考虑电价激励需求响应下多主体微电网电源容量优化

考虑多主体微电网中用户在电价激励需求响应的基础上,实现微电网风-光-柴-储的容量优化配置。在电力市场环境下,通过考虑微电网内多元主体的不同职能,建立两阶段优化模型:阶段1建立微电网运营商与消费者之间的完全信息博弈互动模型,在保障售电商利益的前提下,以消费者盈余最大为优化目标,得到微电网内的最优峰谷分时电价策略,进而得到消费者在电价激励下的需求响应曲线;阶段2通过微电网电源投资商与微电网运营商之间的博弈互动,以微电网电源投资商的利益最大化为优化目标,得到微电网内不同分布式电源容量的最优化配置策略。结合某一地区的历史数据信息进行仿真算例分析,验证所提模型的有效性。     

基于广义斯塔克尔伯格博弈的微电网能源管理

为了实现微电网的能源优化管理,最大化单个微电网和多个用户的群体效益,基于非合作博弈理论,建立单个微电网和多个用户之间的广义斯塔克尔伯格博弈模型,其中,微电网作为博弈的领导者,以最大化收益为目标优化电价,引导用户调整用电策略,用户作为博弈的追随者,以最大化各自的效用函数为目标,在给定电价下决定用电策略,并影响电价制定。将广义纳什均衡问题转化为变分不等式,并采用固定步长的投影算法和最佳响应算法进行求解。某社区微电网的实例验证了所提模型的有效性。     

基于高效遗传算法的电网需求侧调度优化研究及其收敛性分析

智能电网中大功率电器飙升及智能终端的普及,导致需求侧用电负荷增加所造成用电困难的问题。从分布式发电、市电以及居民用电三个角度考虑需求侧调度场景,并对其构建分时电价模型。随后,通过引入居民舒适度、用电经济度和负载方差三个衡量调度性能函数,构建出一种基于调度性能函数的加权优化目标模型。考虑到复杂多方的分时电价模型参与调度,提出了一种改进的遗传算法对需求侧进行用电调度来最小化目标函数。该算法通过加入精英选择策略和进化逆转操作,可有效地减少算法迭代次数,以取得目标函数最优值。然后,从理论上对所改进的遗传算法进行收敛性证明。最后,通过算例仿真验证了算法的有效性,并在满足居民用电舒适度的同时降低了31.29%的用电成本。     

用电需求曲线建模及其应用

根据需求跟随价格变化的原理,用户的用电数量不是确定不变的.本文借鉴经济学的有关理论,从用户调查入手,阐述了用电需求曲线的数学模型及其参数估计方法.在此基础上,分析了用户的电力需求弹性.特别针对电力企业广泛采用的增供促销措施进行了定量分析,发现这种措施在某些情况下可能失效,由此提出了基于用电需求曲线的区别定价方法,指导电力企业采取合理的价格营销策略.     

Arbitration of day-ahead real power market clearing under the influence of voltage dependent load models

Social welfare maximization is used as an objective function to clear day-ahead real power electricity markets with elastic loads. The conventional way is to model loads as voltage independent. This paper investigates behaviour of day-ahead market clearing in the presence of voltage dependent load models at different loading conditions. In a multi-objective framework, different objective functions (load served, generation cost, emission and voltage stability enhancement index) are combined with social welfare so as to examine each function’s behaviour. However, it is observed and demonstrated that the objective functions are either in accord or discord with social welfare at different loading conditions. Therefore, Pareto fronts are obtained to decide the most optimal functioning condition subject to all operating and technical constraints for the judgement making authority. The differential evolution algorithm is applied for single and multi-objective optimization purposes. The model is implemented on IEEE 30 bus system for testing and verification.     

Nodal price volatility reduction and reliability enhancement of restructured power systems considering demand–price elasticity

With the development of restructured power systems, the conventional “same for all customers” electricity price is getting replaced by nodal prices. Electricity prices will fluctuate with time and nodes. In restructured power systems, electricity demands will interact mutually with prices. Customers may shift some of their electricity consumption from time slots of high electricity prices to those of low electricity prices if there is a commensurate price incentive. The demand side load shift will influence nodal prices in return. This interaction between demand and price can be depicted using demand–price elasticity. This paper proposes an evaluation technique incorporating the impact of the demand–price elasticity on nodal prices, system reliability and nodal reliabilities of restructured power systems. In this technique, demand and price correlations are represented using the demand–price elasticity matrix which consists of self/cross-elasticity coefficients. Nodal prices are determined using optimal power flow (OPF). The OPF and customer damage functions (CDFs) are combined in the proposed reliability evaluation technique to assess the reliability enhancement of restructured power systems considering demand–price elasticity. The IEEE reliability test system (RTS) is simulated to illustrate the developed techniques. The simulation results show that demand–price elasticity reduces the nodal price volatility and improves both the system reliability and nodal reliabilities of restructured power systems. Demand–price elasticity can therefore be utilized as a possible efficient tool to reduce price volatility and to enhance the reliability of restructured power systems.     

Optimal real and reactive power dispatch

This paper presents a method for optimal real and reactive power dispatch for the economic operation of power systems. As in other methods, the problem is decomposed into a P-optimization module and a Q-optimization module, but in this method both modules use the same generation cost objective function. The control variables are generator real power outputs for the real power module; and generator reactive power outputs, shunt capacitors/reactors, and transformer tap settings for the reactive power module. The constraints are the operating limits of the control variables, power line flows, and bus voltages.The optimization problem is solved using the gradient projection method (GPM) which is used for the first time in the power systems study. Among other advantages, the GPM allows the use of functional constraints without the need for penalty functions or Lagrange multipliers.Mathematical models are developed to represent the sensitivity relationships between dependent and control variables for both (real and reactive power) optimization modules, and thus eliminate the use of B-coefficients. Results of two test systems are presented and compared with conventional methods.     

基于拉姆齐定价理论的销售电价研究

将拉姆齐定价理论应用于销售电价定价中,通过松建居民用户和工业用户电力需求计量经济学模型,回归估计了近期我国分类用户的短期电力需求价格弹性,并按照拉姆齐定价模型测算出分类用户次优平均销售电价水平。在此基础上提出了基于福利因数的改进拉姆齐定价模型,该模型与居民阶梯式电价有着紧密联系,促使分类用户销售电价机制趋于合理,增加社会总福利。     

Demand response governed swarm intelligent grid scheduling framework for social welfare

Peak load defines the generation, transmission and distribution capacity of interconnected power network. As load changes throughout the day and the year, electricity systems must be able to deliver the maximum load at all times, which will be hard trade for a practical power network. Smart grid technologies show strong potential to optimize asset utilization by shifting peak load to off peak times, thereby decoupling the electricity growth from peak load growth. Under Smart grid trade regulation, with continuous varying demand pattern, electricity price will be uneven as well. On this view point, in order to obtain a flatten demand, without affecting the welfare of the market participants, this paper presents an on-going effort to develop Demand Response (DR) governed swarm intelligence based stochastic peak load modeling methodology capable of restoring the market equilibrium during price and demand oscillations of the real-time smart power networks. This proposed DR based methodology allows generators and loads to interact in an automated fashion in real time, coordinating demand to flatten spikes and thereby minimizing erratic variations of price of electricity. For proper utilization of DR connectivity, a Curtailment Limiting Index (CLI) has been formulated, monitoring which in real time, for each of the Load Dispatch Centers (LDCs), the system operator can shape the electricity demand according to the available capacity of generation, transmission and distribution assets. The proposed methodology can also be highlighted for generating the most economical schedule for social welfare with standard operational status in terms of voltage profile, system loss and optimal load curtailment. The case study has been carried out in IEEE 30 bus scenario as well as on a practical 203 bus-265 line power network (Indian Eastern Grid) with both generator characteristics and price responsive demand characteristics or DR as inputs and illustrious Particle Swarm Optimization (PSO) technique has assisted the fusion of the proposed model and methodology. Encouraging simulation results suggest that, the effective deployment of this methodology may lead to an operating condition where an overall benefit of all the power market participants with standard operational status can be ensured and the misuse of electricity will be minimized.     

考虑用户满意度的需求侧管理价格决策模型

开放需求侧,引入需求侧的价格弹性,利用价格手段实现负荷调控,是电力市场发展的必由之路.作者在用电价格弹性的基础上,分析了不同类别负荷对电价变化的不同响应,建立了统一的用户反应度模型,从而客观地描述各时段负荷大小与整个计算周期内各时段电价之间的关系;通过对用户电力消费行为的分析,提出了一种用户满意度的量化方法;基于上述结果,建立了考虑用户满意度的DSM价格决策模型.文中通过一个具体算例,探讨了市场环境下利用价格手段进行负荷控制的可能性.     

以节能调度为导向的发电侧与售电侧峰谷分时电价联合优化模型

针对中国现阶段还未全面执行电力竞价上网的实际情况,以节能调度为导向,基于需求侧响应,考虑发电机组的上网电量及其在峰、平、谷时段的电量分配问题,在保证发、供、用三方均能从峰谷分时电价中受益的前提下,以实施峰谷分时电价后平均发电能耗成本最低为目标函数,建立发电侧与售电侧峰谷分时电价联合设计的优化模型。在建模过程中,根据各类机组在发电过程中所排放污染物带来的经济损失,为发电机组设置环境价值参数,并给出环境价值参数的确定方法。最后,应用遗传算法给出该优化模型的求解步骤。算例结果表明该优化模型的可行性和有效性。     

基于需求侧响应的多类型负荷协调控制模型

风电的随机波动性影响了电力系统经济运行。为充分挖掘需求侧响应资源,需要考虑不同负荷对电价响应的差异性,并将其融入传统调度中。首先,基于电价弹性矩阵,对用户的电价响应行为建模。其次,分析不同负荷的电价响应特性,对负荷进行分类。在此基础上,基于峰谷分时电价,以系统运行经济性为目标,考虑用户满意度约束,建立多类型负荷协调控制模型。并将模型转化为一个双层优化问题,利用差分进化粒子群算法进行求解。最后,在一10机系统中进行仿真验证。结果表明,所建立的多类型负荷协调控制模型可充分挖掘不同负荷的需求响应能力,能有效降低发电成本,有利于系统经济运行。     

基于戴蒙德模型的用户响应行为分析

针对传统的需求价格弹性理论仅考虑到需求与价格之间的关系,无法同时考虑到用户的意愿和反映用户对于分时电价的真实响应情况等问题,提出了基于戴蒙德模型的同时,可考虑价格与用户意愿的响应行为分析方法。首先,结合需求价格弹性、相对风险厌恶不变函数,考虑价格与用户意愿等因素对用户响应行为进行建模;其次以效用函数最大为目标,结合各时段的用电约束,构建了用户优化决策模型,继而进行优化求解,模拟用户的最优决策行为;最后通过算例验证了该模型改善了居民用户的用电曲线,降低了居民用户的电费成本,可以模拟拥有不同意愿的用户响应情况。     

电力市场初期两部制无功定价方法

提出了一种电力市场初期的两部制无功定价方法,以有功电能成本和无功电量成本最优为目标函数,以潮流方程为等式约束,以运行及安全约束为不等式约束。建议的内点非线性规划法原理简单、计算量小。所提两部制无功电价模型的核心想法为:一方面,提出电力市场下无功源以运行成本参与竞争,以解决现有的一部制电价对无功的运行成本补偿不足的问题;另一方面,提出一种新的无功容量电价分解方案,以解决现有的一部制电价对无功投资成本回收不明确的问题。电量电价加容量电价形成了建议的两部制无功电价。IEEE-30节点系统算例验证了建议的无功优化算法和无功价格模型的有效性和实用性。     

A study of reactive power marginal price in electricity market

Developing an accurate and feasible method for reactive power pricing is significant in the electricity market. The reactive power price cannot be obtained accurately by conventional optimal power flow models which usually ignore the production cost of reactive power. In this paper, the authors include the production cost of reactive power into the objective function of the optimal power flow problem, and use sequential quadratic programming method to solve the optimization problem and obtain reactive power marginal price accordingly. A five-bus test system is used for computer study. The results from eight study cases show clearly the effects of various factors on reactive power marginal price.     

Flexibility now or later? – Impact of market timing on flexibility and social welfare of demand response

Demand side management enables flexible electricity consumers to participate in system services that contribute to enhanced integration of renewable energy sources. The specific market timing, pricing scheme and demand response program decide in which way consumers receive and react to incentives. Aside from pricing, several other parameters were found to greatly influence consumer response. Policy makers can improve the market design of wholesale and balancing markets. This would be a necessary tool to increase the demand side flexibility, but could also be used to allow better forecasts of production. Here, the impact of lead time on the flexibility of consumers is investigated and its impact on social welfare is estimated. The price elasticity of consumers can vary in different ways depending on the lead time and estimations may be uncertain. Therefore, the concept of a demand flexibility gap is proposed in order to quantify how the uncertainty of consumers’ responses may affect the social welfare of such a policy change. We recommend that lead time should be considered in electricity market design, e.g., in consecutive ahead markets in order to tap the full potential of flexibility from the demand side.     

一种面向微电网多自主体系统的实时电价算法

针对微电网中多个可控出力的供电端向多位用户供电的电能优化调度问题,结合多自主体网络的协调控制机制,从而搭建基于全分布式控制的微电网能量管理系统。根据对偶分解,分解社会效益最大化问题为对偶子问题,并将求解对偶问题的最优Lagrange乘子转化为求解实时电价,从而提出基于Lambda-Consensus的实时电价算法。依据信息交互机制,将功率项反馈给电价计量单元,并根据电价参考值更新本地的电价计量值,进而供电端和需求侧的发/用电调度单元分别响应电价,在有功功率输出/输入的约束下调整发/用电行为,最终实现功率平衡和社会效益最大化。实例验证该算法的有效性和即插即用特性,并在大规模网络下也能达到理想的收敛效果。     

负荷价格弹性的季节特性及尖峰电价政策福利效应估算

尖峰电价可以驱动用户主动减少高峰用电负荷并自愿参与需求响应。以江苏省3年的第二产业负荷数据为基础,运用数理统计方法实证研究了尖峰负荷价格弹性的时间特征,给出了尖峰电价政策福利函数,并根据不同弹性下需求响应的程度对尖峰电价政策的福利效应进行了分析和估算。结果表明：尖峰电价可以使社会净福利显著增加,但各月和各时段增幅差异较大。按照社会最大化原则,执行尖峰电价政策及尖峰价格浮动幅度,应依据负荷价格弹性季节和时段特性有选择性地确定。     

考虑分时电价和需求响应的家庭型用户侧微电网优化运行

在考虑分时电价和需求响应的基础上,建立了含有光伏发电、风力发电以及蓄电池的家庭型用户侧微电网优化运行模型,提出了一种家庭型用户侧微电网需求响应方法.优化模型以综合考虑用户的购电费用和舒适度为优化目标,尽可能多地将峰时电价时段的家用负荷功率向谷时电价和平时电价时段移动.采用粒子群优化算法对模型进行了求解,在算例分析中对比了优化前后微电网向电网公司支付的购电费用,验证了所提优化算法的有效性.     

计及用户舒适度与用电成本的空调负荷优化控制方法

智能电网环境下,应用于空调(商业与居民用户最主要的用电负荷)的需求响应措施对电网稳定运行有重要意义。针对用户参与需求响应过程中导致舒适度明显降低的问题,本文提出了一种用户可参与自主决策的空调负荷优化控制方法,基于改进的免疫克隆选择算法,建立了同时考虑用户舒适度与用电成本的空调负荷多目标优化调控模型;并将原始免疫克隆选择算法中的变异算子改进为一种自适应的非一致性变异算子,进一步提高算法的收敛能力,逼近Pareto最优面。仿真及实验结果表明,本文算法在对空调负荷执行基于分时电价的需求响应过程中,能够有效兼顾用户对经济性和舒适性的需求;优化结果相对用户期望值的亲和力得到明显提升,验证了该算法的有效性和优越性。