考虑需求侧响应的园区综合能源系统优化配置

以园区综合能源系统(PIES)的优化配置为研究对象,考虑电力市场分时定价机制下的需求侧响应策略,以PIES的运行成本、污染排放和能耗为优化目标,建立主问题与子问题的迭代优化架构,最后采用多属性决策方法从待选方案中选出最优的系统配置。算例结果表明,可再生能源的接入可以有效地提高PIES的综合效益,引入价格型和替代型需求侧响应后可以进一步提升PIES的经济性,但同时降低了PIES的环保性。     

计及综合需求响应的电热集成系统优化运行

针对我国“三北”地区供暖季弃风严重问题,提出一种计及综合需求响应（CDR）的电热集成系统（IETS）优化调度策略。基于电量电价弹性矩阵构建实时电价需求响应模型以引导用户错峰时移用电;考虑到热用户对供热温度感知的模糊性,将其作为热能需求响应参与调度,分析并阐述电热需求响应促进风电消纳的机理;通过多场景分析刻画新能源预测的不确定性,计及系统安全运行约束,以系统总运行成本最小优化求解日前调度决策与旋转备用容量安排。最后,通过典型算例对比不同模式下的调度策略,验证了提出的计及CDR的优化调度策略能够开阔风电上网空间和提高系统的经济性。     

面向综合需求响应的综合能源系统优化调度

电能和天然气的强耦合使得电气综合能源系统调度问题复杂化,特别是该类系统需求侧的灵活性显著强于电力系统或天然气系统,基于此,该文旨在解决考虑综合需求侧灵活性的电气综合能源系统优化调度问题。为了充分利用需求侧的灵活性,该文建立了详细能量路由器的综合需求响应模型,与传统需求响应方案不同,综合需求侧响应方案须考虑电力需求和天然气需求的灵活性和可控性。此外,该文建立的综合需求响应模型属于一个优化问题,而不是价格响应问题。基于两级优化框架,实现了能量路由器综合需求响应模型与电气综合能源系统的调度模型的能量交互。然后,将上述优化调度问题建模为一个复杂的双层混合整数非线性规划问题,并提出了一种有效解决上述复杂问题的方法。最后,案例研究验证了所提方法的优越性。     

基于需求响应的用户侧微电网多目标优化运行方法

摘要： 需求响应使用户积极参与到电网的优化运行中,是实现用户侧微电网优化运行的重要手段。针对用户侧微电网,提出了基于价格激励与可控负荷的优化运行模型。模型中包括实时电价的分区策略与可控负荷的调控策略。将用户用电成本,空调与热水器温度与目标温度的差值最小化作为优化目标,将可延迟负荷的延迟时间,可计划负荷的工作状态作为决策变量。通过仿真计算与分析,验证了优化模型的可行性与有效性,为用户侧微电网的低成本运行提供了理论支持。     

计及需求侧灵活资源的综合能源系统合作博弈优化运行

综合能源系统能够提高能源的综合利用效率,是能源领域未来的发展趋势。为了充分发挥需求侧灵活资源对综合能源系统运行的优化作用,提出一种计及需求侧灵活资源的综合能源系统合作博弈优化运行方法。首先,构建综合能源系统的需求侧灵活资源运行模型,包括综合需求响应和多类型储能设备;接着,考虑多个综合能源系统间的电能交易合作,以合作联盟效益最大化为目标函数,构建计及需求侧灵活资源的综合能源系统合作博弈优化运行模型;然后,运用数学方法对所提模型进行两阶段等效转换,使其易于求解;最后,通过算例分析验证了所提模型能够充分发挥需求侧灵活资源的优势,提高综合能源系统的运行经济性。     

近零碳园区的综合能源系统优化与评价方法

为降低园区综合能源系统碳排放,基于园区能源需求和负荷特性,设计了基于电能替代技术的综合能源系统方案,建立全工况仿真模型。通过非支配排序遗传算法和Gurobi求解器对多目标协同优化问题进行求解,并采用熵权-优劣解距离法进行综合评价决策,从而确定最优系统容量配置和运行策略。案例分析表明：所提方案使园区碳排放强度降低77%,满足近零碳园区要求;年能源净成本降低61.2%,能源自给率提升至71.3%。优化后的综合能源系统显著提升了能源利用效率和安全性,降低了碳排放,增强了园区能源独立性。     

计及碳交易与需求响应的多典型日综合能源系统优化模型

系统负荷作为综合能源系统的约束条件,常以单个典型日加以表征,这难以描述实际负荷波动性与随机性的特点。本文构建了计及碳交易与需求响应的多典型日优化模型。通过引入谱聚类算法获得多个典型日数据,在表征原始负荷数据波动性与随机性的同时,也基于负荷数据构成实现了数据分类,并以负荷构成为基础分别建立优化模型,引入阶梯型碳交易机制与需求响应机制,在Python语言下调用Gurobi求解器完成模型求解仿真,在此基础上对阶梯型碳交易参数的变化开展研究。结果表明,本文提出的优化模型可很好地针对全年不同的负荷特征实现灵活调度,在兼顾经济性与环保性的同时有利于清洁机组出力。     

计及不确定性与需求响应的综合能源系统优化

为增加可再生能源的吸收能力和缩减碳排放量,提出了计及不确定性与需求响应的综合能源系统优化方案。首先,构建综合能源系统的总体架构,建立数学模型;其次,针对源荷两侧不确定性因素,采用拉丁超立方抽样法和改进K-means聚类算法进行处理,设计双层优化模型,在MATLAB平台下,采用改进天牛须搜索优化算法(BAS-GA)结合CPLEX 12.10求解器进行求解;最终,通过对比不同方案下的系统规划及运营费用进行实例验证。结果表明：实施综合需求响应措施后,电力负荷、热负荷和氢负荷的峰谷差分别降低了16.8%,14.9%和13.6%;在采用需求响应方案后,光伏、蓄电池和电锅炉的装机容量均较采纳需求响应方案前有所降低,减少幅度分别为13.54%,21.83%和11.16%;实施需求响应措施可以使整体装机容量降低313 kW。     

需求侧响应技术浅析

需求侧是电力市场规划的重要组成部分.通过分析需求侧的特征,整合电能系统的供给和使用方式,能够协助系统稳定运行、完善市场定价机制.需求侧响应技术针对电力系统中的峰值负荷,按需调整价格机制,有效转移了用户的负荷使用时段,达到了削峰填谷、维持稳定的目的 .需求侧响应所涉及的价格型响应与激励型响应分别以价格、奖励为基础,针对用...     

计及多能需求的综合能源服务商优化运行策略研究

随着分布式发电技术的不断成熟及发展,未来综合能源服务将是整合不同类型分布式发电并满足用户不同用能需求的有效途径.提出了一种含有多种分布式发电资源同时考虑多用能需求的综合能源服务商优化运行策略模型.首先建立了含有风电、光伏、燃气轮机、电储能、电热泵、辅助锅炉等分布式资源及电、热用能需求的园区综合能源系统优化调度模型;其次...     

考虑电转氢气过程及综合需求响应的电-氢-气综合能源系统协调优化运行

文章介绍了电转氢气、氢气转天然气的过程,并在此基础上构建了电-氢-气综合能源系统。通过考虑综合需求侧响应,建立以最小化系统运行总成本为目标函数的协调优化模型,利用禁忌搜索算法和粒子群算法相结合的改进算法进行求解。经验证,考虑电转氢气过程及综合需求响应的协调优化方案增强了综合能源系统的经济性、环保性,提高了新能源的消纳能力。     

基于主从博弈理论的综合能源系统优化运行方法

综合能源系统作为实现智慧能源的关键技术形式,其优化运行问题是一门重要课题。针对综合能源系统优化运行问题,考虑到系统多能互补协调,基于主从博弈理论进行建模。其中:主体博弈模型以多能互补协调计划为博弈策略,以综合能源系统综合运行成本最小化为博弈支付,计及多能互补协调约束等必要约束条件;从体博弈模型以各个形式能源子网的运行计划为博弈策略,以能源子网运行收益最大化为博弈支付,计及分布式供能设备运行约束等必要约束条件。分析得到主从博弈模型的纳什均衡,基于混沌粒子群算法设计模型求解流程。最后通过仿真算法表明,所建立的模型适用于综合能源系统优化运行方案制定,能够显著降低系统运行成本。     

国际市场煤炭与石油价格相关性研究

煤炭消费增长与我国国民经济增长速度关系密切,2011年以来中国连续成为全球最大煤炭进口国,2013年煤炭进口量达到3.3×108t。国内煤炭价格日益受到国际市场煤炭价格的影响,两者之间的联系愈发紧密。而国际市场上煤炭价格与石油价格的波动具有较强的一致性。通过对煤炭价格和石油价格的相互影响机制进行定性分析、相关性分析以及回归分析,证实煤炭价格与石油价格之间存在显著的正相关关系,石油价格上涨或下跌1%则煤炭价格上涨或下跌0.3654%,石油价格的变化可以解释煤炭价格约18%的变动。煤炭与石油互为能源替代品,石油价格通过替代效应和影响宏观经济来直接、间接地影响煤炭价格。过高的石油价格会使消费者选用相对便宜的煤炭,改变市场供求从而拉高煤炭价格,使两者价格趋向于一致。鉴于外界预测2014年全球石油价格将维持在目前的较高水平,考虑到煤炭与石油价格的正相关性,据此判断国际市场煤炭价格也将保持在现有水平,并随石油价格小幅波动。由于国内外煤炭品质及价格存在差异,因此国内煤炭价格将继续承受下行压力,并逐步向国际市场煤价靠拢。     

Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market

Pollution emission reduction is becoming an inevitable global goal. Incorporating pollution reduction goals into power system operation affects several different aspects, such as unit scheduling and system reliability. At the same time, changes in the energy scheduling change the required optimal reserve amount. Optimal spinning reserve scheduling also affects the energy market scheduling. Optimal reserve allocation changes the energy scheduling, which affect the amount of pollution emission. Therefore, incorporating pollution emission reduction and optimal spinning reserve scheduling cannot be studied separately. Analysis of the system effects of pollution reduction should be performed considering the ancillary service market, specificity the optimal spinning reserve scheduling. This problem is addressed in this paper by incorporating optimal spinning reserve scheduling in a combined environment economic dispatch (CEED) in one objective function. The framework of this paper enables the study of the effect of optimal reserve scheduling and emission reduction as well as an analysis of the system effects of pollution reduction. With the increased AMI and smart grid realization, the reserve supplying demand response (RSDR) is becoming an important player in the reserve market, and thus, these resources are also taken into account. In this paper, the objective function is social cost minimization, including the costs associated with energy provision, reserve procurement, expected interruptions and environmental pollution. A MIP-based optimization method is developed, which reduces the computational burden considerably while maintaining the ability to reach to the optimal solution. The IEEE RTS 1996 is used as a test case for numerical simulations, and the results are presented. The numerical results show that optimal reserve scheduling and RSDR utilization resources have a considerable impact on environmental–economic cost characteristics.     

Theoretical study on the performance of an integrated ground-source heat pump system in a whole year

Being environmental friendly and with the potential of energy-efficiency, ground-source heat pump (GSHP) systems are widely used. However, in southern China, there exists large difference between cooling load in summer and heating load in winter. Thus the increase of soil temperature gradually year-by-year will decrease the COP of the GSHP system. In this paper, the configuration of a vertical dual-function geothermal heat exchanger (GHE) used in an integrated soil cold storage and ground-source heat pump (ISCS&GSHP) system, which charged cold energy to the soil at night and produced chilled water at daytime in summer, and supplied hot water for heating in winter, is presented. This is then followed by reporting the development of the mathematical model for the GHE considering the impact of the coupled heat conduction and groundwater advection on the heat transfer between the GHE and its surrounding soil. The GHE model developed was then integrated with a water-source heat pump and a building energy simulation program together for a whole ISCS&GSHP system. Then the operation performance of the ISCS&GSHP system used for a demonstration building is studied. These simulation results indicated the system transferred 71.505% of the original power consumption at daytime to that at nighttime for the demonstration building. And the net energy exchange in the soil after one-year operation was only 2.28% of the total cold energy charged. Thus we can see the feasibility of the ISCS&GSHP system technically.     

Risk-averse based optimal operational strategy of grid-connected photovoltaic/wind/battery/diesel hybrid energy system in the electricity/hydrogen markets

The techno-economic advantages of grid-connected hybrid energy system (HES) exploit synergies to improve reliability and economic efficiency while maintaining grid stability. Therefore, this paper proposes a risk-averse optimal operational strategy of grid-connected photovoltaic/wind/battery/diesel HES to participate into two energy markets including electricity and hydrogen markets. The grid company can flexibly trade power into two markets to maximally achieve profits based on price arbitrage. The risk influences of the uncertainties, i.e., photovoltaic/wind generation, and electricity prices on the expected revenue are evaluated with CVaR model. For a better exhibition of seasonal variability effects on HES optimal operation strategy, two typical Spring/Summer days are chosen. The proposed risk-averse optimal operational strategy is formulated as a two-stage mixed-integer linear programming (MILP) model. The results in a Spring day simulation under non-risk situation indicate that the overall expected revenue can be improved 2.74 times larger if considering hydrogen market. Moreover, the optimal operational strategy of hydrogen production is considerably affected by unpredictable wind farm. Sensitivity analysis also validates that the changes of PV/WT curtailment penalty have a profound influence than battery degradation coefficient on the HES expected revenue.     

Internalisation of external cost in the power generation sector: Analysis with Global Multi-regional MARKAL model

The Global MARKAL-Model (GMM), a multi-regional “bottom-up” partial equilibrium model of the global energy system with endogenous technological learning, is used to address impacts of internalisation of external costs from power production. This modelling approach imposes additional charges on electricity generation, which reflect the costs of environmental and health damages from local pollutants (SO₂, NO_x) and climate change, wastes, occupational health, risk of accidents, noise and other burdens. Technologies allowing abatement of pollutants emitted from power plants are rapidly introduced into the energy system, for example, desulphurisation, NO_x removal, and CO₂ scrubbers. The modelling results indicate substantial changes in the electricity production system in favour of natural gas combined cycle, nuclear power and renewables induced by internalisation of external costs and also efficiency loss due to the use of scrubbers. Structural changes and fuel switching in the electricity sector result in significant reduction of emissions of both local pollution and CO₂ over the modelled time period. Strong decarbonisation impact of internalising local externalities suggests that ancillary benefits can be expected from policies directly addressing other issues then CO₂ mitigation. Finally, the detailed analysis of the total generation cost of different technologies points out that inclusion of external cost in the price of electricity increases competitiveness of non-fossil generation sources and fossil power plants with emission control.