Using Renewable-Based Microgrid Capabilities for Power System Restoration

Power system restoration (PSR) is a very important procedure to ensure the consumer supply. In this paper, a decentralized multi-agent system (MAS) for dealing with the microgrid restoration procedure is proposed. In this proposed method, each agent is associated to a consumer or microsource (MS) and these will communicate between each other in order to reach a common decision. The agents solve a 0/1 knapsack problem to determine the best load connection sequence during the microgrid restoration procedure. The proposed MAS is tested in two different case studies: a total blackout and a partial blackout, in which the emergency demand response programs are considered. It is developed in the Matlab/Simulink environment and is validated by performing the corresponding dynamic simulations.Power system restoration (PSR) is a very important procedure to ensure the consumer supply. In this paper, a decentralized multi-agent system (MAS) for dealing with the microgrid restoration procedure is proposed. In this proposed method, each agent is associated to a consumer or microsource (MS) and these will communicate between each other in order to reach a common decision. The agents solve a 0/1 knapsack problem to determine the best load connection sequence during the microgrid restoration procedure. The proposed MAS is tested in two different case studies: a total blackout and a partial blackout, in which the emergency demand response programs are considered. It is developed in the Matlab/Simulink environment and is validated by performing the corresponding dynamic simulations

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

New optimization techniques for power system generation scheduling

Generation scheduling in restructured electric power systems is critical to maintain the stability and security of a power system and economical operation of the electricity market. However, new generation scheduling problems (GSPs) are emerging under critical or new circumstances, such as generator starting sequence and black-start (BS) generator installation problems in power system restoration (PSR), and generation operational planning considering carbon dioxide (CO2) emission regulation. This dissertation proposes new optimization techniques to investigate these new GSPs that do not fall into the traditional categories. Resilience and efficient recovery are critical and desirable features for electric power systems. Smart grid technologies are expected to enable a grid to be restored from major outages efficiently and safely. As a result, power system restoration is increasingly important for system planning and operation. In this dissertation, the optimal generator start-up strategy is developed to provide the starting sequence of all BS or non-black-start (NBS) generating units to maximize the overall system generation capability. Then, based on the developed method to estimate the total restoration time and system generation capability, the optimal installation strategy of blackstart capabilities is proposed for system planners to develop the restoration plan and achieve an efficient restoration process. Therefore, a new decision support tool for system restoration has been developed to assist system restoration planners and operators to restore generation and transmission systems in an on-line environment. This tool is able to accommodate rapidly changing system conditions in order to avoid catastrophic outages. Moreover, to achieve the goal of a sustainable and environment-friendly power grid, CO2 mitigation policies, such as CO2 cap-and-trade, help to reduce consumption in fossil energy and promote a shift to renewable energy resources. The regulation of CO2 emissions for electric power industry to mitigate global warming brings a new challenge to generation companies (GENCOs). In a competitive market environment, GENCOs can schedule the maintenance periods to maximize their profits. Independent System Operator\u27s (ISO) functionality is also considered from the view point of system reliability and cost minimization. Considering these new effects of CO2 emission regulation, GENCOs need to adjust their scheduling strategies in the electricity market and bidding strategies in CO2 allowance market. This dissertation proposes a formulation of the emission-constrained GSP and its solution methodology involving generation maintenance scheduling, unit commitment, and CO2 cap-and-trade. The coordinated optimal maintenance scheduling and CO2 allowance bidding strategy is proposed to provide valuable information for GENCOs\u27 decision makings in both electricity and CO2 allowance markets. By solving these new GSPs with advanced optimization techniques of Mixed Integer Linear Programming (MILP) and Mixed Integer Bi-level Liner Programming (MIBLP), this dissertation has developed the highly efficient on-line decision support tool and optimal planning strategies to enhance resilience and sustainability of the electric power grid

A mixed integer linear programming model for minimum backbone grid

Developing a minimum backbone grid in the power system planning is beneficial to improve the power system’s resilience. To obtain a minimum backbone grid, a mixed integer linear programming (MILP) model with network connectivity constraints for a minimum backbone grid is proposed. In the model, some constraints are presented to consider the practical application requirements. Especially, to avoid islands in the minimum backbone grid, a set of linear constraints based on single-commodity flow formulations is proposed to ensure connectivity of the backbone grid. The simulations on the IEEE-39 bus system and the French 1888 bus system show that the proposed model can be solved with higher computational efficiency in only about 30 min for such a large system and the minimum backbone grid has a small scale only 52% of the original grid. Compared with the improved fireworks method, the minimum backbone grid from the proposed method has fewer lines and generators

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

Pre-disaster transmission maintenance scheduling considering network topology optimization

Several devastating experiences with extreme natural disasters demonstrate that improving power system resilience is becoming increasingly important. This paper proposes a pre-disaster transmission maintenance scheduling considering network topology optimization to ensure the power system economics before disasters and power system resilience during disasters. The transmission line fragility is distinguished and considered in the proposed optimization model to determine the maintenance scheduling of defective lines that minimizes load shedding during disasters. The proposed model is established as a tri-level optimization problem that is further reformulated to a bi-level problem utilizing duality theory. The column-and-constraint generation (C&CG) algorithm is employed to solve the equivalent robust optimization problem. Finally, the proposed model and its solution algorithm are implemented on the modified IEEE RTS-79 system. The significant cost savings and increased resilience illustrate the effectiveness of the proposed model

Identification and development of microgrids emergency control procedures

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