Two-Stage Optimal Dispatching of Wind Power-Photovoltaic-Solar Thermal Combined System Considering Economic Optimality and Fairness

Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching model of wind power-photovoltaic-solar thermal combined system considering economic optimality and fairness is proposed.Firstly,the first stage dispatching model takes the overall economy optimization of the system as the goal and the principle of maximizing the consumption of wind and solar output,obtains the optimal output value under the economic conditions of each new energy station,and then obtains the maximum consumption space of the new energy station.Secondly,based on the optimization results of the first stage,the second stage dispatching model uses the dispatching method of fuzzy comprehensive ranking priority to prioritize the new energy stations,and then makes a fair allocation to the dispatching of the wind and solar stations.Finally,the analysis of a specific example shows that themodel can take into account the fairness of active power distribution of new energy stations on the basis of ensuring the economy of system operation,make full use of the consumption space,and realize the medium and long-term fairness distribution of dispatching plan.

Solution of Combined Heat and Power Economic Dispatch Problem Using Direct Optimization Algorithm

This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) production to increase the efficiency of power and heat generation simultaneously having been researched and established, the increasing penetration of CHP systems, and determination of economic dispatch of power and heat assumes higher relevance. The Combined Heat and Power Economic Dispatch (CHPED) problem is a demanding optimization problem as both constraints and objective functions can be non-linear and non-convex. This paper presents an explicit formula developed for computing the system-wide incremental costs corresponding with optimal dispatch. The circumvention of the use of iterative search schemes for this crucial step is the innovation inherent in the proposed dispatch procedure. The feasible operating region of the CHP unit three is taken into account in the proposed CHPED problem model, whereas the optimal dispatch of power/heat outputs of CHP unit is determined using the direct Lagrange multiplier solution algorithm. The proposed algorithm is applied to a test system with four units and results are provided.

Stochastic Accelerated Alternating Direction Method of Multipliers for Hedging Communication Noise in Combined Heat and Power Dispatch

Combined heat and power dispatch(CHPD)opens a new window for increasing operational flexibility and reducing wind power curtailment.Electric power and district heating systems are independently controlled by different system operators;therefore,a decentralized solution paradigm is necessary for CHPD,in which only minor boundary information is required to be exchanged via a communication network.However,a nonideal communication environment with noise could lead to divergence or incorrect solutions of decentralized algorithms.To bridge this gap,this paper proposes a stochastic accelerated alternating direction method of multipliers(SA-ADMM)for hedging communication noise in CHPD.This algorithm provides a general framework to address more types of constraint sets and separable objective functions than the existing stochastic ADMM.Different from the single noise sources considered in the existing stochastic approximation methods,communication noise from multiple sources is addressed in both the local calculation and the variable update stages.Case studies of two test systems validate the effectiveness and robustness of the proposed SAADMM.

Data-driven Surrogate-assisted Method for High-dimensional Multi-area Combined Economic/Emission Dispatch

Multi-area combined economic/emission dispatch(MACEED)problems are generally studied using analytical functions.However,as the scale of power systems increases,ex isting solutions become time-consuming and may not meet oper ational constraints.To overcome excessive computational ex pense in high-dimensional MACEED problems,a novel data-driven surrogate-assisted method is proposed.First,a cosine-similarity-based deep belief network combined with a back-propagation(DBN+BP)neural network is utilized to replace cost and emission functions.Second,transfer learning is applied with a pretraining and fine-tuning method to improve DBN+BP regression surrogate models,thus realizing fast con struction of surrogate models between different regional power systems.Third,a multi-objective antlion optimizer with a novel general single-dimension retention bi-objective optimization poli cy is proposed to execute MACEED optimization to obtain scheduling decisions.The proposed method not only ensures the convergence,uniformity,and extensibility of the Pareto front,but also greatly reduces the computational time.Finally,a 4-ar ea 40-unit test system with different constraints is employed to demonstrate the effectiveness of the proposed method.

Combined Electric and Heat System Testbeds for Power Flow Analysis and Economic Dispatch

In combined electric and heat systems,selecting a suitable testbed for power flow analysis or economic dispatch is not easy:a large number of existing testbeds are not opensource,while others are difficult to be reused by other researchers due to the particularity of system scale,topology,and data.In this paper,we present three open-source testbeds with different scales based on practical combined electric and heat systems.To satisfy researchers"specific demands on the system topology and data,we also discuss how to modify testbeds based on existing topologies and data.Researchers can use the testbeds presented in this paper to test their innovative methods for power flow analysis and economic dispatch,and can also design their own testbeds based on the methodology in this paper,using published topologies and data.

Optimal Combined Heat and Power Economic Dispatch Using Stochastic Fractal Search Algorithm

Combined heat and power(CHP)generation is a valuable scheme for concurrent generation of electrical and thermal energies.The interdependency of power and heat productions in CHP units introduces complications and non-convexities in their modeling and optimization.This paper uses the stochastic fractal search(SFS)optimization technique to treat the highly non-linear CHP economic dispatch(CHPED)problem,where the objective is to minimize the total operation cost of both power and heat from generation units while fulfilling several operation interdependent limits and constraints.The CHPED problem has bounded feasible operation regions and many local minima.The SFS,which is a recent metaheuristic global optimization solver,outranks many current reputable solvers.Handling constraints of the CHPED is achieved by employing external penalty parameters,which penalize infeasible solution during the iterative process.To confirm the strength of this algorithm,it has been tested on two different test systems that are regularly used.The obtained outcomes are compared with former outcomes achieved by many different methods reported in literature of CHPED.The results of this work affirm that the SFS algorithm can achieve improved near-global solution and compare favorably with other commonly used global optimization techniques in terms of the quality of solution,handling of constraints and computation time.

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。

Q学习差分进化算法求解热电动态经济排放调度

热电联产动态经济排放调度同时考虑了燃料成本花费和污染气体排放两个目标值,且下一时间段的热电产量受当前时间段热电产量的影响,这是近年来电力系统运行中的一个重要问题。文中提出一种基于Q学习强化多目标差分进化(Q Learning Multi-Objective Differential Evolution,QLMODE)算法,以此求解热电联产动态经济排放调度(Combined Heat and Power Dynamic Economic Emission Dispatch,CHPDEED)问题。在QLMODE中,采用Q学习技术调整算法的比例因子参数,即在迭代过程中利用子代解和父代解之间的支配关系确定动作奖励和惩罚,并通过Q学习调整参数值,以获得最适合环境模型的算法参数。文中将所提QLMODE用于求解11机组和33机组的热电联产动态经济排放调度问题。仿真结果表明,与4种成熟的多目标优化算法相比,QLMODE算法燃料成本最小,污染气体排放最少,收敛性和多样性指标优于其他4种算法,且QLMODE在两组问题上都获得了更好的Pareto最优前沿。

计及改进阶梯型碳交易和热电联产机组灵活输出的园区综合能源系统低碳调度

为了进一步降低园区综合能源系统(park-level integrated energy system,PIES)碳排放量,优化热电联产(combined heat and power,CHP)机组出力的灵活性,提出一种考虑改进阶梯型碳交易和CHP热电灵活输出的PIES低碳经济调度策略。首先,将遗传算法与模糊控制相结合,设计一种遗传模糊碳交易参数优化器,从而对现有阶梯型碳交易机制进行改进,实现该机制参数的自适应变化;其次,在传统CHP中加入卡琳娜(Kalina)循环与电锅炉(electricboiler,EB),构造CHP热电灵活输出模型,以同时满足电、热负荷的不同需求;然后,提出一种柔性指标——电、热输出占比率,进而计算出电、热输出占比率区间,以衡量CHP运行灵活性;最后,将改进阶梯型碳交易机制和CHP热电灵活输出模型协同优化,以系统运行成本和碳交易成本之和最小为目标,构建PIES低碳经济优化模型。算例分析表明,所提策略可有效降低经济成本和碳排放量,同时还可扩展CHP灵活输出调节范围,能够为PIES低碳经济调度提供参考。

考虑个体自私的热电联产系统完全分布式经济调度

针对传统集中式经济调度策略需要获取全局信息以及无法适应系统灵活拓扑的缺陷,同时考虑能量以电能和热量形式并存出现的情况,基于一致性算法提出了一种完全分布式热电联产经济调度策略.将热电联产系统中各机组电价和热价的增量成本作为一致性变量进行迭代计算,直至增量成本达到收敛,实现系统经济最优.此外,还分析了个别机组为了提升自身利益,向相邻机组传输带有偏差增量成本值的情况,设计了一个增量成本补偿项来消除该自私行为的影响.在3个仿真算例中得到的结果表明,所提策略在解决热电联产系统经济调度问题中具有有效性和可行性.

集成电动汽车的家庭电热综合能源系统负荷调度优化

随着“碳中和”“碳达峰”等政策的提出,新能源和异质能源的调度优化成为减少碳排放的主要措施。而家庭综合能源系统在能源需求侧占比较大,如何构建合理的家庭能源系统以及实现家庭负荷优化的能量管理成为亟待解决的问题。为了实现家庭电热综合能源系统中负荷的灵活调度,充分考虑电动汽车及异质能源间设备的出力影响,提出了一种集成电转热设备热泵和电动汽车的家用燃料电池热电联产(domestic fuel cell-based combined heat and power,DFCCHP)系统综合优化调度方案。首先,根据家庭用电、用热特征将电、热负荷进行细化分类,引入热舒适度评价指标(predicted mean vote,PMV)进行室内温度的控制,建立负荷模型;其次,引入热泵和电动汽车,建立在分时电价和分时气价下以能源购买费用最小为目标的家庭综合能源系统优化调度模型,并使用Cplex求解器对模型求解;最后,通过仿真验证调度模型的合理性、可行性和环保性,以及热泵和电动汽车对系统经济性的影响。结果表明,在不同天气条件下,热泵和电动汽车的引入可有效减少系统的购能成本与碳排放,所得结论为进一步完善家庭综合能源系统拓扑及负荷优化调度提供了一定的理论分析基础。

交直流电力系统与供热系统联合经济调度

随着热电联产等技术的广泛应用,电力系统和供热系统耦合日益紧密,电热联合调度得到了广泛的关注,但现有电热联合调度忽略了热网供电来源的多样性,由此提出了交直流电力系统与供热系统联合经济调度方法。首先,建立交直流电力系统模型,并提出一种线性近似方法对电压源变换器模型所带来的强非线性进行处理;然后,以运行成本最低为目标,建立考虑热网热惯性的交直流电力系统与供热系统联合调度模型,该模型中供热系统通过热电联产机组和交流电锅炉与交流电网耦合,通过直流电锅炉与直流电网耦合;最后,通过算例仿真验证了所提方法的有效性并说明了在电热联合调度中考虑交直流电源的必要性。

Dynamic scheduling of market price-based combined heat–power-constrained renewable microgrid

In this paper,a market price-based combined heat–power dynamic dispatch model for a microgrid is presented.The microgrid comprises cogeneration units and wind and solar power-generation units.A battery and a heat storage tank are incorporated to optimally balance variations in heat-and-power load demands.The proposed model explores the impact of market prices of electricity,heat supply and load variability on the optimal schedule such that profit maximizes and emission,loss and waste heat are minimized.The Weibull probability distribution function is applied to characterize the uncertain renewable power variable in the model and to find the over-and under-scheduling costs.The problem is solved using an improved differential evolution algorithm in which a fuzzy membership module is appended to obtain a solution having the highest attainment for the selected multiple objectives.The results show that the proposed model can handle uncertain heat–power demand and price scenarios to produce feasible and optimal schedules with owner profits,heat utilization and renewable share varying between 10.55–115.97%,72.51–90.39%and 26.82–38.05%,respectively.

Dispatch Model for Integrated Heat and Power Systems Considering Internal Composition of CHP Plants

An integrated heat and power system(IHPS)is a promising approach for alleviating wind curtailment problems.In an IHPS,the combined heat and power(CHP)plant is the key component,which supplies both heat and electric loads,and couples the thermal system and power system.However,existing research commonly ignores or simplifies the internal composition of CHP plants,which could lead to some unavoidable errors.This paper focuses on the internal composition of CHP plants,and models the physical processes in different components and flexible resources in the CHP plant.Furthermore,a joint dispatch problem of an IHPS with the above CHP plant models is formulated,and an iterative algorithm is developed to handle the nonlinearity in this problem.Case studies are performed based on a real CHP plant in Northern China,and the results indicate that the synergistic effect of different energy resources in the CHP plant is realized by the joint dispatch model,which promotes wind power accommodation and reduces fossil fuel consumption.

Feasibility Study for Self-Sustained Wastewater Treatment Plants—Using Biogas CHP Fuel Cell, Micro-Turbine, PV and Wind Turbine Systems

This paper studies the application of renewable energy sources in wastewater treatment plants to achieve self-sustain- ability of power. The data of wastewater treatment plant in the rural city of Toukh-EGYPT are presented as a case-study. The primary objective is to provide an entirely renewable standalone power system, which satisfies lowest possible emissions with the minimum lifecycle cost. Mass balance principle is applied on the biodegradable components in the wastewater to evaluate the volume of digester gas that is produced from sludge through anaerobic digestion process. Using digester gas as a fuel lead to study combined-heat-and-power technologies, where fuel cell is selected in order to abide by the low emissions constraint. The study assessed the electrical power obtained from fuel cell and the utilization of the exhausted heat energy for additional electrical power production using a micro-turbine. After covering the major part of load demand, the use of other renewable energy sources was studied. The strength of both solar and wind energy was determined by the case-study location. Hybrid optimization model for electric renewable (HOMER) software was used to simulate the hybrid system composed of combined-heat-and-power units, wind turbines and photovoltaic systems. Simulation results gave the best system configuration and optimum size of each component beside the detailed electrical and cost analysis of the model.

Schedule for Reducing the Use of Peat and the Possibilities of Replacing It with Forest Chips in Energy Production in Finland

Between 2018 and 2020, an average of 15 TWh of energy peat was consumed in Finland. Energy peat is used in 260 boilers in Finland, which produce district heat and heat and steam for industry, as well as electricity as cogeneration (CHP) in connection with district heating and industrial heat production. Peat accounts for 3% - 5% of the energy sources used in Finland, but its importance has been greater in terms of security of supply. With current use in accordance with the 2018-2020 average, the emissions from peat are almost 6 Mt CO2 per year in Finland, which is 15% of emissions from the energy sector. In this study, the technical limitations related to peat burning, economic limitations related to the availability of biomass, and socio-economic limitations related to the regional economy are reviewed. By 2040, the technical minimum use of peat will fall to 2 TWh. The techno-economical potential may be even lower, but due to socio-economic objectives, peat production will not be completely ceased. The reduction in the minimum share assumes that old peat boilers are replaced with new biomass boilers or are alternatively replaced by other forms of heat production. Based on the biomass reserves, the current use of peat can be completely replaced by forest chips, but regional challenges may occur along the coast and in southern Finland. It is unlikely that the current demand for all peat will be fully replaced by biomass when part of CHP production is replaced by heat production alone and combustion with waste heat sources.

非洲农业园区光伏离网设计案例研究

农业是非洲经济社会发展的重要支柱产业。由于非洲农业生产大多位于偏远地区,远离城市主网,供电问题已成为制约农业发展的重要掣肘。非洲光伏资源丰富,可就地获取并持续利用,采用光伏离网供电可以在农业生产区就近灵活布置,在满足各类用能场景的基础上,有效提升农业生产效率。以非洲某农业园区为例,基于所在地太阳能资源禀赋,在综合考虑园区生产生活用能现状及远期发展规划的基础上,确定园区光伏离网升级改造的设计思路、设计目标,并提出近期和远期两组设计方案。近期方案引入光伏离网发电系统,配置250kW光伏发电单元以及140kW/280kW·h储能单元,旨在满足园区当前用电需求;远期方案引入冷热电三联供系统,配置170kW和590kW光伏发电单元以及85kW/170kW·h储能单元,在满足园区远期综合用能需求的基础上,实现能源梯级利用,提高能源利用效率。分别对两组设计方案进行财务测算和敏感性分析,近期和远期设计方案均属于盈利项目,具有较好的经济、环境和社会效益,在节约电费的同时还可以减少碳排放,推动“新能源+农业”多元化应用。

改进高斯柯西差分进化算法求解热电联产经济调度

热电联产经济调度(combined heat and power economic dispatch,CHPED)是一类复杂的非线性约束最优化问题,智能优化算法则是近年来求解CHPED的主流方法。针对目前智能优化算法求解CHPED存在易于陷入局部最优、精度不佳等问题,提出了一种改进的差分进化算法,即高斯柯西差分进化(Gaussian-Cauchy differential evolution,GCDE)算法。GCDE算法对标准差分进化算法进行了两方面的改进:一方面采用自适应参数策略来调节算法的控制参数,提升算法的求解稳定性;另一方面采用高斯柯西变异策略来进行局部搜索,增强算法的搜索精度。将GCDE算法应用于求解7台机组和24台机组的CHPED模型,并与现有差分进化算法结果进行了对比,结果表明GCDE算法在求解CHPED问题时具有较好的求解精度和稳定性。

基于生物质气化的内燃机-吸收式制冷联合冷热电系统分析

可再生能源发电技术是解决能源短缺和环境污染问题的有效途径之一,本文提出了基于生物质气化的联合冷热电(combined cooling heating and power,CCHP)系统。该系统以生物质气化气为燃料,通过内燃机(internal combustion engine,ICE)发电。为提高燃料的整体利用水平,对内燃机排气的低温余热由吸收式制冷及热水系统进行回收。气化炉模型考虑了热解、还原及氧化反应,内燃机模型考虑了气缸内压力、温度随转角的变化。对于单效溴化锂–水吸收式制冷单元,研究了发生器和吸收器工作温度对制冷量的影响。研究结果表明:采用气化气为燃料的内燃机热效率可达37%。增大内燃机转速使系统电效率提高2%,整体冷热电效率为47%;压缩比的增大使系统电效率提高1%,冷热电效率几乎不变。降低吸收器工作温度和提高发生器工作温度可增大制冷量并提高制冷性能,效能系数可以达到77%。整个联合冷热电系统的电效率为22%,冷热电联合效率可以达到48%,投资回收期为3~4 a。

计及含蒸汽热泵工业园区能量品位转换的综合能源优化调度

沿海工业园区具有大量蒸汽需求,且存在能源利用率低及风电浪费严重的问题。为此,以位于中国江苏省靠近海上风电并网点的某沿海工业园区为研究对象,提出一种考虑高温蒸汽热泵能量品位转换的综合能源系统优化调度方法。首先,构建高温蒸汽热泵能量品位转换模型,以实现电热耦合及能源梯级利用;然后,提出含高温蒸汽热泵的风电消纳策略,以促进系统风电消纳;最后,考虑风电出力预测误差,建立以总运行成本最小为目标函数的综合能源系统随机优化调度模型。仿真结果表明,所提调度方法有效促进了海上风电消纳,可在解耦冷热电联产(CCHP)机组“以热定电”运行约束的同时满足工业园区的蒸汽负荷需求。同时,提升了能源利用效率,具有良好的社会经济效益,可有效保障系统运行的可靠性。