Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope,elevation,speed and route distance preview to achieve optimal energymanagement of plug-in hybrid electric vehicles(PHEVs).Theapproach is to identify route features from historical and real-time traffic data,in which information fusion model and trafficprediction model are used to improve the information accuracy.Then,dynamic programming combined with equivalent con-sumption minimization strategy is used to compute an optimalsolution for real-time energy management.The solution is thereference for PHEV energy management control along the route.To improve the system's ability of handling changing situation,the study further explores predictive control model in the real-time control of the energy.A simulation is performed to modelPHEV under above energy control strategy with route preview.The results show that the average fuel consumption of PHEValong the previewed route with model predictive control(MPC)strategy can be reduced compared with optimal strategy andbase control strategy.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Optimal Economical Sizing of Grid-Connected Hybrid Renewable Energy System

In this paper, an optimized model is proposed to find the best values for decision variables to optimize the grid connected hybrid renewable energy system which consists of photovoltaic panels, wind turbines and battery bank for electrification to North-east region of Afghanistan to meet winter power shortages of the area. In the proposed model, there are three decision variables namely, the total area occupied by the set of PV panels, total swept area by the rotating turbines＇ blades, and the number of batteries. GA （genetic algorithm） is defined to find the optimal values of the decision variables. The objective of this research is to minimize the LCC （life cycle cost） of the hybrid renewable energy system, and ensuring at the same time systems reliability level which is measured in terms of LPSP （loss of power supply probability）.

Design and optimization of equivalent consumption minimization strategy for 4WD hybrid electric vehicles incorporating vehicle connectivity

This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4 WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control(MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge(SOC) sustainability is formulated to optimize the equivalent factors(EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol(UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method.

Economic Assessment of Standby Diesel Generator for Peak Reduction in Commercial and Industrial Buildings:A Case Study in Malaysia

This paper outlines the barriers and potential benefits of using standby diesel generators in mitigating the peak demands for commercial and industrial customers. The feasibility of utilizing the standby diesel generators to reduce the electricity bills for customers is carried out by using the hybrid optimization model for electric renewable(HOMER)software. The size of the standby diesel generator and its operational duration are determined based on the lowest cost of electricity obtained from the evaluations. The economic assessments demonstrate that there is potential to reduce the electricity bills for commercial and industrial customers under the existing fuel price and tariffs. The commercial customers under the tariff C2 have the highest potential to save their electricity bills with the use of standby diesel generators for peak reduction. This study demonstrates the potential of the standby diesel generators in peak reduction.

计及辅助服务的电-气-氢综合能源系统优化调度

氢能是中国能源行业低碳化转型的重要支撑。电制氢技术可将电网难以消纳的新能源转换成绿氢注入天然气管道,实现绿氢的远距离传输与利用。文中提出一种计及绿氢注入的电-气综合能源系统优化调度模型,且计及电网辅助服务以支撑高比例新能源消纳。首先,建立了计及绿氢注入及管道管存的天然气系统准动态运行模型;然后,构建了计及调峰与灵活备用的辅助服务的综合能源系统灵活运行模型。最后,算例测试量化评估了电制氢及多能协同对于支撑新能源消纳及提高系统运行灵活性的价值,并且分析了新能源渗透率、管道掺氢比限制对于综合能源系统调度结果的影响机理。

电-碳耦合市场环境下可再生能源投资规划优化方法

中国面临着可再生能源高速发展和电力市场改革、碳市场建设稳步推进的多重机遇,市场化机制将科学引导可再生能源合理规划与有效投资。电力市场和碳市场虽分开运营,两者的出清结果却紧密耦合,且共同影响市场主体的运营与投资模型。在电-碳耦合市场的背景下,文中提出了一种最大化可再生能源接入容量并保障其投资成本回收的可再生能源投资规划双层优化模型。首先,基于动态碳排放特性推导了常规机组的电-碳耦合报价,提出了考虑碳配额交易影响的电力现货市场机制。然后,在“投资-运行”框架下建立了可再生能源投资规划双层优化模型:上层在能收回投资成本的前提下,最大化可再生能源的投资容量并对其进行定容和选址决策;下层分别模拟电力现货市场和国家核证自愿减排量市场的出清过程,以向上层模型的可再生能源发电商投资决策提供相应的价格信号。通过下层问题最优性条件和线性化方法,将双层优化模型转化为混合整数线性规划进行求解。最后,通过仿真算例验证了所提方法有效性,并探索了碳市场交易、网络容量与储能等不同因素对可再生能源最优投资容量的影响。

考虑不确定性与交互功率的城市综合能源系统两阶段调度

城市综合能源系统运行优化面临新能源发电强随机性和强波动性、负荷功率不确定性以及系统参数不确定性的多重挑战。文中构建考虑聚合特性的用户侧灵活性资源调度模型,提出考虑不确定性与交互功率的城市综合能源系统两阶段优化调度策略。在日前阶段,采用混合随机鲁棒优化方法处理建筑群参数不确定性和源荷不确定性,得到城市配电网与上级电网的最优交互功率区间。在日内阶段,基于新能源发电和负荷的预测结果,采用模型预测控制滚动优化系统的调度策略。算例结果表明,调度策略能够保证用户的热舒适度需求,得到的交互功率区间具有较强的应对日内不确定性能力,同时降低交互功率短时剧烈波动对系统运行安全性和经济性的影响。

基于序列二次规划算法的插电式混合动力汽车模型预测控制策略

为提升插电式混合动力汽车(PHEV)的车速预测精度和燃油经济性,提出基于序列二次规划(SQP)算法的模型预测控制能量管理策略。以卷积神经网络(CNN)构建的车速预测模型为基础,选取三类典型历史工况数据作为CNN车速预测模型的训练集,使用鲸鱼优化算法(WOA)优化CNN参数,通过优化的WOA-CNN模型预测未来时域内的车速;采用SQP算法对模型预测控制策略进行求解,且与基于规则的电量消耗和电量保持(CD-CS)策略和基于全局优化的动态规划(DP)策略的控制结果进行对比分析,验证所提策略的有效性。结果表明:通过WOA-CNN模型可提高车速预测精度,为4.88%~8.39%;与DP控制策略相比,本文提出策略的燃油消耗量高出1.98%,但计算时间减少了74.32%,能量管理的实时性得到大幅提升;与CDCS控制策略相比,提出策略的节油率为20.37%。综合考虑,本文提出策略的整车能量消耗和计算成本较优,可合理实现对PHEV转矩分配的智能控制。

基于HPSOGA的多目标电动汽车充电优化

随着电动汽车保有量的快速上升,电动汽车无序并网充电将会给配电网负荷平稳带来巨大的不确定性,因此对电动汽车的充电进行优化十分重要。为此,提出一种基于混合粒子群优化遗传算法(hybrid particle swarm optimization genetic algorithm,HPSOGA)的多目标电动汽车充电优化策略。使用Monte Carlo法基于用户出行规律建立电动汽车充电负荷曲线,在传统PSO算法的基础上引入GA算法的迭代机制,形成HPSOGA算法并用其对以用户充电费用最少和电网负荷波动率最小建立的多目标优化模型进行求解。结合具体算例进行仿真分析,结果显示基于HPSOGA算法的多目标电动汽车充电优化策略具有更快的优化速度以及更好的优化效果,进一步降低电网负荷峰值、提高电网负荷谷值,电网负荷波动率得到有效降低,同时用户充电成本得到有效减少。

考虑坡度工况的FCHEV队列分层优化控制策略研究

在面对坡度工况时,如何开发同时兼顾车辆间协同控制与能耗经济性的控制策略是提高交通效率与发挥车辆节能潜力的关键技术之一。以燃料电池混合动力汽车队列为研究对象,以安全行驶及优化能耗为目标,提出了一种基于改进粒子群优化算法与Q学习的燃料电池混合动力汽车队列分层优化控制策略。该策略中上层控制器在保证满足与前车距离或速度限制等安全约束的前提下,利用改进的粒子群优化算法获取节能速度轨迹,并使用模型预测控制框架实时调整主车速度遵循节能速度轨迹行驶。下层控制器根据上层求解的车速和需求功率等信息建立Q学习控制器,实现燃料电池混合动力汽车动力电池与燃料电池之间的最优能量分配。仿真结果表明,本文所提出的分层控制策略在坡度工况下,表现出良好的跟踪性能和安全性能,且优化结果与动态规划策略相似,表明该策略的能耗经济性及可行性。

计及可再生能源消纳的电热综合能源系统多目标优化模型

可再生能源因其清洁低碳的特点备受能源行业关注,在能源转型背景下,可再生能源受到关注和重视,已经成为能源转型的核心。可再生能源具有高效、清洁、低碳、环保等特点,发展可再生能源有利于促进生态环境和社会经济可持续发展。所以,我国能源发展的核心和关键环节是加快发展可再生能源。但是,可再生能源的不确定性也带来了严重的消纳问题。提高电力系统的灵活性去解决可再生能源的消纳问题成为当下研究热点之一。本文突破异质能源间的壁垒,基于电热综合能源系统去探索提高可再生能源的消纳能力。同时,立足于经济性与安全性指标,提出一种多目标优化数学模型,并通过对偶转化和行列生成算法对所提模型进行求解。最后,在算例测试中,通过对比电热综合能源系统与电力系统的各种测试指标证明了所提模型的有效性。

含热泵和电热混合储能的建筑微能源网经济优化调度

为实现微能源网经济运行,提出一种含热泵和电热混合储能的建筑微能源网经济优化调度方法。首先基于ETP模型结合蒙特卡洛模拟建立采用通断调节的大规模空调负荷聚合模型,确定逐时冷负荷需求。然后构建考虑相变蓄冷变温特性与风冷热泵出力特性的微能源网优化调度模型。最后以微能源网全寿命周期成本最小为目标函数,应用Python+Gurobi对模型进行求解。分析夏季3种典型天气,4种场景下的优化调度结果,仿真结果表明:配置电热混合储能具有电、冷负荷削峰填谷的效果,能降低初始投资成本与运行成本,系统的经济性和灵活性更优。

Consideration of some optimization techniques to design a hybrid energy system for a building in Cameroon

To solve the problem energy deficit encountered in developing countries,Hybrid Renewable Energy System(HRES)appears to be a very good solution.The paper presents the optimal design of a hybrid renewable energy system considering the technical i.e Loss of Power Supply Probability(LPSP),economic i.e Cost of Electricity(COE)and Net Present Cost(NPC)and environmental i.e Total Greenhouse gases emission(TGE)aspects using Particle Swarm Optimization(PSO),hybrid Particle Swarm Optimization-Grey Wolf Optimization(PSOGWO),hybrid Grey-Wolf Optimization-Cuckoo Search(GWOCS)and Sine-Cosine Algorithm(SCA)for a Community multimedia center in MAKENENE,Cameroon;where inhabitants have to spend at times 3 to 4 days of blackout.Seven configurations(Scenarios)of hybrid energy systems including PV,WT,Battery and Diesel generator are analyzed considering an average daily energy load of 50.22 kWh with a peak load of 5.6 kW.Four values of the derating factor i.e 0.6,0.7,0.8 and 0.9 are used in this analysis and the best value is 0.9.Scenario 3 with LPSP,COE,NPC,TGE and RF of 0.003%,0.15913$/kWh,46953.0485$,2.3406 kg/year and 99.8%respectively when using GWOCS is found to be the most appropriate for the Community multimedia center.The optimal Scenario is obtained for a system comprising of 18 kW of P_(pv-rated)corresponding to 69 solar panels,3 days of AD corresponding to a total battery capacity of 241 kWh and 1 of N_(dg).

含风力发电的市级电网区域可再生能源规划方案确定及重要因素影响研究

以CO2减排目标为主要约束条件,以经济性最优为目标函数,以可再生能源混合优化模型为平台,提出了某市级区域电网基于风资源的可再生能源规划方案,从上级电网购电价格、风电成本、电煤价格、向电网送电量等影响能源规划的因素入手进行灵敏度分析,给出了有实际参考价值的分析结论。研究表明,当风电成本与火电成本可相比拟时,在CO2减排约束下,若不考虑向上级电网输送电量,引入风力发电可以有效减少污染物排放量和燃料消耗,提高的风电比例会随风机数量的增加逐渐达到饱和,电力生产成本会随风机的过度装设而迅速增长。对于设定的CO2减排目标,在安装风机数量适当大于满足减排目标所需的最小风机数量,在区域电网不加装储能装置的条件下,上级电网购电价格以及煤炭价格的升高会直接导致区域电网电力供应经济性的迅速恶化,而向上级电网售电能够有效降低区域电网的电力生产成本,且大电网对区域电网外送风电的消纳能力越强,区域电网的电力生产成本就能降得越低。

电力市场环境下风光储联合运行优化策略

风电、光伏等新能源机组与储能系统联合参与电力市场竞争的策略,有利于提高新能源发电企业的市场竞争力,促进新能源消纳与友好接入。构建风电—光伏—储能联合优化运行决策模型,以利润最大化为目标,充分考虑新能源机组与储能系统运行时的各项技术约束条件,研究在电力市场环境下风光储协调耦合运行的发电策略与出力安排,并分析联合运行模式所带来的经济效益问题。算例分析验证了所提出的风光储联合运行优化决策模型的正确性与有效性。

区域风能规划中的风资源参数及等效风速序列求解方法

在面积较广阔、建有多个风速观测站的区域风能规划中,缺乏能够描述区域等效风资源状况的风速数据以及相应的求解方法。针对区域等效风速序列及风速分布特性参数难于求解的问题,调查总结了常用风能规划模型中用于描述风速变化特性及风资源分布的重要参数,分析了参数选择对风能规划的影响,提出基于多维自回归模型的区域等效参数及区域等效风速序列求解方法,给出一种仅利用月平均风速合成风速数据的方法。以可再生电力混合优化模型为计算平台,以建有多个风速观测站的某区域为算例,参数求解方法及合成风速方法的合理性进行了验证分析。结果表明,2种方法都能够求得合理的区域等效风速数据;利用月平均风速合成风速数据的方法,能够合成具有一定可参考性的、适用于区域风能规划的风速数据。

基于混合储能的大型风电场优化控制

波动性电源的大规模接入,不仅造成电力系统运行调度困难,而且会影响其安全稳定运行。因此,通过配置储能装置来对并网功率进行平滑控制十分必要。基于一定配置容量的混合储能系统(HESS),提出一种用于大型风电场并网的最优控制方法。采用蓄电池和超级电容为储能介质,建立短期优化控制的动态模型,确立相应的控制目标和约束条件,并设计基于粒子群优化(PSO)的求解算法。仿真算例的结果表明该方法是正确、有效的。

插电式四驱混合动力汽车控制策略设计及优化

为有效识别驾驶员的驾驶意图,在保障插电式四驱混合动力汽车动力性的基础上,提高其燃油经济性,提出了一种转矩识别系数计算方法,设计了基于发动机输出转矩最优能量管理控制策略,讨论了每种工作模式的判别条件以及转矩分配方法.为避免单一优化算法运算时间长、容易陷入局部最优的固有缺陷,使用优拉丁超立方的方法进行试验设计,利用径向基函数神经网络(radial basis function,RBF)建立近似模型,使用多岛遗传算法对近似模型进行了优化.研究结果表明:对优化后的控制策略进行离线仿真得出,混合动力汽车在满足动力性能的前提下,百公里油耗降低了16.4%;将优化后的控制策略在d SPACE上进行硬件在环试验表明,所制定的控制策略,可以实现基本的能量管理,且加入转矩识别之后平均车速误差降低了39.9%,百公里油耗降低了8.5%.

基于效率优化的混合动力再生制动控制策略

以CVT混合动力轿车为研究对象,根据镍氢电池组和ISG电机性能试验结果,建立了电机/电池联合效率模型,获取了电机/电池联合最佳效率曲线,提出了实现CVT与ISG电机和镍氢电池组最佳匹配和系统效率优化的再生制动控制策略。仿真结果表明:所提出的再生制动控制策略,在保证整车制动安全的条件下,能实现ISG电机/电池高效工作,可进一步提高制动能量回收率。