|Title:||Advanced system restoration and load modeling in modern power|
|Subject:||Electric power systems -- Maintenance and repair.|
Electric power failures.
Electric power distribution.
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Electrical Engineering|
|Pages:||115 pages : illustrations|
|Abstract:||This thesis deals with both system restoration and load modelling issues in modern power system. With increased penetration of renewables and upgrade of network complexity in modern grid environment, a resilient and efficient restoration strategy is increasingly desired to enhance system reliability. As one of the most important tasks for power system planning and operation, power system restoration is regarded as a multi-objective and multi-stage nonlinear constrained optimization problem involving a great number of generation, transmission, distribution and load constraints. For simplifying problem formulation, the process is usually divided into three stages: preparation, system restoration and load restoration, with emphasis on different restoration objective for each stage. A restoration strategy is attained for dispatchers to start blackstart units, establish transmission path to crank non-blackstart units and pick up necessary loads. In existing methodologies, a strategy usually provides optimal starting timetable for all blackstart and non-blackstart units based on fixed time interval timeline in a way that the overall system generation capability is maximized. The insufficiency of this strategy is the inflexibility of the resultant restoration plan due to the assumption that the operation time for each transmission line and the whole restoration time horizon are preassigned fixed values. In modern power system environment, resilience and efficiency are critical and desirable features for electric power system recovery. Consequently, another variable factor of flexible transmission line operation time is taken into consideration while ensuring grid security at the same time, and a novel methodology named optimal efficiency oriented power system restoration has been developed. The proposed efficiency oriented model consists of transmission path search module, operation time calculation module, startup constraints checking module and load pickup module. Particularly, the operation time calculation module is designed to generate a random time matrix T for modeling variable transmission line operation time which complies with beta distribution. Based on this time matrix, power system restoration is formulated as a permutation-based optimization problem. Different from traditional objective of maximizing available generation capability, the proposed novel optimization objective is to maximize available generation capability per unit time named as restoration efficiency for adapting flexible restoration period. Moreover, the optimization solution in terms of non-blackstart units startup permutation is solved through one optimization process for generator startup permutation and transmission restoration path. For practical application, a flexible restoration schedule is generated according to the optimal solution, and provides information of non-blackstart generator startup timing, charging path and corresponding available generation at each restoration stage. Another contribution is development of a tailored algorithm referred as advanced quantum-inspired differential evolutionary algorithm (AQDE) to solve the proposed permutation-based restoration model. It features with better population diversity and quicker convergence speed. The superior performance of AQDE has been benchmarked through comparison experiments with two other well established meta-heuristic techniques including QDE and GA. Consequently, the proposed AQDE method is successfully applied to solve the system restoration of IEEE 39 and 118 bus systems respectively.|
The optimal efficiency oriented power system restoration methodology is also applied on load restoration stage after its validation on the aforementioned system restoration stage. Traditionally at load restoration stage, maximization of restored load becomes primary objective. When applying the proposed novel methodology, however, maximization of restored load per unit time is designed as the optimization objective. In order to reduce the impacts of service disruption on load loss, load prioritization should be taken into consideration in load pickup process. In general, electrical loads can be divided into three levels based on reliability requirements by customers. Load importance degree is defined according to properly ranking the prioritization of loads with reference to pre-signed contracts with customers, where the expected service quality has been specified. Based on the undirected power system topology model, a novel index Ps combining load prioritization and capacitance is proposed for searching optimal path. Finally, a flexible restoration schedule is obtained to provide information for generator startup and load pickup considering load prioritization. The proposed restoration methodology is applied on IEEE 39 and 57 bus systems respectively. The second part of my research work is load modeling. It is an important issue due to the fact that load model significantly affects power system dynamic simulations. In modern power system, there is increasingly desire of delicate load model with respect to accuracy and computational efficiency. A delicate load model is called for to capture specific load characteristics of various load components. To catch these characteristics, a complete load model at distribution grid level consisting of equivalent capacitor, large motor and small motor is proposed. Furthermore, the other two models, namely the composite load model and dynamic load model at distribution grid level, are applied to assess the accuracy of the developed complete load model. The comparisons of simulation on case studies have demonstrated that the complete load model has superior performance in transient simulations at distribution grid level and capable to capture more accurate load characteristics than the other two models.
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