minimize the increase in the cost due to rescheduling process. Particle swarm optimization is used as an optimization tool to search for the optimal solution to enhance the system stability with a minimum cost. The handling of all system constraints including stability constraints is achieved using a self-adaptive penalty function. Comparison strategy for selecting the best individuals during the optimization process is proposed where the feasible solutions are ever preferable during selection of local and global best particles.Die Schwerpunkte der Dissertation liegen in der Entwicklung eines schnellen und robusten Echtzeit-Bewertungsinstruments für Stabilitätsuntersuchungen in elektrischen Energienetzen und in dem Entwurf von Rahmenbedingungen zur Verbesserung der Systemstabilität. Basierend auf Untersuchungen bezüglich des dynamischen Verhaltens von elektrischen Energienetzen ist das Ziel der vorgeschlagenen Rahmenbedingungen, eine Planungsstrategie zu entwickeln, die marktwirtschaftlich ausgerichtet ist, um so die Stabilitätsgrenze zu verbessern und die erforderliche Systemsicherheit zu gewährleisten. Die dynamische Stabilität von elektrischen Energienetzen wurde bezogen auf die transiente und oszillatorische Stabilität untersucht, welche zur Beurteilung des dynamischen Verhaltens des Systems während Netzstörungen genutzt wird. Das Ziel der Dissertation ist die folgenden Aspekte zu untersuchen: Evaluierung der Dynamischen Stabilität: Die dynamische Stabilität ist durch die Simulation von kritischen Netzereignissen untersucht worden. Ziel war es, Störungen zu ermitteln, die zu kritischen oder gar unsicheren Betriebszuständen führen, und wichtige Beurteilungsparameter über den Zustand des Netzes auszuwählen. Die Beurteilungsparameter über den Zustand des elektrischen Energienetzes sind unter Verwendung der kritischen Fehlerklärungszeit als Indikator für die transiente Stabilität und der minimalen Dämpfung von Oszillationen als Indikator für die ozillatorische Stabilität ermittelt worden. Die übliche Methode bei einer transienten Stabilitätsanalyse in elektrischen Energienetzen basiert auf Simulationen im Zeitbereich und wird unter der Verwendung von vordefinierten netzkritischen Ereignissen genutzt, um die kritische Fehlerklärungszeit präzise zu berechnen. Die Prony-Analyse als eine effiziente Identifizierungstechnik wird zur Schätzung der Zustandsparameter auf eine einer Störung folgenden Zeitantwort verwendet. Der Gebrauch der Prony-Analyse erfasst die Veränderungen im Fehlerort von kleinen Störungen und einen Anstieg von Systemnichtlinearitäten im oszillatorischen Modus. Die mit Hilfe der Modalanalyse berechneten Parameter für den oszillatorischen Modus werden als Referenzsignale während des Abstimmens der Parameter der Prony-Analyse verwendet. Ziel ist die Verbesserung der Identifizierung des Systemmodus. Schnelles Bewertungswerkzeug für die dynamische Stabilität: Ein präzises und schnelles Werkzeug für die Bewertung von dynamischer Stabilität wurde mit Hilfe von künstlichen, neuronalen Netzen entwickelt. Die schnelle Antwort eines künstlichen, neuronalen Netzes ermöglicht es dem Netzbetreiber, geeignete fehlerbehebende Schalthandlungen während kritischer Netzereignisse durchzuführen. So kann die Stabilität des elektrischen Netzes gewährleistet und bevorstehende Netzausfälle verhindert werden. Zwei offline trainierte künstliche neuronale Netze sind entwickelt worden, um a) das dynamische Verhalten unter Verwendung ausgewählter Eingangseigenschaften und b) die berechnete kritische Fehlerklärungszeit als Indikator für die transiente Stabilität und die minimale Dämpfung der Oszillationen als Indikator für ozillatorische Stabilität abzubilden. Künstliche, neuronale Netze bieten vielversprechende Lösungen für schnelle Berechnungen bei online Anwendungen. Als Folge kann die hohe Anzahl an Berechnungen, die zur Untersuchung aller zu erwartenden kritischen Netzereignissen in elektrischen Energienetzen benötigt werden, schnell durchgeführt werden. Dies ermöglicht eine Bewertung der Systemzustände des elektrischen Netzes und eine Initiierung der zu erwartenden Schalthandlungen, um so die Systemstabilität zu verbessern. Für eine genaue Bewertung der dynamischen Stabilität sollten die Eingangseigenschaften für das künstliche, neuronale Netz sorgfältig ausgewählt werden. In dieser Arbeit sind die Eingangseigenschaften aus den gesamten Systemdaten ausgewählt worden, um die folgenden Eigenschaften kennzuzeichnen: i. Veränderungen in der Systemtopologie und des Lastflusses durch Ausfälle oder planmäßige Wartungen von Hauptkomponenten des Systems, wie zum Beispiel Übertragungsleitungen, Erzeugereinheiten oder großen Lasten ii. Veränderungen des Fehlerortes und des Einflusses des Fehlers auf die elektrischen Komponenten iii. Laständerungen und Lastaufteilung zwischen Netzversorgern Die Eingangseigenschaften wurden für viele, unterschiedliche Lastszenarien in Verbindung mit den zu erwartenden Netztopologien erzeugt. Die Anfangsbedingungen sind auf Grund von Erfahrungen mit dem Betrieb von elektrischen Energienetzen und bedingt durch das zu schätzende Ziel vorausgewählt. Die endgültige Auswahl der Eingangseigenschaften ist in drei Schritte unterteilt, um so die Genauigkeit des künstlichen, neuronalen Netzes zu erhöhen, welches die dynamische Stabilität des Energienetzes abbildet. Im ersten Schritt sind die Generatorklemmenspannungseinbrüche direkt nach der Netzstörung die wichtigen ausgewählten Eigenschaften. Hierdurch wird die Schwere des kritischen Netzereignisses aus der Sicht der Erzeugungseinheit gekennzeichnet und die Fehlerstelle lokalisiert. In dem zweiten Schritt werden neue Eingangseigenschaften basierend auf der Massenträgheitskonstante des Systems und der erzeugten Leistung in jedem Gebiet berechnet. So können Veränderungen in der Netztopologie und des Lastflusses unter normalen und gestörten Betriebsbedingungen gekennzeichnet werden. Im dritten Schritt wird eine systematische Cluster-Bildung der Eigenschaften genutzt, um so die wichtigsten Eigenschaften auszuwählen, die Aussagen über die Lastzustände und den Lastfluss über die Leitungen zulassen. Alle ausgewählten Eigenschaften repräsentieren das Eingangsmuster, wobei das Ausgangsmuster der Index der dynamischen Stabilitätsanalyse ist. Die Ergebnisse stellen die Eignung des künstlichen, neuronalen Netzes bei der Bewertung der dynamischen Stabilität dar. Verbesserung der dynamischen Stabilität: Eine online Verbesserung der dynamischen Stabilität kann durch eine vorgeschlagene marktwirtschaftliche Neuplanung des deregulierten Energiesystems und durch eine Neuplanung der Erzeugungseinheiten basierend auf der Empfindlichkeitsanalyse im Falle des Betriebs des Energienetzes durch eine zentrale Einheit erreicht werden. In dem vorgeschlagenen Markt für deregulierte Energiesysteme wird im Falle, dass vorgesehenen Schalthandlungen das Netz nicht in einen stabilen Zustand zurückbringen kann, die Energie zwischen Versorgern und Verbrauchern basierend auf optionalen Leistungsgeboten umgeschichtet. Alle Erzeuger und Verbraucher sind berechtigt an diesem Markt durch freiwillige Leistungsgebote teilzunehmen, um so ihre geplante Menge chancengleich zu erhöhen oder zu verkleinern. Diese Gebote der Marktteilnehmer repräsentieren die angebotene Leistungsmenge und den darauf bezogenen Preis. Teilnehmer, von denen es verlangt ist, Erzeugung oder Verbrauch zu reduzieren, werden für diese Möglichkeit zur Reduzierung bezahlt. So kann der Verlust der Serviceleistung kompensiert werden, während Teilnehmer, deren Leistung erhöht wird, durch den Marktpreis plus zusätzlicher Kosten für zusätzliche Veränderungen entlohnt werden. Das Ziel dieser Rahmenbedingungen ist eine Verbesserung der Systemstabilität kombiniert mit einem Minimum an zusätzlichen Kosten auftretend durch die Neuplanung. Im Falle eines zentralen Energiemarktes wird die Neuplanung der Erzeuger basierend auf der Empfindlichkeitsanalyse durchgeführt, um so eine Verbesserung der Systemstabilität zu erreichen. Die Empfindlichkeitsanalyse bezieht sich auf die Systemantwort des Generators während des belastbarsten kritischen Netzereignisses. Dieses kritische Netzereignis trennt die Erzeugungseinheiten a) in kritische Maschinen, die eine positive Empfindlichkeit besitzen, und b) in nicht-kritische Maschinen mit einer negativen Empfindlichkeit. Die Einteilung in kritische und nicht-kritische Maschinen ermöglicht eine Lösung für die Stabilisierung des Systems. Die Verteilung der verschobenen Leistung zwischen den Generatoren in jeder Gruppe wird unter Verwendung der Generatorleistungen und der Massenträgheitskonstanten berechnet. Dies erleichtert den Optimierungsalgorithmus und beschleunigt das Erhalten einer möglichen Lösung. Das Ziel ist die Minimierung der Erhöhung der Kosten für die absolut erzeugte Leistung auf Grund der Abweichung vom wirtschaftlichen Arbeitspunkt. In dieser Arbeit wird die Particle Swarm Optimierung als Werkzeug verwendet, um damit eine optimale Lösung mit den minimalen Kosten zu erlangen. Dadurch kann eine Verbesserung der dynamischen Stabilität des elektrischen Energienetzes unter Berücksichtigung aller systembedingten Nebenbedingungen erlangt werden. Die Handhabung aller systembedingten Nebenbedingungen inklusive der Nebenbedingungen der dynamischen Stabilität kann durch eine selbstanpassende Straffunktion erreicht werden

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Analysis and management of security constraints in overstressed power systems

Management of operational security constraints is one of the important tasks performed by system operators, which must be addressed properly for secure and economic operation. Constraint management is becoming an increasingly complex and challenging to execute in modern electricity networks for three main reasons. First, insufficient transmission capacity during peak and emergency conditions, which typically result in numerous constraint violations. Second, reduced fault levels, inertia and damping due to power electronic interfaced demand and stochastic renewable generation, which are making network more vulnerable to even small disturbances. Third, re-regulated electricity markets require the networks to operate much closer to their operational security limits, which typically result in stressed and overstressed operating conditions. Operational security constraints can be divided into static security limits (bus voltage and branch thermal limits) and dynamic security limits (voltage and angle stability limits). Security constraint management, in general, is formulated as a constrained, nonlinear, and nonconvex optimization problem. The problem is usually solved by conventional gradient-based nonlinear programming methods to devise optimal non-emergency or emergency corrective actions utilizing minimal system reserves. When the network is in emergency state with reduced/insufficient control capability, the solution space of the corresponding nonlinear optimization problem may be too small, or even infeasible. In such cases, conventional non-linear programming methods may fail to compute a feasible (corrective) control solution that mitigate all constraint violations or might fail to rationalize a large number of immediate post-contingency constraint violations into a smaller number of critical constraints. Although there exists some work on devising corrective actions for voltage and thermal congestion management, this has mostly focused on the alert state of the operation, not on the overstressed and emergency conditions, where, if appropriate control actions are not taken, network may lose its integrity. As it will be difficult for a system operator to manage a large number of constraint violations (e.g. more than ten) at one time, it is very important to rationalize the violated constraints to a minimum subset of critical constraints and then use information on their type and location to implement the right corrective actions at the right locations, requiring minimal system reserves and switching operations. Hence, network operators and network planners should be equipped with intelligent computational tools to “filter out” the most critical constraints when the feasible solution space is empty and to provide a feasible control solution when the solution space is too narrow. With an aim to address these operational difficulties and challenges, this PhD thesis presents three novel interdependent frameworks: Infeasibility Diagnosis and Resolution Framework (IDRF), Constraint Rationalization Framework (CRF) and Remedial Action Selection and Implementation Framework (RASIF). IDRF presents a metaheuristic methodology to localise and resolve infeasibility in constraint management problem formulations (in specific) and nonlinear optimization problem formulations (in general). CRF extends PIDRF and reduces many immediate post-contingency constraint violations into a small number of critical constraints, according to various operational priorities during overstressed operating conditions. Each operational priority is modelled as a separate objective function and the formulation can be easily extended to include other operational aspects. Based on the developed CRF, RASIF presents a methodology for optimal selection and implementation of the most effective remedial actions utilizing various ancillary services, such as distributed generation control, reactive power compensation, demand side management, load shedding strategies. The target buses for the implementation of the selected remedial actions are identified using bus active and reactive power injection sensitivity factors, corresponding to the overloaded lines and buses with excessive voltage violations (i.e. critical constraints). The RASIF is validated through both static and dynamic simulations to check the satisfiability of dynamic security constraints during the transition and static security constraints after the transition. The obtained results demonstrate that the framework for implementation of remedial actions allows the most secure transition between the pre-contingency and post-contingency stable equilibrium points

Energy Management of Prosumer Communities

The penetration of distributed generation, energy storages and smart loads has resulted in the emergence of prosumers: entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably in the available electricity markets. Significant untapped potential remains in the exploitation and coordination of small and medium-sized distributed energy resources. However, such resources usually have a primary purpose, which imposes constraints on the exploitation of the resource; for example, the primary purpose of an electric vehicle battery is for driving, so the battery could be used as temporary storage for excess photovoltaic energy only if the vehicle is available for driving when the owner expects it to be. The aggregation of several distributed energy resources is a solution for coping with the unavailability of one resource. Solutions are needed for managing the electricity production and consumption characteristics of diverse distributed energy resources in order to obtain prosumers with more generic capabilities and services for electricity production, storage, and consumption. This collection of articles studies such prosumers and the emergence of prosumer communities. Demand response-capable smart loads, battery storages and photovoltaic generation resources are forecasted and optimized to ensure energy-efficient and, in some cases, profitable operation of the resources

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Investigation of domestic level EV chargers in the Distribution Network: An Assessment and mitigation solution

This research focuses on the electrification of the transport sector. Such electrification could potentially pose challenges to the distribution system operator (DSO) in terms of reliability, power quality and cost-effective implementation. This thesis contributes to both, an Electrical Vehicle (EV) load demand profiling and advanced use of reactive power compensation (D-STATCOM) to facilitate flexible and secure network operation. The main aim of this research is to investigate the planning and operation of low voltage distribution networks (LVDN) with increasing electrical vehicles (EVs) proliferation and the effects of higher demand charging systems. This work is based on two different independent strands of research. Firstly, the thesis illustrates how the flexibility and composition of aggregated EVs demand can be obtained with very limited information available. Once the composition of demand is available, future energy scenarios are analysed in respect to the impact of higher EVs charging rates on single phase connections at LV distribution network level. A novel planning model based on energy scenario simulations suitable for the utilization of existing assets is developed. The proposed framework can provide probabilistic risk assessment of power quality (PQ) variations that may arise due to the proliferation of significant numbers of EVs chargers. Monte Carlo (MC) based simulation is applied in this regard. This probabilistic approach is used to estimate the likely impact of EVs chargers against the extreme-case scenarios. Secondly, in relation to increased EVs penetration, dynamic reactive power reserve management through network voltage control is considered. In this regard, a generic distribution static synchronous compensator (D-STATCOM) model is adapted to achieve network voltage stability. The main emphasis is on a generic D-STATCOM modelling technique, where each individual EV charging is considered through a probability density function that is inclusive of dynamic D-STATCOM support. It demonstrates how optimal techniques can consider the demand flexibility at each bus to meet the requirement of network operator while maintaining the relevant steady state and/or dynamic performance indicators (voltage level) of the network. The results show that reactive power compensation through D-STATCOM, in the context of EVs integration, can provide continuous voltage support and thereby facilitate 90% penetration of network customers with EV connections at a normal EV charging rate (3.68 kW). The results are improved by using optimal power flow. The results suggest, if fast charging (up to 11 kW) is employed, up to 50% of network EV customers can be accommodated by utilising the optimal planning approach. During the case study, it is observed that the transformer loading is increased significantly in the presence of D-STATCOM. The transformer loading reaches approximately up to 300%, in one of the contingencies at 11 kW EV charging, so transformer upgrading is still required. Three-phase connected DSTATCOM is normally used by the DSO to control power quality issues in the network. Although, to maintain voltage level at each individual phase with three-phase connected device is not possible. So, single-phase connected D-STATCOM is used to control the voltage at each individual phase. Single-phase connected D-STATCOM is able maintain the voltage level at each individual phase at 1 p.u. This research will be of interest to the DSO, as it will provide an insight to the issues associated with higher penetration of EV chargers, present in the realization of a sustainable transport electrification agenda

Decentralized algorithm of dynamic task allocation for a swarm of homogeneous robots

The current trends in the robotics field have led to the development of large-scale swarm robot systems, which are deployed for complex missions. The robots in these systems must communicate and interact with each other and with their environment for complex task processing. A major problem for this trend is the poor task planning mechanism, which includes both task decomposition and task allocation. Task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize the cost while satisfying operational constraints. Task allocation mechanism must be run by each robot, which integrates the swarm whenever it senses a change in the environment to make sure the robot is assigned to the most appropriate task, if not, the robot should reassign itself to its nearest task. The main contribution in this thesis is to maximize the overall efficiency of the system by minimizing the total time needed to accomplish the dynamic task allocation problem. The near-optimal allocation schemes are found using a novel hybrid decentralized algorithm for a dynamic task allocation in a swarm of homogeneous robots, where the number of the tasks is more than the robots present in the system. This hybrid approach is based on both the Simulated Annealing (SA) optimization technique combined with the Discrete Particle Swarm Optimization (DPSO) technique. Also, another major contribution in this thesis is the formulation of the dynamic task allocation equations for the homogeneous swarm robotics using integer linear programming and the cost function and constraints are introduced for the given problem. Then, the DPSO and SA algorithms are developed to accomplish the task in a minimal time. Simulation is implemented using only two test cases via MATLAB. Simulation results show that PSO exhibits a smaller and more stable convergence characteristics and SA technique owns a better quality solution. Then, after developing the hybrid algorithm, which combines SA with PSO, simulation instances are extended to include fifteen more test cases with different swarm dimensions to ensure the robustness and scalability of the proposed algorithm over the traditional PSO and SA optimization techniques. Based on the simulation results, the hybrid DPSO/SA approach proves to have a higher efficiency in both small and large swarm sizes than the other traditional algorithms such as Particle Swarm Optimization technique and Simulated Annealing technique. The simulation results also demonstrate that the proposed approach can dislodge a state from a local minimum and guide it to the global minimum. Thus, the contributions of the proposed hybrid DPSO/SA algorithm involve possessing both the pros of high quality solution in SA and the fast convergence time capability in PSO. Also, a parameters\u27 selection process for the hybrid algorithm is proposed as a further contribution in an attempt to enhance the algorithm efficiency because the heuristic optimization techniques are very sensitive to any parameter changes. In addition, Verification is performed to ensure the effectiveness of the proposed algorithm by comparing it with results of an exact solver in terms of computational time, number of iterations and quality of solution. The exact solver that is used in this research is the Hungarian algorithm. This comparison shows that the proposed algorithm gives a superior performance in almost all swarm sizes with both stable and small execution time. However, it also shows that the proposed hybrid algorithm\u27s cost values which is the distance traveled by the robots to perform the tasks are larger than the cost values of the Hungarian algorithm but the execution time of the hybrid algorithm is much better. Finally, one last contribution in this thesis is that the proposed algorithm is implemented and extensively tested in a real experiment using a swarm of 4 robots. The robots that are used in the real experiment called Elisa-III robots