Generation Expansion Planning With Wind Power Plants

Unlike conventional generation sources, such as coal or gas fired power, hydro power, and nuclear power, etc., wind power is with natures of low-controllable, stochastic, intermittent, and anti-peak-shaving. This dissertation analyses the impact of wind power on generation expansion planning, and studies generation expansion planning problems with large-scale wind farms and sizing and siting of distributed wind generation (DWG). The main innovations of the dissertation are as follows:1) According to the natures of stochastic and intermittent of wind turbines, equivalent energy and frequency function (EEFF) method is proposed for power system probabilistic production simulation, which combines the frequency and duration (FD) approach and equiva1ent energy function (EEF) method. EEFF method keeps the time-dependent behaviour of power load and wind turbine generator, and it can evaluate the effect of wind power and load on the commitment of conventional units, as well as the dynamic cost associated with the commitment of thermal units.2) A novel generator maintenance scheduling model based on equivalent risk principle is present, which takes the minimum of risk standard variance of all the maintainance period as planning objective. Heuristic algorithm based on minimum cumulative risk and plant growth simulation algorithm (PGSA) are put forword to solve the model. Both algorithms calculate the system risk with the method of combined semi-invariant and Gram-Charlier expanding, and take into account the uncertainty of wind power. Minimum cumulative risk algorithm takes the minimum cumulative risk period as the maintenance period of the unit. PGSA takes the start period number of the generators to be maintained as decision variables and search the optimal solution with the plant phototropism mechanism.3) An generation expansion planning model of cost minimization with large-scale wind farms is put forward, considering the impacts of wind farms on system peak regulation, frequency regulation, and environmental protection benefit. In order to take into account the impact of different generation price on investment decision, a bi-level generation expansion planning model is put forword, whose top planning problem is the generator investment decision problem with objective of net benefit maximinum, and lower planning problem is production optimizing decision problem. The PGSA combined with minimum cumulative risk algorithm and EEFF method is present to solve the bi-level programming model.4) A planning scheme based on the chance constrained programming of distributed wind generation in the existing distribution networks is proposed, whose planning objective is to maximize the benefit of the independent power producer (IPP). PGSA is put forword to solve the model and the probabilistic power flow method based on semi-invariant is applied to judge whether the planning schemes satisfy the constraints of both the node voltage operation ranges and the branch transmission capacities.5) A novel bi-level programming model for siting and sizing of distributed wind generation under active management (AM) mode is put forward, which breaks the"fit and forget"installation policy of distributed generation in the passive distribution network. The model takes the maximum expectation of net benefit of DWG as the upper level program objective, and takes the minimum expectation of generation curtailment with voltage and thermal constraints as the lower level program objective, and takes into account the impact of active voltage management algorithm on improvement of branch power flow and node voltage. The PGSA combined with probabilistic optimal power flow algorithm is applied to solve the optimal planning of DWG under AM mode.Numerical examples prove the feasibility and effectivity of the proposed model and methodology in this dissertation.