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.

Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source-load power interval prediction

Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.

A DISPATCHER TRAINING SIMULATOR SYSTEM INTEGRATED WITH THE EXIST SCADA/EMS

To utilize exist SCADA(Supervisory Control and Data Acqui si tion)/EMS (Energy Management System) fully and economize capital, Henan Electric Power Dispatching and Communication Center in China established a set of Henan Dispatcher Training Simulator (HNDTS) base on its exist SCADA/EMS. In order to i ntegrated with exist SCADA/EMS, the integration method and technique are propose d. Graph data integration discussed with emphasis. After integration implemented , HNDTS can share all data with SCADA/EMS and dispatchers can be trained in same environment as real work situation, in the same time it can avoid amout of work of maintenance engineers. Both advantages and disadvantages of integration are analyzed. In the end of paper, the requirement for future DTS is put forward bas e on the experience of author.

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%.

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm:Designed for Dispatch of Integrated Energy System in Coal Mine

The dispatch of integrated energy systems in coal mines(IES-CM)with mine-associated supplies is vital for efficient energy utilization and carbon emissions reduction.However,IES-CM dispatch is highly challenging due to its feature as multi-objective and strong multi-constraint.Existing constrained multi-objective evolutionary algorithms often fall into locally feasible domains with poorly distributed Pareto front,which greatly deteriorates dispatch performance.To tackle this problem,we transform the traditional dispatch model of IES-CM into two tasks:the main task with all constraints and the helper task with constraint adaptive.Then we propose a constraint adaptive multi-tasking differential evolution algorithm(CA-MTDE)to optimize these two tasks effectively.The helper task with constraint adaptive is developed to obtain infeasible solutions near the feasible domain.The purpose of this infeasible solution is to transfer guiding knowledge to help the main task move away from local search.Additionally,a dynamic dual-learning strategy using DE/current-to-rand/1 and DE/current-to-best/1 is developed to maintain task diversity and convergence.Finally,we comprehensively evaluate the performance of CA-MTDE by applying it to a coal mine in Shanxi Province,considering two IES-CM scenarios.Results demonstrate the feasibility of CA-MTDE and its ability to generate a Pareto front with exceptional convergence,diversity,and distribution.

Real-time Risk-averse Dispatch of an Integrated Electricity and Natural Gas System via Condi-tional Value-at-risk-based Lookup-table Ap-proximate Dynamic Programming

The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.

Coordinated Dispatch Based on Distributed Robust Optimization for Interconnected Urban Integrated Energy and Transmission Systems

To improve the economic efficiency of urban integrated energy systems(UIESs)and mitigate day-ahead dispatch uncertainty,this paper presents an interconnected UIES and transmission system(TS)model based on distributed robust optimization.First,interconnections are established between a TS and multiple UIESs,as well as among different UIESs,each incorporating multiple energy forms.The Bregman alternating direction method with multipliers(BADMM)is then applied to multi-block problems,ensuring the privacy of each energy system operator(ESO).Second,robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty.The column and constraint generation(C&CG)algorithm is then employed to solve the robust model.Third,to tackle the convergence and practicability issues overlooked in the existing studies,an external C&CG with an internal BADMM and corresponding acceleration strategy is devised.Finally,numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.

含碳捕集电厂与氢能多元利用的综合能源系统低碳经济调度

随着中国“碳达峰·碳中和”战略的不断推进,综合能源系统的绿色低碳转型迫在眉睫。针对传统碳捕集电厂灵活性较差、风电并网难以消纳等问题,提出一种含碳捕集电厂与氢能多元利用的综合能源系统低碳经济调度模型。首先,引入储液罐对传统碳捕集电厂进行改造,提高电厂应对风电波动的运行灵活性;其次,构建含两段式电转气、氢燃料电池、储氢罐和掺氢热电联产在内的氢能多元利用结构,以充分挖掘氢能利用与碳捕集电厂的协同运行潜力。在此基础上,引入阶梯碳交易机制,建立以碳交易、碳封存、燃煤及购气成本之和最小为优化目标的低碳经济调度模型。算例结果表明,文中模型能够有效提高系统的风电消纳水平和能源利用效率,具有显著的低碳经济效益。此外,碳交易基准价格的合理设定能够引导系统提高碳捕集水平。

State Transition Modeling Method for Optimal Dispatching for Integrated Energy System Based on Cyber-Physical System

The traditional energy hub based model has difficulties in clearly describing the state transition and transition conditions of the energy unit in the integrated energy system(IES).Therefore,this study proposes a state transition modeling method for an IES based on a cyber-physical system(CPS)to optimize the state transition of energy unit in the IES.This method uses the physical,integration,and optimization layers as a three-layer modeling framework.The physical layer is used to describe the physical models of energy units in the IES.In the integration layer,the information flow is integrated into the physical model of energy unit in the IES to establish the state transition model,and the transition conditions between different states of the energy unit are given.The optimization layer aims to minimize the operating cost of the IES and enables the operating state of energy units to be transferred to the target state.Numerical simulations show that,compared with the traditional modeling method,the state transition modeling method based on CPS achieves the observability of the operating state of the energy unit and its state transition in the dispatching cycle,which obtains an optimal state of the energy unit and further reduces the system operating costs.

储液式CCS耦合P2G的综合能源系统低碳经济调度

为应对电转气P2G(power-to-gas)和碳捕集系统CCS(carbon capture system)在可再生能源出力不足时耦合失效的问题,考虑对常规CCS引入CO_(2)储液罐以加强P2G和CCS的耦合。首先,构建储液式CCS和P2G的耦合模型,利用储液罐将CCS捕集的CO_(2)时移至可再生能源富足时段,供P2G再利用;其次,构建储液式CCS与P2G耦合的电热气综合能源系统日前优化调度模型;最后,以北方某工业园区为例进行仿真。结果证明所提模型可以充分利用CCS捕集的CO_(2),从而获得更好的经济和环境效益。

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。

考虑柔性电/热负荷的综合能源系统分布鲁棒优化调度

综合能源系统(integrated energy system,IES)的效益分析不仅取决于能源供给侧的调度方案,也受到需求侧用能方式的影响。基于此,在IES的需求侧引入柔性负荷响应,以平滑负荷曲线,进一步提升IES的风电消纳能力和经济效益;同时为尽量减小供能侧风电出力不确定性的影响、实现调度方案鲁棒性与经济性的均衡,构建了考虑柔性电负荷和柔性热负荷的IES两阶段分布鲁棒优化调度模型:预调度阶段以IES的日前综合调度成本最低为目标;再调度阶段以风电历史数据为基础,寻找最恶劣风电出力概率分布下的最优机组调节方案,并使用列约束生成算法进行求解。最后,采用算例验证了该模型的有效性。

基于近端策略优化算法含碳捕集的综合能源系统低碳经济调度

为了实现园区综合能源系统(PIES)的低碳化经济运行和多能源互补,解决碳捕集装置耗电与捕碳需求之间的矛盾,以及不确定性源荷实时响应的问题,提出了基于近端策略优化算法含碳捕集的综合能源系统低碳经济调度方法。该方法通过在PIES中添加碳捕集装置,解决了碳捕集装置耗电和捕碳需求之间的矛盾,进而实现了PIES的低碳化运行;通过采用近端策略优化算法对PIES进行动态调度,解决了源荷的不确定性,平衡了各种能源的供给需求,进而降低了系统的运行成本。实验结果表明:该方法实现了不确定性源荷的实时响应,并相比于DDPG(deep deterministic policy gradient)和DQN(deep Q network)方法在低碳化经济运行方面具有有效性及先进性。

含整体煤气化燃料电池-碳捕集电厂的风火储系统分布鲁棒调度方法

整体煤气化燃料电池(integrated gasification fuel cell,IGFC)是与碳捕集技术高度适配的清洁、高效、稳定的绿色煤电技术,有望克服传统碳捕集技术在低浓度CO_(2)环境下产生的高成本、高能耗问题。该文构建含IGFC碳捕集电厂的风火储发电系统。研究IGFC碳捕集电厂的运行机理,对IGFC内部各环节建立数学模型,提出工况匹配时燃烧利用率和阴极空气利用率需要满足的运行条件,并针对该型碳捕集电厂的电碳特性进行分析。为计及风电出力的不确定性,构建风火储系统的两阶段分布鲁棒经济调度模型,引入k阶适应性理论,提出基于正交支撑子集策略的求解方法来获得鲁棒对等式。最后,在改进IEEE-30节点系统中进行算例分析,结果表明,该型碳捕集电厂可利用阳极碳富集的优势,实现系统低碳经济调度的目标,并验证所提优化方法能为可行解提供可解释性。

面向易行的高速铁路智能平行调度系统构架及关键技术

针对我国高铁成网运行、管控列车数量和种类多、运营方式复杂等特点,以构建新一代智能动态调度系统为目标,探讨面向易行的高速铁路智能平行调度系统构架,从5个方面提炼出高速铁路智能平行调度系统的技术特征。总结目前高速铁路智能平行调度系统研究亟待解决的关键技术,介绍智能高铁平行调度一体化仿真原型系统研制及实现的部分场景功能。研究工作可为建立面向易行服务的,适应中国高铁“效率变革”“质量变革”需求的智能动态调度理论与技术体系,实现我国高速铁路智能调度系统和运输服务水平的升级发展提供参考。

考虑[火用]效率的区域综合能源系统配置与调度双层优化

文章针对含高比例的可再生能源的区域综合能源系统,综合考虑能量的数量和品质,提出了一种计及[火用]效率的双层优化方法。首先,建立了区域综合能源系统电、气、热、冷多能耦合的模型,并对系统内涉及的能量和设备进行[火用]分析,分别计算其能量品质系数和[火用]效率;然后,建立了兼顾系统容量规划和调度运行的双层协同优化模型,其中上层以[火用]效率和经济性为优化目标进行容量配置,下层以运行成本最低为目标优化机组出力,从而得到最佳方案;最后,选取某区域综合能源系统进行算例分析。与其他方案相比,文章所提双层优化方法可以为区域综合能源系统提供更好的配置和调度方案,[火用]效率可以更好地反映能量真实利用水平,实现高质量用能。

循证训练理念下签派员技能全生命周期全动模拟机融合训练研究

为了培养高素质签派员队伍,借鉴飞行员技能全生命周期管理(PLM)体系建设相关理论,结合试点开展的“飞行员-签派员”融合训练,梳理其实施的可行性和必要性,探索签派员整个职业生涯岗位胜任力提升的实施路径。基于循证训练(EBT)理念总结“飞行员-签派员”融合训练的保障机制,为优化签派员训练模式提供参考。

带时间窗的中心站多车程集卡调度优化研究

为解决铁路集装箱中心站与周边货源地间的公铁联运多车程集卡集疏调度问题,将货源地的每一集装箱集疏需求视为一个任务节点,以集卡调度总成本最小为目标,综合考虑集卡容量和集装箱集疏时间窗等约束,构建允许同一集卡重复利用的多车程集卡调度优化模型,并设计改进的遗传算法求解该模型。为均衡集卡工作时间,以调度模型确定的最佳集卡数量和车程集合为输入,进一步构建以集卡作业时间之差最小为目标的集卡车程分配模型,并借助Gurobi精确求解每一集卡的最佳车程任务。以郑州铁路集装箱中心站为例进行实验验证,结果表明:本文所提方法能有效解决公铁联运集装箱在集疏环节的集卡调度问题;相较于单车程模式,多车程模式下集卡调度成本平均减少了60.31%;车程分配优化可以将集卡作业时间最大差值减少14.3%以上,提高了集卡作业时间的均衡性。

分支定界搜索信息深度引导的电-气互联系统调度决策加速求解方法

电-气互联系统调度决策问题旨在实现天然气系统和电力系统中可调节资源的最佳配置,其精准性与高效性直接影响电-气互联系统运行的安全性与经济性。为描述可调节资源离散状态、非线性运行特性等物理性质,电-气互联系统调度决策问题中含有规模庞大的离散决策变量,模型复杂度高,使得现有依赖于商业混合整数线性规划(MILP)求解器的电力系统运筹优化技术面临“组合爆炸”的计算负担。为此,该文提出一种分支定界搜索信息深度引导的电-气互联系统调度决策加速求解方法。所提方法利用分支定界初始搜索阶段的信息构建小规模辅助MILP模型,并内嵌于分支定界搜索过程,引导剪除更多冗余搜索空间,在不损失最优性的前提下加速收敛。基于RTS-GMLC电力系统和天然气系统不同负荷水平及线性分段数下的30个算例仿真结果说明,相比于直接使用商业MILP求解器,所提方法在不损失最优性的前提下可实现平均4.20倍的加速,验证了所提方法的有效性。

考虑电-氢一体化的微电网低碳-经济协同优化调度

针对微电网中缺乏对碳排放流特性的精确刻画,导致运行层面中微电网的低碳运行方式和经济运行方式之间矛盾凸显,低碳和经济难以兼顾的问题,构建了计及绿电制氢和多能耦合的电-氢一体化运行框架,提出了综合考虑碳排放流和低碳需求响应的微电网低碳-经济协同双层优化调度模型。上层优化调度模型中,以风光储氢一体化工业园区为典型微电网场景,以低碳性和经济性协调最优为目标,将源侧碳交易机制融入调度决策目标中,充分挖掘微电网低碳-经济协同运行方式,以此制定微电网最优低碳经济调度策略;下层低碳需求响应模型中,以微电网用户碳减排所节约的碳排放成本为激励信号,通过建立微电网能量流与碳排放流的映射关系,实现碳排放责任的转移和分摊以及对微电网运行中的碳排放特性精细化评估,进而建立由负荷碳排放强度时空差异性引导用户参与碳减排策略的低碳需求响应(demond response,DR)模型,深入挖掘微电网源-荷间的低碳性和经济性间的协同性。以某工业园区微电网为例,验证了所提模型能有效兼顾系统的低碳性和经济性。