Increasing of fuel cell economic benefits by optimal participation strategy with energy storages and other distributed resources and considering uncertainties and various markets

The Participation of Distributed Energy Resources (DERs) such as Fuel Cell (FC) in Daily Market is one of the main Problems of these resources. Uncertain nature of market price and power system parameters can be affected to economic performance of DERs and is caused deviation penalties during market settlements. This article introduces an optimal combined sharing strategy of fuel cell/wind turbine/battery storage unit and demand response as microgrid to improve all of them profit in market participation. In order to adjust of contracted power and reduce the imbalanced energy, the Adjusted Market (AM) is considered 3–7 h before the delivered time, which the offered power could be updated in this time. Uncertainty of markets and DERs generations is considered using an optimal stochastic optimization approach. The probabilistic scenarios are generated using forecast error database in daily, adjusted and imbalance market as production of DERs. Uncertain variables are predicted by a neuro-fuzzy model. Finally, to demonstrate of the proposed joint model strategy, a test system contain fuel cell/wind turbine/battery storage unit and demand response is considered and the results is calculated during one week, month, and year period in joint and separated conditions.     

基于最优滚动控制域的储能控制策略

针对风电场预测功率与实际功率不匹配以及风力发电不确定性问题,提出一种以补偿风电预测误差和平抑风电波动为目标的储能控制策略。该策略以先进控制理论为基础,结合储能补偿预测区间和储能平抑风电波动区间,提取考虑储能运行成本的储能最优滚动控制域。首先,针对储能补偿预测误差目标,制定储能控制策略,提取允许误差内的储能补偿区间;其次,考虑风电功率波动要求及荷电状态（SOC）约束,采用模型预测控制求解出储能滚动控制序列,确定储能平抑区间。最后,考虑储能运行成本,将补偿区间和平抑区间相结合,制定储能最优滚动控制区间,以此为基础确定储能容量。以中国新疆某风电场为例,对该文提出的储能控制策略与传统控制策略进行对比验证,验证所提策略的可行性和有效性。     

Modelling of energy systems with a high percentage of CHP and wind power

This paper presents the energy system analysis model EnergyPLAN, which has been used to analyse the integration of large scale wind power into the national Danish electricity system. The main purpose of the EnergyPLAN model is to design suitable national energy planning strategies by analysing the consequences of different national energy investments. The model emphasises the analysis of different regulation strategies and different market economic optimisation strategies.At present wind power supply 15% of the Danish electricity demand and ca 50% is produced in CHP (combined heat and power production). The model has been used in the work of an expert group conducted by the Danish Energy Agency for the Danish Parliament. Results are included in the paper in terms of strategies, in order to manage the integration of CHP and wind power in the future Danish energy supply in which more than 40% of the supply is expected to come from wind power.     

Generating short‐term probabilistic wind power scenarios via nonparametric forecast error density estimators

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost‐effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power time series. We estimate nonparametric forecast error densities, specifically using epi‐spline basis functions, allowing us to capture the skewed and nonparametric nature of error densities observed in real‐world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured. We compare the performance of our approach to the current state‐of‐the‐art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Our methodology is embodied in the joint Sandia–University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.     

The energy storage system output control strategy to improve theshort term windpower forecastingaccuracy rate

目前对于储能系统应用于平抑新能源发电的波动性、移峰填谷等场景的控制策略已有文献研究,但对于风功率预测准确率影响风电场效益的机制下储能系统应用的可行性尚未见研究。本文提出了一种以减小风电场短期功率预测偏差为目标的储能系统出力控制策略,控制策略以风电场实时出力数据（秒级）为数据源,采用线性外推加以移动平均优化的方法预测下一时刻风电场出力,通过比较风电场短期功率预测值与实时预测值,计算储能系统期望出力,并根据储能系统不同SOC区间内的出力能力进行约束,输出储能系统出力指令,最后进行了仿真验证。结果表明,本文提出的储能系统出力控制策略,能够使风电场通过配置储能系统,减少短期功率预测准确度考核,对风电场的精益化运行具有指导意义。     

计及超短期预测误差的风储系统跟踪计划出力控制策略

在计划跟踪模式下,为最大化风储系统出力的经济效益,提出风储系统的协调控制方法。首先,基于超短期功率预测,以风电场总收益最大为目标,建立风储系统跟踪计划出力的控制模型;然后,利用超短期功率预测数据和历史实际功率数据,建立基于小波变换和序列到序列（sequence to sequence,seq2seq）的超短期预测功率修正模型,对超短期功率预测数据进行误差修正;最后,将基于超短期功率预测的计划跟踪模型与超短期预测误差修正模型结合,提出一种计及超短期预测误差的风储系统跟踪计划出力的控制策略。仿真结果表明：所提策略能显著提高风电计划跟踪精度和风储系统运行经济性、促进风电的消纳。     

Research progress on fre protection technology of LFP lithium-ion battery used in energy storage power station

随着电化学储能市场的蓬勃发展,电化学储能电池本身的安全性越来越受到关注,如何最大程度地降低储能电池组火灾风险是电化学储能大规模应用时亟需解决的问题。本文综述目前国内外针对锂离子电池热失控已有的研究成果,包括磷酸铁锂电池的燃烧特性、火灾危险等级以及在储能电站预警系统中应用的锂离子电池热失控及热扩散参数;梳理不同灭火剂对电池火灾的灭火效率;同时总结电化学储能电站的灭火系统选择,为电网储能工程应用提供参考,有效支持锂离子储能电池的大规模工程需求。     

An analysis for the need of a battery energy storage system in tracking wind power schedule output

电池储能系统(battery energy storage system,BESS)在风储联合应用中具有多种功能,利用电池储能系统提高风电并网调度运行能力是当前研究的热点之一.文章基于我国北方某风电场历史运行数据与预测数据,依据预测误差评价指标和风电场预报考核指标的综合评价方法对风电场预测数据进行分析研究,归纳了预测误差的概率分布特征;提出利用电池储能系统提高风电跟踪计划出力能力,统计并量化出电池储能系统用于跟踪计划出力场合的作用范围;通过仿真验证电池储能系统在风储联合系统中提高风电跟踪计划出力控制策略的有效性和可行性.     

From probabilistic forecasts to statistical scenarios of short‐term wind power production

Short‐term (up to 2–3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time‐dependent and multistage decision‐making problems, e.g. optimal operation of combined wind‐storage systems or multiple‐market trading with different gate closures. This issue is addressed here by describing a method that permits the generation of statistical scenarios of short‐term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion of series of prediction errors to a multivariate Gaussian random variable, the interdependence structure of which can then be summarized by a unique covariance matrix. Such matrix is recursively estimated in order to accommodate long‐term variations in the prediction error characteristics. The quality and interest of the methodology are demonstrated with an application to the test case of a multi‐MW wind farm over a period of more than 2 years. Copyright © 2008 John Wiley & Sons, Ltd.     

满足主变N-1的变电站储能定容方法及经济分析

[目的]在保证变电站平均负载率相对合理、提高变电站利用小时数的前提下,为了解决目前电网存在的高峰负荷过高造成的变电站主变压器短时间重载问题,延缓电网的建设投资,保证电网经济运行,文章提出了基于满足主变N-1供电要求的变电站储能定容方法。[方法]首先,建立储能定容数学模型实现对负荷运行曲线削峰填谷、降低变压器最高运行负载率;然后,构建锂离子电化学储能定容经济目标函数,并开展建设锂离子电化学储能和扩建主变的建设投资对比,分析储能定容经济性;最后,结合实际变电站,提出储能配建规模的建议及经济可行的储能建设成本。[结果]通过分析,随着未来锂离子电化学储能单位造价降低,储能功率可按照变电站容量15%、充放电时长2 h考虑,储能系统单位造价降低至1 200元/kWh左右时,建设储能的经济优势明显。[结论]所提出的储能定容方法可以有效解决变电站短时间重载问题,并具备较常规输变电工程更优的经济性。     

Machine learning methods for short‐term bid forecasting in the renewable energy market: A case study in Italy

In liberalized markets, there usually exists a day‐ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day‐ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1‐day‐ahead forecast. The input‐output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012‐2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play.     

Energy storage sizing for wind power: impact of the autocorrelation of day‐ahead forecast errors

The availability of day‐ahead production forecast is an important step toward better dispatchability of wind power production. However, the stochastic nature of forecast errors prevents a wind farm operator from holding a firm production commitment. In order to mitigate the deviation from the commitment, an energy storage system connected to the wind farm is considered. One statistical characteristic of day‐ahead forecast errors has a major impact on storage performance: errors are significantly correlated along several hours. We thus use a data‐fitted autoregressive model that captures this correlation to quantify the impact of correlation on storage sizing. With a Monte Carlo approach, we study the behavior and the performance of an energy storage system using the autoregressive model as an input. The ability of the storage system to meet a production commitment is statistically assessed for a range of capacities, using a mean absolute deviation criterion. By parametrically varying the correlation level, we show that disregarding correlation can lead to an underestimation of a storage capacity by an order of magnitude. Finally, we compare the results obtained from the model and from field data to validate the model. Copyright © 2013 John Wiley & Sons, Ltd.     

金属有机框架及其衍生金属氧化物在锂和钠离子电池中的应用

高性能锂和钠离子电池是未来便携电子设备、电动汽车和大规模储能电站的重要组成部分,受到了各行业的广泛关注。目前商用的锂离子电池和研发中的钠离子电池都面临着一些技术瓶颈,主要表现为能量密度低、充放电慢等,导致无法满足市场的需求。具有独特结构、高比表面积的金属有机框架及其衍生金属氧化物可作为电化学储能器件新型电极材料,满足高性能锂和钠离子电池的要求。本文综述了近年来金属有机框架及其衍生金属氧化物作为锂和钠离子电池电极材料的研究进展,同时指出了金属有机框架及其衍生金属氧化物在实际应用中的不足及未来可能的一些改进措施。     

Large-scale integration of wind power into different energy systems

The paper presents the ability of different energy systems and regulation strategies to integrate wind power. The ability is expressed by the following three factors: the degree of electricity excess production caused by fluctuations in wind and Combined Heat and Power (CHP) heat demands, the ability to utilise wind power to reduce CO₂ emission in the system, and the ability to benefit from exchange of electricity on the market. Energy systems and regulation strategies are analysed in the range of a wind power input from 0 to 100% of the electricity demand. Based on the Danish energy system, in which 50% of the electricity demand is produced in CHP, a number of future energy systems with CO₂ reduction potentials are analysed, i.e. systems with more CHP, systems using electricity for transportation (battery or hydrogen vehicles) and systems with fuel-cell technologies. For the present and such potential future energy systems different regulation strategies have been analysed, i.e. the inclusion of small CHP plants into the regulation task of electricity balancing and ancillary grid stability services and investments in electric heating, heat pumps and heat storage capacity. The results of the analyses make it possible to compare short-term and long-term potentials of different strategies of large-scale integration of wind power.     

Study on multi‐objective optimization of load dispatch including renewable energy and CCS technologies

This paper mainly studies the multi‐objective optimization of load dispatch of power systems including renewable energy and CO₂ capture and storage (CCS) technologies. The improved environmental/economic load dispatch model for the power system is constructed, considering the renewable energy utilization and CCS technologies. A novel singular weighted method (SWM) has been proposed in this paper for solving this kind of multi‐objective and multi‐constraint optimization problem. A power system with five generators has been applied in one case study to test the model and SWM. It was concluded that the share that each unit takes is not linear; however, the optimal results are largely relevant to the characteristics of the units. In addition, the research results showed that with the increment of the weight coefficient for a certain objective function, the optimization result was closer to the single optimization result for that objective function; and with the increase of forecast demand load, a 35 MW wind energy unit and a 200 MW water energy unit should be built. Copyright © 2009 John Wiley & Sons, Ltd.     

An integrated approach for optimal coordination of wind power and hydro pumping storage

The increasing wind power penetration in power systems represents a techno‐economic challenge for power producers and system operators. Because of the variability and uncertainty of wind power, system operators require new solutions to increase the controllability of wind farm output. On the other hand, producers that include wind farms in their portfolio need to find new ways to boost their profits in electricity markets. This can be done by optimizing the combination of wind farms and storage so as to make larger profits when selling power (trading) and reduce penalties from imbalances in the operation. The present work describes a new integrated approach for analysing wind‐storage solutions that make use of probabilistic forecasts and optimization techniques to aid decision making on operating such systems. The approach includes a set of three complementary functions suitable for use in current systems. A real‐life system is studied, comprising two wind farms and a large hydro station with pumping capacity. Economic profits and better operational features can be obtained from the proposed cooperation between the wind farms and storage. The revenues are function of the type of hydro storage used and the market characteristics, and several options are compared in this study. The results show that the use of a storage device can lead to a significant increase in revenue, up to 11% (2010 data, Iberian market). Also, the coordinated action improves the operational features of the integrated system. Copyright © 2013 John Wiley & Sons, Ltd.     

On the fluctuating power generation of large wind energy converters,with and without storage facilities

The power fluctuations and time duration patterns of large, hypothetical wind energy generators are analysed, using meteorological data for Denmark. It is found that the fluctuations, relative to a load which varies through the year in a manner similar to the actual load, are no greater than the fluctuations relative to a constant load (base-load application). The addition of a hypothetical short-term storage, capable of delivering the average power for 10–20 hr, makes the wind energy system as dependable as one large nuclear power plant, being capable of delivering the average power for about 70 per cent of the time. For full coverage by a wind energy system, an additional long-term storage facility must be added. It is found that for the wind energy conditions of the Danish site considered, a storage holding 40–60 days of average load and with a recovery percentage of 50, will be needed. Due to the energy losses in the storage systems, the installed capacity must be augmented by about 35 per cent. A few remarks are made about the prospects for developing suitable storage facilities, and about the cost of total wind energy systems relative to other means of producing electricity.     

Wind park reliable energy production based on a hydrogen compensation system. Part II: Economic study

Power production from renewable energy resources is increasing day by day. In the case of Spain, in 2009 it represented 26.9% of installed power and 20.1% of energy production. Wind energy makes the most important contribution to this production. Wind generators are greatly affected by the restrictive operating rules of electricity markets because, as wind is naturally variable, wind generators may have serious difficulties in submitting accurate generation schedules on a day-ahead basis, and in complying with scheduled obligations. Weather forecast systems have errors in their predictions depending on wind speed. Therefore, if wind energy becomes an important actor in the energy production system, these fluctuations could compromise grid stability. In the previous paper in this brief series [1], the authors showed technical results of the proposed solution, which consists of combining wind energy production with a biomass gasification system and a hydrogen generation system based on these two sources. In the present paper it is shown the economic results of the study, considering the most profitable technical configurations and three possible economic scenarios.     

Comparing electricity,heat and biogas storages’ impacts on renewable energy integration

Increasing penetration of fluctuating energy sources for electricity generation, heating, cooling and transportation increase the need for flexibility of the energy system to accommodate the fluctuations of these energy sources. Controlling production, controlling demand and utilising storage options are the three general categories of measures that may be applied for ensuring balance between production and demand, however with fluctuating energy sources, options are limited, and flexible demand has also demonstrated limited perspective. This article takes its point of departure in an all-inclusive 100% renewable energy scenario developed for the Danish city Aalborg based on wind power, bio-resources and low-temperature geothermal heat. The article investigates the system impact of different types of energy storage systems including district heating storage, biogas storage and electricity storage. The system is modelled in the energy systems analyses model energyPRO with a view to investigating how the different storages marginally affect the amount of wind power that may be integrated applying the different storage options and the associated economic costs. Results show the largest system impact but also most costly potential are in the form of electricity storages.     

Analysis of the wind energy market in Denmark and future interactions with an emerging hydrogen market

The transition from fossil fuels to renewable energy sources is critical to reduce future emissions and mitigate the consequences hereof. Yet, the expansion of renewable energy, especially the highly fluctuating production of wind energy, poses economic challenges to the existing energy system in Denmark. This paper investigates the economic feasibility of integrating a 250 kW, 500 kW, 750 kW and 1 MW water electrolysis system in the existing Danish energy market to exploit excessive off- and onshore wind energy for hydrogen production used as fuel for transportation purposes. In 2018, Danish wind turbines produced excess energy during 1238 h, which poses a capacity constraint as the electrolysis systems are limited to only produce hydrogen for 14% of the total available annual hours. This paper concludes that the net present value of each investment is negative as the fixed and variable production costs exceeds the generated revenues and it is therefore not economical feasible to invest in an electrolysis system with the purpose of only operating whenever excess off- and onshore wind energy is available.