低碳环境下考虑多重不确定性的负荷裕度分析理论与方法研究

By challenges of energy shortage and environmental restrictions, the power industry is witnessing an increasing trend of low carbon-emission developments, such as wind power generation and Plug-in Hybrid Electric Vehicle (PHEV). However, these kinds of low carbon-emission electric power are totally different from traditional ones due to their stochastic, intermittent, and fuzzy natures. Thus it is difficult for traditional analysis methods to determine the load margin of a power system requiring real-time power balance. In addition, it also becomes hard to achieve effective multi-objective coordination among the load margin, pool purchase cost, and emission of carbon dioxide, etc. This research aims at developing methodologies for load margin analysis and multi-objective coordination theory. We are going to develop new models of power injections including multi-uncertainty generation and load, new approaches to obtain the maximal load margin, and new methods for multi-objective coordination and risk identification..Firstly, we will set up a daily sequence model describing the stochastic and fuzzy variables of the injection power for both the wind generation and the load. This model can be achieved based on forecasting techniques and the credibility theory. Secondly, we will develop an approach to find the voltage-collapse critical point of the system with multi-uncertainty injection power of wind generation and load. This approach will be based on the random fuzzy simulation, Dynamic Continual Power Flow (DCPF), and Optimal Power Flow (OPF). Thirdly, we will derive an estimation equation of the static-voltage-stability-security region hyper-plane to obtain its boundary description in the extended full injection space for normal and post-fault system states. This equation will take into account the control mode of the Doubly-fed Induction Generator (DFIG) in addition to the static-voltage characteristics of the load. Moreover, considering requirements of static voltage stability, real and reactive power reserve, and steady-state security constraints, we will propose a new methodology for multi-objective coordination and risk identification considering multi-uncertainty power injections. This methodology will be based on the random fuzzy chance-constrained programming and multi-objective dynamic OPF. Finally, the proposed models and methods will be tested by data of the IEEE standard examples and real power systems to verify their effectiveness.

大力发展风电、插入式电动汽车和积极推进节能减排的低碳电力是应对能源和环境危机的必然选择。在低碳环境多重不确定注入下，电力系统供需实时平衡中负荷裕度确定、多目标协调及风险识别等面临挑战。本项目借助数据挖掘和可信性理论，建立风电出力、负荷水平及增长方向的随机模糊变量序列化模型；基于随机模糊模拟、动态连续潮流和最优潮流，提出多重不确定注入系统电压崩溃临界点求取模型和算法；考虑双馈异步风力发电机控制方式和负荷静态电压特性，推导静态电压稳定安全域边界切平面解析式，获取正常和故障后的静态电压稳定域边界描述；基于随机模糊机会约束规划、多目标动态最优潮流，提出多重不确定注入下多目标协调及风险识别新思路和新方法；最后采用IEEE算例和实际电网数据验证模型与算法的有效性。通过对电源和负荷多重不确定特征提取和建模、最大负荷裕度求取、多目标协调和风险识别的研究，构建低碳环境多重不确定注入下负荷裕度分析理论和方法。

随着风电、电动汽车和常规机组节能减排等低碳电力推进，电力系统源荷注入功率不确定性增强且兼具随机模糊特征，从而使电力系统运行中负荷裕度确定及其与低碳经济等多目标协调面临挑战。本项目对此进行研究，相关研究工作与取得成果主要如下：.1）负荷裕度求取中源荷双侧注入功率多重不确定特征提取及建模。在对风电和负荷等历史数据进行质量评估及挖掘基础上，依据不确定理论，定义其为随机模糊变量，结合概率分布特征提取及其参数模糊隶属函数确定获取其机会测度函数，进而基于随机模糊模拟进行仿真分析，从而获得兼顾随机性和模糊性的注入功率，实例数据验证了该方法正确有效。.2）考虑注入功率随机模糊性的电力系统静态电压崩溃极限点求取方法。提出含风电电力系统随机模糊潮流算法，结合随机模糊模拟技术和牛顿拉夫逊算法获取兼顾随机性和模糊性的潮流结果；进而提出含DFIG稳态模型的随机模糊连续潮流模型，结合随机模糊模拟和预估校正算法获得负荷裕度机会测度函数，IEEE算例仿真结果表明上述方法正确有效。.3）研究得到含DFIG风电场电力系统静态电压稳定域切平面数学描述及风速对其影响灵敏度；进而获得DFIG不同无功控制方式下的静态电压稳定域边界切平面的解析描述，算例仿真结果表明恒电压控制模式更利于负荷裕度提升；进而计及风速随机模糊性，提出含DFIG电力系统的P-Q-V静态电压稳定域切平面方法及安全边界机会测度，得到兼顾随机模糊性的负荷裕度数学描述，采用IEEE算例结果验证了上述方法。.4）多重不确定注入下电力系统负荷裕度和低碳经济多目标协调及风险识别。提出基于随机机会约束规划的主动配电网分布式风光双层优化配置方法，获得适应多容量渗透率边界下兼顾安全经济环境优化的规划方案；研究考虑激励型需求响应的含风电电力系统经济低碳日前机组组合调度方法，通过需求响应消纳逆调峰风电；提出考虑风速随机模糊性与P-Q-V静态电压稳定安全域约束的随机模糊多目标动态最优潮流，基于随机模糊机会约束规划，获取满足置信水平的兼顾随机模糊性的最优潮流；提出风电并网的状态转移因子识别风电注入功率的潮流转移敏感线路。上述方法均得到算例验证正确有效。.综上，项目构建提出低碳环境多重不确定注入下负荷裕度分析理论和方法，按预期计划实现了研究目标，为低碳电力系统的分析与运行提供一定理论支持。