Two-Stage Low-Carbon Economic Dispatch of Integrated Demand Response-Enabled Integrated Energy System with Ladder-Type Carbon Trading

Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response(IDR)is proposed in this paper for the integrated energy system(IES),where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR.In contrast,the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables.In addition,to fully exploit the system’s emission reduction potential,a carbon trading cost model with segmented CO_(2) emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism,and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side.The CPLEX optimizer resolves the two-stage model,and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.

Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.

考虑“响应-碳排放”责任耦合交易的IDR策略研究

综合需求响应(integrated demand response,IDR)作为区域综合能源系统(regional integrated energy system,RIES)维持供需平衡、实现分布式能源就地消纳的重要途径,已成为当下的研究热点。但现有IDR研究存在3个方面不足:综合能源服务商(integrated energy service provider,IESP)制定激励策略时,未考虑区域内用户的响应疲劳特性;在实施IDR的复杂场景下,未考虑区域IESP间的响应责任交易;忽略响应责任与碳排放责任的耦合关系。为此,首先分析用户响应意愿随响应次数的演化特性,通过引入响应疲劳函数实现对用户响应疲劳特性的有效建模;在此基础上,根据IESP的响应任务类型,设置多个IESP参与IDR的复杂场景。进一步,为实现各IESP参与IDR的经济性与低碳性,在考虑复杂场景下IESP间响应责任交易时的能源转移与碳排放责任转移基础上,建立响应责任-碳排放责任耦合交易机制。最终,采用基于自适应迭代步长的议价方法以确定最优交易价格。通过仿真算例验证所提模型的有效性:考虑用户响应疲劳特性的改进模型使用户的总收益提升27%;所提交易机制不仅使各场景下IESP的总成本分别降低15.8%、9.8%、94.1%,还使典型场景下IESP的碳排放量降低17.8%,提高IESP参与IDR的经济性与低碳性,实现IESP与用户的共赢。

Station-and-network–coordinated planning of integrated energy system considering integrated demand response

The integrated energy system(IES)is an important energy supply method for mitigating the energy crisis.A station-and-network–coordinated planning method for the IES,which considers the integrated demand responses(IDRs)of flexible loads,electric vehicles,and energy storage is proposed in this work.First,based on load substitution at the user side,an energy-station model considering the IDR is established.Then,based on the characteristics of the energy network,a collaborative planning model is established for the energy station and energy network of the IES,considering the comprehensive system investment,operation and maintenance,and clean energy shortage penalty costs,to minimize the total cost.This can help optimize the locations of the power lines and natural gas pipelines and the capacities of the equipment in an energy station.Finally,simulations are performed to demonstrate that the proposed planning method can help delay or reduce the construction of new lines and energy-station equipment,thereby reducing the investment required and improving the planning economics of the IES.

A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Research on Operation Optimization of Energy Storage Power Station and Integrated Energy Microgrid Alliance Based on Stackelberg Game

With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage.To solve the problem of the interests of different subjects in the operation of the energy storage power stations(ESS)and the integrated energy multi-microgrid alliance(IEMA),this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game.In the upper layer,ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.In the lower layer,IEMA optimizes the output of various energy conversion coupled devices within the IEMA,as well as energy interaction and demand response(DR),based on the energy interaction prices provided by ESS.The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs.

Low Carbon Economic Dispatch of Integrated Energy System Considering Power Supply Reliability and Integrated Demand Response

Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy.This paper studies an electric-gas-heat integrated energy system,including the carbon capture system,energy coupling equipment,and renewable energy.An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost,carbon emission and enhance the power supply reliability.Firstly,the lowcarbon mathematical model of combined thermal and power unit,carbon capture system and power to gas unit(CCP)is established.Subsequently,we establish a low carbon multi-objective optimization model considering system operation cost,carbon emissions cost,integrated demand response,wind and photovoltaic curtailment,and load shedding costs.Furthermore,considering the intermittency of wind power generation and the flexibility of load demand,the low carbon economic dispatch problem is modeled as a Markov decision process.The twin delayed deep deterministic policy gradient(TD3)algorithm is used to solve the complex scheduling problem.The effectiveness of the proposed method is verified in the simulation case studies.Compared with TD3,SAC,A3C,DDPG and DQN algorithms,the operating cost is reduced by 8.6%,4.3%,6.1%and 8.0%.

Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.

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。

考虑需求响应及不确定性的综合能源优化调度

如何在区域综合能源系统中建立有效的需求侧模型,对于需求响应政策的实施至关重要。为提高用户参与需求响应积极性,引入实时定价机制,同时考虑系统的经济低碳性和电气热用户的用能满意度,构建兼顾系统和用户利益的综合需求响应主从博弈模型。针对用户参与需求响应的不确定性,引入贝叶斯方法更新负荷曲线。算例分析表明,所提博弈模型及贝叶斯方法能够有效平衡系统和用户之间的利益,提高了综合需求响应的可靠性。

考虑超碳需求响应的综合能源系统低碳优化调度

西北地区新能源发展迅速,充分利用当地独有的光热资源优势,同时结合火电低碳化改造与新能源协同运行,有利于推动当地能源系统绿色低碳转型。为尽最大限度地提升系统的减碳能力,提出一种基于源荷协同降碳的超碳需求响应模型,将动态碳排放因子作为分时电价的惩罚因子,从而将源侧碳信号传递至荷侧,驱使用户侧进行低碳性状态转移。首先,在日前调度阶段,构建预调度-再调度两阶段调度运行机制,再调度根据预调度的系统状态信息进行超碳需求响应,来深度降低系统碳排量。其次,将光热电站引入综合能源系统,与风电场、碳捕集电厂协同运行,从而构建高比例新能源场景,来验证超碳需求响应在此场景下的减碳效益。最后,建立了基于超碳需求响应的预调度-再调度两阶段低碳调度模型。经算例仿真分析表明,所提源荷协同降碳的新思路能有效提高系统的减碳能力,深入挖掘系统的降碳空间,提升系统的经济效益。

考虑不确定性的综合能源供应商日前优化运行策略

综合能源供应商作为多能源供应主体,可以参与市场化交易。为探索综合能源供应商在电力市场环境下新的盈利点,构建了考虑需求响应的多主体互动机制,并充分考虑光伏出力、多元用能需求、市场电价以及需求响应资源潜力的不确定性,采用区间优化方法构建了表征综合能源供应商日前运行优化问题的混合整数线性规划模型,得到综合能源供应商的购能、设备运行以及需求响应资源调用等策略。算例验证结果表明,需求响应机制可以有效降低购电成本,增加综合能源供应商的运行收益。

考虑用户比价行为的综合需求响应低碳优化策略

在我国“双碳”背景下,建立综合能源系统(integrated energy system,IES)已经成为实现“碳达峰”、“碳中和”目标,加快能源结构转型的重要举措,而综合需求响应(integrated demand response,IDR)是综合能源系统减少碳排放、缓解供需双侧不平衡的有效途径。然而,现有关于IDR的研究大多仅考虑其经济效益,未考虑其环境效益,忽略了需求侧用户对不同时段激励价格的比价行为以及不同用户的差异化响应特性。该文提出了基于碳排放因子的IDR碳排放折算模型,并将碳排放成本加入综合能源系统服务商(integrated energy system provider,IESP)的目标函数中;通过建立激励交叉弹性耦合矩阵对用户的比价行为进行了有效建模以提升模型的精确性;同时计及用户的差异化响应特性,通过制定差异化的激励策略以充分挖掘用户的响应潜力。所提模型被建立为一个IESP-用户双层优化模型,并将该模型转化为了一个单层的凸优化模型以实现高效地求解。最后通过仿真算例验证了模型的有效性,不仅减少了碳排放量,同时降低了IESP的响应成本并提升了用户的舒适度,实现了多方共赢。

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。

基于组合赋权与灰云模型的综合能源系统需求响应效益评价

考虑到当前综合能源系统研究缺乏完善的需求响应量化评估方案,难以在优化运行层面综合评估需求响应效益这一问题,提出了一种基于改进AHP群决策—CRITIC组合赋权与灰云模型的综合能源系统需求响应效益评价方法。首先,从综合能效性、社会经济性和需求侧互动性三个维度构建了需求响应效益评价指标体系。其次,采用一致性和权重拟合性更优的指数标度法构造判断矩阵,降低赋值误差,并通过AHP群决策法确定主观权重,从而削弱主观极值偏差对权重的影响;在由CRITIC法确定客观权重后,基于最小欧氏距离建立组合权重模型,并通过非线性规划求取最优组合权重;针对评价等级信息的模糊性与隶属等级的随机性,采用正态灰云白化权模型确定指标分类等级与场景评分。最后,以北方某综合能源系统为例,根据用户参与需求响应方式设置了4种运行场景,分析了不同需求响应对系统运行的影响与作用,算例结果表明所提指标体系与评价方法科学有效。

考虑含HRD的光热电站和综合需求响应的综合能源系统低碳经济调度

在“双碳”的背景下,为进一步提升综合能源系统(Integrated Energy System, IES)的经济性和环境效益,文中提出一种在奖惩阶梯型碳交易机制下考虑含热回收装置(Heat Recycling Device, HRD)的光热电站和综合需求响应的IES系统低碳经济调度方法。首先,在源侧构建含热回收装置的光热电站与加装碳捕集的热电联产机组联合运行的IES架构,并分析电转气两阶段的运行原理,建立计及余热回收的电转气设备模型。其次,考虑到负荷侧电、热、气三种负荷的柔性特性,在负荷侧建立电热气综合需求响应模型。最后,引入奖惩阶梯型碳交易机制,进一步减小系统碳排放量,构建调度周期内以含购能成本、运维成本、碳交易成本等系统总运行成本最小为目标的综合能源系统低碳优化调度模型。通过算例分析结果表明,所提方法不仅能提高机组的运行潜力,而且有效降低了系统总运行成本与碳排放量。

计及多重需求响应的综合能源系统多时间尺度低碳运行

为挖掘需求侧资源响应潜力,文中提出一种计及多重需求响应的综合能源系统(integrated energy system,IES)多时间尺度低碳调度策略。首先,考虑到需求侧资源在不同时间尺度下的响应差异性,建立计及价格型和激励型的多重综合需求响应(integrated demand response,IDR)模型。然后,为减少源、荷预测误差对IES运行的影响,分别构建日前低碳经济调度模型和日内双时间尺度滚动优化平抑模型。最后,算例仿真设置不同场景进行对比分析。结果表明,相比传统IDR,多重IDR能有效挖掘用户响应潜力,提升系统经济性。此外,计及多重IDR的多时间尺度调度策略能有效缓解源、荷误差带来的功率波动并降低系统碳排放量,实现IES低碳、经济和稳定运行。

计及源荷不确定性的智能小区能源系统两阶段鲁棒优化调度

为解决电力系统中电源侧和负荷侧的不确定性对电网调度计划的影响,针对电源侧,考虑风电与光伏出力的不确定性,分别建立风电与光伏的概率密度函数模型,通过拉丁超立方采样方法生成场景并进行缩减,从而得出风电与光伏出力区间;针对负荷侧,考虑柔性负荷对电网消峰填谷的作用,提出基于智能小区的综合需求响应两阶段鲁棒优化模型。在日前阶段,以电网系统运行成本和碳交易成本最小为优化目标,考虑源荷的不确定性,基于价格需求响应模型,从而确定日前调度方案。在日内阶段,基于日前阶段优化结果,以智能小区运行成本和碳交易成本最小为优化目标,建立两阶段鲁棒优化模型,通过列约束生成(column-and-constraint generation,C&CG)算法将目标函数进行转换,采用Karush-Kuhn-Tucker条件和Big-M约束方法将max-min形式优化问题转化为混合整数线性规划(mixed integer linear programming,MILP)模型。最终,通过算例验证了所提模型的正确性以及算法的有效性。

考虑氢能利用与碳捕集联合灵活运行的综合能源系统源荷低碳优化

为提高综合能源系统的低碳特性和新能源消纳能力,提出一种考虑氢能利用与碳捕集电厂联合灵活运行的综合能源系统源荷低碳优化调度策略。首先,引入溶液存储器和烟气旁路系统,建立碳捕集电厂综合灵活运行模型,针对氢能转换特性建立氢能利用模型,并提出碳捕集电厂-氢能利用的联合灵活运行模型;其次,构建计及价格型、激励型的综合需求响应模型;最后,以最优经济和低碳为目标,构建了综合能源系统低碳经济调度模型。仿真结果表明,考虑碳捕集电厂-氢能利用联合运行模型能有效降低系统碳排放量,实现综合能源系统经济、环保运行。