考虑风光消纳的源荷联合多目标优化调度研究

发布时间：2018-06-26 00:07

本文选题：新能源消纳 + 源荷联合　；参考：《西安理工大学》2017年硕士论文

【摘要】：风能、太阳能等清洁能源发电形式在缓解化石能源枯竭、减轻环境污染等方面发挥着重要作用。但风电、光伏发电间歇、波动、不可控的特性给电网运行控制带来显著影响,逐年加重的弃风弃光现象严重阻碍了我国实现清洁替代与能源转型。为平衡风光波动、解决大规模新能源消纳矛盾,本文以优化全网资源、提升电网动态平衡性能为切入点,研究包含新能源的电力系统多目标优化调度策略,力求兼顾新能源利用效率和电网安全、经济运行。首先从电网调度层面协调“源”“荷”双侧资源,深入分析风力、光伏发电、梯级水电、火电的短期运行及互补特性和柔性负荷响应特性,确定源荷联合优化运行可行性;针对柔性负荷资源数量多、难以直接控制等特点,结合我国市场建设现状,探索基于负荷聚合商调度架构和双边合同互动方式的需求侧优化调控策略。本文以减弃增效和提高系统经济性为优化目标,将柔性负荷资源作为“虚拟互动电厂”参与电量平衡和备用,构建最优潮流下基于复杂时空耦合约束的考虑新能源利用率、常规火电平稳、梯级水电和系统经济性的源荷联合多目标调度模型,将其解耦为具有优化时序的风光火荷多方协调和梯级水电站经济分配两阶段优化。其次,提出基于Pareto最优和精英归档机制的多目标萤火虫算法(MOFA)用于求解高维复杂多目标模型。针对萤火虫算法搜索效率低的缺点,采用粒子群算法中全局最优指导飞行方向的思想和种群合并等策略对其改进,通过标准测试系统验证了算法的正确性。应用多目标萤火虫算法和单目标萤火虫算法先后求解两阶段优化模型。最后在IEEE-30标准测试系统下,对比不同新能源接入水平下不同场景的调度计划,结果表明本文所建模型的合理性和源荷联合运行在提高经济、社会、环境效益上的有效性;所提算法在电力系统多目标模型求解中表现出收敛速度快、寻优效果佳的性能,具有较强的适应性。
[Abstract]:Clean energy, such as wind energy and solar energy, plays an important role in alleviating fossil energy depletion and environmental pollution. However, the intermittent, fluctuating and uncontrollable characteristics of wind power, photovoltaic power generation have a significant impact on power grid operation and control. The phenomenon of abandonment of wind and light has seriously hindered the realization of clean substitution and energy transformation in China. In order to balance the fluctuation of scenery and solve the problem of large scale new energy consumption, this paper studies the multi-objective optimal dispatching strategy of power system including new energy by optimizing the whole network resources and improving the dynamic balance performance of the power network. Strive to take into account the efficiency of new energy use and grid security, economic operation. Firstly, coordinating the "source" and "load" resources from the grid dispatching level, deeply analyzing the short-term operation and complementary characteristics and flexible load response characteristics of wind power, photovoltaic power generation, cascade hydropower, thermal power, and determining the feasibility of combined optimization operation of source and load. In view of the large quantity of flexible load resources and the difficulty of direct control, combined with the current situation of market construction in China, this paper explores the demand-side optimal regulation strategy based on load aggregator scheduling architecture and bilateral contract interaction. In order to reduce the efficiency and improve the system economy, this paper takes flexible load resource as "virtual interactive power plant" to participate in the power balance and reserve, and constructs a new energy utilization factor based on complex space-time coupling constraints under the optimal power flow. The conventional thermal power stable cascade hydropower and system economy combined multi-objective dispatching model is decoupled into the multi-coordination of wind-fire load with optimal time series and the two-stage optimization of economic distribution of cascade hydropower stations. Secondly, a multi-objective firefly algorithm (MOFA) based on Pareto optimization and elite archiving mechanism is proposed to solve the complex multi-objective model with high dimension. Aiming at the low search efficiency of the firefly algorithm, the particle swarm optimization (PSO) algorithm is improved by using the idea of global optimal flight direction guidance and the strategy of population merging. The correctness of the algorithm is verified by a standard test system. The multi-objective firefly algorithm and the single-objective firefly algorithm are used to solve the two-stage optimization model. Finally, in the IEEE-30 standard test system, the scheduling plans of different scenarios under different new energy access levels are compared. The results show that the rationality of the proposed model and the effectiveness of the combined operation of source and load in improving economic, social and environmental benefits; The proposed algorithm has the advantages of fast convergence, good optimization effect and strong adaptability in solving the multi-objective model of power system.
【学位授予单位】：西安理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73

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