Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

Demand and Capacity Modelling in Healthcare Using Discrete Event Simulation

The NHS is right now confronting huge pressures relating to demand and capacity in radiology. The purpose of this research has been to provide information about MRI usage, details of operational aspects of MRI services, and to ascertain the planning intentions of NHS radiology services to keep up and create MRI capacity. The report expands on using Discrete Event Simulation (DES) to inspect and plan the utilisation of NHS hospital resources for the radiology department to help a 24 hr service that is available to outpatients which will help with diminishing patient waiting time, better resource usage, understanding the capacity and demand. Consequently, this research examines to adjust staff and resources with the demand of the MRI. The research was investigated using DES in various scenarios to find which resources are inactive;patients are treated slowly. DES helped in discovering resource utilisation and outpatient throughout the system. It additionally helped in distinguishing the bottlenecks in patient flow. The DES simulation results demonstrated that time for the outpatient in the system is less and more outpatients have been treated too. There is a higher level of outpatient patients leaving the system under 120 minutes. The report uncovered an MRI report interpretation time. Reception room time and MRI waiting room time are decreased significantly. It additionally exhibited with an expanded outflow of outpatients, resources, for example, MRI capacity and radiographer utilisation expanded.

The 3D simulation and optimized management model of groundwater systems based on eco-environmental water demand

Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item and unification of groundwater抯 economic, environmental and ecological functions were taken into account. Based on eco-environmental water demand at Da抋n in Jilin province, a three-dimensional simulation and optimized management model of groundwater systems was established. All water balance components of groundwater systems in 1998 and 1999 were simulated with this model and the best optimal exploitation scheme of groundwater systems in 2000 was determined, so that groundwater resource was efficiently utilized and good economic, ecologic and social benefits were obtained.

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.

Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach

This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.

Numerical Study on the Suitability of Passive Solar Heating Technology Based on Differentiated Thermal Comfort Demand

Indoor thermal comfort and passive solar heating technologies have been extensively studied.However,few studies have explored the suitability of passive solar heating technologies based on differentiated thermal comfort demands.This work took the rural dwellings in Northwest China as the research object.First,the current indoor and outdoor thermal environment in winter and the mechanism of residents’differentiated demand for indoor thermal comfort were obtained through tests,questionnaires,and statistical analysis.Second,a comprehensive passive optimized design of existing buildings was conducted,and the validity of the optimized combination scheme was explored using DesignBuilder software.Finally,the suitability of passive solar heating technology for each region in Northwest China was analyzed based on residents’differentiated demand for indoor thermal comfort.The regions were then classified according to the suitability of the technology for these.The results showed that the indoor heating energy consumption was high and the indoor thermal environment was not ideal,yet the solar energy resources were abundant.Indoor comfort temperature indexes that match the functional rooms and usage periods were proposed.For the buildings with the optimized combination scheme,the average indoor temperature was increased significantly and the temperature fluctuation was decreased dramatically.Most regions in Northwest China were suitable for the development of passive solar heating technology.Based on the obtained suitability of the technology for the regions of Northwest China,these were classified into most suitable,more suitable,less suitable,and unsuitable regions.

Integrated optimal method for cell formation and layout problems based on hybrid SA algorithm with fuzzy simulation

To adapt to the complex and changeable market environment,the cell formation problems(CFPs) and the cell layout problems(CLPs) with fuzzy demands were optimized simultaneously. Firstly,CFPs and CLPs were described formally. To deal with the uncertainty fuzzy parameters brought,a chance constraint was introduced. A mathematical model was established with an objective function of minimizing intra-cell and inter-cell material handling cost. As the chance constraint of this problem could not be converted into its crisp equivalent,a hybrid simulated annealing(HSA) based on fuzzy simulation was put forward. Finally,simulation experiments were conducted under different confidence levels. Results indicated that the proposed hybrid algorithm was feasible and effective.

The Identification of Peak Period Impacts When a TMY Weather File Is Used in Building Energy Use Simulation

When typical meteorological year (TMY) data are used as an input to simulate the energy used in a building, it is not clear which hours in the weather data file might correspond to an electric or natural gas utility’s peak demand. Yet, the determination of peak demand impacts is important in utility resource planning exercises and in determining the value of demand-side management (DSM) actions. We propose a formal probability-based method to estimate the summer and winter peak demand reduction from an energy efficiency measure when TMY data and model simulations are used to estimate peak impacts. In the estimation of winter peak demand impacts from some example energy efficiency measures in Texas, our proposed method performs far better than two alternatives. In the estimation of summer peak demand impacts, our proposed method provides very reasonable results which are very similar to those obtained from the Heat Wave approach adopted in California.

Simulation for Water Quality Management in Inohana Lake Estuary, Japan

The Inohana Lake is a branch lake of the Hamana Lake. The Inohana Lake is an estuary rather than a brackish lake, and has suffered environmental problems such as eutrophication and bottom hypoxic water. In this study, the coupled hydrodynamic and ecological models （eco-hydrodynamic model） were used to construct the strategy for preventing the bottom hypoxic water and improving or recovering the water quality in the lake. Using the model input obtained from the summertime data over 1998-2002, the summer-average flow field and oxygen concentration and budget of the standard run were calculated. Remedial measures used in this study are divided into two parts： the biogeochemical and physical changes in the present situation. For the remedial measures including the biogeochemical changes in the present situation, the simulations considering the reductions of the nutrient inputs from the river, main lake （land） and bottom sediment, and the sediment oxygen demand （SOD） were carried out. For the remedial measures including the physical changes, the 50 and 100 m extensions of the inlet width were considered in the model runs. These simulated results were compared in terms of changes in the dissolved oxygen （DO） concentration and oxygen budget in the bottom layer in the Inohana Lake. There was no significant change in the DO concentration and oxygen stock in the simulations for the reduction of the nutrient inputs from the land and bottom sediment, however increases in those in the simulations for the reduction of SOD. When SOD was reduced by 50%, the bottom DO concentration increased by approximately 2 mg/L and the oxygen stock in the bottom layer increased by 47% comparing the present situation （the standard run） of the lake. The simulation results for inlet width extension showed that the extension of width makes the DO concentration and oxygen stock lower. The remedial measures for the sediment control were proposed to prevent the bottom hypoxia and manage the water quality.

A Predictive Nighttime Ventilation Algorithm to Reduce Energy Use and Peak Demand in an Office Building

The effect of two nighttime ventilation strategies on cooling and heating energy use is investigated for a prototype office building in several northern America climates, using hourly building energy simulation software （DOE2.1E）. The strategies include： scheduled-driven nighttime ventilation and a predictive method for nighttime ventilation. The maximum possible energy savings and peak demand reduction in each climate is analyzed as a function of ventilation rate, indoor-outdoor temperature difference, and building thermal mass. The results show that nighttime ventilation could save up to 32% cooling energy in an office building, while the total energy and peak demand savings for the fan and cooling is about 13% and 10%, respectively. Consequently, finding the optimal control parameters for the nighttime ventilation strategies is very important. The performance of the two strategies varies in different climates. The predictive nighttime ventilation worked better in weather conditions with fairly smooth transition from heating to cooling season.

Determination of Effectiveness of Energy Management System in Buildings

Building Energy Management Systems(BEMS)are computer-based systems that aid in managing,controlling,and monitoring the building technical services and energy consumption by equipment used in the building.The effectiveness of BEMS is dependent upon numerous factors,among which the operational characteristics of the building and the BEMS control parameters also play an essential role.This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS.The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions.The simulation is developed using Visual Basic Application(VBA)in Microsoft Excel,based on discrete-event Monte Carlo Simulation(MCS).The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model.Further,during the second simulation,the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case.The model compares the energy consumption from the first simulation and the second simulation.Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5(1 being poor effectiveness and 5 being excellent effectiveness of BEMS).This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

The Application of Information Systems to Improve Ambulance Response Times in the UK

Emergency ambulance services in the UK are tasked with providing pre-hospital patient care and clinical services with a target response time between call connect to on-scene attendance.In 2017,NHS England introduced four new response time categories based on patient needs.The most challenging is to be on-scene for a life-threatening situation within seven minutes of the call being connected when such calls are random in terms of time and place throughout a large territory.Recent evidence indicates emergency ambulance services regularly fall short of achieving the target ambulance response times set by the National Health Service(NHS).To achieve these targets,they need to undertake transformational change and apply statistical,operations research and artificial intelligence techniques in the form of five separate modules covering demand forecasting,plus locate,allocate,dispatch,monitoring and re-deployment of resources.These modules should be linked in real-time employing a data warehouse to minimise computational data and generate accurate,meaningful and timely decisions ensuring patients receive an appropriate and timely response.A simulation covering a limited geographical area,time and operational data concluded that this form of integration of the five modules provides accurate and timely data upon which to make decisions that effectively improve ambulance response times.

新基建视域下物联网专业实验教学体系研究

建立科教协同的实验教学平台,推动智能实验室建设是新基建的重点方向。物联网工程作为支撑国家战略性新兴产业发展的新工科专业,其实验教学在人才的培养过程中至关重要。然而,现有实验教学体系受资金、场地、师资力量、网络条件等因素制约,很难直观呈现规模化、创意化物联网实验,尤其是过程化物联网综合实验。重点研究了新基建视域下如何构建符合物联网产业需求和人才培养的实验教学过程和实验教学体系。通过对物联网专业课程结构和人才需求进行分析,结合学校人才培养定位和物联网产业链现状,讨论了现有实验教学平台的不足。同时,以物联网的技术体系结构为主线,对物联网实验教学知识点进行了梳理,提出了一种虚实结合的物联网实验教学平台建设方案。提出的实验平台能够满足基础实验、工程实训、创新创客、科研辅助及行业应用等需求。

后疫情时代医学生突发公共卫生事件应急能力的问卷调查及教学探讨

目的:调查医学生突发公共卫生事件的应急能力,为后期课程改革提供依据。方法:选用信度和效度均较高的EPIQ改良问卷进行调查,采用整群随机抽样的方法选取2018级医学各专业学生409名,比较不同性别、专业突发公共卫生事件预防能力、准备能力、救援能力和应急能力情况。结果:409名医学生中突发事件应急能力调查合格率为64.79%;不同性别、专业应急能力合格率差异无统计学意义(P>0.05);准备能力和救援能力合格率较低,分别为68.22%和64.30%,预防能力合格率较高,为89.49%。分条目分析发现,除手卫生的正确实施得分较高外,其他各项目得分水平中等。结论:临床医学生突发事件应急能力水平整体偏下,主观教学需求较大,教学形式需求多样化,以技能操作和情境模拟训练为主。

简易续约规则对谈判药品价格和持续供应的影响:现象及可行性方案探究

目的:探索在2022年医保目录简易续约背景下是否存在一类特殊现象,即部分药品会不断触发连续降价条件以致最终退出医保目录。方法:本文通过构建需求价格弹性模型,结合医保目录谈判和续约规则进行数值模拟,对多轮简易续约情况进行讨论,以验证简易续约规则是否影响药品可持续供应。最后,本文对可行性方案的适用条件进行数理分析。结果:在2022年简易续约规则下,市场上确实存在一类药品,当其首次医保谈判药品降价幅度和其弧弹性满足一定条件时,会陷入无限降价的困境。该现象可能是由于企业错估药品需求或是进入医保目录后药品销量飙升,量价线性关系被打破。减少续约次数和重新谈判能够降低此类现象发生的可能性。结论:2023年简易续约规则一定程度上避免了此类现象的发生,后续政策可将阶梯型降价标准改为连续型。

SOD_M、DTA-6和Cc对大豆生育中后期功能叶片生理特性的影响

在大田栽培条件下，以垦农4号为材料，通过叶面喷施植物生长调节剂SOD模拟物（SODM）、2-N，N-二乙氨基乙基己酸酯（DTA-6）、氯化胆碱（Cc），研究其对大豆生育中后期功能叶片某些生理特性的影响。结果表明，喷施后5～20d，SODM、DTA-6、Cc处理提高了叶片的Chla、Chlb及Chl（a＋b）含量，降低了Chla／Chlb比值，其中SODM调控效果最佳。SODM、DTA-6提高了叶片可溶性蛋白质和可溶性糖含量。SODM、DTA-6、Cc处理在多数测定时期内均提高了叶片SOD和POD活性，其中DTA-6对SOD活性的调控作用最强，SODM次之；SODM对POD活性调控效果最佳，DTA-6次之。同时，DTA-6和SODM提高了叶片CAT活性，减缓了MDA含量的升高，而Cc对上述多项指标的调控作用均不明显。综合分析表明，叶面喷施DTA-6和SODM，调节了大豆生育后期功能叶片的生理代谢水平，延缓了大豆植株的衰老进程，提高了大豆产量。

面向供需协同的城际通道高速列车开行方案优化研究

探讨城际通道短距离多线路列车优化问题,建立城际通道高速列车开行方案优化模型,提升列车运输效益和服务水平。针对城际通道线路特点,以多线路、多列车类型为基础构造列车流路径网络;分析旅客出行费用偏差,将客票费用偏差和旅行时间费用偏差相结合考虑,通过引入旅客对不同因素的容忍度参数,提出旅客出行需求与运输供给的协同偏差,进而建立供需协同的城际通道列车开行方案优化模型。以模拟退火算法为基础,设计模型求解算法。最后通过算例进行仿真验证,结果表明,利用该方法得到的开行方案,能在满足旅客需求条件下,在列车运输效率、运营收益和服务质量方面得到一定程度的提高,从而为高速铁路列车开行方案优化提供新思路。

角蛋白组氨酸金属结合体的制备及其对SOD酶的模拟

本文将羽毛角蛋白(FK)与组氨酸(His)复合,再与金属离子M(Cu^(II)、Zn^(II)、Ni^(II)、Mn^(II)络合成功制得了角蛋白组氨酸金属结合体FK-HisM_x(M=Cu、Zn、Ni、Mn)。采用紫外光谱(UV-Vis)、红外光谱(IR)、圆二色谱(CD)、热重分析(TGA)、扫描电镜(SEM)等手段对FK-HisM_x(M=Cu、Zn、Ni、Mn)的结构进行了表征。用核黄素/DL-甲硫氨酸-NBT还原法考察了FK-HisM_x对超氧阴离子自由基(O_2·^-)的清除作用,研究了其清除机理。结果发现:FK-His M_x均具有清除O_2·^-的能力。其中,FK-HisCu_x具有较高的O_2·^-清除活性,FK-HisCu_(60)的O_2·^-清除活性最好,其抗O_2·^-的EC_(50)值为0.0115μmol·L^(-1),对Cu,ZnSOD的模拟度高达356.5%。

混合超启发式算法求解复杂两级车辆路径问题

针对模糊需求下的绿色两级车辆路径问题,以最小化车辆运营成本和油耗成本之和为优化目标,提出一种混合超启发式算法进行求解.首先,考虑两级问题解空间庞大且相互耦合,设计一种聚类分解策略将该问题分解为多个子问题,以合理缩小问题搜索空间;然后,提出增强超启发式分布估计算法(enhanced hyperheuristic estimation of distribution algorithm,EHHEDA)对各个子问题进行求解,进而获得原问题的解.EHHEDA基于超启发式算法框架,在高层策略域设计一种基于三维概率模型的分布估计算法,动态确定由底层操作域中各搜索算子所组成的排列(即高层个体),可有效控制和引导整个算法的搜索行为;同时,在底层操作域设计10种有效邻域搜索算子,并加入重升温操作的模拟退火机制作为问题解(即底层个体)的接受准则,有利于在问题解空间中执行深入搜索.仿真实验结果表明,所提出的算法在大多数测试集上优于近年来用于求解类似问题的算法,验证了所提出算法的有效性.

采用改进北方苍鹰算法的微电网优化调度研究

微电网系统通常由多种分布式电源组成,为降低运行成本,常使用智能算法对微电网进行调度。智能算法在求解微电网调度模型时容易陷入局部最优解,导致求解精度差,因此在北方苍鹰算法的基础上,提出了一种混合策略改进的北方苍鹰算法(HNGO),利用反向学习、Metropolies准则以及自适应t分布变异提高求解精度,同时构建了考虑可再生能源出力特性的需求响应模型,使负荷曲线与可再生能源出力曲线更贴近,然后建立日运行成本最低的微电网优化调度模型,并利用HNGO求解。对比仿真结果显示所提算法具有更好的求解精度,且所提需求响应模型能显著降低燃料成本。