考虑多风电场相关性的电网动态经济调度二阶段带补偿算法

大规模多风电场的并网给电网经济调度带来了巨大挑战,文中对含多风电场的电网日前动态经济调度问题进行研究。首先,引入Copula理论构建了多风电场间输出功率的联合分布。随后,建立了电网日前24h动态经济调度随机优化模型。针对约束中风电输出功率的随机性,引入二阶段带补偿算法求解,将原调度模型转化为二阶段模型:第一阶段为仅含常规变量约束,而在目标函数中增加补偿量期望值的优化模型;第二阶段为含随机变量约束,并以补偿量惩罚期望值最小为目标的优化模型;通过二阶段模型间的交替迭代求得最优解。基于IEEE 118节点系统和某省级大电网的算例对该方法的有效性进行了验证。     

考虑可再生能源不确定性的热电联供型微网随机鲁棒经济调度

为应对可再生能源出力不确定性和传统微网供能形式单一造成经济性较低的问题,提出了多能源微网两阶段随机鲁棒优化模型。模型考虑了电网与热网的网架结构,目标函数旨在最小化最恶劣风电出力场景下的两阶段微网成本,其中包括第一阶段启停成本与第二阶段运行成本。由于第一阶段与第二阶段的决策与优化结果相互影响,因此两阶段优化问题难以直接求解,文章采用线性决策随机鲁棒优化框架对模型求解。首先,应用线性决策方式相关理论对第二阶段进行转化;其次,采用锥化模糊集刻画可再生能源出力的不确定性;最后,将第二阶段的sup-min问题推导为锥优化的min问题,进而与第一阶段的min问题合并,得到能够直接求解的单层锥优化问题,并采用求解器求得最优解。仿真结果验证了所提模型和方法的有效性。     

考虑风电不确定性的多区电力系统分散式随机调度方法

在考虑风电出力不确定性的基础上研究了多区域电网分散式优化问题。首先,对区域边界共享节点进行复制以实现互联电网的解耦,然后,基于场景法对风电随机性进行建模,构建区域电网两阶段随机优化模型,最后,采用同步型交替方向乘子法(Synchronous Alternating Direction Method of Multipliers,SADMM)交替求解全网分散优化问题和区域两阶段随机优化问题。采用修改的新英格兰39节点系统构建3区域互联电网进行仿真测试,验证了所提模型能够有效应对风电的随机不确定性,并实现各区域电网调度的分散自治。     

基于数据驱动的多微电网互联系统分布鲁棒运行优化

针对多微电网互联系统调度中分布式电源出力不确定性以及运行效率低下等问题,提出基于数据驱动的多微电网互联系统分布鲁棒运行优化策略。首先基于多元正态Copula理论,计及风电功率预测误差的时间相关性及其与预测值的条件相关性,运用聚类方法对处理后的分布式电源数据进行聚类分析,通过场景削减方法生成具有代表性的多变量典型场景,得出分布式电源出力的概率分布模糊集。然后,建立多微电网两阶段滚动调度优化模型,考虑微电网内新能源实时出力的不确定性,提出一种基于列约束生成算法（C&CG）与交替方向乘子法（ADMM）的分布式求解算法,以实现两阶段分布式迭代求解。仿真结果表明,该方法可有效降低源荷预测不确定性对多微电网系统安全运行的影响,提高多微电网区域的经济效益。     

考虑风电出力不确定性的分布鲁棒主备协同优化调度

随着大量可再生能源如风电接入电网,如何最大化地利用可再生能源、减小传统火电机组的成本成为亟需解决的难题,特别是机组运行和备用容量的协同调度成为现在研究的一大热点。考虑风电出力的不确定性,建立了成本最小化的两阶段经济调度模型。在第一阶段,即日前调度,根据预测的风电出力制定机组预调度方案。在第二阶段,即实时调度阶段,根据风电的实时出力对第一阶段调度方案进行反馈调节。针对风电出力的不确定性,应用Kullback-Leibler离散度原理对不确定因素进行建模。结合分布鲁棒方法,建立了极端概率分布下的两阶段分布鲁棒主备协同优化模型,并将其转化为可解的混合整数非线性规划问题。基于广义Benders分解方法,提出了分解协调算法对优化模型进行求解。最后以IEEE 6 节点系统和IEEE 118 节点系统进行算例仿真分析,并对比传统鲁棒及随机规划方法,验证了所提方法的可行性和优越性。     

基于内点法和邻域搜索解耦动态规划法的区域电网动态无功优化方法

动态规划法可高效、准确求解小规模区域电网动态无功优化问题,但随着电网规模的增大,存在组合爆炸而导致求解时间急剧增长的问题。为此,提出了基于内点法和邻域搜索解耦动态规划法的区域电网动态无功优化两阶段混合方法。第1阶段,采用Sigmoid函数实现原模型的连续化,然后采用内点法求连续最优解;第2阶段,在连续解基础上,采用启发式邻域搜索策略确定解耦动态规划法搜索空间,通过站间解耦、调压和无功补偿设备的解耦协调以及站内的动态规划求解区域电网动态无功优化问题。该两阶段方法既保证了优化解的质量又可以有效避免离散变量求解状态组合爆炸问题,大幅度提高了动态规划法的计算效率。以某220 kV控制分区的仿真分析,验证了所提方法的有效性。     

不确定机组组合处理方法评述

机组组合问题本质上是不确定性优化问题,随着以风力发电为代表的可再生分布式发电大量接入电网,该问题的随机性更加突出。分析了基于方案树的随机规划、模糊优化和机会约束规划3种不同的技术,从所需数据量、计算复杂度、对参数的依赖性和求解方法等方面进行了定性比较和评述。结果表明,3种方法在不同的情况下都有其各自的优势,需根据实际情况选择合理的处理方法。     

考虑不确定性的综合能源博弈规划

随着电力系统与天然气系统的耦合性不断增强,以电网为核心的综合能源系统应运而生。然而在不确定条件下,当前规划方法仍无法保证各厂商的利益最优,因此本文提出了基于非合作博弈论的电-气系统的协同规划方法。首先,以各能量单元和电、气网公司作为独立的利益团体,以其年净收益最大为优化目标,建立电-气综合系统中风电机组,燃气机组、电转气机组、输电线路和天然气管道的综合非线性规划模型。针对风电、负荷的不确定性,以及经典两阶段鲁棒优化的保守性问题,提出了一种改进的两阶段鲁棒优化方法——期望场景最优,任意场景可行。最后,以修改的IEEE39节点电力系统与比利时20节点天然气系统构造电-气网络,验证了模型及算法的有效性。     

基于行波波形综合相似度比较的电缆故障选线

针对现有小电流接地故障行波选线方法高度依赖行波初始波头的不足,通过分析三芯电缆故障行波折、反射过程,提出了基于行波电流波形综合相似度比较的小电流接地系统电缆故障选线方法.该方法首先利用S变换提取电缆故障产生的金属护层接地线行波电流时频信息,然后构造行波电流信号的时频能量矩阵和波形相似度矩阵,进而根据波形相似度矩阵计算每...     

光储充一体化快充站日前运行策略

针对电动汽车光储充一体化快充站的优化运行提出一种日前运行策略.考虑光伏出力与快充负荷的不确定性,应用基于场景的随机优化方法,以期望运行成本最低为目标,在满足储能系统循环寿命损耗限制的条件下,制定下一日的交换功率参考值.快充负荷场景利用序贯蒙特卡洛模拟方法基于非齐次泊松过程假设生成.利用分段线性化的储能循环寿命损耗计算模型,对与放电深度相关的储能寿命损耗进行精细化建模.通过算例验证了文中策略在应用储能系统响应电价信号进行削峰填谷方面的有效性.     

计及风电预测误差不确定性的风电参与网架重构优化

在大停电后的电力系统恢复过程中,充分考虑风电场的功率支援可加快系统的恢复速度,而风电场出力又具有不确定性,这为系统恢复方案的制定带来了新的挑战。为此文中提出了一种考虑风电场出力不确定性的网架重构恢复方法,该方法首先以预测误差不确定性描述风电场出力不确定性,然后采用风电场限出力接入策略消除风功率波动性对网架重构带来的负面影响,同时定义网架恢复成功率指标描述风电场预测出力不确定性可能导致的恢复失败风险。之后,在机组节点恢复成功率不低于一定置信水平的前提下建立随机相关机会目标规划模型,以最大化网架恢复成功率和最小化网架恢复时间为优化目标,采用离散粒子群算法和随机模拟技术组成的混合智能算法求解模型。最后,以IEEE 39节点为算例对该方法与传统恢复方法进行了分析比较,结果表明,提出的恢复方法在有效应对风电场出力不确定性的同时能够有效缩短网架恢复时间,加快系统恢复进程。     

Optimal offering strategy considering the risk management for wind power producers in electricity market

This paper provides a technique based on stochastic programming to optimally solve the wind power problem faced by the uncertainty. Uncertainties regarding the wind availability, market prices, and balancing energy needs are considered throughout the paper. The objective of this paper is to derive the best offering strategy for a wind power producer in a Short-Term electricity market, while limiting the risk of expected profit and required reserve due to wind speed forecast volatility. Risk aversion is explicitly modeled using the conditional value-at-risk methodology. ARIMA techniques are used to predict next-day electricity prices and wind speed forecast. For more performance the probability distribution function of the error between forecasted value and realized value to scenario generation is used. A realistic of numerical case studies demonstrates the interest and the effectiveness.     

基于通用分布的含风电电力系统随机动态经济调度

随着风电大规模并网,其不确定性给电力系统经济调度带来了新的挑战。文中利用通用分布模型拟合不同风电功率预测水平下的实际风电功率分布,并以此建立了考虑风电低估、高估成本的日前动态经济调度的随机优化模型。通过对目标函数和约束条件的转化与分析,将随机优化模型转化为一个非线性凸优化问题。结合二次规划算法和内点法,提出了一种两阶段优化算法用以求解对应的经济调度问题。最后,在含风电场的IEEE 30节点系统上,验证了所提基于通用分布的随机动态经济调度方法的有效性。     

基于动态场景集和需求响应的二阶段随机规划调度模型

风电及负荷场景集作为随机规划调度的输入,对决策结果影响大。为使场景集刻画出风电及负荷的波动性,采用考虑随机变量相关性的动态场景法生成风电、负荷及净负荷的场景集,并从与调度接轨的角度提出相应的指标对场景质量进行评价。建立了一种基于here-and-now和waitand-see(HN-WS)的二阶段随机规划调度模型。该模型包含了日前与实时的关联性,决策过程融入了对实时场景已实现情况下的考虑,优化目标涵盖了日前阶段的燃料成本及实时阶段的平衡矫正期望成本。此外,为减少弃风,在调度模型中引入了激励型需求响应,与火电机组的备用容量进行协同优化。最后,采用爱尔兰电网的风电数据、Elia电网的负荷数据及改进的IEEE-118节点系统验证了所提调度模型的有效性。     

基于楼宇分布式能源提升配电网韧性的智能表计优化配置方法

针对现有配电网韧性研究未充分考虑需求侧资源价值的问题,提出了一种新的利用需求侧智能楼宇(smart building,SB)实现供电韧性的解决方案。首先,基于SB中能量转换存储设备的可调特性,分析了灾后SB对配电网的能量支撑作用。在此基础上,构建了基于二阶段随机规划的需求侧资源最优开发模型。在充分计及极端天气对电网损伤不确定性影响的基础上,以系统投资运行费用及灾后负荷损失成本综合最小作为目标,通过优化智能能量表计在SB用户中安装位置,实现对需求侧分布式能源在灾后负荷恢复中的贡献作用。最后以我国东部某沿海城市实际配电网为例,分析了基于SB供能系统提升配电网韧性的效果及成本效益,并验证了所提方法的有效性。     

Static transmission capacity expansion planning under uncertainty

This paper presents a new formulation of the static optimization problem in long-range transmission capacity expansion planning. A quantitative reliability technique is used in conjunction with a stochastic load flow formulation in order to accommodate uncertainty in system component availability and demand projections. The problem is solved using chance-constrained programming. Application of the formulation is demonstrated using the IEEE five-bus test system.     

Stochastic optimization and modelling of large hydrothermal systems for long-term regulation

Methods are presented for long-term regulation of hydrothermal power systems which take account of unit availability, stochastic load and inflow variations. Two general classes of methods for taking into account the inflow availability are presented, namely two-stage linear programming under uncertainty and chanced constrained programming. The computational feasibility of both methods is illustrated by solution of a sample problem. In addition, the practical viability of the first approach is verified by application to a large system.     

Market-clearing with stochastic security-part I: formulation

The first of this two-paper series formulates a stochastic security-constrained multi-period electricity market-clearing problem with unit commitment. The stochastic security criterion accounts for a pre-selected set of random generator and line outages with known historical failure rates and involuntary load shedding as optimization variables. Unlike the classical deterministic reserve-constrained unit commitment, here the reserve services are determined by economically penalizing the operation of the market by the expected load not served. The proposed formulation is a stochastic programming problem that optimizes, concurrently with the pre-contingency social welfare, the expected operating costs associated with the deployment of the reserves following the contingencies. This stochastic programming formulation is solved in the second companion paper using mixed-integer linear programming methods. Two cases are presented: a small transmission-constrained three-bus network scheduled over a horizon of four hours and the IEEE Reliability Test System scheduled over 24 h. The impact on the resulting generation and reserve schedules of transmission constraints and generation ramp limits, of demand-side reserve, of the value of load not served, and of the constitution of the pre-selected set of contingencies are assessed.     

Stochastic Optimal Load Flow Using Newton-Raphson Iterative Technique

ABSTRACT

In this paper we consider the problem of stochastic optimal power flow for an all thermal electric power system. The formulation includes the effects of variables uncertainty modeled as stochastic and normally distributed. The system performance is represented by the load flow equations in polar form, reformulated to include the uncertainty aspect. Inequality constraints are handled using Powell's penalty function method. We employ a variational approach to derive the optimality conditions resulting in a set of nonlinear equations. Newton's method is employed, to actually implement the optimum strategy by solving the nonlinear set of optimality conditions iteratively. An application example involving the IEEE standard 14 bus system is presented and the stochastic solution is compared with a deterministic solution for the same system in order to demonstrate the applicability of the proposed method.     

Semi-definite Programming Approach to Stochastic Combined Heat and Power Environmental/Economic Dispatch Problem

This article presents a solution to the stochastic multi-objective combined heat and power environmental/economic dispatch problem using the semi-definite programming formulation. The vector objective is reduced to an equivalent scalar objective using the weighted sum method. The resulting optimization problem is formulated as a convex optimization via semi-definite programming relaxation. The convex optimization problem was solved to obtain Pareto-optimal solutions. Improvement in the distribution of solution set was achieved through non-linear selection of the weight factor. Simulation was performed on a test problem to investigate the effectiveness of the proposed approach. Results showed that the semi-definite programming based weighted sum method has inherently good convergence property and can have its diversity property improved through weight adaptation.