消纳大规模风电的电力系统经济调度研究

发布时间：2017-12-26 19:24

本文关键词：消纳大规模风电的电力系统经济调度研究　出处：《华南理工大学》2016年硕士论文　论文类型：学位论文

【摘要】：风能是一种绿色环保的可再生能源,发展风电有利于改善能源结构。但由于风电具有不可调度性、随机波动性、反调峰特性,大规模风电并网将使传统的电力系统确定性优化调度方法难以适用。本文详细综述了国内外含风电并网的电力系统经济调度研究的相关文献,并指出先进的调度技术是充分消纳风电的基础和保障。针对大规模风电的接入问题,本文从三个方面对现有调度体系作出改进:1)采用多时间尺度的调度策略,逐级降低风电的预测偏差对电网的影响;2)在各时间尺度中分别采用先进的调度方法,实现调度的综合效益最大化;3)用户侧参与优化调度,进一步提高风电接纳能力。具体工作如下:首先,详细介绍了能较好处理不确定性参数的鲁棒优化理论,并介绍了原对偶内点法、遗传算法、粒子群算法的基本原理,为后续章节奠定了基础。其次,建立了消纳大规模风电的多时间尺度鲁棒调度方法。一方面通过日前计划、日内滚动计划、实时计划的三个时间尺度逐级消纳风电,实现了“多级协调,逐级细化”的优化效果;另一方面,采用鲁棒调度方法处理风电出力的不确定性,保证了调度的鲁棒性。在某省级电网的算例分析中发现,本文方法能有效避免弃风和切负荷损失,且保证线路潮流在风电波动情况下不发生越限。再次,为解决鲁棒调度的经济性和鲁棒性的矛盾冲突问题,通过对调度方案进行弃风和切负荷的风险评估,将调度的鲁棒性转换为经济指标,从而建立了以综合成本(发电成本与风险成本之和)最小的不确定集优化模型。通过构造一种双层优化算法求解不确定集优化模型,内层模块为原对偶内点法求解经济调度模型的过程,外层模块为智能算法搜索最优不确定集的过程。所述方法在IEEE 39节点系统中得到了验证。最后,为充分利用负荷侧的可调度资源,提出一种考虑多种可控负荷的电源-负荷协同调度方法。在日前调度模型的基础上,通过对可中断负荷和可转移负荷进行建模,使发电侧和用电侧均参与电网运行的资源优化配置。算例结果表明:源荷协同调度方式能使峰谷差下降389MW,并提高了电网的风电接纳能力,其最大能接纳的风电渗透率由11%上升至21%。
[Abstract]:Wind energy is a green and renewable energy. The development of wind power is beneficial to the improvement of energy structure. However, because wind power has the characteristics of non schedulability, stochastic volatility and anti peak shaving, large scale wind power integration will make traditional power system deterministic optimal scheduling method difficult to apply. In this paper, the literature on economic dispatch of power system with wind power integration at home and abroad reviewed in detail, and points out that the advanced scheduling techniques is the foundation and guarantee the full consumption of wind power. Aiming at the problem of access to large-scale wind power, this paper from the three aspects of the existing scheduling system to make improvements: 1) with multiple time scale scheduling strategy, gradually reducing the prediction error of wind power on the grid; 2) using advanced scheduling method in different time scales, the maximum comprehensive benefit realization of scheduling; 3) scheduling user participation, to further improve the ability to accept wind power. The specific work is as follows: first, the robust optimization theory which can deal with uncertain parameters is introduced in detail, and the basic principles of primal dual interior point method, genetic algorithm and particle swarm optimization are introduced, which lays the foundation for subsequent chapters. Secondly, a multi time scale robust scheduling method for accommodating large scale wind power. On the one hand, before the days of rolling plan, plan, plan in time of three time scale by consumptive wind power, to achieve a "multi-level coordination, the optimization effect is gradually refined"; on the other hand, the robust scheduling method of the uncertainty in wind power output, to ensure the robustness. In a case study of a provincial power grid, it is found that this method can effectively avoid abandoned wind and load shedding, and ensure that the line power flow does not exceed the limit under the condition of wind power fluctuation. Again, to resolve the contradictions and conflicts between robust scheduling and robust economy, through the risk assessment of abandoned wind and load shedding on scheduling scheme, robust scheduling for converting economic indicators, so as to establish a comprehensive cost (cost and risk cost) minimum uncertainty optimization model. A bilevel optimization algorithm is constructed to solve the uncertain set optimization model. The inner module is the process of solving the economic dispatch model by the primal dual interior point method, and the outer module is the process of searching the optimal uncertain set by the intelligent algorithm. The method described is verified in the IEEE 39 node system. Finally, in order to make full use of the schedulable resources on the load side, a power load cooperative scheduling method, which considers a variety of controllable loads, is proposed. Based on the day before the scheduling model, based on interruptible load and load transfer model, the generation and optimization of resource allocation of electricity side are involved in the operation of the power grid. The example shows that the source load cooperative scheduling method can reduce the peak valley difference by 389MW, and improve the wind power acceptance ability of the grid. The maximum wind energy penetration can be increased from 11% to 21%.
【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM614;TM73

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