Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Development Strategy of Urban Cultural Scene to Promote the Upgrading of Regional Cultural Consumption in Shaanxi

Regional cultural patterns and characteristics play a positive role in economic and social development.By planning and constructing cultural amenities and creating cultural scenes,the spatial quality and quality of life in a region can be enhanced,facilitating the expansion of cultural consumption.Shaanxi,with its rich historical and cultural resources,positions the capital city of Xi’an as a“world historical city”,boasting a vast number of cultural amenities represented by“cultural facilities”,“cultural activities”,“cultural experiences”,and“cultural services”.The development of urban cultural scene,with the aim of promoting the upgrading of regional cultural consumption in Shaanxi,requires comprehensive planning and a multifaceted approach,particularly in integrating provincial cultural scenes,clarifying the positioning of cultural scenes,innovating cultural scene experience projects,creating cultural scene intellectual property(IP),and empowering cultural scenes through the application of science and technology.

Compensation/consumption hierarchical control strategy based on wind-solar-hydrogen coupling grid connection

In the process of grid-connected wind and solar power generation,there are problems of high rate of abandoning wind and light and insufficient energy.In order to solve these problems,we construct a grid-connected wind-solar hydrogen storage(alkaline electrolyzer(AE)-hydrogen storage tank-battery-proton exchange membrane fuel cell(PEMFC))coupled system architecture.A grid-connected compensation/consumption hierarchical control strategy based on wind-solar hydrogen coupling is proposed.During the grid-connected process of wind and solar power generation,the upper-level control allocates power reasonably to the hydrogen energy storage system by dispatching the power of wind and solar power generation.At the same time,the control strategy ensures that the pressure of the hydrogen storage tank is within the safety range limit,and the lower control completes the control of the duty cycle of the converter in the system.Due to the randomness of wind and light,the hydrogen energy storage system is divided into three working conditions,namely compensation,balance and consumption,and five working modes.The simulation results show that the hydrogen energy storage system compensates for 40%of the power shortage,and consumes 27.5%of the abandoned wind and solar energy,which improves the utilization rate of clean energy.

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

A Novel Method for the Application of the ECMS(Equivalent Consumption Minimization Strategy)to Reduce Hydrogen Consumption in Fuel Cell Hybrid Electric Vehicles

Fuel cell hybrid electric vehicles are currently being considered as ideal means to solve the energy crisis and global warming in today’s society.In this context,this paper proposes a method to solve the problem related to the dependence of the so-called optimal equivalent factor(determined in the framework of the equivalent consumption minimum strategy-ECMS)on the working conditions.The simulation results show that under typical conditions(some representative cities being considered),the proposed strategy can maintain the power balance;for different initial battery’s states of charge(SOC),after the SOC stabilizes,the fuel consumption is 5.25 L/100 km.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

A DIRECT METHOD IN OPTIMAL PORTFOLIO AND CONSUMPTION CHOICE

In this paper we use a direct method to solve the optimal portfolio andconsumption choice problem in the security market for a specific case,in which theutility function is of a given homogenous form, i.e. the so-called CRRA case. The ideacomes from the completion technique ever used in LQ optimal control.

Fuel Consumption Potential of the Pushbelt CVT

The efforts to further reduce fuel consumption of vehicles equipped with a pushbelt type Continuously Variable Transmission(CVT) focus on different sources of loss.In this paper the magnitude of these losses and their potential for reduction is described.Inside the CVT,the variator,its control strategy and the hydraulic actuation circuit can be distinguished as the main potentials.A major opportunity is offered by a new control strategy that takes the actual slip between belt and pulley as the control parameter.The resulting decrease of clamping forces on the pushbelt leads to a reduction of variator and actuation losses.Further potential is found in the hydraulic actuation circuit by an improved tuning of the power supply to the actual power requirement.Outside the CVT additional potential is found in start-stop functionality as supported by measures inside the transmission.The paper describes the theoretical background as well as practical fuel savings of up to 5.5% that were obtained in tests on vehicle level.Slip control adds an inherent robustness to the operation of the pushbelt and opens up the fuel saving potential of the CVT thus reinforcing its position as the benchmark for the near future.

Dynamic GM（1,1） Model Based on Cubic Spline for Electricity Consumption Prediction in Smart Grid

Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Using piecewise polynomial interpolation thought,this model can dynamically predict the general trend of time series data.Combined with low-order polynomial,the cubic spline interpolation has smaller error,avoids the Runge phenomenon of high-order polynomial,and has better approximation effect.Meanwhile,prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment.Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.

China's Petroleum Consumption and Petroleum Security

Petroleum consumption and petroleum security in China can directly affect the development of national economy. This article briefs the conditions in world's developed countries with respect to oil consumption and strategic oil reserves. Taking into account the quick increase in China's oil consumption this article proposes to increase the oil resources along with consumption reduction and avoidance of risks in order to safeguard China's oil supplies.

关于大食物安全战略背景下我国油料保障底线目标及对策的商榷

树立大食物观和实施大食物安全战略是我国新时代推动食物来源更多元、数量更充足、品质更营养、产业更高效、环境更友好、调控更自主、消费更健康、治理更有力的迫切需要。1978年我国实行改革开放以来,包括油料在内的主要农产品生产、贸易、消费发生了历史性巨变,人民生活水平不断提高,但也存在生产和消费结构失衡导致的慢性疾病增多、环境压力加大、安全风险升高等不容忽视的突出问题,其中油料和食用植物油近十多年处于既自给不足又消费超量的两难局面。基于我国自然资源禀赋和经济社会发展的实际,保障未来油料供给安全必须坚持“一靠油菜、二靠花生、多油并举、健康消费”的发展思路,通过综合施策在十年左右将国产植物油产量稳定提高到2000万吨,以确保居民食用油基本消费需求的安全自主供给底线,并不断改善油料产品质量、提高产业竞争力和促进脂质产品的科学健康消费。

融合工况预测的燃料电池汽车里程自适应等效氢耗最小控制策略

为有效地提高插电式燃料电池汽车的经济性,实现燃料电池和动力电池的功率最优分配,考虑到行驶工况、电池荷电状态(State of charge,SOC)、等效因子与氢气消耗之间的密切联系,制定融合工况预测的里程自适应等效氢耗最小策略.通过基于误差反向传播的神经网络来实现未来短期车速的预测,分析未来车辆需求功率变化,同时借助全球定位系统规划一条通往目的地的路径,智能交通系统便可获取整个行程的交通流量信息,利用行驶里程和SOC实时动态修正等效消耗最小策略中的等效因子,实现能量管理策略的自适应性.基于MATLAB/Simulink软件,搭建整车仿真模型与传统的能量管理策略进行仿真对比验证.仿真结果表明,采用基于神经网络的工况预测算法能够较好地预测未来短期工况,其预测精度相较于马尔可夫方法提高12.5%,所提出的能量管理策略在城市道路循环工况(UDDS)下的氢气消耗比电量消耗维持(CD/CS)策略下降55.6%.硬件在环试验表明,在市郊循环工况(EUDC)下的氢气消耗比CD/CS策略下降26.8%,仿真验证结果表明了所提出的策略相比于CD/CS策略在氢气消耗方面的优越性能,并通过硬件在环实验验证了所提策略的有效性.

考虑驾驶风格的混合动力汽车强化学习能量管理策略

为了提升混合动力汽车能量管理策略对不同风格驾驶员的适应性,基于深度强化学习和等效燃油消耗最小策略(equivalent consumption minimization strategy,ECMS),提出一种考虑驾驶风格的混合动力汽车能量管理策略。通过实车试验采集驾驶员行驶数据,基于采集数据进行驾驶员驾驶风格的聚类分析,建立驾驶风格识别模型;构建基于强化学习和ECMS的能量管理策略,将驾驶风格系数作为强化学习状态变量,利用多种驾驶风格的组合工况训练深度确定性策略梯度智能体,获取不同工况和驾驶风格下ECMS等效因子,采用ECMS求解最优发动机、电机转矩分配以及变速箱挡位;搭建硬件在环测试平台,并基于实际采集的不同驾驶员驾驶数据构建测试工况,验证所提出控制策略的有效性。研究结果表明,相较于基于规则策略、基于等效因子比例修正的自适应ECMS以及DRL-SAC策略,提出的考虑驾驶风格的强化学习能量管理策略使整车能量消耗分别降低16.35%、11.11%和7.56%,所提控制策略的有效性得到了验证。

考虑冬季供暖的风电—CSP电站联合参与现货市场运行策略

随着“双碳”目标的提出,以风电为代表的可再生能源参与电力现货市场已是大势所趋。但由于具有不确定性和波动性,风电在市场中常处于不利地位。风电与具有灵活调节能力的光热电站(Concentrated Solar Power,CSP)联合能够减少实时出力偏差,进而降低不平衡成本。基于此,本文针对风电—CSP电站联合参与现货市场的运行策略开展研究。首先,对风电—CSP电站联合参与现货市场的机理进行分析,在此基础上,以经济性最优为目标,综合考虑供电收益、冬季供暖收益和不平衡惩罚等因素,提出了考虑冬季供暖的风电—CSP电站联合参与电力现货市场运行策略,并基于Shapley值法对联盟收益进行分配,最后分析了储热容量对联盟收益的影响。算例表明所提联合运行策略能够充分利用CSP电站灵活性,显著提高双方收益,减少弃风损失。

某酒店制冷机房运行策略优化

对华东地区某酒店制冷机房运行策略进行了优化,利用能耗模拟软件对多种运行方案进行了仿真分析。结果表明:原方案仅对冷水泵采用变频控制,制冷机房能效比为4.62;优化方案1采取传统变频方式,对冷水泵、冷却水泵、冷却塔风机采用变频控制,制冷机房能效比达到5.32,与原方案相比提升了15.1%;优化方案2通过对冷水机组、冷水泵、冷却水泵、冷却塔风机进行优化控制,制冷机房能效比为5.48,与原方案相比能效比提升了18.6%,且每年可减少约79 t碳排放量,节能效果显著。

基于充电行为电量规划的自适应能量管理策略

为提高插电式混合动力汽车(PHEV)的燃油经济性,以并联式PHEV为研究对象,考虑驾驶员充电行为,提出基于充电行为电量规划和等效燃油消耗最小策略(ECMS)的自适应能量管理策略。构建了包含不同类型工况的组合工况。根据驾驶员的充电行为特征(充电频率、充电上限、放电下限)进行充电行为分析,并进行4类工况电量消耗与燃油消耗ECMS策略下的离线仿真数据获取。根据驾驶员充电行为以及离线数据进行SOC轨迹的规划。结果表明:在电池SOC初始值分别为0.9、0.6时,提出的能量管理策略相较于基于电量消耗维持的等效燃油消耗策略,整车燃油经济性分别提升7.87%和13.1%。

计及负荷预测的风光柴储联供型微网系统的运行优化

微电网系统优化运行能够有效推进落实“双碳战略”,但微电网运行时未考虑负荷的动态变化及运行的多目标特性,限制了系统运行优化和可再生能源的消纳能力。针对该问题,建立由运行成本费用和环境治理费用构成的微电网总运行成本最低,以及可再生能源消纳最大化的多目标优化运行模型;针对电网负荷的动态变化,采用Holt-winters模型预测微电网系统负荷变化;最后用改进的多目标粒子群算法对微电网模型进行求解。为验证算法的可行性,以IEEE数据库中高比例新能源HRP-38标准系统和华东地区微电网为研究对象。算例表明,计及负荷预测下两类微电网系统中风电利用率分别提高了8.13%、5.23%,光伏利用率分别提高了8.59%、6.49%,系统总运行成本分别降低了8.29%、8.89%。因此,所提方法既能有效提高系统对可再生能源的消纳能力,又能降低系统的总运行成本,同时实现环境效益最大化,对微电网运行具有指导意义。

地铁活塞效应的节能综述研究

随着地铁行业的蓬勃发展,利用活塞风降低车站能耗成为目前研究的热点之一。首先整理与活塞效应相关的国内外文献,研究活塞效应的形成机理和特性,分析活塞效应对地铁环控系统产生的影响,发现两者间潜在的节能关系。其次分别对隧道活塞风与车站活塞风的利用策略进行综述研究,这些策略主要与通风井的相关设计、列车的行驶、站台门的选择以及门帘的安装等有关,合理地采用这些策略可以为车站降低能耗。最后提出了活塞风的利用原则,在活塞风满足乘客需求的情况下应当优先利用活塞通风,应根据风量、温湿度、二氧化碳浓度以及颗粒物浓度等参数评价活塞风是否满足需求。

韩国吸引境外医疗消费的国际化战略及中国镜鉴

韩国医疗旅游的繁荣得益于其先进的医疗技术、优质的医疗服务、完善的服务流程、卓越的国际声誉、政府的政策支持以及丰富的旅游体验等,使其在全球医疗旅游市场中占据重要地位。为了吸引境外医疗消费,韩国积极实施以国际战略为导向,打造医疗消费中心、以政策法规为保障,发挥政府职能作用、以服务质量为根本,致力医疗品牌打造、以宣传营销为抓手,提升医疗国际形象、以培养引进为途径,加强人才队伍建设、以国际合作为契机,拓展境外医疗市场、以旅游资源为依托,吸引境外医疗游客等国际化战略。我国可以借鉴韩国的有益经验,实施加强法规与政策支持、提升医疗服务品质、实施合理的国际化战略、拓展医疗旅游市场等措施,吸引境外医疗消费。