Effective Energy Management Scheme by IMPC

The primary purpose of the Energy Management Scheme(EMS)is to monitor the energy fluctuations present in the load profile.In this paper,the improved model predictive controller is adopted for the EMS in the power system.Emperor Penguin Optimization(EPO)algorithm optimized Artificial Neural Network(ANN)with Model Predictive Control(MPC)scheme for accurate prediction of load and power forecasting at the time of preoptimizing EMS is presented.For the power generation,Renewable Energy Sources(RES)such as photo voltaic(PV)and wind turbine(WT)are utilized along with that the fuel cell is also presented in case of failure by the RES.Such a setup is connected with the grid and applies to the household appliances.In improved model predictive control(IMPC),the set of constraints for the powerflow in the system is optimized by the ANN,which is trained by EPO.Such a tuning based prediction model is presented in the IMPC technique.The proposed work is implemented in the MATLAB/Simulink platform.The energy management capability of the proposed system is analyzed for different atmospheric conditions.The total system cost,life cycle cost and annualized cost for IMPC are 48%,45%and 15%,respectively.From the performance analysis,the cost obtained by the proposed method is very low compared to that obtained by the existing techniques.

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.

Real-time Energy Management Method for Electric-hydrogen Hybrid Energy Storage Microgrids Based on DP-MPC

With the increasing presence of intermittent energy resources in microgrids,it is difficult to precisely predict the output of renewable resources and their load demand.In order to realize the economical operations of the system,an energy management method based on a model predictive control(MPC)and dynamic programming(DP)algorithm is proposed.This method can reasonably distribute the energy of the battery,fuel cell,electrolyzer and external grid,and maximize the output of the distributed power supply while ensuring the power balance and cost optimization of the system.Based on an ultra-shortterm forecast,the output power of the photovoltaic array and the demand power of the system load are predicted.The offline global optimization of traditional dynamic programming is replaced by the repeated rolling optimization in a limited period of time to obtain power values of each unit in the energy storage system.Compared with the traditional DP,MILP-MPC and the logic based real-time management method,the proposed energy management method is proved to be feasible and effective.

Modular energy cost optimization for buildings with integrated microgrid

Buildings are becoming suitable for application of sophisticated energy management approaches to increase their energy efficiency and possibly turn them into active energy market participants.The paper proposes a modular coordination mechanism between building zones comfort control and building microgrid energy flows control based on model predictive control. The approach opens possibilities to modularly coordinate technologically heterogeneous building subsystems for economically-optimal operation under user comfort constraints. The imposed modularity is based on a simple interface for exchanging building consumption and microgrid energyprice profiles. This is a key element for technology separation,replication and up-scaling towards the levels of smart grids and smart cities where buildings play active roles in energy management. The proposed coordination mechanism is presented in a comprehensive realistic case study of maintaining comfort in an office building with integrated microgrid. The approach stands out with significant performance improvements compared to various non-coordinated predictive control schemes and baseline controllers. Results give detailed information about yearly cost-effectiveness of the considered configurations,which are suitable for deployment as short-and long-term zero-energy building investments.

Real-time Energy Management of Low-carbon Ship Microgrid Based on Data-driven Stochastic Model Predictive Control

With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affect safe and stable operation of the ship microgrid.In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy,we propose a real-time energy management strategy based on data-driven stochastic model predictive control.First,we establish a ship photovoltaic and load scenario set consid-ering time-sequential correlation of prediction error through three steps.Three steps include probability prediction,equal probability inverse transformation scenario set generation,and simultaneous backward method scenario set reduction.Second,combined with scenario prediction information and rolling op-timization feedback correction,we propose a stochastic model predictive control energy management strategy.In each scenario,the proposed strategy has the lowest expected operational cost of control output.Then,we train the random forest machine learn-ing regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control.Finally,a low-carbon ship microgrid with photovoltaic is simulated.Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy,as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.Index Terms-Data-driven stochastic model predictive control,low-carbon ship microgrid,machine learning,real-time energy management,time-sequential correlation.

船舶混合动力系统能量管理预测控制方法研究

针对混合动力系统控制稳定性和节能减排问题,明确模型预测控制在船舶能量管理中的作用、研究现状及趋势。借助CiteSpace对船舶能量管理进行可视化分析,理清了船舶能量管理的发展脉络,并通过不同能量管理策略的对比分析揭示模型预测控制在船舶能量管理中的重要性;从预测建模、优化目标及约束、求解和改进策略等3个方面开展了船舶能量管理预测控制方法的分析;最后从考虑可再生能源的能量管理策略、建模与验证的标准化、协同优化和多维度评估等方面对船舶能量管理预测控制的未来研究进行展望。结果表明:模型预测控制在智能船舶、多能源船舶等复杂的动力系统的实时控制中具有重要潜力。与其他算法结合开展多目标算法融合是提升控制精度和计算实时性的重要途径,开展多维度测试评估有利于推动策略的实船应用。

基于情景分析法的安徽省能源消耗及碳排放分析

为了从区域层面开展能源消耗和碳排放量预测分析,以安徽省能源消耗和碳排放量为研究对象,通过构建LEAPAnhui预测模型,设计了基准情景(BAS)、发展规划情景(DPS)、节能减排情景(ERS)3种情景,预测了安徽省未来3种不同的发展路径。结果显示:到2035年,BAS情景下能源消耗总量达到23459×10^(4)t(以吨标准煤计),相比BAS情景,DPS与ERS情景能源消耗总量分别下降了20.6%、30.7%,其中ERS情景的能源消耗总量于2030年实现达峰,峰值为16416×10^(4)t;BAS情景下,安徽省碳排放总量到2035年已达到512.2×10^(6)t,年均增长率为2.6%。DPS与ERS情景下,碳排放总量分别于2030年(389.1×10^(6)t)、2025年(357.2×10^(6)t)达到峰值,均能够完成我国提出的争取在2030年前实现碳达峰的目标承诺。基于碳约束目标对于安徽省能源结构进行了优化分析,在约束路径下,分配给三大系统清洁能源的比例分别为:第三产业39.3%、居民生活23.3%、第二产业37.4%,最小清洁能源需求量为107×10^(16)J,在能源需求总量中的占比为20.6%。

基于车速预测的PHEV能量管理策略

为兼顾插电式混合动力汽车能量管理策略的节油能力和实时性,设计了一种基于车速预测的能量管理策略。建立LSTM神经网络车速预测模型,实现对车速的准确预测;在模型预测控制框架下,采用动态规划算法对电机转矩分配进行优化,建立基于车速预测的能量管理策略。仿真结果表明,该策略具有良好的实时性,同时较之基于规则的能量管理策略油耗降低了14.85%。

基于模型预测框架的燃料电池汽车混合动力系统能量管理

混合动力系统作为燃料电池汽车的关键组成部分,系统的能耗与寿命优化是推动其进一步商业化发展的关键,而能量管理策略(EMS)在提高燃料电池汽车混合动力系统的效率和燃料经济性方面发挥着重要作用。为有效降低混合动力系统的内部损耗,进一步提高系统经济性,提出了一种基于模型预测框架的混合动力系统能耗优化能量管理控制方法。首先构建包含动力系统内部损耗与氢气消耗的目标函数,然后利用模型预测架构求解目标函数来控制燃料电池的输出电流,以实现合理的能量分配,最后在Matlab中进行仿真,验证所制定方法的合理性和有效性。与应用较为广泛的等效氢耗最小策略(ECMS)相比,结果表明所提方法可以显著降低动力系统的内部损耗和氢气消耗。此外,该策略还可以有效保持蓄电池的荷电状态(SOC),延长动力系统的使用寿命。

基于新能源消纳的高温电解制氢系统建模与控制方法研究

基于新能源随机波动以及低温电解制氢系统转化效率较低且无法切换到发电状态,提出一种高温电解制氢变负载情形下的高效最大产氢点多模型优化控制方法。首先构建计及辅助设备在内的高温固体氧化物电解系统整体协同运行的多能耦合优化模型,分析其影响系统电解的工作温度、电流强度、物料流速等诸多因素并导出高能安全产氢率;其次,在PID控制的基础上,给出一种既能实现变负荷同步准确跟踪产氢轨迹和优化电网调控需求,又能最大优化产氢效率的自适应时变线性变参数模型预测控制方法;最后,通过算例仿真验证所提方法较PID控制电解系统升温速率快,过渡时间短,电解电流、水蒸气流速、H2流速、电炉功率都能平稳过渡至稳态点,具有一定的理论和实用价值。

基于支持向量机与模型预测控制的混合动力船舶能量管理策略

为了提高混合动力船舶能量利用效能,提升混合动力船舶燃油经济性,将在线工况识别与实时优化策略相结合,提出了基于支持向量机(support vector machine,SVM)与模型预测控制(model predictive control,MPC)的混合动力船舶能量管理策略。引入SVM理论,使用“美维凯越”号新能源游轮的实船数据,优化核函数类型及关键参数,构建船舶运行4个典型工况的在线识别模型;再通过对船舶当前时刻工况特征参数的分析与判断,确定船舶实时运行工况。以最小燃油消耗和保持储能单元荷电状态(state of charge,SOC)稳定为目标,以主机和复合储能单元之间的实时输出功率为优化变量,以功率需求预测模型为约束条件,构建模型预测控制MPC模型;为提高不同工况下功率需求预测的精度,提出基于多步马尔科夫模型的功率预测模型,并根据实时工况识别结果,更新MPC模型中的功率需求预测模型约束,实现船舶能量实时动态优化。最后,采用小波变换方法,将最优功率解分解成高频信号和低频信号;再根据复合储能单元中不同动力源工作特性的差异,将高频信号和低频信号分别分配给具有高功率密度的超级电容和具有高能量密度的蓄电池。为验证方法的有效性,开展了基于MATLAB的仿真实验,结果表明:在相同工况下,所提策略累计燃油消耗量为4 404.556 1 g,平均燃油消耗率为202.973 7 g/kWh;与基于单一模型预测控制的能量管理策略相比,所提方法可节省燃油消耗4.55%,验证了所提能量管理策略的有效性。

增程式电动物流车队列的能量管理策略研究

以增程式电动物流车队列为研究对象,为提高整个队列的燃油经济性,从队列的协同自适应巡航控制和能量管理策略两方面进行研究。利用车对车通信和前车领航车跟随式通信拓扑结构,基于分布式模型预测控制,设计了以稳定性、舒适性和经济性为优化目标的协同自适应巡航控制器。将增程式电动物流车队列的能量管理策略描述为一个完全合作类型的多智能体强化学习问题,所有智能体共同探索在不同车辆状态下的最优控制动作,提出了基于多智能体强化学习的能量管理策略。仿真结果表明,所设计的生态协同自适应巡航控制策略能够有效地平衡车辆队列的稳定性和经济性。以动态规划为基准,与单智能体算法相比,基于多智能体深度确定性策略梯度算法的能量管理策略可以在显著提高学习速率的同时获得近似最优解。

基于模型预测控制的家庭能量管理优化调度方法研究

在分布式可再生能源大规模接入和分时电价实施的背景下,为降低电费成本、提高用户舒适度并提高可再生能源消纳率,提出了一种基于模型预测控制的家庭能量管理策略。建立由分布式光伏和各类用电负载等组成的家庭能量系统,分析各类设备的工作特性,提出相应的舒适度评价指标,特别针对空调这一典型功率可变负荷,结合建筑的热动态特性,建立室内温度预测模型。在建立家庭能量系统的基础上,使用遗传算法进行优化管理,并在模型预测控制框架下不断执行和更新。最后,实验对比结果表明,文中提出的基于模型预测控制的家庭能量管理策略可以有效实现能量的优化调度,并在预测不确定场景下具有较强鲁棒性。

基于需求功率预测的电动拖拉机能量管理策略

针对电动拖拉机在犁耕工况下电机需求电流波动比较大的特点,为了改善动力电池的输出电流过高或过低及电动拖拉机犁耕持续作业时间短的现象,利用超级电容高功率密度的特点,设计了一种锂电池+超级电容结构的双电源电动拖拉机,并建立了Amesim/Simulink联合仿真模型。以模型预测控制作为双电源系统的能量管理方法,基于长短期记忆神经网络建立电动拖拉机犁耕工况下的需求功率预测模型,使用动态规划算法求解最佳的锂电池输出电流。仿真结果表明:相比于模糊控制策略,基于模型预测控制策略有效降低了锂电池大电流放电的频率且峰值电流降低了40%,有效提高了锂电池的使用寿命;超级电容的SOC保持在比较高的范围内,且电动拖拉机在犁耕工况下的单位里程能量消耗降低了2.17%,实现了双电源电流分配最优,提高了电动拖拉机的动力性和经济性。

基于多层ReLU网络的楼宇暖通空调系统能量管理策略

提升现代智能楼宇的能量管理能力,是电力紧平衡背景下促进电网节能增效的重要举措。文中提出一种数据驱动的楼宇暖通空调系统节能控制策略,避免了传统模型预测控制对精确热力学建模的依赖。首先,在搭建楼宇热网络仿真模型的基础上,采用延时嵌入法构造输入特征,建立基于修正线性单元的多层神经网络模型,实现室内温度时间序列的多步预测。然后,针对电网的分时电价,构建滚动时域优化模型,并将其重构为混合整数线性规划的形式,实现有限控制周期内优化模型的高效求解。最后,基于Simscape搭建楼宇热仿真模型,验证了所提控制策略的有效性。仿真结果表明,所提策略能够满足温度舒适度要求,并提高楼宇的经济运行水平。

产品服务系统视角下建筑能源管理系统的设计介入路径 与策略研究

在双碳目标与建筑运行走向电气化智慧化的背景下,推广和应用能源管理系统已成为一种实现节能降耗、降本增效的有效途径。现有建筑能源管理系统研究多以节能技术创新为主,缺乏以用户为中心、兼顾用户舒适度等价值的视角,同时也面临场景差异化应用的挑战。本研究针对能源管理系统这种典型的B端设计问题,借助设计学科的产品服务系统视角研究面向B/C端应用层、采集感知层的设计介入路径,据此提出涉及劝导式设计、服务体验流程和系统创新等设计介入策略。通过典型能源管理系统案例分析验证上述策略,并探讨其相关设计方法,以提升能源管理系统的用户体验和节能目标。

分布式光伏发电接入智能电网功率预测模型优化研究

在可再生能源中,随着光伏发电量逐年增加,光伏发电的波动性可能对电网系统带来多种不利影响,准确的光伏发电量预测有益于确保电网的稳定运行。对光伏发电的直接预测进行了系统的阐述,讨论了输入输出数据的相关性以及模型输入数据预处理的重要性,基于不同类别的几种光伏功率预测模型进行性能分析,考虑了不同预测模型的优劣势并进行了评估。研究结果表明,经过优化的算法显著提高了模型的预测精度,遗传算法被认为是最可行的优化方法之一。

基于双向LSTM-Attention模型的火电厂负荷预测研究

准确预测电厂负荷可指导火电厂制定发电计划和调度安排,有利于降低能源成本和污染物排放,对电厂的经济性和环保性有重要意义。本文提出一种基于双向LSTM-Attention的火电厂负荷预测方法。首先,通过皮尔逊系数筛选出关键特征变量;其次利用双向长短期记忆网络提取关键变量之间的长期依赖关系与短期变化特征,最后融合注意力权重机制以进一步突出关键时序信息,进而实现负荷的准确预测。以某在役600 MW超临界机组为对象进行验证。结果表明:相较于单向LSTM、双向LSTM、单向LSTM-Attention,本文所提方法的决定系数R^(2)、均方根误差S_(RMSE)和平均绝对误差S_(MAE)均为最优,分别为0.9566、16.3159、13.5043,能更准确地捕捉到负荷快速波动的趋势,为电厂的负荷预测和能源管理提供可行的方法。

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

海上平台能源路由器母线电压控制策略研究

适用于海上平台能源路由器直流母线电压稳定性控制方法目前还不够完善,为了解决海上平台脉冲负载对能源路由器直流母线电压造成严重冲击的问题,引入混合储能系统,提出一种考虑电压波动系数的双向DC/DC变换器功率预测控制策略及考虑混合储能能量限值的功率管理方法.经过仿真,系统的直流母线电压动态响应达到预期需求.搭建海上平台能源路由器实验平台,通过实验验证了该方案具有较高的动态响应及稳定性.