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The Mechanism And Control Of Demand Response In The Frequency Control Of The Power Systems

Posted on:2016-09-07Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y Q BaoFull Text:PDF
GTID:1222330488457736Subject:Electrical engineering
Abstract/Summary:PDF Full Text Request
With the development of the smart metering technologies, demand response is able to participate in the frequency control of the power systems. By refined management of power consumption of the grid friendly appliances (GFAs) in accordance to the frequency deviation, the system frequency can be adjusted real-timely. In order to well utilize the demand response resources in the frequency control, it is necessary to implement research on the mechanism and the control theory of the demand response for the frequency control. Nowadays, the increasingly penetration of renewable energies makes the power system stability and reliability facing more and more challenges. In such situation, researching on the demand response for the frequency control has very important practical significance.In this paper, the mechanisms of conventional primary and secondary frequency control are reviewed, and the mechanisms of demand response for the frequency control are analyzed. The possibility and necessity of demand response for the frequency control is clarified. And the system frequency response model considering demand response is developed. Then the research is carried out based on four key technologies of demand response in the frequency control:1. A hybrid hierarchical demand response control method is proposed. The method consists of two layers of control logic:the control logic of the control center and the control logic of the individual GFA controllers. The control center is responsible for sending parameters setting signal to each GFA. And each individual GFA controller is responsible for the frequency detection and concrete realization of the control logics. In order to improve the performance of the overall system, the genetic algorithm (GA) is used to optimize the control parameters of the demand response included control system. The proposed hybrid hierarchical control method is verified on numerical examples.2. The demand response control method for the frequency control of a multi-area power system is specially designed. By the feedback of the frequency deviation signal and the tie-line power deviation signal between areas, the area frequency as well as the tie-line power fluctuation can be well stabilized. To obtain the optimal control parameters, the control system is transformed into a multi-objective optimization problem, and solved by the GA method. The test examples based on a three area power system verifies the proposed control strategy and the optimization method.3. The optimal scheduling of the multi-scale demand response resources is effectively solved. The frequency control problem is incorporated in the scheduling problem through a frequency limit constraint. By piecewise linearization, the frequency limit constraint is transformed into an inequality relationship between the generation unit commitment and the available demand response resources. And the optimization problem is solved by the Mixed Integer Linear Programming (MILP). The test examples verify the proposed scheduling method.4. A hardware based GFA controller is systematically designed and developed. The phase-lock-loop (PLL) is adopted to realize high precision detection of the system frequency, and the delay compensation block is designed to relieve the undesirable effect of the response delay. The overall GFA controller is programmed using the Verilog code, and implemented on a FPGA chip. The Quartus-Matlab co-simulation platform is established to verify the performance of the GFA controller by developing the GFA model and the frequency response model on the Quartus and Matlab software, respectively. At last, the control logic is programmed into a FPGA demo-board and further verifies the performance of the designed GFA controller.
Keywords/Search Tags:Demand Response, Frequency Control, Genetic Algorithm, FPGA, Co-simulation
PDF Full Text Request
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