含风电场的电力系统机组优化调度研究

发布时间：2018-05-28 03:14

本文选题：风电场 + 机组优化调度　；参考：《重庆大学》2014年硕士论文

【摘要】：随着全球范围内能源需求的增长和环境问题的日益突出，风能作为可再生能源中最具经济发展前景的清洁能源，逐渐受到世界各国的重视和青睐，风电并网容量逐年增加。但是，与常规发电机组不同，由于风电功率具有随机性和波动性，大规模风电的接入必然会给电力系统机组的优化调度和运行带来一系列的挑战和要求。因此，合理制定含风电场电力系统的机组调度计划对于提高风电的利用率具有重要意义。本文围绕含风电场电力系统的机组优化调度问题，开展了如下的研究工作：计及负荷和风电功率的不确定性，结合拉丁超立方抽样和场景技术分析了负荷和风电功率的联合多时段场景模型。针对风电功率预测误差缺乏统一的概率分布模型，通过建立风电功率预测误差的经验分布函数，结合样条插值法建立了风电功率预测误差总体分布函数的解析表达式，并采用拉丁超立方技术对负荷和风电功率的预测误差进行抽样，，结合场景削减技术分析了负荷和风电功率了负荷和风电功率的联合多时段场景模型。算例分析结果表明了该方法的可行性和有效性。计及不同场景间负荷和风电功率的波动性对机组优化调度的影响，以所有场景下发电成本的期望值和方差的加权和为目标函数，建立了综合考虑负荷及风电功率不确定性影响的机组优化调度模型，并采用改进的粒子群优化算法对模型进行求解。针对负荷和风电功率的不确定性影响，基于不同场景之间负荷和风电功率的极限波动区间确定系统的正负旋转备用需求。为提高算法迭代过程中的收敛性能，提出以种群最优值为引导动态调整机组的出力范围。以某典型10机测试系统为例进行算例分析，验证了所提模型和算法的正确性和有效性。考虑到机组优化调度与环境成本、负荷预测误差以及风电渗透率等参数密切相关，为分析这些参数变化对机组优化调度的影响，给出了含风电场电力系统机组优化调度的算例仿真分析。算例分析结果表明：环境成本对机组调度的发电成本影响不大，但对机组调度的环境效益影响显著；负荷预测误差和风电渗透率对机组调度的发电成本影响较大。
[Abstract]:With the increase of global energy demand and the increasingly prominent environmental problems, wind energy, as the most promising clean energy in renewable energy, has been paid more and more attention and favor in the world, and the wind power grid capacity is increasing year by year. However, unlike conventional generators, because of the randomness and volatility of wind power, large-scale wind power access will inevitably bring a series of challenges and requirements to the optimal scheduling and operation of power system units. Therefore, it is of great significance to make the dispatching plan of wind farm power system reasonably for improving the utilization ratio of wind power. In this paper, the following research work is carried out on the optimal dispatching of units with wind farm power system: Considering the uncertainty of load and wind power, combined with Latin hypercube sampling and scenario technology, the combined multi-period scenario model of load and wind power is analyzed. In view of the lack of a unified probability distribution model for wind power prediction error, an analytical expression of wind power prediction error overall distribution function is established by establishing the empirical distribution function of wind power prediction error and combining spline interpolation method. The prediction error of load and wind power is sampled by using Latin hypercube technique, and the combined multi-period scenario model of load and wind power is analyzed with scene reduction technology. The results of an example show that the method is feasible and effective. Considering the effect of fluctuation of load and wind power between different scenarios on the optimal scheduling of generating units, the weighted sum of expected value and variance of generation cost in all scenarios is taken as the objective function. An optimal scheduling model considering the uncertainty of load and wind power is established, and the improved particle swarm optimization algorithm is used to solve the model. According to the uncertainty of load and wind power, the requirement of positive and negative rotation reserve is determined based on the limit fluctuation range of load and wind power between different scenarios. In order to improve the convergence performance of the iterative algorithm, the optimal population value is proposed to guide the dynamic adjustment of the generating range. Taking a typical 10-machine test system as an example, the correctness and validity of the proposed model and algorithm are verified. Considering that the optimal scheduling of the unit is closely related to the environmental cost, load forecasting error and wind power permeability, the influence of these parameters on the optimal scheduling of the unit is analyzed. An example of optimal dispatching of power system units with wind farm is presented. The result of example analysis shows that the environmental cost has little effect on the generation cost of unit dispatching, but it has a significant effect on the environmental benefit of unit dispatching, and the load forecasting error and wind power permeability have great influence on the generation cost of unit dispatching.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM614;TM73

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