A Distribution Network Reconfiguration and Islanding Strategy

With the development of Smart Grid, the reliability and stability of the power system are significantly improved. However, a large-scale outage still possibly occurs when the power system is exposed to extreme conditions. Power system blackstart, the restoration after a complete or partial outage is a key issue needed to be studied for the safety of power system. Network reconfiguration is one of the most important steps when crews try to rapidly restore the network. Therefore, planning an optimal network reconfiguration scheme with the most efficient restoration target at the primary stage of system restoration is necessary and it also builds the foundation to the following restoration process. Besides, the utilization of distributed generators (DGs) has risen sharply in the power system and it plays a critical role in the future Smart Grid to modernize the power grid. The emerging Smart Grid technology, which enables self-sufficient power systems with DGs, provides further opportunities to enhance self-healing capability. The introduction of DGs makes a quick and efficient restoration of power system possible. In this thesis, based on the topological characteristics of scale-free networks and the Discrete Particle Swarm Optimization (DPSO) algorithm, a network reconfiguration scheme is proposed. A power system structure can be converted into a system consisting of nodes and edges. Indices that reflect the nodes’ and edges’ topological characteristics in Graph Theory can be utilized to describe the importance of loads and transmission lines in the power system. Therefore, indices like node importance degree, line betweenness centrality and clustering coefficient are introduced to weigh the importance of loads and transmission lines. Based on these indices, an objective function which aims to restore as many important loads and transmission lines as possible and also subjected to constraints is formulated. The effectiveness of potential reconfiguration scheme is verified by Depth First Search (DFS) algorithm. Finally, DPSO algorithm is employed to obtain the optimal reconfiguration scheme. The comprehensive reconfiguration scheme proposed by my thesis can be the theoretical basis for the power grid dispatchers. Besides, DGs are introduced in this thesis to enhance the restoration efficiency and success rate at the primary stage of network restoration. Firstly, the selection and classification principle of DGs are introduced in my thesis. In addition, the start sequence principle of DGs is presented as a foundation for the following stability analysis of network restoration with DGs. Then, the objective function subjected to constraints that aims to restore as many important loads as possible is formulated. Based on the restoration objective, islands that include part of important and restorable loads are formed because the DGs’ capacity cannot ensure an entire restoration of the outage areas. Finally, DPSO is used to obtain the optimal solution of islanding strategy and the state sequence matrix is utilized to represent the solution space. It is believed that this work will provide some useful insight into improving the power system resiliency in the face of extreme events such as natural or man-made disasters

Subsystem partitioning for power system black-start considering restoration reliability

Za slučaj nestanka struje velikih razmjera, u energetskom sustavu mogu postojati više od jedne jedinice za crni start. Racionalna shema za obnovu podjele podsustava mogla bi ubrzati čitavi postupak uspostavljanja sustava i pomagati pouzdanost uspostavljanja. U radu se predlaže strategija podjele podsustava uzimajući u obzir pouzdanost uspostavljanja. Proračun pouzdanosti uspostavljanja čvorova zasnovan je na nepovezanosti algoritma binarnog dijagrama odlučivanja. Tada se, prema različitoj pouzdanosti uspostavljanja čvorova koji se trebaju uspostaviti pomoću različitih jedinica za crni start ili odgovarajućih područja, kao i prema vremenu uspostavljanja putanja, prepoznaju odgovarajući podsustavi. Predložena podjele podsustava uspostavljanja posebnu pozornost posvećuje nesigurnosti dalekovoda i transformatora. Funkcija kriterija podjele, koja se rješava genetičkim algoritmom, postavljena je da procijeni rezultat podjele. Konačno, učinkovitost predložene metode potvrđena je sustavom sabirnice IEEE 118.After large scale blackout, there may be more than one black-start unit in power system. A rational subsystem partitioning restoration scheme could speed up the entire system restoration procedure and promote restoration reliability. In the paper, a subsystem partitioning strategy considering the restoration reliability is proposed. The calculation of nodes restoration reliability based on the disjoint of Binary Decision Diagram (BDD) algorithm. Then according to different restoration reliability of nodes to be restored by different black-start units or charged areas, as well as its restoration time of the paths, the belonging subsystems are identified. The proposed subsystem partitioning restoration pays extra attention to the uncertainty of transmission lines and transformers which fits the practical operation conditions better. A partitioning criterion function which is solved by genetic algorithm is established to evaluate the partitioning result. Finally, the effectiveness of the proposed method is validated by IEEE 118 bus system

Genetic Algorithms Applications to Power System Security Schemes

This thesis details the approaches which aim to automatically optimize power system security schemes. In this research, power system security scheme includes two main plans. The first plan, which is called the defence plan scheme, is about preventing cascading blackouts while the second plan, which is called the restoration plan, is about rebuilding the power system in case of failure of the first plan. Practically, the defence plan includes under-frequency load shedding and under-frequency islanding schemes. These two schemes are always considered the last stage of the defensive actions against any severe incident. It is recognized that it is not easy for any power system’s operational planner to obtain the minimum amount of load shedding or the best power system islanding formation. In the case of defence plan failure, which is always possible, a full or partial system collapse may occur. In this situation, the power system operator is urgently required to promptly restore the system. This is not an easy task, since the operator must not violate many power system security constraints. In this research, genetic algorithms and expert systems are employed, as optimization methods, to identify the best amount of load shedding and island formation for the defence plan and the shortest path to rebuild the power system for the restoration plan. In the process of designing the power system security scheme, the majority of the electromechanical power system security constraints are considered. It is well known that power system optimization problems often have a huge solution space. In this regard, many successful techniques have been used to reduce the size of the solution spaces associated with the optimization of the power system security schemes in this work. The Libyan power system is used as an industrial case study to validate the practicality of the research approaches. The results clearly show that the new methods that have been researched in this PhD work have shown great success. Using the Libyan power system, the optimized defence plan has been compared to the current defence plan. The results of this comparison have shown that the optimized defence plan outperforms the current one. Regarding the optimized restoration plan, the results present the fact that the Libyan power system can be restored in reasonable time.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Decision aid function for restoration of transmission power systems after a blackout

This thesis, based on a project realised in cooperation with Électricité de France (EDF), proposes a new concept for a Decision Aid Function FOr Restoration (DAFFOR) of transmission power systems after a blackout. DAFFOR is an interactive computer tool which provides the operators in power system control centres with guidance concerning the actions to execute during the restoration, in real-time conditions. In other words, it takes into account the real-time state of the power system, including the unforeseen events that may happen during the restoration. Since time is a limiting factor and the decision making is a highly combinatorial problem, a knowledge-based system is proposed in order to solve it. The restoration process can be decomposed into two main stages. The first one, skeleton creation, consists of starting the production units and connecting some transmission devices in order to energize a strong network. The second stage, load pickup, aims to supply the consumers. In DAFFOR, EDF's strategy for the first restoration stage has been implemented, and a new strategy for the load pickup stage has been proposed and implemented in the form of rules. The above restoration strategies represent DAFFOR's knowledge, which has been enhanced with a number of heuristics. DAFFOR consists of two kernels: the Reasoning kernel and the Real Time Update kernel. The Reasoning kernel has the task of assisting the operator during the restoration process and is the interactive guidance part of DAFFOR. It can either suggest a control action to execute on the power system to the operators or assess a control action provided by the operators. The control action is suggested with respect to operating limits (over- and under-voltages, frequency excursions and overloads) and according to knowledge (restoration strategy and heuristics). The feasibility of an action is tested within an internal dynamic simulator, which also takes into account the time necessary to physically execute an action (e.g., telephone a person in the field). The Reasoning kernel can adapt its operation via data generated by the Real Time Update (RTUpd) kernel. The RTUpd kernel steadily reads real-time power system data from System Control and Data Acquisition (SCADA) function and those entered by the operators (if unavailable from SCADA). It generates a coherent data set, which is the only real-time information available to the Reasoning kernel, and the message which indicates to the Reasoning kernel how to continue its operation. In addition to the real-time data, the RTUpd kernel has two feedback inputs internal to DAFFOR: a coherent data set generated in the previous data processing by the RTUpd kernel itself, and a simulated data set generated by the Reasoning kernel (i.e., its internal dynamic simulator). With these three inputs, the RTUpd kernel generates the current image of the power system, and identifies unforeseen events. Thanks to the RTUpd kernel, the Reasoning kernel may keep up with the dynamic evolution of the power system. The stand-alone prototype of DAFFOR has been tested with data provided by EDF, and shown very good efficiency. At present, it is about to be coupled with the EDF's operator training simulator in order to test its real-time functionality. This work also proposes an original method aimed at the determination of a strategy for the load pickup stage. A genetic algorithm has been developed which generates the optimized sequences of manoeuvres for different initial states of the power system for the second restoration stage. It uses the dynamic simulator as its evaluation function. The obtained results have shown that some additional manipulations should be done in order to deduce generic rules for the load pickup strategy. At present, the obtained sequences are classified in a decision tree, which permits the most adequate sequence for the initial state to be chosen

Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topi

Identification and development of microgrids emergency control procedures

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200