Research On Coordinative Optimization Strategy Of Power System Restoration

Modern power systems have become more and more complex due to the introduction of the deregulated and unbundled power market operational mechanism. Conflict between increased load demand and the aged equipments has also pushed power systems to be operated close to their limits, which is increasing the risk of large blackouts after the cascading failures. Accordingly, how to restore power systems rapidly and effectively after blackouts has become more and more important issue of interest in power engineering. Based on the existing literature survey investigated thoroughly to power system restoration, a novel global intelligent optimization framework towards modern power system restorative control is proposed in this thesis. The key points of this research involve the construction of optimization model, the solution strategy, the behavior of collaborative optimization, the overall optimization target, the concept of time step and the interactive strategy, etc. This research can enhance the restorative control level and improve the whole optimization results, on the other hand, can implement the transformation of decision-making mode of power system restoration control from the traditional experience-driven strategy to the standardization and normalization strategy. The main achievements are as follows:1. With respect to the specific optimization objective, the whole power system restoration problem can be supposed to be divided into the optimal units start-up, the optimal network reconfiguration, and the optimal load recovery. The corresponding optimization models and the constraint conditions were studied in this thesis. The framework of the optimal strategy for the power system restoration was proposed, which can provide a new way to implement the optimization of the power system restoration.2. An optimal strategy involving the corresponding model and approach to units start-up were presented. The objective of the model is to maximize the total power generation capability (MWh) over a restoration period whilst being subjected to the specific constraints. The proposed model is a typical multi-constraint knapsack problem from the mathematical point of view. The combination of the data envelopment analysis (DEA) method and by solving the knapsack problem was employed to determine the units to be cranked. The proposed method, to some extent, can make the trade off between the simulation accuracy and the computing efforts better.3. An optimal strategy involving the corresponding model and approach for the network reconfiguration were presented. The goal of the proposed model is to find the shortest weighted path for units start-up or load recovery in restoration duration whilst considering all kinds of constraints. The proposed model is considered as a typical Steiner tree problem from the mathematical point of view. The genetic algorithm method with characteristics of global optimization and handling the discrete variables easily and effectively was employed to solve this problem. Furthermore, the performance of genetic algorithm was optimized in order to improve calculation speed, stability and search efficiency further.4. A novel evaluation model for the importance of load and a load recovery optimization model as well as the solution strategy with respect to the period when the network is still reconfiguring, which are different from the traditional concept of the load recovery, were proposed in this thesis. From the mathematical point of view, the proposed model is a multi-attribute decision making problem. The DEA method with preference information and the value of density of pi/wi greedy algorithm were employed to determine the loads to be recovered.5. The interactive and collaborative mechanism during units start-up, network reconfiguration and load recovery was studied in this thesis. An interative strategy with respect to the restorative control based on the short-term-target-network was presented. The whole restorative control problem was discretized as a series of optimization problem in each time step. In each time step, according to the restored system in last time step, the corresponding optimal target networks with the units, networks and loads to be restored at current time step can be determined based on the global optimization technique. These restored short-term-target-networks can guide the restoration process step by step effectively. Furthermore, the decision making process can be combined with the actual restoration process by the interative techniques. A Multi-Agent System based optimal strategy framework towards restoration control was proposed. The corresponding collaborative mechanism among Agents was studied as well. Finally, the coordination model of the optimal strategy towards restorative control was presented.