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Parallel restoration is an efficient way to speed up the restoration process after a wide‐area outage or a complete blackout of a power system. To implement efficient parallel restoration, an appropriate network partitioning method is necessary. Given this background, a two‐step network partitioning strategy for parallel power system restoration is presented. In order to minimise the restoration time of generating units, a grouping model for non‐black‐start units is developed first. Based on the graph theory, a power network partitioning model is presented with the objectives of minimising the number of the interconnected lines and maximising the electrical distance of the interconnected lines among different restoration subsystems. The well‐established AMPL/CPLEX is employed to solve the developed optimisation models. Finally, the IEEE New England 10‐unit 39‐bus system and a part of Zhejiang provincial power system in China are employed to illustrate the feasibility and effectiveness of the developed models. [ABSTRACT FROM AUTHOR]

The applications of battery energy storage systems (BESSs) in power systems have been paid increasing attention in recent years. Apart from all the benefits that BESSs can bring to the power systems, their potential applications in assisting power system restoration after a major blackout have not yet been systematically investigated. The flexible charging and discharging characteristics of BESSs could help maintain the balance between power supply and demand during the system restoration process, thus the recovery speed of the power system concerned after a blackout could be accelerated. Given this background, the applications of BESSs in power system restoration is investigated. First, the potential applications of BESSs during power system restoration process are discussed. A multi‐objective optimisation model is next proposed, aiming at minimising the number of circuit breaker operations and outage durations of both the non‐black‐start generating units and the important loads. Meanwhile, the application of intermittent renewable energy sources for improving power system restoration is also discussed. The operation strategy of BESSs will also be optimised after the system restoration scheme is attained through the multi‐objective optimisation. The proposed method is demonstrated by numerical tests on the IEEE 30‐bus and 162‐bus test systems. [ABSTRACT FROM AUTHOR]

The energy stored in the batteries of electric vehicles (EVs) could be employed for starting generators when a blackout or a local outage occurs. Considering the feature of the battery swapping mode, an available capacity model of the batteries in a centralised charging station is first developed. Then, the authors analyse the start‐up characteristics of a generator powered by batteries and propose a bi‐level optimisation‐based network reconfiguration model to determine the restoration paths with an objective of maximising the overall generation capability. In the upper‐level optimisation model, the generator start‐up sequence is optimised, whereas the restoration paths are optimised in the lower‐level one. Moreover, they consider the uncertainties associated with the available capacity of the batteries. The bi‐level optimisation model for the network reconfiguration is developed in the chance‐constrained programming framework and solved by the well‐established particle swarm optimisation algorithm. Finally, case studies are employed to demonstrate the effectiveness of the presented model. Simulation results show that a centralised EV charging station could act as a power source to effectively restore a power system without black‐start (BS) generators or with insufficient cranking power from BS generators, and the presented model could be used to guide actual system restorations. [ABSTRACT FROM AUTHOR]

Network reconfiguration after complete blackout of a power system is an essential step for power system restoration. A new node importance evaluation method is presented based on the concept of regret, and maximisation of the average importance of a path is employed as the objective of finding the optimal restoration path. Then, a two‐stage method is presented to optimise the network reconfiguration strategy. Specifically, the restoration sequence of generating units is first optimised so as to maximise the restored generation capacity, then the optimal restoration path is selected to restore the generating nodes concerned and the issues of selecting a serial or parallel restoration mode and the reconnecting failure of a transmission line are next considered. Both the restoration path selection and skeleton‐network determination are implemented together in the proposed method, which overcomes the shortcoming of separate decision‐making in the existing methods. Finally, the New England 10‐unit 39‐bus power system and the Guangzhou power system in South China are employed to demonstrate the basic features of the proposed method. [ABSTRACT FROM AUTHOR]

Restoring a large-scale power system has always been a complicated and important issue. A lot of research work has been done on different aspects of the whole power system restoration procedure. However, more time will be required to complete the power system restoration process in an actual situation if accurate and real-time system data cannot be obtained. With the development of the wide area monitoring system (WAMS), power system operators are capable of accessing to more accurate data in the restoration stage after a major outage. The ultimate goal of the system restoration is to restore as much load as possible while in the shortest period of time after a blackout, and the restorable load can be estimated by employing WAMS. Moreover, discrete restorable loads are employed considering the limited number of circuit-breaker operations and the practical topology of distribution systems. In this work, a restorable load estimation method is proposed employing WAMS data after the network frame has been reenergized, and WAMS is also employed to monitor the system parameters in case the newly recovered system becomes unstable again. The proposed method has been validated with the New England 39-Bus system and an actual power system in Guangzhou, China. [ABSTRACT FROM PUBLISHER]

As the first stage of power system restoration after a blackout, an optimal black‐start scheme is very important for speeding up the whole restoration procedure. Up to now, much research work has been done on generating or selecting an optimal black‐start scheme by a single round of decision‐making. However, less attention has been paid for improving the final decision‐making results through a multiple‐round decision‐making procedure. In the group decision‐making environment, decision‐making results evaluated by different black‐start experts may differ significantly with each other. Thus, the consistency of black‐start decision‐making results could be deemed as an important indicator in assessing the black‐start group decision‐making results. Given this background, an intuitionistic fuzzy distance‐based method is presented to analyse the consistency of black‐start group decision‐making results. Moreover, the weights of black‐start indices as well as the weights of decision‐making experts are modified in order to optimise the consistency of black‐start group decision‐making results. Finally, an actual example is served for demonstrating the proposed method. [ABSTRACT FROM AUTHOR]

• A novel generator start-up sequence optimization strategy is proposed in this work, taking the integration time of wind-solar hybrid systems into account. • The proposed model requires fewer variables compared with other models, therefore the computational efficiency can be greatly improved. • The confidence gap decision theory based generator start-up sequence strategy is proposed to address the generation uncertainty of wind-solar hybrid systems. • Better generator start-up sequence solutions with higher computational efficiency and less conservatism can be attained by employing the proposed strategy. A reasonable start-up sequence of generators considering renewable energy generation integration can effectively improve the restoration efficiency of the power system concerned. The restoration strategy of generators is time-sensitive, however, the computation procedure of existing methods is not fast enough to meet the requirements of practical applications. In this paper, the generator start-up sequence optimization (GSSO) problem is addressed with the integration of wind-solar hybrid systems (WSSs) considered in bulk power systems. Specifically, a deterministic model for GSSO is formulated to maximize the total generation capability during the restoration procedure, in which fewer auxiliary variables are required to promote the computational efficiency. On this basis, in order to deal with the inherent and significant uncertainties of WSSs, a confidence gap decision theory (CGDT) based approach is proposed combining the advantages of stochastic optimization and the information gap decision theory based method while considering the worst-case realization of unknown probability distributions, so that a chance-constrained robust generator start-up solution is thereby attained. Moreover, the formulated CGDT based model is transformed into a mixed integer linear programming problem. Finally, the IEEE 39-bus power system and a modified version of an actual power system in China are employed to demonstrate the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]