水火风发电系统多周期联合优化调度模型及方法
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摘要
为了促进能源、经济、环境的可持续发展,大力开发利用水电、风电等清洁能源已经得到了广泛关注。随着流域梯级水电站群的规模化发展,梯级上下游电站间的时空相关性更加紧密,在水电优化调度中考虑电站间水流滞时动态变化和水电机组检修计划的影响,将能够促进上下游电站统筹协调运行,充分发挥流域梯级水电节能效益。此外,随着风电并网容量的增加,风电功率的随机波动性、不可控性以及难以预测性增加了电网调峰的难度和优化调度的不确定性。为了促进风电的消纳,充分发挥梯级水电、火电的调节能力,开展多种能源的联合优化调度,已经成为当前电网调度面临的迫切需要解决的问题。基于上述背景,本文围绕水火风电力系统中的多周期优化调度问题,开展了以下的研究工作：
在水电短期优化调度方面,提出了考虑水流滞时优化的优化调度模型。该模型建立了水流滞时和出库流量之间的非线性函数关系,从而反映了梯级水电站间水流滞时动态变化的特性。同时,针对水流滞时、水电转换和分段出力限制等复杂的非线性函数,提出了线性化建模的新方法,通过引入多组状态变量及相关约束,将短期水电优化调度问题转换为混合整数线性规划问题进行求解。仿真结果表明,在短期水电站群优化调度中考虑水流滞时的优化,能够有效提高调度方案的可行性和经济性。
在水火发电系统中长期优化调度方面,提出了发电计划和检修计划联合优化的多场景调度模型。首先,针对中长期来水和负荷预测信息的不确定性,建立了场景树模型。然后,鉴于设备检修计划的连续性,在预测场景树的基础上,通过节点和场景关联矩阵,实现多场景下设备检修模型的构建。再者,鉴于中长期调度计划中发电计划和检修计划对时段间隔要求的不同,分别设置电量相关节点和电力相关节点,实现中长期发电计划和检修计划的协调。最后,通过大规模实际水火电系统的实例分析,结果表明检修计划和发电计划的协调优化调度,能够更好地提升系统的节能和经济效益,使得检修计划和发电计划的制定更为合理；根据多场景模型制定的调度方案更具适用性。
在水火风发电系统短期优化调度方面,提出了计及下一调度周期调峰约束的随机机组组合模型。首先,针对风电的随机性,利用缩减后的场景进行模拟,并建立了各个场景下的风水火耦合的运行约束。然后,针对下一调度周期高峰和低谷的调峰问题,增加了机组启停相关约束。最后,采用混合整数线性规划法对所提模型进行了求解,仿真结果表明,优化得到的机组组合方案,既能够保证各个场景下各类型机组出力的可行性,又能够满足下一调度周期的高峰负荷需求并减少了低谷时段的弃风量。
在水火风发电系统中长期优化调度方面,提出了基于点估计的不确定性调度方法。首先,综合考虑水力、风力、热力与电力相互耦合的复杂约束,建立了水火风发电系统中长期优化调度模型。为了降低求解难度,将这一大规模、多约束、非线性的优化问题转换为线性混合整数模型予以求解。然后,针对模型中各时段水文径流与风速的随机性,利用点估计法构造随机变量的估计点,结合每一个估计点向量进行确定性的中长期优化调度模型的求解,进而得到优化调度决策变量的期望值。最后,通过算例分析,结果表明本文所提调度方法能够在确保计算精度的前提下,加快求解速度,此外通过水火风的联合优化调度,能够提高系统清洁能源消纳的能力、减少化石能源的消耗。
论文上述研究成果可以为实际发电计划优化软件研发提供理论支撑,并在水火和水火风发电系统优化运行中具有应用的前景。
With the purpose of promoting sustainable development of energy, economy, and environment, it has been given abroad attention to make a great effort on the development of clean energy generation like hydro and wind power. Due to large-scale development of basin cascade hydropower stations, the spatial-temporal correlation between upstream and downstream hydropower plants becomes more closely. In the hydropower optimal scheduling model, the consideration of variation of water time delay and the influence of maintenance will promote coordination of dispatch between upstream and downstream stations and the improvement of energy-saving benefit. In addition, as the wind integration increases, it would become more difficult for grid to perform peak regulation and optimal operation because of the stochastic, uncontrollable and unpredictable characters of wind power. For the better consumption of wind power, it is urgent to realize combined optimal operation among various enegies in order to make full use of the regulation potential of cascade hydropower and thermal power. Based on the above backgrounds, this dissertation focuses on the multi-period optimal scheduling. The main research work is as follows:
In aspect of short-term hydropower optimal scheduling, the model considering the optimization of water time delay is proposed. This model establishes nonlinear functions of water time delay and reservoir outflow to represent the dynamic change of water time delay among cascade hydropower stations. Meanwhile, in view of the complicated non-linear function, like cascade delay time, hydraulic-electric conversion and piecewise output limit, a novel linear modeling method is proposed. The short-term optimal scheduling problem is changed into a linear mixed integer programming problem by introducing multiple sets of state variables and constraints. Simulation results show that the feasibility and economics of scheduling can be improved by this model.
In aspect of mid-long term hydro-themal optimal scheduling, the multi-scenario model considering joint optimization between generation and maintenance scheduling is proposed. Firstly, the scenario tree model is established to describe the uncertainty of water inflow and load prediction. Secondly, in consideration of the continuity of equipment maintenance scheduling, nodes are divided into different scenarios based on the predicting scenario tree, and equipment maintenance model of multi-scenario scheduling is founded through the scenario-node incidence matrix. Furthermore, electric power-related nodes and energy-related nodes were set respectively to coordinate generation and maintenance scheduling, in view to their different scheduling intervals. Finally, application examples of an actual large-scale hydrothermal system are tested and analyzed. Results demonstrated that coordinate optimization between generation and maintenance scheduling can improve energy and economic efficiency, which enhances the feasibility of generation schedules in return. It also indicates that the multi-scenario optimization can improve the applicable of the system scheduling scheme.
In aspect of short-term hydro-thermal-wind optimal scheduling, the stochastic unit commitment model with the peak and valley regulation constraints in next scheduling period is proposed. Firstly, in respond to the randomness of wind power, the reduced scenarios are used to simulate wind power, and the coupling operational constraints of hydro, thermal, wind power in each scenario are formulated. Secondly, for the peak regulation issue in next scheduling period, the constraints about unit start and stop status are introduced. Finally, the proposed model is sloved by mixed integer linear programming. Simulation results show that the optimized unit commitment can ensure the output feasibility of each unit type under all scenarios, and the peak demand could be satisfied while wind curtailment during low demand time could be reduced.
In aspect of mid-long term hydro-themal-wind optimal scheduling, the uncertain scheduling method based on point estimate is put forward. Firstly, taking into account the complex constraints, the long-term optimal scheduling model of wind-hydro-thermal system is established. In the proposed model, all of the coupled hydraulic, wind, thermal and electricity constraints are included. In order to reduce the solution difficulty, the above large-scale, multi-constrained, nonlinear optimization problem is converted into a mixed integer linear model. Secondly, the point estimation method is used and the estimation points of stochastic variables are generated. Then the model is solved on each estimation point vector and thus the expected value of decision variables could be calculated. Finally, numerical examples show that the proposed model could accelerate the calculation speed while ensuring the precision. In addition, the combined hydro-thermal-wind optimal scheduling could enhance the system capacity to integrate clean energy and reduce the consumption of fossil fuels.
The above achievements can provide theoretical support for research and development of actural generation scheduling software, and also have the application prospect in the hydro-thermal and hydro-thermal-wind power systems.
在水电短期优化调度方面,提出了考虑水流滞时优化的优化调度模型。该模型建立了水流滞时和出库流量之间的非线性函数关系,从而反映了梯级水电站间水流滞时动态变化的特性。同时,针对水流滞时、水电转换和分段出力限制等复杂的非线性函数,提出了线性化建模的新方法,通过引入多组状态变量及相关约束,将短期水电优化调度问题转换为混合整数线性规划问题进行求解。仿真结果表明,在短期水电站群优化调度中考虑水流滞时的优化,能够有效提高调度方案的可行性和经济性。
在水火发电系统中长期优化调度方面,提出了发电计划和检修计划联合优化的多场景调度模型。首先,针对中长期来水和负荷预测信息的不确定性,建立了场景树模型。然后,鉴于设备检修计划的连续性,在预测场景树的基础上,通过节点和场景关联矩阵,实现多场景下设备检修模型的构建。再者,鉴于中长期调度计划中发电计划和检修计划对时段间隔要求的不同,分别设置电量相关节点和电力相关节点,实现中长期发电计划和检修计划的协调。最后,通过大规模实际水火电系统的实例分析,结果表明检修计划和发电计划的协调优化调度,能够更好地提升系统的节能和经济效益,使得检修计划和发电计划的制定更为合理；根据多场景模型制定的调度方案更具适用性。
在水火风发电系统短期优化调度方面,提出了计及下一调度周期调峰约束的随机机组组合模型。首先,针对风电的随机性,利用缩减后的场景进行模拟,并建立了各个场景下的风水火耦合的运行约束。然后,针对下一调度周期高峰和低谷的调峰问题,增加了机组启停相关约束。最后,采用混合整数线性规划法对所提模型进行了求解,仿真结果表明,优化得到的机组组合方案,既能够保证各个场景下各类型机组出力的可行性,又能够满足下一调度周期的高峰负荷需求并减少了低谷时段的弃风量。
在水火风发电系统中长期优化调度方面,提出了基于点估计的不确定性调度方法。首先,综合考虑水力、风力、热力与电力相互耦合的复杂约束,建立了水火风发电系统中长期优化调度模型。为了降低求解难度,将这一大规模、多约束、非线性的优化问题转换为线性混合整数模型予以求解。然后,针对模型中各时段水文径流与风速的随机性,利用点估计法构造随机变量的估计点,结合每一个估计点向量进行确定性的中长期优化调度模型的求解,进而得到优化调度决策变量的期望值。最后,通过算例分析,结果表明本文所提调度方法能够在确保计算精度的前提下,加快求解速度,此外通过水火风的联合优化调度,能够提高系统清洁能源消纳的能力、减少化石能源的消耗。
论文上述研究成果可以为实际发电计划优化软件研发提供理论支撑,并在水火和水火风发电系统优化运行中具有应用的前景。
With the purpose of promoting sustainable development of energy, economy, and environment, it has been given abroad attention to make a great effort on the development of clean energy generation like hydro and wind power. Due to large-scale development of basin cascade hydropower stations, the spatial-temporal correlation between upstream and downstream hydropower plants becomes more closely. In the hydropower optimal scheduling model, the consideration of variation of water time delay and the influence of maintenance will promote coordination of dispatch between upstream and downstream stations and the improvement of energy-saving benefit. In addition, as the wind integration increases, it would become more difficult for grid to perform peak regulation and optimal operation because of the stochastic, uncontrollable and unpredictable characters of wind power. For the better consumption of wind power, it is urgent to realize combined optimal operation among various enegies in order to make full use of the regulation potential of cascade hydropower and thermal power. Based on the above backgrounds, this dissertation focuses on the multi-period optimal scheduling. The main research work is as follows:
In aspect of short-term hydropower optimal scheduling, the model considering the optimization of water time delay is proposed. This model establishes nonlinear functions of water time delay and reservoir outflow to represent the dynamic change of water time delay among cascade hydropower stations. Meanwhile, in view of the complicated non-linear function, like cascade delay time, hydraulic-electric conversion and piecewise output limit, a novel linear modeling method is proposed. The short-term optimal scheduling problem is changed into a linear mixed integer programming problem by introducing multiple sets of state variables and constraints. Simulation results show that the feasibility and economics of scheduling can be improved by this model.
In aspect of mid-long term hydro-themal optimal scheduling, the multi-scenario model considering joint optimization between generation and maintenance scheduling is proposed. Firstly, the scenario tree model is established to describe the uncertainty of water inflow and load prediction. Secondly, in consideration of the continuity of equipment maintenance scheduling, nodes are divided into different scenarios based on the predicting scenario tree, and equipment maintenance model of multi-scenario scheduling is founded through the scenario-node incidence matrix. Furthermore, electric power-related nodes and energy-related nodes were set respectively to coordinate generation and maintenance scheduling, in view to their different scheduling intervals. Finally, application examples of an actual large-scale hydrothermal system are tested and analyzed. Results demonstrated that coordinate optimization between generation and maintenance scheduling can improve energy and economic efficiency, which enhances the feasibility of generation schedules in return. It also indicates that the multi-scenario optimization can improve the applicable of the system scheduling scheme.
In aspect of short-term hydro-thermal-wind optimal scheduling, the stochastic unit commitment model with the peak and valley regulation constraints in next scheduling period is proposed. Firstly, in respond to the randomness of wind power, the reduced scenarios are used to simulate wind power, and the coupling operational constraints of hydro, thermal, wind power in each scenario are formulated. Secondly, for the peak regulation issue in next scheduling period, the constraints about unit start and stop status are introduced. Finally, the proposed model is sloved by mixed integer linear programming. Simulation results show that the optimized unit commitment can ensure the output feasibility of each unit type under all scenarios, and the peak demand could be satisfied while wind curtailment during low demand time could be reduced.
In aspect of mid-long term hydro-themal-wind optimal scheduling, the uncertain scheduling method based on point estimate is put forward. Firstly, taking into account the complex constraints, the long-term optimal scheduling model of wind-hydro-thermal system is established. In the proposed model, all of the coupled hydraulic, wind, thermal and electricity constraints are included. In order to reduce the solution difficulty, the above large-scale, multi-constrained, nonlinear optimization problem is converted into a mixed integer linear model. Secondly, the point estimation method is used and the estimation points of stochastic variables are generated. Then the model is solved on each estimation point vector and thus the expected value of decision variables could be calculated. Finally, numerical examples show that the proposed model could accelerate the calculation speed while ensuring the precision. In addition, the combined hydro-thermal-wind optimal scheduling could enhance the system capacity to integrate clean energy and reduce the consumption of fossil fuels.
The above achievements can provide theoretical support for research and development of actural generation scheduling software, and also have the application prospect in the hydro-thermal and hydro-thermal-wind power systems.
引文
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