| Energy shortage and environmental pollution problems make the exploitation of renewable energy sources become the development trend of the electric power industry. Wind power generation, which is a kind of clean and environmentally friendly renewable resource with an abundant of reservation, is the maturest renewable energy generation technology. However, the random characteristic of wind power makes the safety and stability of power system operation face new challenges after wind power integration. In order to realize power system with wind power integration clearly and offer effective suggestions about the safety and stability of power system operation, this dissertation taking wind-storage system as the study object, researches the optimization configuration method of wind-storage system and associated power system operation problem with wind-storage system integration, explores the effect to power system brought by wind power integration.Optimized wind farm integration capacity not only can promise the economy of wind farm itself, but also can guarantee the safety and stability of power system operation after wind power integration. It is conducive to the global optimum operation of power system incorporating wind power generation, so the solution of optimum wind power integration capacity is researched in this dissertation. Aiming at the situation that the wind speed prbobability distributions are different in each month, the parameter set of wind speed probability distribution function in each month is adopted to describe the annual wind speed variation rule. Based on the parameter set of wind speed probability distribution, the scenario tree construction method is used to split the continuous probability density function of wind speed into discrete scenario power set and produce the annual scenario power tree, which approximates wind speed random variable reasonably. On the basis of wind speed random variable treatment, an optimization model of wind power integration capacity considering the variation process of power system is proposed making the maximization of net generation benefit of wind farm as objective function and making the requirement of safe and stable operation of power system as constraint conditions. A hierarchical optimization algorithm combining particle swarm optimal algorithm with w-weight(w-PSO) and social emotional optimal algorithm(SEOA) is proposed to solve the proposed optimization model. The case study verifies the correctness of the proposed model and algorithm; the factors of affecting wind farm integration capacity are researched simultaneously.In order to enhancing the regulation capability of output power in wind farm and improving the fluctuation characteristic of wind power, the optimization configuration method of energy storage system is researched in this dissertation. Wind power fluctuation characteristic index is proposed to quantificationally describe the output characteristic of wind power. Taking minimization of the investment or annually total cost of energy storage system as objective, on the basis of the wind power fluctuation characteristic index and coordinated control strategy of hybrid energy storage system, the double-level optimization configuration model of hybrid energy storage system combined with battery and supercapacitor is founded taking the output power fluctuation request of wind farm and the technical and economic constraints of hybrid energy storage system into account, nested particle swarm optimization algorithm is adopted to solve the proposed model. The case study indicate that severer wind power fluctuation index corresponds to better output power smoothness and higher investement or annually total cost of energy storage system; the variation of investment is inversely proportional to the variation of annually total cost; adopting hybrid energy storage mode can decrease annually total cost of energy storage system.Wind power integration makes the constitution of power system operation plan which has an important effect on the normal operation of power system change obviously, so the dissertation researches the optimization model of daily unit commitment problem of power system with wind-storage system and the corresponding solution. A multi-objective unit commitment optimization model which overall reflects the real operation situation of power system is proposed making the maximization of total energy utilization rate of power system and the minimization of total generation cost of power system as objective functions and considering the system power balance requirement, the system reserve capacity requirement, the generation unit ramp rate, the restraint of start-up and shutdown time of generation unit, the restraint of wind-storage system itself as model constraint conditions on the basis of wind power forecasting data. Fuzzy modeling technique is used to deal with multi-objective functions; an improved combined particle swarm optimal algorithm is proposed to solve the optimization model which is dealt with by fuzzy processing method; the effect of the two control modes of storage system and forecasting data error are analyzed according to calculation results. The results of case study indicate that the proposed model can reduce the fuel consumption of low combustion efficiency generation unit; different control modes and forecasting precision have affect on the calculation results of proposed optimization model.Due to the integration of wind power, voltage stability analysis method being an important part of the safety and stability analysis of power system operation is also affected by wind power randomicity, traditional deterministic voltage stability analysis method is lacking in the ability of acquiring the randomicity information of power system voltage stability variation with wind power integration. Aiming at the new changes of power system voltage stability problem brought by wind power randomicity, the probability voltage stability margin problem in power system with wind-storage system integration is studied in this dissertation. The proposed probability analysis method based on polynomial normal transformation and maximum entropy estimate utilizes polynomial normal transformation technique to deal with the correlation among random variables, uses point estimate method to get the estimate points of random variables, uses predictor corrector primary dual interior point method(PCPDIPM) to solve the founded deterministic loadability margin optimization model based on the estimate points and then calculate the statistical characteristics of loadability margin, finally adopts maximum entropy estimate method to get the estimation function of loadability margin probability distribution of power system with wind power integration. The case study verifies the correctness of proposed method, discusses the effect to loadability margin index brought by the variation of correlative wind power parameters and the improvement action to loadability margin index brought by configuring energy storage system. The calculation results indicate that the loadability margin indexes are different under different wind power parameter conditions; the fluctuation of loadability margin is decreased with the consideration of energy storage system; loadability margin probability information acquired by proposed method has satisfied calculation accuracy, the solving process of proposed method is simpler and higher efficiency in comparison with Monte-Carlo simulation. |
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