CN115081700A - Comprehensive energy storage technology-based data center multi-energy collaborative optimization method and system - Google Patents
Comprehensive energy storage technology-based data center multi-energy collaborative optimization method and systemDownload PDFInfo
- Publication number
- CN115081700A CN115081700ACN202210654961.9ACN202210654961ACN115081700ACN 115081700 ACN115081700 ACN 115081700ACN 202210654961 ACN202210654961 ACN 202210654961ACN 115081700 ACN115081700 ACN 115081700A
- Authority
- CN
- China
- Prior art keywords
- energy
- data center
- cold
- time
- optimization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明涉及综合能源系统优化技术领域，具体涉及一种基于综合储能技术的数据中心多能协同优化方法及系统。The invention relates to the technical field of comprehensive energy system optimization, in particular to a multi-energy collaborative optimization method and system for a data center based on a comprehensive energy storage technology.
背景技术Background technique
截止2020年底，我国数据中心机架已超过400万架，近5年来，保持近30％的平均增速。预计到2024年，我国数据中心总耗电量占当年全国电力消耗总量的5％～8％。数据中心成为重要的典型电力用户形式。在“双碳”及“构建以新能源为主体的新型电力系统”背景下，微电网、分布式能源将快速发展，数据中心在能源供应形式和安全保障等方面将面临新挑战。储能系统(电池储能、暖通储能)对于数据中心具有可作为后备应急能源、促进本地可再生能源消纳、参与“削峰填谷”、减少运行成本等重要作用。By the end of 2020, the number of data center racks in my country has exceeded 4 million, maintaining an average growth rate of nearly 30% in the past five years. It is estimated that by 2024, the total power consumption of data centers in my country will account for 5% to 8% of the total national power consumption in that year. Data centers have become an important form of typical electricity users. In the context of "dual carbon" and "building a new power system with new energy as the main body", microgrids and distributed energy will develop rapidly, and data centers will face new challenges in terms of energy supply and security. The energy storage system (battery energy storage, HVAC energy storage) plays an important role in the data center as backup emergency energy, promoting local renewable energy consumption, participating in “peak shaving and valley filling”, and reducing operating costs.
目前，数据中心普遍存在能源系统的能耗高、系统配置单一且能源系统经济性有很大提升空间等问题，对于含有多种能源技术的数据中心综合能源系统规划优化、运行优化还少有深入研究。当前数据中心综合能源系统优化往往只考虑应急水蓄冷、应急电池储能技术，而缺乏对于数据中心复杂运行动态和多场景的考虑，针对目前配置更加多样化的新型储能系统的数据中心缺少优化分析。At present, data centers generally have problems such as high energy consumption of energy systems, single system configuration, and great room for improvement in energy system economy. There is little in-depth planning and optimization of comprehensive energy system planning and operation optimization of data centers containing multiple energy technologies. Research. The current data center comprehensive energy system optimization often only considers emergency water cooling and emergency battery energy storage technologies, but lacks consideration of the complex operation dynamics and multiple scenarios of the data center, and lacks optimization for the current data centers with more diversified new energy storage systems. analyze.
同时，利用储电、储冷等储能技术，在低谷电价时间段进行储能，在高峰电价时间段向系统释放能量，从而降低系统的用能成本。但是，如果储能容量配置过大，容易造成投资过大，不易收回设备投资；储能容量配置过小，则没有充分利用本地资源。且储电和储冷也分别具有不同的经济特性，因此，如何合理优化配置储电、储冷系统，并与数据中心用电系统进行耦合优化，是一个比较大的难题。At the same time, energy storage technologies such as electricity storage and cold storage are used to store energy during the low electricity price period, and release energy to the system during the peak electricity price period, thereby reducing the energy cost of the system. However, if the energy storage capacity configuration is too large, it is easy to cause excessive investment, and it is difficult to recover the equipment investment; if the energy storage capacity configuration is too small, the local resources are not fully utilized. In addition, electricity storage and cooling storage also have different economic characteristics. Therefore, how to reasonably optimize the configuration of electricity storage and cooling storage systems, and how to optimize the coupling with the data center power system is a relatively big problem.
因此，急需要解决目前关于数据中心综合能源系统的研究存在未从系统角度研究数据中心采用储电和储冷等技术路径比选和效益分析、储能系统优化配置方法、数据中心综合能源系统运行策略优化等问题。Therefore, there is an urgent need to solve the current research on the comprehensive energy system of the data center. There is no systematic research on the technology path comparison and benefit analysis of the data center using electricity storage and cooling storage, the optimal configuration method of the energy storage system, and the operation of the integrated energy system of the data center. policy optimization, etc.
发明内容SUMMARY OF THE INVENTION
本申请提供了一种基于综合储能技术的数据中心多能协同优化方法及系统，用于解决现有技术中数据中心在同时考虑应急蓄冷、应急储电、调峰蓄冷、调峰储电的综合能源系统优化设计与调度问题。The present application provides a multi-energy collaborative optimization method and system for a data center based on a comprehensive energy storage technology, which is used to solve the problem that the data center in the prior art simultaneously considers emergency cold storage, emergency power storage, peak-shaving cold storage, and peak-shaving power storage. Optimal design and scheduling of integrated energy systems.
本发明提供了一种基于综合储能技术的数据中心多能协同优化方法，包括：The present invention provides a multi-energy collaborative optimization method for a data center based on comprehensive energy storage technology, including:
S1：构建数据中心综合能源系统的各个设备的数学模型，所述设备包括蓄冷设备、电池储能设备、供电设备、供冷设备和供热设备中的一种或多种；S1: construct a mathematical model of each device of the comprehensive energy system of the data center, the device includes one or more of cold storage device, battery energy storage device, power supply device, cooling device and heating device;
S2：根据所述数学模型，构建数据中心综合能源系统以碳排放为导向的目标函数和以经济性为导向的目标函数；S2: According to the mathematical model, construct a carbon emission-oriented objective function and an economy-oriented objective function of the comprehensive energy system of the data center;
S3：根据能量平衡原理和数据中心综合能源系统的工程实际确定约束条件；S3: Determine the constraints according to the energy balance principle and the engineering practice of the integrated energy system of the data center;
S4：根据所述以碳排放为导向的目标函数、所述以经济性为导向的目标函数和所述约束条件，建立多目标优化模型；S4: Establish a multi-objective optimization model according to the carbon emission-oriented objective function, the economy-oriented objective function and the constraint conditions;
S5：进行优化求解多目标优化模型；S5: Perform optimization to solve the multi-objective optimization model;
S6：分析求解结果是否符合工程实际，若否，则调整相关设备参数与约束条件后，返回执行步骤S5，若是，则执行步骤S7；S6: analyze whether the solution result conforms to the actual engineering, if not, after adjusting the relevant equipment parameters and constraints, return to step S5, if yes, execute step S7;
S7：根据分析结果得到最优优化模型，进行数据中心综合能源系统的优化设计和调度。S7: Obtain the optimal optimization model according to the analysis results, and carry out the optimal design and scheduling of the comprehensive energy system of the data center.
优选地，所述步骤S1具体包括：Preferably, the step S1 specifically includes:
构建蓄冷设备数学模型：Build a mathematical model of cold storage equipment:
其中，Q(t)表示蓄冷设备t时刻的蓄冷量，Q(t-1)表示蓄冷设备t-1时刻的蓄冷量。Pin(t)和Pout(t)分别表示蓄冷设备t时刻的蓄冷功率和放冷功率。ηin和ηout分别表示蓄冷设备的蓄冷效率和放冷效率，Δt表示计算时间间隔；Here, Q(t) represents the cool storage capacity of the cool storage facility at time t, and Q(t-1) represents the cool storage capacity of the cool storage facility at time t-1. Pin (t) and Pout (t) represent the cool storage power and the cool discharge power of the cool storage device at time t, respectively. ηin and ηout represent the cold storage efficiency and cooling efficiency of the cold storage equipment, respectively, and Δt represents the calculation time interval;
构建电池储能设备数学模型：Build a mathematical model of a battery energy storage device:
E(t)＝E(t-1)+(Pc(t)ηc-Pd(t)/ηd)ΔtE(t)=E(t-1)+(Pc (t)ηc -Pd (t)/ηd )Δt
其中，E(t)表示电池储能设备t时刻的总能量，E(t-1)表示电池储能设备t-1时刻的总能量，Pc(t)表示电池储能设备t时刻的充电功率，Pd(t)表示电池储能设备t时刻的放电功率，ηc表示电池储能设备的充电效率，ηd表示电池储能设备的充电效率，Δt表示计算时间间隔；Among them, E(t) represents the total energy of the battery energy storage device at time t, E(t-1) represents the total energy of the battery energy storage device at time t-1, and Pc (t) represents the charging of the battery energy storage device at time t Power, Pd (t) represents the discharge power of the battery energy storage device at time t, ηc represents the charging efficiency of the battery energy storage device, ηd represents the charging efficiency of the battery energy storage device, and Δt represents the calculation time interval;
构建供电设备、供冷设备和供热设备数学模型：Build mathematical models of power, cooling, and heating equipment:
其中，i表示能源转换设备，t表示时刻，P、C、H分别表示电能、冷能、热能，in、out分别表示输入和输出，
优选地，所述步骤S2包括：Preferably, the step S2 includes:
根据所述数学模型，建立数据中心综合能源系统以碳排放为导向的目标函数minCO2emission：According to the mathematical model, the carbon emission-oriented objective function minCO2 emission of the integrated energy system of the data center is established:
其中，t表示时刻，eele表示大电网中生产一个单位电能所排放的CO2量，egas表示燃烧一单位天然气所排放的CO2量，
优选地，所述步骤S2包括：Preferably, the step S2 includes:
根据所述数学模型，建立数据中心综合能源系统以经济性为导向的目标函数cost：According to the mathematical model, the economic-oriented objective function cost of the comprehensive energy system of the data center is established:
其中，cost表示总成本，k表示设备编号常数，c表示成本，t表示时刻，dt表示时间变量，inv表示初投资年化成本，om表示运维成本，
优选地，所述步骤S3包括：Preferably, the step S3 includes:
确定电、冷、热系统的能量平衡约束：Determine the energy balance constraints for electric, cooling, and heating systems:
其中，t表示时刻，i表示能源转换设备，k表示设备编号常数，
优选地，所述步骤S3包括：Preferably, the step S3 includes:
确定蓄冷设备约束：Determine cold storage device constraints:
Qmin≤Q(t)≤QmaxQmin ≤Q(t)≤Qmax
μin(t)+μout(t)≤1μin (t)+μout (t)≤1
其中，t表示时刻，Qmin表示蓄冷设备最小蓄冷容量，Qmax表示蓄冷设备最大蓄冷容量，Q(t)表示蓄冷设备t时刻的蓄冷量，Vmax表示数据中心允许安装蓄冷罐的最大体积，ρ表示蓄冷水密度，取1000kg/m3，Cp表示冷水的比热容，取值4.18kJ/(kg·℃)，η表示蓄冷罐的有效利用体积，Δt表示供回水温度差，取5～7℃，Pin(t)表示蓄冷设备t时刻的蓄冷功率，Pout(t)表示蓄冷设备t时刻的放冷功率，Pin和
优选地，所述步骤S3包括：Preferably, the step S3 includes:
确定电池储能设备约束：Determine battery energy storage device constraints:
Emin≤E(t)≤EmaxEmin ≤E(t)≤Emax
Bc(t)+Bd(t)≤1Bc (t)+Bd (t)≤1
其中，t表示时刻，E(t)表示电池储能设备t时刻的总能量，Emin、Emax分别表示电池储能设备最小和最大储电容量，Pc(t)、Pd(t)分别表示电池储能设备t时刻的充电和放电功率，
优选地，所述步骤S5包括：Preferably, the step S5 includes:
进行优化求解多目标优化模型的方法为：The optimization method to solve the multi-objective optimization model is as follows:
使用计算机软件中设定平台编写优化问题程序，调用设定平台中内嵌的单纯型法求解器进行优化求解多目标优化模型。Use the setting platform in the computer software to write the optimization problem program, and call the simplex method solver embedded in the setting platform to optimize and solve the multi-objective optimization model.
本发明提供一种基于综合储能技术的数据中心多能协同优化系统，其特征在于，包括：The present invention provides a data center multi-energy collaborative optimization system based on comprehensive energy storage technology, which is characterized by comprising:
数学模型构建模块，用于构建数据中心综合能源系统的各个设备的数学模型，所述设备包括蓄冷设备、电池储能设备、供电设备、供冷设备和供热设备中的一种或多种；Mathematical model building module, used to construct mathematical models of various equipments of the comprehensive energy system of the data center, the equipments include one or more of cold storage equipment, battery energy storage equipment, power supply equipment, cooling equipment and heating equipment;
目标函数构建模块，用于根据所述数学模型，构建数据中心综合能源系统以碳排放为导向的目标函数和以经济性为导向的目标函数；an objective function building module, used for constructing a carbon emission-oriented objective function and an economy-oriented objective function of the comprehensive energy system of the data center according to the mathematical model;
约束条件构建模块，用于根据能量平衡原理和数据中心综合能源系统的工程实际确定约束条件；Constraints building module, used to determine constraints according to the energy balance principle and the engineering practice of the integrated energy system of the data center;
多目标优化模型建立模块，用于根据所述以碳排放为导向的目标函数、所述以经济性为导向的目标函数和所述约束条件，建立多目标优化模型；a multi-objective optimization model establishment module, configured to establish a multi-objective optimization model according to the carbon emission-oriented objective function, the economy-oriented objective function and the constraint conditions;
多目标优化模型求解模块，用于进行优化求解多目标优化模型；The multi-objective optimization model solving module is used to optimize and solve the multi-objective optimization model;
多目标优化模型分析模块，用于分析求解结果是否符合工程实际，若否，则调整相关设备参数与约束条件后，再进行优化求解多目标优化模型，若是，则将分析结果发送给优化设计和调度模块；The multi-objective optimization model analysis module is used to analyze whether the solution results conform to the actual engineering. If not, after adjusting the relevant equipment parameters and constraints, the optimization is performed to solve the multi-objective optimization model. If so, the analysis results are sent to the optimization design and scheduling module;
优化设计和调度模块，用于根据分析结果得到最优优化模型，进行数据中心综合能源系统的优化设计和调度。The optimization design and scheduling module is used to obtain the optimal optimization model according to the analysis results, and carry out the optimal design and scheduling of the comprehensive energy system of the data center.
本发明提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机软件产品，所述计算机软件产品包括的若干指令，用以使得一台计算机设备执行所述一种基于综合储能技术的数据中心多能协同优化方法。The present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer software product, and the computer software product includes several instructions to enable a computer device to execute the integrated energy storage-based Technology's data center multi-energy collaborative optimization approach.
与现有技术相比，本发明具有如下的有益效果：Compared with the prior art, the present invention has the following beneficial effects:
1)在现有的大多数只考虑数据中心供电、应急蓄冷、应急储电的基础上，同步增加考虑了调峰蓄冷、调峰储电等内容，从规划层面为数据中心综合能源系统提供最优优化模型，进一步降低系统经济成本，降低二氧化碳排放；从运行层面，提出数据中心综合能源系统优化设计和调度，进一步提供数据中心在某时段或典型日的综合能源系统运行优化设计和调度；1) On the basis of most of the existing data center power supply, emergency cold storage, and emergency power storage, the content of peak-shaving cold storage, peak-shaving power storage, etc. is simultaneously added to provide the most comprehensive energy system for the data center from the planning level. The optimal optimization model can further reduce the economic cost of the system and reduce carbon dioxide emissions; from the operation level, the optimal design and scheduling of the integrated energy system of the data center is proposed to further provide the optimal design and scheduling of the integrated energy system operation of the data center during a certain period of time or on a typical day;
2)在分别设定数据中心的总碳排放最低、能源经济成本最低两个优化目标时，与传统的通常只考虑电力、燃气两大常规能源的基础上，同步增加考虑了其他能源的碳排放与经济性，以提升本发明的扩展性与适用性；2) When the two optimization goals of the lowest total carbon emission and the lowest energy economic cost of the data center are respectively set, on the basis of the traditional two conventional energy sources of electricity and gas, the carbon emission of other energy sources is simultaneously increased. and economy to improve the expansibility and applicability of the present invention;
3)可以弥补数据中心在同时考虑应急蓄冷、应急储电、调峰蓄冷、调峰储电的综合能源系统优化配置方面研究的缺失。3) It can make up for the lack of research on the optimal configuration of the comprehensive energy system that simultaneously considers emergency cold storage, emergency power storage, peak-shaving cold storage, and peak-shaving power storage.
附图说明Description of drawings
为了更清楚地说明本发明实施案例或现有技术中的技术方案，下边将对实施例中所需要使用的附图做简单介绍，通过参考附图会更清楚的理解本发明的特征和优点，附图是示意性的而不应该理解为对本发明进行任何限制，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，可以根据这些附图获得其他的附图。其中：In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below, and the features and advantages of the present invention will be more clearly understood by referring to the drawings, The accompanying drawings are schematic and should not be construed as any limitation to the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative effort. in:
图1为一种基于综合储能技术的数据中心多能协同优化方法步骤流程图；Figure 1 is a flow chart showing the steps of a multi-energy collaborative optimization method for a data center based on comprehensive energy storage technology;
图2为机柜负载率30％时7、8月蓄冷、放冷策略示意图；Figure 2 is a schematic diagram of the cooling storage and cooling strategy in July and August when the cabinet load rate is 30%;
图3为机柜负载率80％时7、8月蓄冷、放冷策略示意图；Figure 3 is a schematic diagram of the cooling storage and cooling strategy in July and August when the cabinet load rate is 80%;
图4为尖峰电价时段蓄电池充放电示意图；Figure 4 is a schematic diagram of battery charging and discharging during peak electricity price periods;
图5为非尖峰电价时段蓄电池充放电示意图；Figure 5 is a schematic diagram of battery charging and discharging during non-peak electricity price periods;
图6为夏季典型日系统运行示意图；Figure 6 is a schematic diagram of a typical day system operation in summer;
图7为冬季典型日系统运行示意图；Figure 7 is a schematic diagram of the operation of the system on a typical day in winter;
图8为数据中心在不同配置方案、不同负载率下全年能源系统运行成本示意图；Figure 8 is a schematic diagram of the annual energy system operation cost of the data center under different configuration schemes and different load rates;
图9为一种基于综合储能技术的数据中心多能协同优化系统示意图。FIG. 9 is a schematic diagram of a data center multi-energy collaborative optimization system based on integrated energy storage technology.
具体实施方式Detailed ways
为了能够更清除地理解本发明的上述目的、特征和优点，下面结合附图和具体实施方式对发明进行进一步的详细描述。需要说明的是在不冲突的情况下，本申请的实施例及实施例中的特征可以相互结合。In order to more clearly understand the above objects, features and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features of the embodiments may be combined with each other without conflict.
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是，本发明还可以采用其他不同于在此描述的其他方式来实施，因此，本发明的保护范围并不受下面公开的具体实施例的限制。Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific details disclosed below. Example limitations.
实施例一Example 1
如图1所示，本发明提供了一种基于综合储能技术的数据中心多能协同优化方法，包括：As shown in FIG. 1 , the present invention provides a multi-energy collaborative optimization method for a data center based on comprehensive energy storage technology, including:
S101：构建数据中心综合能源系统的各个设备的数学模型，所述设备包括蓄冷设备、电池储能设备、供电设备、供冷设备和供热设备中的一种或多种；S101: Construct a mathematical model of each device of the data center comprehensive energy system, the device includes one or more of cold storage device, battery energy storage device, power supply device, cooling device and heating device;
S102：根据所述数学模型，构建数据中心综合能源系统以碳排放为导向的目标函数和以经济性为导向的目标函数；S102: According to the mathematical model, construct a carbon emission-oriented objective function and an economy-oriented objective function of the comprehensive energy system of the data center;
S103：根据能量平衡原理和数据中心综合能源系统的工程实际确定约束条件；S103: Determine the constraints according to the energy balance principle and the engineering practice of the integrated energy system of the data center;
S104：根据所述以碳排放为导向的目标函数、所述以经济性为导向的目标函数和所述约束条件，建立多目标优化模型；S104: Establish a multi-objective optimization model according to the carbon emission-oriented objective function, the economy-oriented objective function, and the constraint conditions;
S105：进行优化求解多目标优化模型；S105: Perform optimization to solve the multi-objective optimization model;
S106：分析求解结果是否符合工程实际，若否，则调整相关设备参数与约束条件后，返回执行步骤S105，若是，则执行步骤S107；S106: Analyze whether the solution result conforms to the engineering practice, if not, after adjusting the relevant equipment parameters and constraints, return to step S105, if yes, execute step S107;
S107：根据分析结果得到最优优化模型，进行数据中心综合能源系统的优化设计和调度。S107: Obtain an optimal optimization model according to the analysis result, and perform optimal design and scheduling of the comprehensive energy system of the data center.
本发明提供基于综合储能技术的数据中心多能协同优化方法，通过构建数据中心综合能源系统优化模型，通过对优化模型进行求解，得到最优优化模型，实现数据中心综合能源系统的优化设计和调度，使得数据中心经济效益和环境效益的整体最优。The invention provides a multi-energy collaborative optimization method for a data center based on an integrated energy storage technology. By constructing an optimization model of the integrated energy system of the data center, and by solving the optimization model, the optimal optimization model is obtained, so as to realize the optimal design and optimization of the integrated energy system of the data center. Scheduling makes the overall optimal economic and environmental benefits of the data center.
进一步地，在S101的步骤中，具体包括：Further, in the step of S101, it specifically includes:
构建蓄冷设备数学模型：Build a mathematical model of cold storage equipment:
其中，Q(t)表示蓄冷设备t时刻的蓄冷量，Q(t-1)表示蓄冷设备t-1时刻的蓄冷量。Pin(t)和Pout(t)分别表示蓄冷设备t时刻的蓄冷功率和放冷功率。ηin和ηout分别表示蓄冷设备的蓄冷效率和放冷效率，Δt表示计算时间间隔；Here, Q(t) represents the cool storage capacity of the cool storage facility at time t, and Q(t-1) represents the cool storage capacity of the cool storage facility at time t-1. Pin (t) and Pout (t) represent the cool storage power and the cool discharge power of the cool storage device at time t, respectively. ηin and ηout represent the cold storage efficiency and cooling efficiency of the cold storage equipment, respectively, and Δt represents the calculation time interval;
构建电池储能设备数学模型：Build a mathematical model of a battery energy storage device:
E(t)＝E(t-1)+(Pc(t)ηc-Pd(t)/ηd)ΔtE(t)=E(t-1)+(Pc (t)ηc -Pd (t)/ηd )Δt
其中，E(t)表示电池储能设备t时刻的总能量，E(t-1)表示电池储能设备t-1时刻的总能量，Pc(t)表示电池储能设备t时刻的充电功率，Pd(t)表示电池储能设备t时刻的放电功率，ηc表示电池储能设备的充电效率，ηd表示电池储能设备的充电效率，Δt表示计算时间间隔；Among them, E(t) represents the total energy of the battery energy storage device at time t, E(t-1) represents the total energy of the battery energy storage device at time t-1, and Pc (t) represents the charging of the battery energy storage device at time t Power, Pd (t) represents the discharge power of the battery energy storage device at time t, ηc represents the charging efficiency of the battery energy storage device, ηd represents the charging efficiency of the battery energy storage device, and Δt represents the calculation time interval;
构建供电设备、供冷设备和供热设备数学模型：Build mathematical models of power, cooling, and heating equipment:
其中，i表示能源转换设备，t表示时刻，P、C、H分别表示电能、冷能、热能，in、out分别表示输入和输出，
数据中心综合能源系统包括的设备一般有：供电设备：电网供电、光伏发电、燃气轮机等；供冷设备：冷水机、吸收式制冷机、其他热泵等；调控室及宿舍等场所的供热设备：热泵、燃气锅炉等。需要说明的是，如果数据中心不需要考虑值班室和宿舍区供热需求，可以将本发明实施例中供热部分相应参数置0。为了考虑本发明提出的方法有更一般的适用性和完整性，本发明实施中保留了供热部分。The equipment included in the comprehensive energy system of the data center generally includes: power supply equipment: grid power supply, photovoltaic power generation, gas turbine, etc.; cooling equipment: chiller, absorption chiller, other heat pumps, etc.; heating equipment in control rooms and dormitories: Heat pumps, gas boilers, etc. It should be noted that, if the data center does not need to consider the heating demand of the duty room and the dormitory area, the corresponding parameters of the heating part in the embodiment of the present invention may be set to 0. In order to consider the more general applicability and integrity of the method proposed by the present invention, the heating part is reserved in the implementation of the present invention.
进一步地，在S102的步骤中，包括：Further, in the step of S102, including:
根据所述数学模型，建立数据中心综合能源系统以碳排放为导向的目标函数minCO2emission：According to the mathematical model, the carbon emission-oriented objective function minCO2 emission of the integrated energy system of the data center is established:
其中，t表示时刻，eele表示大电网中生产一个单位电能所排放的CO2量，egas表示燃烧一单位天然气所排放的CO2量，
进一步地，在S102的步骤中，包括：Further, in the step of S102, including:
根据所述数学模型，建立数据中心综合能源系统以经济性为导向的目标函数cost：According to the mathematical model, the economic-oriented objective function cost of the comprehensive energy system of the data center is established:
其中，cost表示总成本，k表示设备编号常数，c表示成本，t表示时刻，dt表示时间变量，inv表示初投资年化成本，om表示运维成本，
进一步地，在S103的步骤中，包括：Further, in the step of S103, including:
包括确定电、冷、热系统的能量平衡约束：This includes determining the energy balance constraints for the electrical, cooling, and heating systems:
其中，t表示时刻，i表示能源转换设备，k表示设备编号常数，
进一步地，在S103的步骤中，包括：Further, in the step of S103, including:
确定蓄冷设备约束：Determine cold storage device constraints:
Qmin≤Q(t)≤QmaxQmin ≤Q(t)≤Qmax
μin(t)+μout(t)≤1μin (t)+μout (t)≤1
其中，t表示时刻，Qmin表示蓄冷设备最小蓄冷容量，Qmax表示蓄冷设备最大蓄冷容量，Q(t)表示蓄冷设备t时刻的蓄冷量，Vmax表示数据中心允许安装蓄冷罐的最大体积，ρ表示蓄冷水密度，取1000kg/m3，Cp表示冷水的比热容，取值4.18kJ/(kg·℃)，η表示蓄冷罐的有效利用体积，Δt表示供回水温度差，取5～7℃，Pin(t)表示蓄冷设备t时刻的蓄冷功率，Pout(t)表示蓄冷设备t时刻的放冷功率，Pin和
进一步地，在S103的步骤中，包括：Further, in the step of S103, including:
确定电池储能设备约束：Determine battery energy storage device constraints:
Emin≤E(t)≤EmaxEmin ≤E(t)≤Emax
Bc(t)+Bd(t)≤1Bc (t)+Bd (t)≤1
其中，t表示时刻，E(t)表示电池储能设备t时刻的总能量，Emin、Emax分别表示电池储能设备最小和最大储电容量，Pc(t)、Pd(t)分别表示电池储能设备t时刻的充电和放电功率，
进一步地，在S104的步骤中，包括：Further, in the step of S104, comprising:
根据所述以碳排放为导向的目标函数、所述以经济性为导向的目标函数和所述约束条件，建立多目标优化模型。According to the carbon emission-oriented objective function, the economy-oriented objective function and the constraints, a multi-objective optimization model is established.
进一步地，在S105的步骤中，包括：Further, in the step of S105, including:
进行优化求解多目标优化模型的方法为：使用计算机基于Matlab软件中的Tomlab平台编写优化问题程序，调用Tomlab平台中内嵌的单纯型法求解器进行求解。The method of optimizing and solving the multi-objective optimization model is: using a computer to write the optimization problem program based on the Tomlab platform in the Matlab software, and calling the simplex solver embedded in the Tomlab platform to solve it.
进一步地，在S106的步骤中，包括：Further, in the step of S106, comprising:
分析求解结果是否符合工程实际，若否，则调整相关设备参数与约束条件后，再进行优化求解多目标优化模型，若是，则执行步骤S107。It is analyzed whether the solution result conforms to the engineering practice, if not, after adjusting the relevant equipment parameters and constraints, the optimization is performed to solve the multi-objective optimization model, if so, step S107 is performed.
在进一步地，在S107的步骤中，包括：Further, in the step of S107, comprising:
根据分析结果得到最优优化模型，进行数据中心综合能源系统的优化设计和调度。According to the analysis results, the optimal optimization model is obtained, and the optimal design and scheduling of the integrated energy system of the data center are carried out.
以下将根据本发明的实施例提供应用例如下：The following will provide application examples according to embodiments of the present invention as follows:
选取北京某数据中心为研究对象，该数据中心规划建设1850个机柜，每台机柜平均功率约4.4kW，北京某数据中心负荷需求分析如表1所示：A data center in Beijing is selected as the research object. The data center plans to build 1850 cabinets, and the average power of each cabinet is about 4.4kW. The load demand analysis of a data center in Beijing is shown in Table 1:
表1：北京某数据中心负荷需求分析Table 1: Analysis of load demand of a data center in Beijing
根据数据中心负荷需求分析，考虑不同储能设备配置的数据中心综合能源系统：According to the load demand analysis of the data center, consider the comprehensive energy system of the data center with different energy storage device configurations:
方案一：只考虑应急蓄冷系统，为常见的基本配置，主要配置包括变频离心式冷水机组、应急蓄冷罐；Option 1: Only consider the emergency cold storage system, which is a common basic configuration. The main configuration includes variable frequency centrifugal chillers and emergency cold storage tanks;
方案二：在考虑应急水蓄冷系统基础上，增加了调峰水蓄冷系统，利用峰谷电价差，降低了系统运行成本；Option 2: On the basis of considering the emergency water cooling system, a peak-shaving water cooling system is added, and the system operating cost is reduced by utilizing the difference in electricity price between peaks and valleys;
方案三：在方案二的基础上，增加了锂电池储能系统，更加充分的利用峰谷电价差，进一步降低了系统的运行成本；Option 3: On the basis of
方案四：在方案二的基础上，增加了全钒液流电池系统，利用峰谷价差，降低系统运行成本。Option 4: On the basis of
需要说明的是，计算机优化工具理论分析得出的数据中心电池储能最佳配置容量为11MW/33MWh，但受实际工程施工现场空间限制，电池储能系统配置没有到达最佳值，实地空间可配置5MW/16.6MWh锂电池储能系统或3.2MW/9.6MWh全钒液流电池储能系统。It should be noted that the optimal configuration capacity of data center battery energy storage obtained by theoretical analysis of computer optimization tools is 11MW/33MWh, but due to the limitation of actual construction site space, the configuration of battery energy storage system does not reach the optimal value, and the field space can be used. Equipped with 5MW/16.6MWh lithium battery energy storage system or 3.2MW/9.6MWh all-vanadium flow battery energy storage system.
其中，系统配置为：离心式冷水机组3517kW*4(三用一备)Among them, the system configuration is: centrifugal chiller 3517kW*4 (three uses and one backup)
调峰蓄冷8000RTh(不包括应急蓄冷750RTh)Peak shaving cold storage 8000RTh (excluding emergency cold storage 750RTh)
电储储能3.2MW/9.6MWhElectric storage and energy storage 3.2MW/9.6MWh
根据电价信息，结合实际负荷需求，考虑电池储能与水蓄冷的运行策略，其基本原则是充分利用谷段、合理利用平段储能(冷能、电能)满足尖峰段、峰段的能源需求。According to the electricity price information, combined with the actual load demand, the operation strategy of battery energy storage and water storage is considered. .
运行策略分别见图2、图3，图2为机柜负载率30％时7、8月蓄冷、放冷策略示意图；图3为机柜负载率80％时7、8月蓄冷、放冷策略示意图。冬季利用自然冷却系统，无需启动冷水机组和水蓄冷系统。The operation strategies are shown in Figure 2 and Figure 3 respectively. Figure 2 is a schematic diagram of the cooling storage and cooling strategy in July and August when the cabinet load rate is 30%; Figure 3 is a schematic diagram of the cooling storage and cooling strategy in July and August when the cabinet load rate is 80%. Using the natural cooling system in winter, there is no need to start the chiller and the water storage system.
夏季尖峰电价时段，采用“三充三放”策略，因谷电期间，冷水机需要增加蓄冷功率，受总容量限制，故在此期间，利用低功率充满电，在非尖峰电价时段，采用“两充两放”策略。如图4、图5所示，图4为尖峰电价时段蓄电池充放电示意图，图5为非尖峰电价时段蓄电池充放电示意图。During the peak electricity price period in summer, the strategy of "three charges and three discharges" is adopted. During the valley electricity period, the chiller needs to increase the cold storage power, which is limited by the total capacity. Therefore, during this period, use low power to fully charge, and in the non-peak electricity price period, adopt the "three charging and three discharging" strategy. "Two charge and two discharge" strategy. As shown in Figures 4 and 5, Figure 4 is a schematic diagram of battery charging and discharging during peak electricity price periods, and Figure 5 is a schematic diagram of battery charging and discharging during non-peak electricity price periods.
系统运行策略优化分析：System operation strategy optimization analysis:
图6为夏季典型日系统运行示意图，储能曲线正值表示向系统释放能量，负值表示处于储能状态。电池储能、水蓄冷理想充放策略是基于数据中心的电负荷、冷负荷特性，尽量避免尖峰和高峰电价时段用电网电。如11:00-13:00及16:00-17:00电价尖峰时段，此时，变频离心冷水机组停运，电储能、水蓄冷向系统释放能量。Figure 6 is a schematic diagram of the operation of the system on a typical day in summer. The positive value of the energy storage curve indicates that energy is released to the system, and the negative value indicates that it is in the state of energy storage. The ideal charging and discharging strategy of battery energy storage and water cooling storage is based on the characteristics of electric load and cooling load of the data center, and try to avoid using grid electricity during peak and peak electricity price periods. Such as 11:00-13:00 and 16:00-17:00 electricity price peak period, at this time, the variable frequency centrifugal chiller is out of operation, and the electric energy storage and water cold storage release energy to the system.
图7是冬季典型日系统运行示意图，冬季典型日因为采用自然冷却技术，系统的冷水机和水蓄冷系统处于停运状态，制冷系统只有水泵在运转。水泵的电功率约为夏季所有冷机运行功率的15％。Figure 7 is a schematic diagram of the operation of the system on a typical day in winter. On a typical day in winter, due to the use of natural cooling technology, the chiller and water storage system of the system are in a shutdown state, and only the water pump is running in the refrigeration system. The electrical power of the water pump is about 15% of the operating power of all chillers in summer.
将基础数据及运行策略输入优化配置模型中，以不同的储能设备配置的数据中心综合能源系统作为优化变量，以充分利用谷段、合理利用平段储能满足尖峰段、峰段的能源需求为优化策略，以数据中心综合能源系统经济性最优作为优化目标。通过求解得到数据中心在不同配置方案、不同负载率下全年能源系统运行成本，如图8所示，从图8可以看出：随着负载率的上升，数据中心全年能源系统的运行成本上升；配置水蓄冷和锂电池储能系统的方案三最具经济性。Input the basic data and operation strategy into the optimal configuration model, and take the comprehensive energy system of the data center configured with different energy storage equipment as the optimization variable, so as to make full use of the energy storage in the valley section and reasonably use the energy storage in the flat section to meet the energy demand of the peak section and the peak section. In order to optimize the strategy, the optimal economical efficiency of the comprehensive energy system of the data center is taken as the optimization goal. The annual operating cost of the data center's energy system under different configuration schemes and different load rates is obtained by solving, as shown in Figure 8. It can be seen from Figure 8 that with the increase in the load rate, the annual operating cost of the data center's energy system Rising; the third option of configuring water cooling and lithium battery energy storage systems is the most economical.
分析本发明应用例的碳排量：通过计算分析，本应用例的四个方案中，方案一的全年二氧化碳排放总量最低，约为1.138万吨(电网碳排因子取0.6276kg/kWh)，其他三个方案因为增加了储能系统，虽然利用峰谷电价差节约了能源成本，但是储能系统也不可避免增加了系统充、放损耗。而本案例因为资源禀赋所限，没有清洁能源可以使用，因此，因为损耗反而导致增加了碳排放。也说明在数据中心应充分利用清洁能源，配合综合储能技术，不但可以节约能源成本，同时可以降低二氧化碳排放。Analysis of the carbon emission of the application example of the present invention: Through calculation and analysis, among the four schemes of this application example, the total annual carbon dioxide emission of
本发明应用例通过对比分析多种储能设备的配置及其运行策略和经济效益，说明通过合理配置电池储能和调峰水蓄冷，可以明显降低系统运行成本和设备年化投资成本。The application example of the present invention compares and analyzes the configuration of various energy storage devices, their operation strategies and economic benefits, and shows that by rationally configuring battery energy storage and peak-shaving water storage, the system operating cost and equipment annual investment cost can be significantly reduced.
通过本发明应用例可以得出：Through the application example of the present invention, it can be concluded that:
1)利用峰谷电价设置调峰水蓄冷系统，可有效降低系统运行费用，系统初期投资小，静态投资回收期短，投资收益稳定。同时，水蓄冷系统大大增加数据中心的供冷安全性，合理采用蓄冷系统也是打造数据中心绿色建筑重要的评分项。1) Using the peak-valley electricity price to set up the peak-shaving water cooling system can effectively reduce the operating cost of the system. The initial investment of the system is small, the static investment payback period is short, and the investment income is stable. At the same time, the water cooling system greatly increases the cooling security of the data center, and the rational use of the cooling system is also an important scoring item for building a green building of the data center.
2)利用电池储能可有效降低系统运行费用。电池储能的布置受现场安装空间影响，其中锂电池是电池中比能量最高的实用型电池，其电池效率高、响应速度快、单位投资成本低、静态回收期短；全钒液流电池循环次数大大超过锂电池，电池寿命更长，但其初投资高于普通锂电池。2) The use of battery energy storage can effectively reduce system operating costs. The layout of battery energy storage is affected by the on-site installation space. Among them, lithium batteries are practical batteries with the highest specific energy. They have high battery efficiency, fast response speed, low unit investment cost, and short static payback period; all-vanadium flow battery cycle The number of times is much higher than that of lithium batteries, and the battery life is longer, but its initial investment is higher than that of ordinary lithium batteries.
实施例二
如图9所示，本发明提供了一种基于综合储能技术的数据中心多能协同优化系统，包括：As shown in FIG. 9 , the present invention provides a multi-energy collaborative optimization system for a data center based on comprehensive energy storage technology, including:
数学模型构建模块901，用于构建数据中心综合能源系统的各个设备的数学模型，所述设备包括蓄冷设备、电池储能设备、供电设备、供冷设备和供热设备中的一种或多种；Mathematical
目标函数构建模块902，用于根据所述数学模型，构建数据中心综合能源系统以碳排放为导向的目标函数和以经济性为导向的目标函数；an objective
约束条件构建模块903，用于根据能量平衡原理和数据中心综合能源系统的工程实际确定约束条件；The constraint
多目标优化模型建立模块904，用于根据所述以碳排放为导向的目标函数、所述以经济性为导向的目标函数和所述约束条件，建立多目标优化模型；a multi-objective optimization
多目标优化模型求解模块905，用于进行优化求解多目标优化模型；The multi-objective optimization
多目标优化模型分析模块906，用于分析求解结果是否符合工程实际，若否，则调整相关设备参数与约束条件后，再进行优化求解多目标优化模型，若是，则将分析结果发送给优化设计和调度模块；The multi-objective optimization
优化设计和调度模块907，用于根据分析结果得到最优优化模型，进行数据中心综合能源系统的优化设计和调度。The optimal design and
所述系统用以实现实施例一所述的一种基于综合储能技术的数据中心多能协同优化方法，在此不再赘述。The system is used to implement the multi-energy collaborative optimization method for a data center based on the integrated energy storage technology described in the first embodiment, and details are not described herein again.
实施例三
本发明提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机软件产品，所述计算机软件产品包括的若干指令，用以使得一台计算机设备执行实施例一所述的一种基于综合储能技术的数据中心多能协同优化方法，在此不再赘述。The present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer software product, and the computer software product includes several instructions to enable a computer device to execute the one described in the first embodiment The multi-energy collaborative optimization method of data center based on comprehensive energy storage technology will not be repeated here.
以上对本发明所提供的一种基于综合储能技术的数据中心多能协同优化方法及系统进行了详细介绍，本文中对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。A method and system for multi-energy collaborative optimization of data centers based on integrated energy storage technology provided by the present invention have been described above in detail. The principles and implementations of the present invention are described in this paper. The descriptions of the above embodiments are only used to help Understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation and application scope. In summary, the content of this specification does not It should be understood as a limitation of the present invention.
以上所述，以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions described in the embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the present application.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210654961.9ACN115081700A (en) | 2022-06-10 | 2022-06-10 | Comprehensive energy storage technology-based data center multi-energy collaborative optimization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210654961.9ACN115081700A (en) | 2022-06-10 | 2022-06-10 | Comprehensive energy storage technology-based data center multi-energy collaborative optimization method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115081700Atrue CN115081700A (en) | 2022-09-20 |
Family
ID=83251742
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210654961.9APendingCN115081700A (en) | 2022-06-10 | 2022-06-10 | Comprehensive energy storage technology-based data center multi-energy collaborative optimization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115081700A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117477627A (en)* | 2023-12-25 | 2024-01-30 | 宁波亮控信息科技有限公司 | Energy-saving intelligent control method for data center energy system based on hybrid energy storage |
| CN117937480A (en)* | 2024-03-22 | 2024-04-26 | 杭州阿里云飞天信息技术有限公司 | A data center energy consumption assessment method, device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110363353A (en)* | 2019-07-16 | 2019-10-22 | 厦门大学 | A distributed integrated energy system optimization design and scheduling method and system |
| CN113722895A (en)* | 2021-08-18 | 2021-11-30 | 国网上海市电力公司 | Comprehensive energy system optimal configuration method based on multi-station fusion |
| US20210376614A1 (en)* | 2019-12-12 | 2021-12-02 | State Grid Zhejiang Electric Power Co., Ltd. Taizhou power supply company | Hierarchical control method for island power grid energy storage system for increasing new energy generation fluctuation |
| CN113762708A (en)* | 2021-07-01 | 2021-12-07 | 国网江西省电力有限公司赣州供电分公司 | Park level comprehensive energy system planning method considering multi-target cooperation |
| CN113837429A (en)* | 2021-07-13 | 2021-12-24 | 国网江苏省电力有限公司苏州供电分公司 | Coordinated operation optimization method for multiple types of energy storage equipment of park comprehensive energy system |
- 2022
- 2022-06-10CNCN202210654961.9Apatent/CN115081700A/enactivePending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110363353A (en)* | 2019-07-16 | 2019-10-22 | 厦门大学 | A distributed integrated energy system optimization design and scheduling method and system |
| US20210376614A1 (en)* | 2019-12-12 | 2021-12-02 | State Grid Zhejiang Electric Power Co., Ltd. Taizhou power supply company | Hierarchical control method for island power grid energy storage system for increasing new energy generation fluctuation |
| CN113762708A (en)* | 2021-07-01 | 2021-12-07 | 国网江西省电力有限公司赣州供电分公司 | Park level comprehensive energy system planning method considering multi-target cooperation |
| CN113837429A (en)* | 2021-07-13 | 2021-12-24 | 国网江苏省电力有限公司苏州供电分公司 | Coordinated operation optimization method for multiple types of energy storage equipment of park comprehensive energy system |
| CN113722895A (en)* | 2021-08-18 | 2021-11-30 | 国网上海市电力公司 | Comprehensive energy system optimal configuration method based on multi-station fusion |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117477627A (en)* | 2023-12-25 | 2024-01-30 | 宁波亮控信息科技有限公司 | Energy-saving intelligent control method for data center energy system based on hybrid energy storage |
| CN117477627B (en)* | 2023-12-25 | 2024-04-12 | 宁波亮控信息科技有限公司 | Energy-saving intelligent control method for data center energy system based on hybrid energy storage |
| CN117937480A (en)* | 2024-03-22 | 2024-04-26 | 杭州阿里云飞天信息技术有限公司 | A data center energy consumption assessment method, device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109919478B (en) | Comprehensive energy microgrid planning method considering comprehensive energy supply reliability | |
| CN109118024B (en) | A multi-resource scheduling method for power system considering multi-stage state transition of thermal power units | |
| CN112234604B (en) | Multi-energy complementary power supply base optimal configuration method, storage medium and equipment | |
| CN108808744A (en) | There are the active control method for coordinating and system of the grid-connected power generation system of energy storage participation | |
| CN109993345B (en) | Garden-oriented dynamic economic dispatching method for multi-energy complementary system for island operation | |
| CN111668878B (en) | Optimal configuration method and system for renewable micro energy network | |
| CN113722895A (en) | Comprehensive energy system optimal configuration method based on multi-station fusion | |
| CN114757414A (en) | Comprehensive energy system robust optimization planning method considering supply and demand uncertainty | |
| CN112307603B (en) | Hybrid energy storage capacity optimization configuration method and system considering large-scale wind power access | |
| CN112862253A (en) | Electricity-gas-heat energy storage collaborative planning method and system considering wind power uncertainty | |
| CN115081700A (en) | Comprehensive energy storage technology-based data center multi-energy collaborative optimization method and system | |
| CN115238991A (en) | Multi-time scale collaborative optimization design method and system for comprehensive energy system | |
| CN102593855A (en) | Method for stabilizing fluctuation of output power of renewable energy power supply in power system | |
| CN114936720A (en) | A low-carbon economic dispatch method for integrated energy systems with source-load-storage coordination | |
| CN117391764A (en) | Comprehensive energy system optimal scheduling method and system | |
| CN114255137A (en) | Low-carbon comprehensive energy system optimization planning method and system considering clean energy | |
| CN117277373A (en) | Variable-period source load storage optimal scheduling method considering adjustment resource demand difference | |
| CN118630837A (en) | Peak load scheduling method, system, electronic device and storage medium for virtual power plant | |
| Yang et al. | Prosumer data center system construction and synergistic optimization of computing power, electricity and heat from a global perspective | |
| CN116720873A (en) | Park integrated energy system carbon capture-energy storage combined system optimization method and system | |
| CN108683211A (en) | A kind of virtual power plant combined optimization method and model considering distributed generation resource fluctuation | |
| CN113364042A (en) | Data center optical storage collaborative optimization configuration method and system | |
| CN110535186A (en) | A kind of medium-term and long-term timing production analogy method and system containing pump-up power station | |
| CN118572703A (en) | Step water storage wind-solar-fire planning operation method, system, equipment and medium | |
| CN118381073A (en) | A new energy station energy storage sharing configuration evaluation method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |