基于条件风险价值的含风电电力系统优化调度研究
发布时间：2018-12-17 05:06
【摘要】：风力发电是目前技术最成熟、最具有成本竞争力的可再生能源发电形式,风电大规模并网缓解了我国能源压力并带来巨大的经济和环境效益。然而风电作为间歇性电源,其大规模并网势必增加电力系统运行与控制难度,给系统运行带来风险。准确地量化风电接入所带来的影响,优化发电与备用计划,制定综合考虑系统经济性和安全性的系统调度策略,成为未来高比例可再生能源接入电力系统所面临的重要问题。针对这一问题,本文从风电功率场景优化建模和调度计划优化建模两个方面进行研究。本文将含风电电力系统运行调度问题与风险理论相结合,把条件风险价值(Conditional Value-at-Risk,CVaR)引入系统运行风险分析中,更为准确地反映系统运行风险及调度决策风险态度对调度计划制定的影响。主要进行了以下研究:(1)提出了考虑风电功率波动与预测误差条件分布特性的场景生成及场景消减方法。风电功率波动性与不确定性的准确量化及建模分析对系统调度计划的制定至关重要。本文基于历史数据分析了风电功率波动与预测误差在不同维度上的条件分布特性。采用考虑时序相关性的动态场景生成技术,提出了综合考虑风电功率波动特性和预测误差条件分布特性的风电功率场景生成方法。针对常规聚类消减算法的缺点,引入改进的K-medoids聚类算法进行场景消减,生成能够准确反映风电功率波动性与不确定性的代表性场景。(2)提出了基于概率场景和条件风险价值的发电与备用协调优化模型。代表性场景能够准确刻画系统的随机过程,以评估系统的调节能力和备用需求。本文利用概率场景的形式描述风电功率波动性和不确定性,建立了考虑风电功率波动性及不确定性的发电与备用协调优化模型。由于基于期望均值的评价方法把不同损失程度的概率场景均一化看待,难以准确体现系统决策的风险态度以及所关注的损失范围的。为准确评价风险实现风险管控,将发电与备用协调优化问题与风险理论相结合,引入条件风险价值理论对弃风、失负荷经济性损失进行风险度量,分析了系统调度不同风险倾向对发电计划与备用分配的影响。(3)引入条件风险价值理论建立了综合考虑储能与需求响应的优化调度模型。针对未来含高比例风电的电力系统仅依靠常规机组难以实现风电消纳且调节成本高的问题,本文将储能和需求响应纳入到日前发电调度模型中。从场景分析的角度建立了考虑储能调节能力的储能模型和计及用户满意度约束的需求响应模型。引入条件风险价值理论进行风险度量,对比分析了不同调度模式下不同风险态度对系统调度的影响。算例结果表明综合考虑储能和需求响应的调度模型能够有效提高风电接纳水平和系统运行经济性,有效降低极端场景带来的风险损失。
[Abstract]:Wind power generation is the most mature and the most competitive renewable energy generation form at present. Wind power large-scale grid connection alleviates the energy pressure and brings huge economic and environmental benefits. However, wind power as an intermittent power supply, its large-scale grid connection is bound to increase the difficulty of power system operation and control, bring risks to the system operation. Accurately quantifying the impact of wind power access, optimizing power generation and standby plans, and formulating system scheduling strategies that take into account the economy and security of the system are important issues facing the future access of high proportion renewable energy sources to power systems. In order to solve this problem, this paper studies wind power scene optimization modeling and scheduling planning optimization modeling. In this paper, the operation scheduling problem of wind power system is combined with the risk theory, and the conditional risk value (Conditional Value-at-Risk,CVaR) is introduced into the system operation risk analysis. It more accurately reflects the impact of system running risk and scheduling decision risk attitude on scheduling planning. The main contributions are as follows: (1) A method of scene generation and scene reduction considering wind power fluctuation and prediction error condition distribution is proposed. Accurate quantification and modeling analysis of wind power fluctuation and uncertainty are very important for system scheduling planning. Based on historical data, the conditional distribution characteristics of wind power fluctuation and prediction error in different dimensions are analyzed in this paper. Based on the dynamic scene generation technique considering the correlation of time series, a method for generating wind power scene is proposed, which takes into account the fluctuation characteristics of wind power and the distribution of prediction error conditions. Aiming at the shortcomings of the conventional clustering subtractive algorithm, an improved K-medoids clustering algorithm is introduced for scene reduction. Representative scenarios which can accurately reflect the volatility and uncertainty of wind power are generated. (2) an optimal model of coordination between generation and reserve based on probabilistic scenarios and conditional risk value is proposed. The representative scene can accurately describe the stochastic process of the system to evaluate the system's adjustment ability and reserve requirement. In this paper, the volatility and uncertainty of wind power are described in the form of probabilistic scenarios, and an optimal model of coordination between generation and reserve is established considering the volatility and uncertainty of wind power. Because the evaluation method based on the expected mean value equalizes the probability scenarios of different loss degrees, it is difficult to accurately reflect the risk attitude of the system decision and the range of loss concerned. In order to accurately evaluate risk and realize risk control, combining the optimization problem of power generation and reserve with the risk theory, the conditional risk value theory is introduced to measure the economic loss of wind and load loss. The influence of different risk propensity of system scheduling on generation plan and reserve allocation is analyzed. (3) the optimal scheduling model considering energy storage and demand response is established by introducing conditional risk value theory. In order to solve the problem that the power system with high proportion of wind power in the future is difficult to achieve wind power absorption and high regulation cost only relying on conventional units, this paper brings the energy storage and demand response into the pre-day generation scheduling model. From the perspective of scenario analysis, the energy storage model considering the capacity of energy storage regulation and the demand response model considering the constraints of user satisfaction are established. The theory of conditional risk value is introduced to measure the risk, and the influence of different risk attitude on system scheduling under different scheduling modes is analyzed. The simulation results show that the scheduling model which takes energy storage and demand response into account can effectively improve the level of wind power acceptance and system operation economy and effectively reduce the risk loss caused by extreme scenarios.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73
[Abstract]:Wind power generation is the most mature and the most competitive renewable energy generation form at present. Wind power large-scale grid connection alleviates the energy pressure and brings huge economic and environmental benefits. However, wind power as an intermittent power supply, its large-scale grid connection is bound to increase the difficulty of power system operation and control, bring risks to the system operation. Accurately quantifying the impact of wind power access, optimizing power generation and standby plans, and formulating system scheduling strategies that take into account the economy and security of the system are important issues facing the future access of high proportion renewable energy sources to power systems. In order to solve this problem, this paper studies wind power scene optimization modeling and scheduling planning optimization modeling. In this paper, the operation scheduling problem of wind power system is combined with the risk theory, and the conditional risk value (Conditional Value-at-Risk,CVaR) is introduced into the system operation risk analysis. It more accurately reflects the impact of system running risk and scheduling decision risk attitude on scheduling planning. The main contributions are as follows: (1) A method of scene generation and scene reduction considering wind power fluctuation and prediction error condition distribution is proposed. Accurate quantification and modeling analysis of wind power fluctuation and uncertainty are very important for system scheduling planning. Based on historical data, the conditional distribution characteristics of wind power fluctuation and prediction error in different dimensions are analyzed in this paper. Based on the dynamic scene generation technique considering the correlation of time series, a method for generating wind power scene is proposed, which takes into account the fluctuation characteristics of wind power and the distribution of prediction error conditions. Aiming at the shortcomings of the conventional clustering subtractive algorithm, an improved K-medoids clustering algorithm is introduced for scene reduction. Representative scenarios which can accurately reflect the volatility and uncertainty of wind power are generated. (2) an optimal model of coordination between generation and reserve based on probabilistic scenarios and conditional risk value is proposed. The representative scene can accurately describe the stochastic process of the system to evaluate the system's adjustment ability and reserve requirement. In this paper, the volatility and uncertainty of wind power are described in the form of probabilistic scenarios, and an optimal model of coordination between generation and reserve is established considering the volatility and uncertainty of wind power. Because the evaluation method based on the expected mean value equalizes the probability scenarios of different loss degrees, it is difficult to accurately reflect the risk attitude of the system decision and the range of loss concerned. In order to accurately evaluate risk and realize risk control, combining the optimization problem of power generation and reserve with the risk theory, the conditional risk value theory is introduced to measure the economic loss of wind and load loss. The influence of different risk propensity of system scheduling on generation plan and reserve allocation is analyzed. (3) the optimal scheduling model considering energy storage and demand response is established by introducing conditional risk value theory. In order to solve the problem that the power system with high proportion of wind power in the future is difficult to achieve wind power absorption and high regulation cost only relying on conventional units, this paper brings the energy storage and demand response into the pre-day generation scheduling model. From the perspective of scenario analysis, the energy storage model considering the capacity of energy storage regulation and the demand response model considering the constraints of user satisfaction are established. The theory of conditional risk value is introduced to measure the risk, and the influence of different risk attitude on system scheduling under different scheduling modes is analyzed. The simulation results show that the scheduling model which takes energy storage and demand response into account can effectively improve the level of wind power acceptance and system operation economy and effectively reduce the risk loss caused by extreme scenarios.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73
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