Multi-objective Optimization of Integrated Energy Systems Considering Ladder-type Carbon Emission Trading and Refined Load Demand Response

In this paper,a novel multi-objective optimization model of integrated energy systems(IESs)is proposed based on the ladder-type carbon emission trading mechanism and refined load demand response strategies.First,the carbon emission trading mechanism is introduced into the optimal scheduling of IESs,and a ladder-type carbon emission cost calculation model based on rewards and penalties is established to strictly control the carbon emissions of the system.Then,according to different response characteristics of electric load and heating load,a refined load demand response model is built based on the price elasticity matrix and substitutability of energy supply mode.On these basis,a multi-objective optimization model of IESs is established,which aims to minimize the total operating cost and the renewable energy source(RES)curtailment.Finally,based on typical case studies,the simulation results show that the proposed model can effectively improve the economic benefits of IESs and the utilization efficiency of RESs.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Inverse Proportion Technique Based Scheduling Strategy for Energy Storage System Considering Load Demand Differences

The energy storage system(ESS)as a demand-side management(DSM)resource can effectively smooth the load power fluctuation of a power system.However,designing a more reasonable ESS operational strategy will be a prerequisite before incorporating the energy storage device into DSM.As different load levels have different demands for the real-time chargedischarge power of an ESS,this paper proposes a heuristic ESS operation scheduling strategy which can take into account the electrical load demand differences.In this paper,firstly,two demand degree concepts for charging power and discharging power are defined to describe the differentiated ESS demand under the condition of different electrical load levels.Secondly,an inverse proportion technique based ESS scheduling strategy,with the consideration of the load demand difference,is proposed in this paper.Thirdly,some evaluating indices are defined in this paper for describing the influence of the proposed strategy on the smoothing degree of the daily load curve.Finally,several case studies are designed to verify the validity and correctness of the proposed technique,and the results show that the proposed technique can effectively smooth the load curve and improve the ability of peak shaving and valley filling.

Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

计及成本和价值的长周期负荷侧调节能力投入策略

随着新型电力系统的发展,负荷侧调节能力建设日益受到关注,对于负荷侧调节能力成本、价值及其投入策略的研究,具有重要意义。基于近10 a来中国电力工业发展的基础数据,结合新型电力系统发展态势,分析了负荷侧调节能力需求的背景,通过对发用电两侧调节能力特点的比较,从电力系统整体角度梳理了负荷侧调节能力的投入所带来的增量价值,分析了负荷侧调节能力的应用价值盈余,进一步结合具体需求提出了3种长周期下负荷侧调节能力投入的优化策略模型,并进行了算例分析。结果显示:负荷侧调节能力的投入有利于降低电力系统的总成本、增加系统调节能力,但是由于其成本曲线的变化以及“非持久特性”等因素,其投入策略需要综合长周期市场发展需求和发用电两侧成本因素进行权衡,所提出的3种优化模型各有其适用场景。

基于节点碳势需求响应的电力系统双层优化调度

为实现电力系统低碳排放、助力经济提升,在建立碳势引导多元柔性负荷模型的基础上,提出一种基于节点碳势需求响应的双层优化调度策略。首先,利用比例共享原则追踪碳排放流,搭建碳排放流模型,从时空维度感知各节点的碳势变化规律。其次,将碳流分析纳入负荷侧需求响应机制中,利用节点碳势建立负荷聚合商需求响应碳排放模型,并厘清不同碳势强度下负荷聚合商调度差异,构建基于节点碳势需求响应的电力系统双层优化调度模型。模型上层为电网运营商最优经济调度,模型下层为负荷聚合商需求响应经济调度。最后,以改进IEEE 30节点系统为例,验证了所提方法的有效性。

极端灾害下基于综合需求响应的电-水-气综合能源系统负荷恢复策略

在地震等极端自然灾害下,电-水-气综合能源系统的元件故障概率会大幅提升,进而严重影响系统的正常运行。针对目前忽略用户在提升系统弹性上的潜力,基于综合需求响应对电-水-气综合能源系统负荷恢复策略进行研究。首先基于多能源之间的耦合特性与用户的动态响应特性建立综合需求响应模型。其次设计系统与用户协同的需求响应参与机制,将用户的综合需求响应作为灵活性资源融入极端灾害下的负荷恢复策略中,有效、合理地提升系统弹性。最后建立电-水-气综合能源系统负荷恢复优化模型,通过改造的电-水-气综合能源系统对所提出的方法进行了模拟和验证。结果表明,所提出的负荷恢复策略能够提高灾后系统负荷的恢复比例,有效增强系统的弹性。

Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas

Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.

Intelligent Load Management Scheme for a Residential Community in Smart Grids Network Using Fair Emergency Demand Response Programs

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modelling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent load management scenario.

考虑阶梯式碳交易与电热需求响应的综合能源系统经济调度

在能源安全新战略背景下,推动可再生能源发展、降低碳排放将是未来发展趋势。为了消纳大规模风电的弃风功率以提升电网的调峰能力,首先考虑阶梯式碳交易机制,采用碳交易市场,利用碳交易价格达到控制碳排放的目的;其次基于分时电价形成用户用电舒适度与用热舒适度,构建电热负荷需求响应模型,在掌握电锅炉、储热装置和热需求可时移特性的基础上,通过风电供给价格弹性系数描述风电出力与电锅炉协议电价之间的变化;最后通过综合满意度指标评估负荷响应能力,构建了考虑需求响应和阶梯式碳交易机制的含蓄热式电锅炉综合能源系统优化调度模型,实现系统经济运行,并采用CPLEX求解器对所构建模型进行求解。结果表明所提方法能够有效促进风电消纳,减少碳排放量且有效降低系统运行成本。

基于多类需求响应的配电网优化调度

针对当前激励型定价补偿机制无法满足多类型负荷差异化需求的问题,提出了一种分段式补偿价格形式的可中断负荷补偿方法,同时引入负荷转移时间和负荷转移功率的二维交变函数,建立了量化负荷转移成本的可转移负荷补偿模型;计及可中断负荷不确定性,建立了多类型需求响应参与配电网优化运行模型,针对模型中非凸非线性的约束,利用二阶锥松弛方法将模型转化为混合整数二阶锥规划模型,采用CPLEX求解器对模型进行求解,并引入贡献度和置信度作为评估用户响应能力的评价指标。仿真结果表明,新型补偿机制能更加合理地指导用户调整用电负荷,平滑负荷曲线,提高配电网运行经济性。

面向需求响应市场的柔性负荷调度方法研究

为保障新能源的充分消纳和电力系统的安全性及可靠性,需要更多的灵活性资源参与调度,以市场化手段制定负荷聚合商等新型主体的调度运行机制,充分发挥其调节能力,对新型电力系统的建设和发展具有重要意义。从参与主体、准入条件、响应规则和补偿规则4方面分析了目前柔性负荷参与需求响应现状,根据负荷控制场景下的调频及调峰需求,构建柔性负荷优化调度模型。按照目前大部分省市所应用单边报量报价的模式,以“价格优先、容量优先”竞价原则,决定补贴价格及各负荷主体响应量。利用包括独立负荷及负荷聚合商在内的5000个柔性负荷规模系统测试调度优化速度,测试表明模型满足需求响应市场下快速高效调整负荷的需求。

基于低代码云平台的低碳园区负荷聚集商系统的研究及实现

中小型负荷分布不集中,弹性水平低是造成负荷侧能源利用效率低、调度困难及需求侧响应难以有效开展的因素之一。针对上述问题,将新兴IT技术与智能电网、虚拟电厂建设等电力行业的需求结合起来,梳理并完成负荷聚集商业务主线逻辑设计,并充分利用低代码云平台上开发效率高、开发周期短、架构灵活、扩展性强和运营维护成本低的特点,基于低代码云平台,设计出一套完整的负荷聚集商系统并加以实现。该系统完整实现了负荷聚集商的业务逻辑,验证了基于低代码云平台系统研发的技术可行性,为负荷聚集商技术支持系统的建设提供了有价值的参考方案。

计及负荷灵活特性的工业园区综合能源系统日前优化调度研究

研究工业灵活负荷可调特性是提升电网动态调节能力的有效途径。工业园区用户侧存在大量具备调节潜力的工业负荷资源,针对此特性,基于能源集线器构建包含分布式发电系统、储能电站、燃气轮机、燃气锅炉、灵活负荷等在内的工业园区综合能源系统数学模型,并提出一种考虑负荷灵活特性的园区能源系统日前优化调度方法。首先,考虑系统需求响应侧电解铝等灵活性负荷的可平移、可转移、可中断的负荷特性;其次,在计及生产运行要素的基础上,以购能成本、补偿成本以及运行和维护成本之和最小为目标函数,建立系统优化调度模型;最后,基于Matlab软件结合Cplex求解器,并通过3种不同典型场景对比分析,验证了所提模型的有效性。算例分析表明,考虑工业灵活电、热负荷耦合优化,成本最低,比没有灵活负荷参与时的成本降低了6.6%。研究分析表明,工业灵活负荷资源优化配置有助于促进新能源的消纳、减轻工业用电压力和提高工业园区系统运行的经济性。

价格-积分联合激励下考虑消费舒适度的居民需求响应优化策略

针对居民家庭生活能耗占比高、碳排放量基数大、用电分散性与随机性明显的问题,为进一步发掘居民用户的节能降碳潜力,引导其规模有序地参与电力需求响应,提出了价格信号与积分制联合激励下考虑消费舒适度的居民用户需求响应优化策略。首先,分析价格-积分联合激励的运营架构,构建考虑居民用电消费心理学的行为不确定性模型。其次,提出价格-积分联合激励量化模型与积分激励兑换策略,结合家用电器运行特征,构建居民柔性负荷的积分激励数学模型。进而,考虑居民用电量、时间与温度多元舒适度,构建计及消费舒适度与边界效用递减效应的居民用户需求响应多目标优化模型。最后,以我国北方某社区某个居民家庭负荷为例进行仿真计算,结果表明,所提模型能在保障居民电力消费舒适度的前提下,降低日电费支出18.21%,相比于其他两种策略,在负荷电量转移率、居民负荷峰值削减量方面的优势更为显著。

考虑多重轮换调控的空调负荷调控潜力评估与控制策略

调度空调柔性负荷是缓解电网高峰负荷的有效措施,对稳定电网运行具有重要意义。首先,构建空调负荷模型,兼顾考虑空调可投切和空调允许关断时长,引入微元法的思想,提出考虑控制时长影响的空调调控潜力评估方法。其次,分析负荷聚合商系统发展需求,基于软件定义网络提出空调状态监控系统架构。然后,以空调群聚合功率变化量与电网调控需求量差值最小为目标,以用户舒适度为重要约束,考虑空调投切状态与空调调控总时长,构建考虑多重轮换调控的空调控制策略。最后,通过仿真分析验证了所提模型不仅能根据不同响应时长准确评估空调调控潜力,而且能够提升空调可调控能力。

面向新能源消纳的需求侧灵活性资源聚合响应分析

新型电力系统中需求侧灵活性资源的重要性日益凸显。现有研究对大量负荷参与新能源消纳的需求侧响应改善效果与影响机制研究不足。首先分析了需求侧资源提供灵活性的机理,提出了需求侧灵活性资源可调节空间的计算方法;然后,考虑可平移负荷、可转移负荷和可削减负荷约束条件,构建面向新能源消纳的多类型需求响应聚合协同优化运行模型;最后,以某省级电网算例开展仿真研究,分析了各种类型需求响应的改善效果与机制。结果表明:负荷响应配合机组启停可以得到超过响应量的新能源消纳量,提出的多类型需求响应协同优化运行模型能够有效促进新能源消纳。

用于快速响应负荷需求的两段式斜坡重力储能系统放电功率调节方法

斜坡式重力储能系统(GESS)放电时通常将上堆场重物块运送至下堆场。然而,当放电过程中系统负荷需求出现波动时,现有方案难以通过及时调整重物块数量来调节输出功率,导致重力储能系统响应不及时并造成额外能量损耗。通过在斜坡两侧增加辅助堆场并将斜坡分为两段,通过控制顶部堆场重物块释放数量、斜坡堆场释放高度以及斜坡堆场重物块抓取位置,实现了重力储能系统输出功率的精确控制与负荷需求的快速响应。基于Matlab/Simulink平台搭建了两段式斜坡重力储能系统仿真模型,验证了所提方法的正确性与有效性。研究成果为斜坡式重力储能系统放电功率的快速调节与精确控制提供了重要参考。

虚拟电厂两阶段准线型需求响应优化及收益共享-风险共担决策方法

末端分散式资源的需求响应(demand response,DR)是支撑电力系统灵活调节的重要形式,但末端资源通常规模大且单体容量小,迫切需要虚拟电厂(virtual power plant,VPP)等市场主体进行聚合代理,从而间接参与电网公司的调节服务。聚焦于准线型需求响应这一新兴的响应模式,重点研究VPP内部分散式资源的利益-风险互动协调策略,提出面向准线型DR的VPP两阶段优化及收益共享-风险共担决策方法,构建VPP与电网、内部用户互动协调新模式。首先,在准线型激励下VPP以整体收益最大化为目标,考虑柔性负荷响应积极度与新能源准确度因子,进行日前-日内两阶段DR优化;其次,在收益共享-风险共担决策方法下,VPP与柔性负荷共享准线型激励、与新能源共担不确定性风险,并以改进的Shapley法对内部柔性负荷进行利益分配。仿真结果表明,相较于传统DR,准线型DR给出全时段响应目标,更具有优越性;共享-共担决策吸引柔性负荷参与VPP调节,促使VPP与用户双赢。