考虑事故频率约束的微网机组组合能效评估方法

A Method for Microgrid Unit Commitment Efficiency Assessment Adware of Contingency Frequency Constraints

新能源消纳水平的提升为系统电源规划带来了新的变革。将系统运行风险融入电源规划和灵活性改造,对于保障微网的可靠运行至关重要。鉴于微网的低惯量特性,文章提出一种考虑事故频率约束的微网机组组合能效评估方法,旨在为考虑系统运行风险的电源规划问题提供有效的分析手段。首先,建立机组组合能效概念,并提出期望电量不足率(expected energy not supplied ratio,EENSR)指标,结合常用的条件风险价值(conditional value-at-risk,CVa R)作为系统风险的基本量化指标;其次,对于给定的风光储荷边界条件,建立考虑事故频率约束的机组组合混合整数线性规划模型,用于量化给定场景下的最小负荷损失;然后,再通过蒙特卡洛模拟法量化机组组合能效;最后,通过算例分析,借助风险指标与边界参数的灵敏度关系,分析探讨富源型、平衡性以及重载型3种微网分别在联网和自治运行时,各机组对于系统风险改善的重要程度,论证了所提方法对于微网机组组合能效评价的有效性及其对实际微网规划的指导意义。

The promotion of renewable uptake level has brought new changes to the power planning. Integrating the system operation risks into the power supply planning and flexibility enhancement is essential to ensure the reliable operation of microgrids. In view of the general low inertia characteristics of a microgrid, this paper proposes a unit commitment efficiency assessment method considering the contingency frequency constraints, the purpose of which is to conduct effective analysis for the power planning considering the system operation risks. First, this paper establishes the concept of unit commitment efficiency, and proposes an indicator termed expected energy not supplied ratio(EENSR). This ratio and the commonly used Conditional Value-at-Risk(CVa R), are taken as the two basic quantitative indicators of the system risks. Secondly, for the given boundary conditions established by the wind power, the solar power, the load demand and the storage, a mixed-integer linear programming model of unit commitment considering the contingency frequency constraints is established to quantify the minimum load loss of a given scenario. On top of that, the classical Monte Carlo simulation is used to summarize the unit commitment energy efficiency. Finally, through case studies conducted on three networked microgrids with distinct features of rich supply, balanced supply-demand and heavy demand respectively, the importance of each unit for the system risk mitigation is analyzed by means of the sensitivity relationship between the risk indicators and the boundary parameters. The case study validates the effectiveness of the proposed method to evaluate the microgrid unit commitment efficiency, and also the instructional value for micro grid planning.