基于半方差风险计量模型的水电站发电优化调度研究

在电力市场环境下,水电站不能按发电计划执行往往会遭受经济效益损失,且欠发风险与过发风险带来的效益损失不尽相同.因此,针对水电站发电风险,提出一种分离考虑水电站欠发风险与过发风险的随机发电优化调度模型,将半方差风险计量模型引入水电站发电优化调度中,以正负风险系数分别衡量管理人员对于过发、欠发风险的主观偏好.制定三峡水电站...     

基于多随机变量风险约束的水电站长期优化调度

针对市场环境下水电站如何综合考虑电价和来水等不确定性因素下的优化调度是当前研究的热点和难点,建立了考虑电价和来水等不确定性因素的风险约束水电站长期优化调度模型。根据此模型发电商可方便地在预期的目标收益和风险之间进行权衡,达到以较小的风险获得较大的收益的目的。以二滩电厂实算为例,表明了该模型的有效性。     

水电站水库预报发电调度的模糊风险分析

针对径流预报误差给水电站水库发电调度带来的风险,在采用组合模型对月径流进行预报和优化调度计算的基础上,构建了水电站水库预报发电调度的模糊风险分析模型框架,并在对入库径流进行随机模拟的条件下设计了模糊风险分析模型的求解流程。三峡水电站水库的实例应用表明,预报误差在5、10月对发电调度的影响较大,且水电站在枯水期的发电模糊风险比汛期高。     

考虑流量演进过程的丰水期梯级水电风险调度

丰水期梯级水电流域来水充沛,上下游水电站之间的流量演进过程对于水电站的优化调度具有重要影响。为了细致描述上下游出入库流量传播过程,结合槽蓄方程和水量平衡方程,运用马斯京根法建立了上下游流量的耦合关系,同时针对丰水期天然来水和市场电价的双重不确定性,综合考虑发电商的风险喜恶行为对调度决策的影响,构建了梯级水电风险调度模型,并利用混合整数线性规划法予以求解。算例结果表明,丰水期考虑河道流量演进过程的梯级水电优化调度模型精确性更高,在不同风险偏好下,可有效地平衡风险和发电效益之间的矛盾。     

不同电价政策下流域梯级水电站群长期优化调度

为定量分析、比较不同电价政策下流域梯级水电站群运营特点,结合发电侧市场开放后梯级水电站群发电计划制定的实际,考虑不同电价政策,以水电站群发电收益最大为目标,建立了长期优化调度模型,并采用粒子群算法求解模型.以我国西南某流域梯级电站群为例,探讨了不同电价政策对梯级电站群优化调度的影响.     

基于分时电价的水库调度图优化研究

以芹山水库为例,结合分时电价原理与水库优化调度理论,划分了分时电价的时段,建立了考虑电价的以水电站水库发电效益最大为目标、兼顾保证出力要求的优化调度模型,应用改进的遗传算法优化水库常规调度图.结果表明,考虑分时电价影响后平均出力变小,能多发峰电,增加发电效益.     

考虑电价风险的水电站长期优化调度及风险评估

研究了电价的不确定性给电力市场环境下水电站长期优化调度带来的风险,建立了基于VaR的期望—风险效用函数模型。借鉴于金融学中的VaR风险度量方法计量并评估风险,运用进化规划算法求解模型,并给出算例对模型进行了验证,分析说明如何应用风险评估方法进行风险度量和评估。     

基于POA优化的双江口水电站对下游梯级发电补偿效益研究

为分析大渡河双江口水电站补偿调节对其下游梯级电站的发电补偿效益,建立了发电量最大化兼顾最小出力最大化的中长期水库群优化调度模型,采用逐步优化算法(POA)求解模型,比较分析了有无双江口水电站补偿下的梯级发电情况,研究了双江口水电站对大渡河梯级的发电补偿效益。结果表明,双江口水电站对下游梯级的发电补偿效益显著,其补偿调节可增加大渡河双江口以下24个梯级多年平均发电量约31.2×108kW·h,增加多年平均枯水期电量约60.2×108kW·h,提升多年枯水期平均出力166.6×104kW,提升多年枯水期最小出力205.5×104kW;按照目前上网电价水平,并考虑分期分时电价的情况下,双江口水电站的补偿调节可增加其下游24个梯级收入共计19.101×108元(含税)。     

考虑收益风险的梯级水电站群中长期合约电量最优组合模型

针对梯级水电站群参与不同时间尺度的合约电量交易时天然来水和交易电价的不确定性因素导致不同市场份额难以确定、水资源利用不充分及不同时间尺度电量分配不合理导致收益存在较大风险问题，提出了考虑收益风险的梯级水电站群中长期合约电量最优组合模型，该模型采用给定置信度下整个梯级发电收益风险最小为目标，其求解首先由多年来水数据生成随机月尺度来水过程，然后根据梯级水电站占市场比重不同模拟出三种电价函数，采用逐步优化和逐次逼近混合算法对电站出力和合约电量组合循环迭代求解。最后以西南地区某流域梯级水电站为例进行验证。结果表明，所提出的优化调度模型能够合理制定年度和月度双边合约电量申报策略，有效降低收益风险，为梯级水电站群参与不同时间尺度的双边交易市场提供可靠依据。     

大规模水电站群短期发电优化调度算法改进

大规模水电站群短期发电优化调度计算量大,实际应用中为满足计算过程和结果可复现需求,多采用各类改进动态规划算法,求解速度难以满足需求。对此,结合大规模水电站群短期发电优化调度模型,同时给定起始状态和最终状态的特性,分析了从起始状态和最终状态同时进行求解的可行性,提出了改进的双向动态规划并行解算方法,实例计算表明,应用此方法可使大规模流域水电站群短期发电优化调度计算速度提高一倍。研究成果可供类似工程参考。     

基于电价波动率的现货电价风险度量研究

风险度量因子的选取是风险度量的核心工作之一,统计性质好的指标通常能降低建模复杂度,提高精度。根据电价序列的特征及电价风险度量模型的特点,提出以电价波动率替代电价作为风险度量因子,避开电价序列的非平稳性,建立GARCH-VaR模型用于现货电价风险度量,以北欧电力市场的电价风险度量为例,对模型的可行性和有效性进行检验,并将所提出的电价风险度量方法与电价波动率正态分布法、电价ARMA-GARCH模型度量的电价风险进行比较。结果表明,所提方法不仅能有效降低电价风险度量的模型复杂度,还可提高风险度量的准确性。     

Oil price risk evaluation using a novel hybrid model based on time-varying long memory

The volatility of crude oil price has a great influence on the world economy. In order to measure the crude oil price risk (VaR) and explain the dynamic relationship between investment income and risk in the oil market more clearly, this paper uses a variety of fractional GARCH models to describe typical volatility characteristics like long memory, volatility clustering, asymmetry and thick tail. The autoregressive conditional heteroscedasticity in the mean model (ARCH-M) and peaks-over-threshold model of extreme value theory (EVT-POT) are taken into account to develop a hybrid time-varying long memory GARCH-M-EVT model for calculation of static and dynamic VaR. Empirical results show that the WTI crude oil has a significantly long memory feature. All the fractional integration GARCH models can describe the long memory appropriately and the FIAPARCH model is the best in regression and out of sample one-step-ahead VaR forecasting. Back-testing results show that the FIAPARCH-M-EVT model is superior to other GARCH-type models which only consider oil price fluctuation characteristics partially and traditional methods including Variance-Covariance and Monte Carlo in price risk measurement. Our conclusions confirm that considering long memory, asymmetry and fat tails in the behavior of energy commodity return combined with effectively dynamic time-varying risk reflection such as the ARCH-M model and reliable tail extreme filter processes such as EVT can improve the accuracy of crude oil price risk measurement, provide an effective tool for analyzing the extreme risk of the tail of the oil market and facilitate the risk management for oil market investors.     

Managing volumetric risk of long-term power purchase agreements

A negative dependence between wind power production and electricity spot price exists. This is an important fact to consider for risk management of long-term power purchase agreements (PPAs). In this study we investigate this dependence by constructing a joint model using constant as well as time-varying copulas. We propose to use score-driven models as marginal model for the spot price of electricity as these are more robust to extreme events compared to ARMA–GARCH models. We apply the new model to pricing and risk management of PPAs and benchmark it against the ARMA–GARCH specification. Our comparison shows that the score-driven model results in a statistically significant improvement of predicting the Value-at-Risk (VaR), which is of high importance for risk management of long-term PPAs. Further, comparing constant and time–varying copulas we find that all time-varying copulas are significantly better than their constant counterparts at predicting the VaR, hence time–varying copulas should be used in risk management of PPAs.     

短期水电优化调度系统设计与开发

大规模水电站群联合优化调度运行对于水电厂自身和电网安全稳定运行至关重要,需制定高效实用的水电站短期发电计划指导其运行。因此,该系统研发了一套基于B/S模式的短期水电优化调度系统,阐述了该系统的总体功能设计和关键实现技术。系统应用表明,该系统具有友好的人机交互界面、良好的健壮性和可扩展性,为制定短期水电系统联合优化发电计划提供了便利。     

基于市场偏差考核的梯级水电调度模型研究

偏差考核现已成为电力市场交易的常规环节,发电企业若不处理好相关问题、合理调度运行,将影响其市场收益,不利于企业的健康持续发展,进而对电力市场的健全发展造成一定影响。因此,为给企业合理应对偏差考核提供参考,首先对偏差电量考核规则进行概述,建立了计及偏差考核的电站收益模型,随后考虑梯级各电站间水力联系,基于单站运行约束条件,构建了基于偏差考核的梯级水电调度模型,并以四川省某流域梯级共8级电站为例,利用逐步优化算法进行计算。结果表明,梯级各站各考核时段电量偏差率介于-0.799%～0.558%之间,均免受偏差考核。     

Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty

In view of the intermittency and uncertainty associated with both the electricity production sector of restructured power system and their competitive markets, it is necessary to develop an appropriate risk managing scheme. So that it is desirable to trade-off between optimum utilization of intermittent generation resources (i.e. renewable energy resources), uncertain market prices and related risks in order to maximize participants' benefits and minimize the corresponding risks in the multi-product market environment. The main goal of this paper is to investigate risk management by introducing a novel multi-risk index to quantify expected downside risk (EDR) which is caused by both the wind power and market price uncertainties. Value-at-Risk (VaR) method is used to assess the mentioned risk issue by the proposed weighted EDR, so that an optimal trade-off between the profit and risk is made for the system operations. Also, the roulette wheel mechanism is employed for random market price scenario generation wherein the stochastic procedure is converted into its respective deterministic equivalents. Moreover, the autoregressive integrated moving average (ARIMA) model is employed to characterize the stochastic wind farm (WF) generation by predetermined mean level and standard deviation of wind behavior as well as temporal correlation. The problem is formulated as a mixed-integer stochastic framework for a hydro-wind power system scheduling and tested on a generation company (GENCO).