电力系统负荷及负荷率的可靠性影响模型
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摘要
电力系统可靠性性能受系统负荷、元件可靠性性能、元件电气特性和网络结构等的影响。这些因素中，系统负荷具有明显的分布特性，用单一负荷水平（比如：峰荷、平均负荷）进行系统可靠性评估，将难以真实地反映系统可靠性水平。通过需求侧管理、储能等手段，可实现系统负荷率的变化，这将对给定时段内系统的可靠性性能产生较大的影响。因此，研究系统可靠性随负荷、负荷率的变化规律，以实现系统可靠性的快速评估，优化系统运行策略，提高系统可靠性，具有重要的理论意义和工程价值。
本文受国家自然科学基金项目“电力系统可靠性非同调机理及非同调元件辨识研究”（50777067）等的资助，对系统负荷、负荷率与电力系统可靠性之间的相互影响关系展开深入探讨，取得的成果如下：
结合支撑向量回归法（SVR，Support Vector Regression）具有求解小样本、非线性、高维数、局部极小点等问题的优点，带扩展记忆的粒子群优化算法(PSOEM,Particle Swarm Optimization with Extended Memory)具有搜索方向明确、收敛快以及粒子个体经验积累等特点，建立短期负荷预测的PSOEM-SVR模型。深入分析ε不敏感损失函数、惩罚因子c等参数对SVR模型精度的影响，应用PSOEM方法实现模型参数的优化计算。将该模型应用于某城市电网进行负荷预测，结果表明：PSOEM-SVR模型的预测精度优于传统BP神经网络等模型，其夏季、冬季负荷预测的最大绝对百分误差分别为2.24%和2.29%。该模型可为负荷率调整策略的制订、可靠性随负荷率变化规律等分析提供负荷预测的基础。
分析发电系统、发输电组合系统主要可靠性指标，如：失负荷概率（LOLP，loss of load probability）、失负荷频率（LOLF，loss of load frequency)、失电量期望（EENS，expected energy not supplied），随系统负荷的变化规律，以拟合的规律曲线连续、光滑等为目标，建立系统可靠性指标随系统负荷变化的三次样条插值模型。通过插值边界条件、插值点导数等信息，即可求解该模型。结合该模型，可建立电力系统可靠性预测估计模型，以避免负荷变化时可靠性的重复评估，可为负荷率变化对系统可靠性影响的分析提供快速计算的基础。将该模型应用于RBTS、IEEE-RTS79、IEEE-RTS96等系统。算例表明：系统可靠性随负荷呈非线性的变化规律；当负荷变化时，可直接应用该模型预测估计系统可靠性，其平均绝对百分误差约2.0%。
基于削峰填谷和等比例调整等负荷率调整策略，提出调整策略对应的负荷调整算法；基于此，建立系统可靠性指标随系统负荷率变化的样条插值模型。两种负荷率调整策略均针对给定时段（如一年或一天）内，在满足负荷电能需求下，调整负荷谷峰差及负荷曲线，即在系统平均负荷不变的前提下改变系统最大负荷及其他时段负荷，以达到提高负荷率的目的。应用RBTS、IEEE-RTS79等系统验证了模型的有效性。算例表明：随着系统负荷率的提高，系统可靠性呈非线性上升规律，即提高系统负荷率可提高系统可靠性；但是，当负荷率提高到一定程度时，系统可靠性的提高会出现明显的饱和现象。
随着负荷率的提高，电力系统可靠性将得到改善，但其通常以牺牲系统运行的经济性为代价。因此，应综合计入负荷率的调整成本、可靠性效益等因素，确定电力系统运行的最优负荷率，以提高系统的可靠性和经济性。鉴于此，挖掘负荷率调整过程中电力系统可靠性、经济性的变化规律，基于系统峰谷差电价、停电损失等因素，建立最优负荷率的非线性规划模型，并应用黄金分割算法求解该模型。应用该模型对RBTS、IEEE-RTS79等系统的负荷率进行了优化分析。算例表明：通过负荷率的优化，可取得较大的经济效益；负荷调整策略对最优负荷率有较大影响。
The reliability of power systems depend on the system load demands, electricalparameters of power system components and power system structure. Due to thedistribution characteristics of system load, it is nearly impossible to evaluate thereliability of a power system using a single system load level, such as the peak load andaverage load. The power system load rate will be changed when the demand sidemanagement is introduced to a power system, hence the reliability of power system willbe changed. Therefore, the studies on the relationship between the system reliability andthe system load rate is critical for power systems, in addition, the study on the fastevaluation method for the power system reliability is also important.
The thesis is supported in part by the Natural Science Foundation Project (No.50777067), which is titled “Recognizing the noncoherent mechanism and noncoherentcomponents of power systems”. The main achievements in this thesis are as follows:
A novel method based on the Support Vector Regression (SVR) is presented in thepaper to forecast the short-term load, which can overcome the defects of the traditionalmethods, such as poor generalization capability, easy to fall into local minimum valueand slow convergence. The Particle Swarm Optimization with Extended Memory(PSOEM) has the following merits: a clearance of the searching direction, fastconvergence and particle accumulation of individual experience. A model based onSVM and PSOEM is proposed to forecast the short-term load of power system, inaddition, the influence on the accuracy of SVM caused by the insensitive loss function εand penalty factor c is investigate, finally optimization on the parameters is carried out.Then the model is used to forecast the short-term load in a Chinese city, and resultsindicated that the PSOEM-SVR model is more accurate than the traditional BP neutralnetwork method, and the calculated errors for the summer load and winter load are2.24%and2.29%respectively. The model can also be used to adjust the strategy ofsystem load rate and the forecast of system load.
The rules between the reliability indices of power system, such as: LOLP (loss ofload probability), LOLF (loss of load frequency), EENS (expected energy not supplied),and the system load were analyzed. The relationship model between the power systemreliability and system load rate is modeled using the cubic splines, and the model can besolved according to the interpolation boundary conditions, the interpolation points and derivative information. Therefore, a fast evaluation technique based on the proposedmodel for the reliability of power systems can be obtained, which can avoid theduplication of reliability evaluation with multi load levels. The proposed model isapplied to the RBTS, IEEE-RTS79, and IEEE-RTS96system. Results indicate that therelationship between the system reliability and load rate is non-linear variation. Theaverage error for the evaluation on the reliability in these cases is about2.0%when theload changes.
Load rate adjustment strategies based on reducing peak and filling valley and theproportion of load shifting are presented, and the algorithm for the load adjustment isalso presented. The relationship between the system reliability and the load rate ismodeled using the spline interpolation method. Both the two load rate adjustmentstrategies can adjust the load demand between the peak and valley periods, the systemload curve on the premise of satisfying the electricity demand for the peak load. In thisway the system load rate can be decreased. The proposed model and method are used tothe RBTS and IEEE-RTS79, and results indicate that with the increase of the systemload rates, the system reliability increase in a non-linear way, which also means thereliability performance of power systems can be improved by increase the system loadrate. However, when the loading rate increased to a certain extent, saturation will arisein the system reliability.
With the increase of system load rate, the power system reliability will beimproved, which is usually associated with a higher cost of system expense. Therefore,the load rate adjustment cost and benefits of reliability improvement should be takeninto account in the model of optimizing the system load rate. The optimum load ratewith a high efficiency and low economic cost can be obtained. Based on the changingrules of the power system reliability and cost with the adjustment of system load rate, anon-linear model for the optimization of the system load rate is built, in which thedifference for the electricity price during the peak and valley, and the costs of load pointoutages have been considered. The golden section algorithm was used to solve theproposed model. Case studies on the RBTS, IEEE-RTS79indicate that great economicbenefits can be obtained by optimizing the system load rate, and the adjustment strategyfor the system load rate have a great influence on the optimum load rate.
本文受国家自然科学基金项目“电力系统可靠性非同调机理及非同调元件辨识研究”（50777067）等的资助，对系统负荷、负荷率与电力系统可靠性之间的相互影响关系展开深入探讨，取得的成果如下：
结合支撑向量回归法（SVR，Support Vector Regression）具有求解小样本、非线性、高维数、局部极小点等问题的优点，带扩展记忆的粒子群优化算法(PSOEM,Particle Swarm Optimization with Extended Memory)具有搜索方向明确、收敛快以及粒子个体经验积累等特点，建立短期负荷预测的PSOEM-SVR模型。深入分析ε不敏感损失函数、惩罚因子c等参数对SVR模型精度的影响，应用PSOEM方法实现模型参数的优化计算。将该模型应用于某城市电网进行负荷预测，结果表明：PSOEM-SVR模型的预测精度优于传统BP神经网络等模型，其夏季、冬季负荷预测的最大绝对百分误差分别为2.24%和2.29%。该模型可为负荷率调整策略的制订、可靠性随负荷率变化规律等分析提供负荷预测的基础。
分析发电系统、发输电组合系统主要可靠性指标，如：失负荷概率（LOLP，loss of load probability）、失负荷频率（LOLF，loss of load frequency)、失电量期望（EENS，expected energy not supplied），随系统负荷的变化规律，以拟合的规律曲线连续、光滑等为目标，建立系统可靠性指标随系统负荷变化的三次样条插值模型。通过插值边界条件、插值点导数等信息，即可求解该模型。结合该模型，可建立电力系统可靠性预测估计模型，以避免负荷变化时可靠性的重复评估，可为负荷率变化对系统可靠性影响的分析提供快速计算的基础。将该模型应用于RBTS、IEEE-RTS79、IEEE-RTS96等系统。算例表明：系统可靠性随负荷呈非线性的变化规律；当负荷变化时，可直接应用该模型预测估计系统可靠性，其平均绝对百分误差约2.0%。
基于削峰填谷和等比例调整等负荷率调整策略，提出调整策略对应的负荷调整算法；基于此，建立系统可靠性指标随系统负荷率变化的样条插值模型。两种负荷率调整策略均针对给定时段（如一年或一天）内，在满足负荷电能需求下，调整负荷谷峰差及负荷曲线，即在系统平均负荷不变的前提下改变系统最大负荷及其他时段负荷，以达到提高负荷率的目的。应用RBTS、IEEE-RTS79等系统验证了模型的有效性。算例表明：随着系统负荷率的提高，系统可靠性呈非线性上升规律，即提高系统负荷率可提高系统可靠性；但是，当负荷率提高到一定程度时，系统可靠性的提高会出现明显的饱和现象。
随着负荷率的提高，电力系统可靠性将得到改善，但其通常以牺牲系统运行的经济性为代价。因此，应综合计入负荷率的调整成本、可靠性效益等因素，确定电力系统运行的最优负荷率，以提高系统的可靠性和经济性。鉴于此，挖掘负荷率调整过程中电力系统可靠性、经济性的变化规律，基于系统峰谷差电价、停电损失等因素，建立最优负荷率的非线性规划模型，并应用黄金分割算法求解该模型。应用该模型对RBTS、IEEE-RTS79等系统的负荷率进行了优化分析。算例表明：通过负荷率的优化，可取得较大的经济效益；负荷调整策略对最优负荷率有较大影响。
The reliability of power systems depend on the system load demands, electricalparameters of power system components and power system structure. Due to thedistribution characteristics of system load, it is nearly impossible to evaluate thereliability of a power system using a single system load level, such as the peak load andaverage load. The power system load rate will be changed when the demand sidemanagement is introduced to a power system, hence the reliability of power system willbe changed. Therefore, the studies on the relationship between the system reliability andthe system load rate is critical for power systems, in addition, the study on the fastevaluation method for the power system reliability is also important.
The thesis is supported in part by the Natural Science Foundation Project (No.50777067), which is titled “Recognizing the noncoherent mechanism and noncoherentcomponents of power systems”. The main achievements in this thesis are as follows:
A novel method based on the Support Vector Regression (SVR) is presented in thepaper to forecast the short-term load, which can overcome the defects of the traditionalmethods, such as poor generalization capability, easy to fall into local minimum valueand slow convergence. The Particle Swarm Optimization with Extended Memory(PSOEM) has the following merits: a clearance of the searching direction, fastconvergence and particle accumulation of individual experience. A model based onSVM and PSOEM is proposed to forecast the short-term load of power system, inaddition, the influence on the accuracy of SVM caused by the insensitive loss function εand penalty factor c is investigate, finally optimization on the parameters is carried out.Then the model is used to forecast the short-term load in a Chinese city, and resultsindicated that the PSOEM-SVR model is more accurate than the traditional BP neutralnetwork method, and the calculated errors for the summer load and winter load are2.24%and2.29%respectively. The model can also be used to adjust the strategy ofsystem load rate and the forecast of system load.
The rules between the reliability indices of power system, such as: LOLP (loss ofload probability), LOLF (loss of load frequency), EENS (expected energy not supplied),and the system load were analyzed. The relationship model between the power systemreliability and system load rate is modeled using the cubic splines, and the model can besolved according to the interpolation boundary conditions, the interpolation points and derivative information. Therefore, a fast evaluation technique based on the proposedmodel for the reliability of power systems can be obtained, which can avoid theduplication of reliability evaluation with multi load levels. The proposed model isapplied to the RBTS, IEEE-RTS79, and IEEE-RTS96system. Results indicate that therelationship between the system reliability and load rate is non-linear variation. Theaverage error for the evaluation on the reliability in these cases is about2.0%when theload changes.
Load rate adjustment strategies based on reducing peak and filling valley and theproportion of load shifting are presented, and the algorithm for the load adjustment isalso presented. The relationship between the system reliability and the load rate ismodeled using the spline interpolation method. Both the two load rate adjustmentstrategies can adjust the load demand between the peak and valley periods, the systemload curve on the premise of satisfying the electricity demand for the peak load. In thisway the system load rate can be decreased. The proposed model and method are used tothe RBTS and IEEE-RTS79, and results indicate that with the increase of the systemload rates, the system reliability increase in a non-linear way, which also means thereliability performance of power systems can be improved by increase the system loadrate. However, when the loading rate increased to a certain extent, saturation will arisein the system reliability.
With the increase of system load rate, the power system reliability will beimproved, which is usually associated with a higher cost of system expense. Therefore,the load rate adjustment cost and benefits of reliability improvement should be takeninto account in the model of optimizing the system load rate. The optimum load ratewith a high efficiency and low economic cost can be obtained. Based on the changingrules of the power system reliability and cost with the adjustment of system load rate, anon-linear model for the optimization of the system load rate is built, in which thedifference for the electricity price during the peak and valley, and the costs of load pointoutages have been considered. The golden section algorithm was used to solve theproposed model. Case studies on the RBTS, IEEE-RTS79indicate that great economicbenefits can be obtained by optimizing the system load rate, and the adjustment strategyfor the system load rate have a great influence on the optimum load rate.
引文
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