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CN115882523A - Power system optimization operation method, system and equipment including distributed energy storage - Google Patents

Power system optimization operation method, system and equipment including distributed energy storage Download PDF

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CN115882523A
CN115882523A CN202310081216.4A CN202310081216A CN115882523A CN 115882523 A CN115882523 A CN 115882523A CN 202310081216 A CN202310081216 A CN 202310081216A CN 115882523 A CN115882523 A CN 115882523A
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power
energy storage
distribution network
storage device
cost
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曾成碧
李卓雅
苗虹
赵昱翔
陈一涵
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Sichuan University
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Abstract

本发明涉及分布式储能技术领域,涉及一种含分布式储能的电力系统优化运行方法、系统及设备,包括:步骤1:获取系统的初始参数;步骤2:建立含分布式储能的配电网优化运行模型;以配电网一日运行成本最低为优化目标,确定出系统运行各部分约束条件,使用混合整数规划法与二阶锥线性规划法,对建立的配电网日前经济调度模型进行求解;步骤3:调用Gurobi求解器对模型进行优化求解;步骤4:通过改变储能装置接入配电网系统的位置或储能装置的充放电功率参数,研究储能装置接入配电网位置或充放电功率参数对风电消纳与系统运行经济状况的影响。本发明能较佳地优化电力系统。

Figure 202310081216

The present invention relates to the technical field of distributed energy storage, and relates to a power system optimization operation method, system and equipment including distributed energy storage, including: step 1: obtaining initial parameters of the system; The optimization operation model of the distribution network; with the lowest daily operation cost of the distribution network as the optimization goal, the constraints of each part of the system operation are determined, and the established distribution network is established using the mixed integer programming method and the second-order cone linear programming method. Dispatch the model to solve; Step 3: call the Gurobi solver to optimize the model; Step 4: Study the energy storage device access by changing the location of the energy storage device connected to the distribution network system or the charging and discharging power parameters of the energy storage device The impact of distribution network location or charging and discharging power parameters on wind power consumption and system operation economic status. The invention can better optimize the power system.

Figure 202310081216

Description

含分布式储能的电力系统优化运行方法、系统及设备Method, system and device for optimizing operation of power system containing distributed energy storage

技术领域Technical Field

本发明涉及分布式储能技术领域,具体地说,涉及一种含分布式储能的电力系统优化运行方法、系统及设备。The present invention relates to the field of distributed energy storage technology, and in particular to a method, system and device for optimizing operation of a power system containing distributed energy storage.

背景技术Background Art

当前,新能源接入配电网后优化运行策略的研究成为了电力系统领域的热点课题之一。与传统电力系统的能源出力相比,新能源发电有绿色环保、能源利用率高、经济效益高等优点,但同时,新能源出力的不确定性,也给电网稳定运行带来了挑战。新能源出力不确定性引起的配电网安全性及可靠性下降问题可通过接入储能装置进行解决,同时,分布式储能装置接入对降低配电网经济成本有积极作用。At present, the research on optimizing operation strategies after the access of renewable energy to distribution networks has become one of the hot topics in the field of power systems. Compared with the energy output of traditional power systems, renewable energy power generation has the advantages of green environmental protection, high energy utilization rate, and high economic benefits. However, the uncertainty of renewable energy output also brings challenges to the stable operation of the power grid. The problem of reduced safety and reliability of the distribution network caused by the uncertainty of renewable energy output can be solved by accessing energy storage devices. At the same time, the access of distributed energy storage devices has a positive effect on reducing the economic cost of the distribution network.

风力发电技术是指将风能转化为电能,是目前的新能源发电中技术理论发展较为完善的、最具有发展潜力的新能源技术之一。风电等新能源大规模接入电网丰富了能源结构,但配电网结构也因此会发生深刻转变,由于系统供电侧与用电侧随机性都太强,且独立性高,电网出力与负荷可能发生失衡,电网运行的安全性与经济性都降低。经大量研究证明,对于新能源发电所遇到的瓶颈,可以采用接入分布式储能装置的方法,降低由于风电等新能源发电方式出力不确定性与间歇性给电网带来的危害。Wind power generation technology refers to the conversion of wind energy into electrical energy. It is one of the new energy technologies with the most complete technical theory and development potential in the current new energy power generation. The large-scale access of new energy such as wind power to the power grid has enriched the energy structure, but the distribution network structure will also undergo a profound change. Since the system power supply side and the power consumption side are too random and highly independent, the power grid output and load may be unbalanced, and the safety and economy of the power grid operation will be reduced. A large number of studies have shown that for the bottlenecks encountered in new energy power generation, the method of connecting to distributed energy storage devices can be used to reduce the harm to the power grid caused by the uncertainty and intermittency of the output of new energy power generation methods such as wind power.

分布式储能技术是指根据负荷、电源位置将储能装置在接入配电网中的特定位置接入,多应用于本实施例提供了一种分布式储能参与含风电并网的配电网调峰填谷优化运行策略方法,通过在系统中加入多个分布式储能装置优化风电的消纳情况,降低弃风情况发生概率,通过分布式储能装置调峰填谷的作用,增加系统经济效益。建立了以优化全网一日运行经济效益最高为目标函数,考虑了节点功率平衡约束、电压降与支路电流约束、火电机组出力约束、火电机组爬坡率约束、火电机组启停约束、风电出力约束、分布式储能装置容量约束、分布式储能装置充放电运行约束,再对节点平衡非线性约束进行一定程度的松弛,将其转化为混合整数二阶锥优化模型进行求解。输配电侧、微电网与用户侧,参与调峰填谷、缓解电网阻塞与提升供电可靠性等多方面,接入方式更灵活,且分布式储能的功率、容量的规模相对较小。Distributed energy storage technology refers to the connection of energy storage devices to specific locations in the distribution network according to the load and power supply location. It is mostly used in this embodiment to provide a distributed energy storage participating in the peak-shaving and valley-filling optimization operation strategy method of the distribution network including wind power grid connection. By adding multiple distributed energy storage devices to the system, the consumption of wind power is optimized, the probability of wind abandonment is reduced, and the peak-shaving and valley-filling effect of distributed energy storage devices is increased. The objective function is established to optimize the highest economic benefit of the entire network operation in one day, taking into account the node power balance constraint, voltage drop and branch current constraint, thermal power unit output constraint, thermal power unit climbing rate constraint, thermal power unit start-stop constraint, wind power output constraint, distributed energy storage device capacity constraint, distributed energy storage device charging and discharging operation constraint, and then the node balance nonlinear constraint is relaxed to a certain extent, and it is converted into a mixed integer second-order cone optimization model for solution. The transmission and distribution side, microgrid and user side participate in peak-shaving and valley-filling, alleviate grid congestion and improve power supply reliability. The access method is more flexible, and the power and capacity of distributed energy storage are relatively small.

分布式储能技术在可再生能源领域的项目最多,增势也最明显,这一类项目通常是指将分布式储能技术应用于风电等新能源接入的电网,采用储能及技术进行电力系统的调峰填谷与能量的协同调度。Distributed energy storage technology has the most projects in the field of renewable energy and its growth is the most obvious. This type of project usually refers to the application of distributed energy storage technology to the power grid to which new energy sources such as wind power are connected, and the use of energy storage and technology to perform peak-shaving and valley-filling of the power system and coordinated energy scheduling.

要实现能源结构由煤炭为主进入到以新能源为主的跨越式转型。以新能源为主的分布式电源并网很大程度上影响着配电网的安全、经济运行,因此,分布式电源的并网导致的诸多电能质量问题引起了国内外的高度重视。To achieve a leapfrog transformation of the energy structure from coal-based to new energy-based. The grid connection of distributed power sources based on new energy has a great impact on the safe and economic operation of the distribution network. Therefore, many power quality problems caused by the grid connection of distributed power sources have attracted great attention at home and abroad.

新能源渗透率不断增加,这给配电网也带来了巨大的挑战。引入分布式储能装置与分布式电源构成多能互补的综合能源电力系统,进行协同配合调度、调峰填谷,维持系统的安全性与经济性。分布式储能装置接入系统的目的是参与配电网的优化调度,例如削峰填谷,以此降低配电网运行成本。现有技术对分布式储能装置的投资优化的根本目的是“提升配电网资产经济性”,在其优化策略中仅单一的讨论了分布式储能装置的投资优化问题,若做出相应的优化,可能导致系统其他机组出力变化进而出现整个系统运行成本不降反升的情况,这样的情况显然不利于配电网长期运行健康发展。因此,在考虑对分布式储能装置的配置与运行优化时,因将系统中的其它机组与电力负荷并入考虑范围,做出对整个系统的运行成本优化。The penetration rate of new energy sources continues to increase, which also brings huge challenges to the distribution network. The introduction of distributed energy storage devices and distributed power sources constitutes a comprehensive energy and power system with multi-energy complementarity, and conducts coordinated scheduling, peak-shaving and valley-filling to maintain the safety and economy of the system. The purpose of connecting distributed energy storage devices to the system is to participate in the optimization scheduling of the distribution network, such as peak-shaving and valley-filling, so as to reduce the operating cost of the distribution network. The fundamental purpose of the existing technology for investment optimization of distributed energy storage devices is to "improve the economic efficiency of distribution network assets". In its optimization strategy, only the investment optimization problem of distributed energy storage devices is discussed. If corresponding optimization is made, it may cause changes in the output of other units in the system, and then the operating cost of the entire system will increase instead of decrease. Such a situation is obviously not conducive to the long-term operation and healthy development of the distribution network. Therefore, when considering the configuration and operation optimization of distributed energy storage devices, other units and power loads in the system are taken into consideration to optimize the operating cost of the entire system.

发明内容Summary of the invention

本发明的内容是提供一种含分布式储能的电力系统优化运行方法、系统及设备,其能够解决分布式储能装置参与含分布式电源能源系统的优化调度问题。The present invention provides a method, system and device for optimizing the operation of an electric power system including distributed energy storage, which can solve the problem of optimizing the scheduling of distributed energy storage devices participating in an energy system including distributed power sources.

根据本发明的一种含分布式储能的电力系统优化运行方法,其包括以下步骤:According to the present invention, a method for optimizing operation of a power system containing distributed energy storage comprises the following steps:

步骤1:获取系统的初始参数,包括配电网结构参数、风电场预测出力、储能装置运行参数、向上级电网购电费用、弃风惩罚成本、火电机组运行成本、储能装置运行成本、切负荷成本;Step 1: Obtain the initial parameters of the system, including distribution network structure parameters, wind farm forecast output, energy storage device operating parameters, electricity purchase costs from the superior power grid, wind power abandonment penalty costs, thermal power unit operating costs, energy storage device operating costs, and load shedding costs;

步骤2:建立含分布式储能的配电网优化运行模型,模型包括:火电机组、风力发电机组、储能装置、电力负荷;以配电网一日运行成本最低为优化目标,确定出系统运行各部分约束条件,使用混合整数规划法与二阶锥线性规划法,对建立的配电网日前经济调度模型进行求解;Step 2: Establish an optimal operation model of the distribution network with distributed energy storage. The model includes: thermal power units, wind turbines, energy storage devices, and power loads. Take the lowest daily operation cost of the distribution network as the optimization goal, determine the constraints of each part of the system operation, and use mixed integer programming and second-order cone linear programming to solve the established distribution network day-ahead economic dispatch model.

步骤3:在Matlab软件中调用Gurobi求解器对模型进行优化求解,形成日前购电量、风电机组与火电机组出力及分布式储能装置充放电计划,以及分布式储能装置在参与系统调峰填谷的作用及其对系统经济效益的提高情况;Step 3: Invoke the Gurobi solver in Matlab software to optimize and solve the model, and form the day-ahead power purchase amount, wind turbine and thermal power unit output, and distributed energy storage device charging and discharging plan, as well as the role of distributed energy storage devices in participating in system peak-shaving and valley-filling and their improvement on system economic benefits;

步骤4:通过改变储能装置接入配电网系统的位置或储能装置的充放电功率参数,研究储能装置接入配电网位置或充放电功率参数对风电消纳与系统运行经济状况的影响。Step 4: By changing the location where the energy storage device is connected to the distribution network system or the charging and discharging power parameters of the energy storage device, the impact of the location where the energy storage device is connected to the distribution network or the charging and discharging power parameters on wind power consumption and the economic conditions of system operation is studied.

作为优选,步骤2中,建立含分布式储能的配电网优化运行模型,以最小化配电网的总运行成本为优化目标;配电网运行成本包括:配电网从上级电网的购电成本、火电机组发电成本、风电机组弃风惩罚成本、储能装置的运行成本以及失负荷成本,由此得出配电网优化运行模型的目标函数如下:As a preferred embodiment, in step 2, an optimized operation model of a distribution network including distributed energy storage is established, with minimizing the total operation cost of the distribution network as the optimization goal; the operation cost of the distribution network includes: the cost of purchasing electricity from the upper power grid, the cost of generating electricity from thermal power units, the penalty cost of wind turbines for abandoning wind, the operation cost of the energy storage device, and the cost of load loss, thereby obtaining the objective function of the distribution network optimized operation model as follows:

Figure SMS_1
;
Figure SMS_1
;

其中:

Figure SMS_15
为配电网从上级电网购电费用,
Figure SMS_5
为火电机组运行成本,
Figure SMS_12
为弃风惩罚成本,
Figure SMS_8
为储能装置运行成本,
Figure SMS_13
为失负荷成本;
Figure SMS_9
t时段配电网从上级电网的购电成本,
Figure SMS_14
t时段配电网从上级电网的购电有功功率;A、B、C为火电机组发电成本系数;
Figure SMS_3
表示火电机组gt时段的出力;
Figure SMS_17
风电机组
Figure SMS_2
的弃风惩罚成本;
Figure SMS_11
表示风电场wt时段的预测出力;
Figure SMS_7
表示风电场wt时段的出力;
Figure SMS_10
为储能使用成本,
Figure SMS_4
表示储能dt时段的放电量;
Figure SMS_16
为配电网切负荷成本,
Figure SMS_6
表示在shed处在t时段的切负荷电量。in:
Figure SMS_15
The cost of electricity purchased by the distribution network from the upper power grid.
Figure SMS_5
is the operating cost of thermal power units,
Figure SMS_12
Penalty costs for wind curtailment,
Figure SMS_8
is the operating cost of the energy storage device,
Figure SMS_13
is the loss of load cost;
Figure SMS_9
is the cost of electricity purchased by the distribution network from the upper power grid during period t ,
Figure SMS_14
is the active power purchased by the distribution network from the upper power grid during period t ; A, B, C are the power generation cost coefficients of thermal power units;
Figure SMS_3
It represents the output of thermal power unit g in period t ;
Figure SMS_17
Wind turbines
Figure SMS_2
The penalty cost of wind curtailment;
Figure SMS_11
represents the predicted output of wind farm w in period t ;
Figure SMS_7
represents the output of wind farm w in period t ;
Figure SMS_10
is the cost of energy storage,
Figure SMS_4
It represents the discharge amount of energy storage d in time period t ;
Figure SMS_16
is the load shedding cost of the distribution network,
Figure SMS_6
It represents the load shedding power in the shed during the period t .

作为优选,步骤2中,利用DistFlow模型来描述配电网节点功率平衡,系统运行约束条件表示为:Preferably, in step 2, the DistFlow model is used to describe the power balance of the distribution network nodes, and the system operation constraints are expressed as:

节点功率平衡约束:Node power balance constraints:

Figure SMS_18
;
Figure SMS_18
;

其中,集合

Figure SMS_37
为连接到配电网节点j的设备集合;
Figure SMS_24
为配电网中以j为首端节点的支路末端节点集合;
Figure SMS_31
为配电线路ij的电阻,
Figure SMS_25
为配电线路ij的电抗;
Figure SMS_33
t时段配电线路ij的电流;
Figure SMS_38
t时段节点i的电压大小;
Figure SMS_41
t时段第g台火电机组的有功发电功率;
Figure SMS_28
为第w台风电机组在t时段的有功发电功率;
Figure SMS_34
表示分布式储能装置dt时段的有功放电量,
Figure SMS_19
表示储能dt时段的有功充电量;
Figure SMS_32
t时段上级电网向配电网输送的有功功率;
Figure SMS_22
t时段上级电网向配电网输送的无功功率;
Figure SMS_39
t时刻失电负荷功率,
Figure SMS_36
t时段负荷d的功率因数;
Figure SMS_40
t时段配电线路ij段的有功功率;
Figure SMS_23
t时段配电线路ij段的无功功率;
Figure SMS_27
t时段配电线路ij段的有功功率;
Figure SMS_20
t时段配电线路ij段的无功功率;
Figure SMS_35
分别为t时段负荷d的有功负荷值和无功负荷值;
Figure SMS_21
Figure SMS_29
为输电线路ij段的电流大小限制;
Figure SMS_26
Figure SMS_30
为节点i的电压大小限制。Among them, the collection
Figure SMS_37
is the set of devices connected to the distribution network node j ;
Figure SMS_24
is the set of branch end nodes with j as the head end node in the distribution network;
Figure SMS_31
is the resistance of the distribution line ij ,
Figure SMS_25
is the reactance of the distribution line ij ;
Figure SMS_33
is the current of the distribution line ij during period t ;
Figure SMS_38
is the voltage of node i at time period t ;
Figure SMS_41
is the active power generation of the g- th thermal power unit in period t ;
Figure SMS_28
is the active power generated by the w- th wind turbine in period t ;
Figure SMS_34
represents the active discharge of distributed energy storage device d in period t ,
Figure SMS_19
It represents the active charge of energy storage d in period t ;
Figure SMS_32
is the active power transmitted from the upper power grid to the distribution network during period t ;
Figure SMS_22
is the reactive power transmitted from the upper power grid to the distribution network during period t ;
Figure SMS_39
is the power of the power-off load at time t ,
Figure SMS_36
is the power factor of load d in period t ;
Figure SMS_40
Refers to the active power of the ij section of the distribution line during time period t ;
Figure SMS_23
Refers to the reactive power of the ij section of the distribution line during time period t ;
Figure SMS_27
Refers to the active power of the ij section of the distribution line during time period t ;
Figure SMS_20
Refers to the reactive power of the ij section of the distribution line during time period t ;
Figure SMS_35
are respectively the active load value and reactive load value of load d in period t ;
Figure SMS_21
and
Figure SMS_29
is the current limit of the ij section of the transmission line;
Figure SMS_26
and
Figure SMS_30
is the voltage limit of node i .

作为优选,步骤2中,火电机组具体运行约束条件表示为:As a preference, in step 2, the specific operating constraints of the thermal power unit are expressed as:

火电机组出力约束:

Figure SMS_42
;Output constraints of thermal power units:
Figure SMS_42
;

火电机组爬坡约束:

Figure SMS_43
;Thermal power unit climbing constraints:
Figure SMS_43
;

火电机组启停约束:

Figure SMS_44
;Constraints on starting and stopping thermal power units:
Figure SMS_44
;

其中:

Figure SMS_47
为状态变量,表示火电机组的工作状态;
Figure SMS_48
火电机组出力的最小限制;
Figure SMS_51
火电机组提供出力的最大限制;
Figure SMS_46
Figure SMS_50
分别为火电机组g的上、下爬坡率;
Figure SMS_52
Figure SMS_53
分别指火电机组g的开机和停机时间计数器;
Figure SMS_45
Figure SMS_49
分别指燃气机组g的最小开机和停机时间。in:
Figure SMS_47
is a state variable, indicating the working state of the thermal power unit;
Figure SMS_48
Minimum limit on the output of thermal power units;
Figure SMS_51
The maximum limit of the output provided by thermal power units;
Figure SMS_46
and
Figure SMS_50
are the up and down climbing rates of thermal power unit g respectively;
Figure SMS_52
and
Figure SMS_53
They refer to the start-up and shutdown time counters of thermal power unit g respectively;
Figure SMS_45
and
Figure SMS_49
They respectively refer to the minimum startup and shutdown time of gas generator set g .

作为优选,步骤2中,风电机组具体运行约束条件表示为:Preferably, in step 2, the specific operating constraints of the wind turbine generator set are expressed as follows:

风电机组出力约束:

Figure SMS_54
;Wind turbine output constraints:
Figure SMS_54
;

风电机组实际出力、弃风大小、风电机组预测出力的关系:

Figure SMS_55
;The relationship between the actual output of wind turbines, the amount of wind abandonment, and the predicted output of wind turbines:
Figure SMS_55
;

其中:

Figure SMS_56
为第w台风电机组在t时段的预测发电功率,
Figure SMS_57
为第w台风电机组在t时段的弃风量。in:
Figure SMS_56
is the predicted power generation of the w- th wind turbine in period t ,
Figure SMS_57
is the wind abandonment volume of the w- th wind turbine in period t .

作为优选,步骤2中,储能装置具体约束条件表示为:Preferably, in step 2, the specific constraints of the energy storage device are expressed as:

储能装置充放电功率约束:

Figure SMS_58
;Energy storage device charging and discharging power constraints:
Figure SMS_58
;

每次充放电前后电量约束:

Figure SMS_59
;Capacity limit before and after each charge and discharge:
Figure SMS_59
;

每次充放电功率限制:

Figure SMS_60
;Each charge and discharge power limit:
Figure SMS_60
;

24小时充放电后电量与当天初始时刻的电量相等约束:

Figure SMS_61
The power after 24 hours of charging and discharging is equal to the power at the beginning of the day:
Figure SMS_61

其中:

Figure SMS_63
Figure SMS_67
为储能装置充放电功率限制的最小值与最大值;
Figure SMS_69
是储能装置t时段的电量;
Figure SMS_64
是储能装置初始时刻的电量,
Figure SMS_66
是储能装置一天结束时刻的电量;
Figure SMS_68
是储能装置的在t时段的充放电功率;
Figure SMS_70
是储能装置的充放电效率;
Figure SMS_62
为储能装置在t时段充放电的状态变量,取值为0或1;
Figure SMS_65
为储能装置充放电功率的最大值限制。in:
Figure SMS_63
,
Figure SMS_67
The minimum and maximum values of the charging and discharging power limits of the energy storage device;
Figure SMS_69
is the amount of electricity in the energy storage device during period t ;
Figure SMS_64
is the initial charge of the energy storage device,
Figure SMS_66
is the amount of electricity in the energy storage device at the end of the day;
Figure SMS_68
is the charging and discharging power of the energy storage device in period t ;
Figure SMS_70
is the charging and discharging efficiency of the energy storage device;
Figure SMS_62
is the state variable of the energy storage device charging and discharging during period t , and its value is 0 or 1;
Figure SMS_65
It is the maximum value limit of charging and discharging power of energy storage device.

作为优选,步骤2中,电压降与支路电流约束表示的配电网节点电流方程是线性非凸的,所以对节点平衡非线性约束进行一定程度的松弛,将其转化为二阶锥优化模型进行求解:Preferably, in step 2, the distribution network node current equation represented by the voltage drop and branch current constraints is linear and non-convex, so the node balance nonlinear constraints are relaxed to a certain extent and converted into a second-order cone optimization model for solution:

首先定义

Figure SMS_71
Figure SMS_72
这两个中间变量,结合支路电流与功率关系式:First define
Figure SMS_71
and
Figure SMS_72
These two intermediate variables, combined with the branch current and power relationship:

Figure SMS_73
Figure SMS_73
;

对接点电压降落方程进行松弛处理得到:Relaxing the junction voltage drop equation yields:

Figure SMS_74
Figure SMS_74
;

进一步转换得到二阶锥方程:Further transformation yields the second-order cone equation:

Figure SMS_75
Figure SMS_75
;

二阶锥松弛后的电流和电压约束为:

Figure SMS_76
。The current and voltage constraints after the second-order cone relaxation are:
Figure SMS_76
.

作为优选,步骤3中,将系统参数、目标函数与约束条件转换成Matlab软件中的语句进行编译,并在Matlab软件中调用Gurobi商业求解器对模型进行优化求解,得出风电机组、火电机组的出力计划,以及分布式储能装置在参与系统调峰填谷的作用与对系统经济效应的提高情况。Preferably, in step 3, the system parameters, objective function and constraints are converted into statements in Matlab software for compilation, and the Gurobi commercial solver is called in Matlab software to optimize and solve the model, so as to obtain the output plans of wind turbines and thermal power units, as well as the role of distributed energy storage devices in participating in the peak-loading and valley-filling of the system and the improvement of the economic effect of the system.

本发明还提供了一种含分布式储能的电力系统优化运行系统,其采用上述的含分布式储能的电力系统优化运行方法,并包括:The present invention also provides a power system optimization operation system including distributed energy storage, which adopts the above-mentioned power system optimization operation method including distributed energy storage, and comprises:

数据获取模块,用于获取一日内配电网结构参数、风电场预测出力、储能装置运行参数、向上级电网购电费用、弃风惩罚成本、火电机组运行成本、储能装置运行成本、切负荷成本;The data acquisition module is used to obtain the distribution network structure parameters, wind farm forecast output, energy storage device operating parameters, electricity purchase costs from the superior power grid, wind abandonment penalty costs, thermal power unit operating costs, energy storage device operating costs, and load shedding costs within a day;

建模模块,用于基于日前配电网各机组出力与运行状态、储能装置运行状态数据构建风电并网的分布式储能装置参与配电网优化调度模型;Modeling module, used to build a model for distributed energy storage devices connected to the wind power grid to participate in the distribution network optimization dispatching based on the output and operating status of each unit in the distribution network and the operating status data of the energy storage device;

优化计算模块,用于将调度模型构建为易于求解的混合整数规划模型,引入0-1二进制状态变量对部分变量进行约束,采用混合整数二阶锥规划法对配电网节点的非线性潮流约束进行改写处理,并通过设置实例分析求解结果;The optimization calculation module is used to construct the dispatch model into an easy-to-solve mixed integer programming model, introduce 0-1 binary state variables to constrain some variables, use mixed integer second-order cone programming to rewrite the nonlinear power flow constraints of distribution network nodes, and analyze the solution results by setting examples;

输出出力计划模块,用于输出优化计算模块的优化计算结果,并且得出系统各机组出力日前计划。The output plan module is used to output the optimization calculation results of the optimization calculation module and obtain the day-ahead output plan of each unit in the system.

本发明还提供了一种含分布式储能的电力系统优化运行设备,其包括处理器,处理器执行计算机程序时实现如上述的含分布式储能的电力系统优化运行方法。The present invention also provides an optimized operation device for an electric power system including distributed energy storage, which includes a processor. When the processor executes a computer program, the optimized operation method for an electric power system including distributed energy storage as described above is implemented.

本发明模拟了含分布式储能的配电网系统,建立了风电、分布式储能装置接入的配电网日前优化调度模型,模拟实际系统运行情况。本发明能达到的技术效果内容如下:The present invention simulates a distribution network system containing distributed energy storage, establishes a day-ahead optimization dispatching model for a distribution network connected to wind power and distributed energy storage devices, and simulates the actual system operation. The technical effects that the present invention can achieve are as follows:

(1)确定配电网系统框架,对配电网内风力发电、火电机组发电、分布式储能装置建立出力数学模型。在数学模型的基础上,建立了并网模式下配电网日前经济调度模型;根据各分布式电源出力特点,提出以一日运行成本最小为目的的目标函数,目标函数中考虑了配电网从上级电网的购电成本、火电机组发电成本、风电机组弃风惩罚成本、储能一日运行成本、失负荷成本,并通过如功率平衡约束、电网电压电流安全约束、风力发电机出来约束、电网安全约束、火电机组出力爬坡率约束、储能装置充放电功率约束等将模型变得更贴近实际配电网系统。(1) Determine the distribution network system framework and establish a mathematical model for the output of wind power generation, thermal power generation, and distributed energy storage devices in the distribution network. Based on the mathematical model, a day-ahead economic dispatch model for the distribution network under the grid-connected mode is established; according to the output characteristics of each distributed power source, an objective function with the goal of minimizing the daily operating cost is proposed. The objective function takes into account the distribution network's electricity purchase cost from the upper power grid, the thermal power generation cost, the wind turbine wind abandonment penalty cost, the energy storage daily operating cost, and the load loss cost. The model is made closer to the actual distribution network system through power balance constraints, grid voltage and current safety constraints, wind turbine output constraints, grid safety constraints, thermal power output ramp rate constraints, and energy storage device charging and discharging power constraints.

(2)将调度模型构建为易于求解的混合整数规划模型,引入0-1二进制状态变量对部分变量进行约束,采用混合整数二阶锥规划法对配电网节点的非线性潮流约束进行改写处理,并通过设置实例分析求解结果体现本发明的技术效果,在Matlab中编程后采用商业求解器Gurobi进行求解。在不同的实例中,分别考虑了接入风电机组和分布式储能装置、风电渗透率、储能装置位置与功率参数对系统运行和系统经济效益的影响,并且得出系统各机组出力日前计划。通过对各实例数据分析,通过本发明的技术可得出结论:分布式储能装置的引入,可使配电网在负荷低谷时段将剩余电量存入储能装置,提高系统对风电的消纳;在有功率缺额时由分布式储能装置进行放电来进行补偿,避免在电价较高时大量购电,达到调峰填谷的效果,提高系统运行的经济效益。分布式储能装置的位置、装置的参数也影响着其参与系统能源调度的情况。(2) The dispatch model is constructed as a mixed integer programming model that is easy to solve. 0-1 binary state variables are introduced to constrain some variables. The nonlinear power flow constraints of the distribution network nodes are rewritten using the mixed integer second-order cone programming method. The technical effect of the present invention is reflected by setting examples to analyze the solution results. After programming in Matlab, the commercial solver Gurobi is used to solve. In different examples, the influence of the connected wind turbines and distributed energy storage devices, wind power penetration rate, energy storage device location and power parameters on the system operation and system economic benefits are considered respectively, and the day-ahead output plan of each unit in the system is obtained. Through the analysis of the data of each example, it can be concluded that the introduction of distributed energy storage devices can enable the distribution network to store the remaining electricity in the energy storage device during the low load period, thereby improving the system's absorption of wind power; when there is a power shortage, the distributed energy storage device discharges to compensate, avoiding the purchase of a large amount of electricity when the electricity price is high, achieving the effect of peak-to-valley shifting, and improving the economic benefits of system operation. The location and parameters of the distributed energy storage device also affect its participation in the system energy dispatch.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

图1为实施例1中一种含分布式储能的电力系统优化运行方法的流程图;FIG1 is a flow chart of a method for optimizing operation of a power system including distributed energy storage in Example 1;

图2为实施例1中电力系统动态经济调度优化方法的应用环境图;FIG2 is an application environment diagram of the power system dynamic economic dispatch optimization method in Example 1;

图3为实施例1中计算机设备的示意图;FIG3 is a schematic diagram of a computer device in Example 1;

图4为实施例2中配电网系统节点结构图;FIG4 is a node structure diagram of the distribution network system in Example 2;

图5(a)为实施例2中实例1经过优化后得出的各机组出力日前计划示意图;FIG5 (a) is a schematic diagram of the day-ahead output plan of each unit obtained after optimization of Example 1 in Example 2;

图5(b)为实施例2中实例2经过优化后得出的各机组出力日前计划示意图;FIG5( b ) is a schematic diagram of the day-ahead output plan of each unit obtained after optimization of Example 2 in Embodiment 2;

图6为实施例2中实例3中三种运行方式的系统结构示意图;FIG6 is a schematic diagram of the system structure of three operating modes in Example 3 of Embodiment 2;

图7为实施例2中实例3中储能装置内部电量

Figure SMS_77
的变化情况示意图;FIG. 7 is a diagram showing the internal charge of the energy storage device in Example 3 of Embodiment 2.
Figure SMS_77
Schematic diagram of the changes in

图8为实施例2中实例4中储能装置内部电量

Figure SMS_78
的变化情况示意图。FIG. 8 is a diagram showing the internal charge of the energy storage device in Example 4 of Embodiment 2.
Figure SMS_78
Schematic diagram of the changes in .

具体实施方式DETAILED DESCRIPTION

为进一步了解本发明的内容,结合附图和实施例对本发明作详细描述。应当理解的是,实施例仅仅是对本发明进行解释而并非限定。In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

实施例1:Embodiment 1:

如图1所示,本实施例提供了一种含分布式储能的电力系统优化运行方法,其包括以下步骤:As shown in FIG1 , this embodiment provides a method for optimizing operation of a power system including distributed energy storage, which includes the following steps:

步骤1:获取系统的初始参数,包括配电网结构参数、风电场预测出力、储能装置运行参数、向上级电网购电费用、弃风惩罚成本、火电机组运行成本、储能装置运行成本、切负荷成本;Step 1: Obtain the initial parameters of the system, including distribution network structure parameters, wind farm forecast output, energy storage device operating parameters, electricity purchase costs from the superior power grid, wind power abandonment penalty costs, thermal power unit operating costs, energy storage device operating costs, and load shedding costs;

步骤2:建立含分布式储能的配电网优化运行模型,模型包括:火电机组、风力发电机组、储能装置、电力负荷;以配电网一日运行成本最低为优化目标,确定出系统运行各部分约束条件,使用混合整数规划法与二阶锥线性规划法,对建立的配电网日前经济调度模型进行求解;Step 2: Establish an optimal operation model of the distribution network with distributed energy storage. The model includes: thermal power units, wind turbines, energy storage devices, and power loads. Take the lowest daily operation cost of the distribution network as the optimization goal, determine the constraints of each part of the system operation, and use mixed integer programming and second-order cone linear programming to solve the established distribution network day-ahead economic dispatch model.

步骤3:在Matlab软件中调用Gurobi求解器对模型进行优化求解,形成日前购电量、风电机组与火电机组出力及分布式储能装置充放电计划,以及分布式储能装置在参与系统调峰填谷的作用及其对系统经济效益的提高情况;Step 3: Invoke the Gurobi solver in Matlab software to optimize and solve the model, and form the day-ahead power purchase amount, wind turbine and thermal power unit output, and distributed energy storage device charging and discharging plan, as well as the role of distributed energy storage devices in participating in system peak-shaving and valley-filling and their improvement on system economic benefits;

步骤4:通过改变储能装置接入配电网系统的位置或储能装置的充放电功率参数,研究储能装置接入配电网位置或充放电功率参数对风电消纳与系统运行经济状况的影响。Step 4: By changing the location where the energy storage device is connected to the distribution network system or the charging and discharging power parameters of the energy storage device, the impact of the location where the energy storage device is connected to the distribution network or the charging and discharging power parameters on wind power consumption and the economic conditions of system operation is studied.

图2是本实施例提供的电力系统动态经济调度优化方法的应用环境图。本实施例提供的电力系统动态经济调度优化方法可以但不限于应用于该应用环境。如图2所示,该应用环境包括:电力数据采集设备、计算机设备104和调度中心105。FIG2 is an application environment diagram of the power system dynamic economic dispatch optimization method provided in this embodiment. The power system dynamic economic dispatch optimization method provided in this embodiment can be applied to, but is not limited to, this application environment. As shown in FIG2 , the application environment includes: power data acquisition equipment, computer equipment 104 and dispatch center 105.

电力数据采集设备101~103可以是机电一体式电表、全电子式电表等智能电表,在此不作限定。具有用户端控制、多种数据传输模式的双向数据通信等智能化的功能。计算机设备104可以是服务器(Server)、终端(Terminal)等,在此不作限定。服务器,可以在网络环境下为客户机(Client,此处通常指可以发出调度需求指令的计算机)提供某种服务的专用计算机,服务器安装有网络操作系统(如Windows Server、Linux、Unix等)和各种服务器应用系统软件(此处主要指可以完成含分布式储能的电力系统优化运行策略过程的Matlab软件)的计算机。终端通常是指那些与集中式主机系统(例如IBM大型计算机)相连的用户设备,终端从用户接收键盘输入的数据采集指令与优化运算指令,并且将这些输入发送给主机系统。调度中心105配置在线调度计算机设备104,通过人机对话方式完成对含分布式储能的电力系统优化运行策略的计算。The power data acquisition equipment 101~103 can be an intelligent meter such as an electromechanical integrated meter and a fully electronic meter, which is not limited here. It has intelligent functions such as user-side control and two-way data communication in multiple data transmission modes. The computer device 104 can be a server (Server), a terminal (Terminal), etc., which is not limited here. The server is a dedicated computer that can provide certain services to the client (Client, here usually refers to a computer that can issue a dispatch demand instruction) in a network environment. The server is installed with a network operating system (such as Windows Server, Linux, Unix, etc.) and various server application system software (here mainly refers to Matlab software that can complete the process of optimizing the operation strategy of the power system containing distributed energy storage). The terminal usually refers to user devices connected to a centralized host system (such as an IBM mainframe computer). The terminal receives data acquisition instructions and optimization operation instructions input by the user from the keyboard, and sends these inputs to the host system. The dispatch center 105 is equipped with an online dispatch computer device 104 to complete the calculation of the optimal operation strategy of the power system containing distributed energy storage through human-computer dialogue.

调度中心105向计算机设备104发送优化指令。计算机设备104用于在收到优化指令后,向目标区域内的电力数据采集设备101~103发送采集指令。电力数据采集设备101~103用于在收到采集指令后,采集电力系统在一日内的各机组运行数据和储能装置运行数据,并将采集到的数据发送到计算机设备104中。计算机设备104在接收到电力系统在一日内的各机组运行数据和储能装置运行数据后,建立风电、分布式储能装置接入的配电网日前优化调度模型,并将得到的各机组一日运行计划出力结果发送到调度中心105中。The dispatch center 105 sends an optimization instruction to the computer device 104. After receiving the optimization instruction, the computer device 104 is used to send a collection instruction to the power data collection devices 101-103 in the target area. After receiving the collection instruction, the power data collection devices 101-103 are used to collect the operation data of each unit and the operation data of the energy storage device in the power system within a day, and send the collected data to the computer device 104. After receiving the operation data of each unit and the operation data of the energy storage device in the power system within a day, the computer device 104 establishes a day-ahead optimization dispatching model for the distribution network connected to wind power and distributed energy storage devices, and sends the obtained daily operation plan output results of each unit to the dispatch center 105.

步骤2中,建立含分布式储能的配电网优化运行模型,以最小化配电网的总运行成本为优化目标;配电网运行成本包括:配电网从上级电网的购电成本、火电机组发电成本、风电机组弃风惩罚成本、储能装置的运行成本以及失负荷成本,由此得出配电网优化运行模型的目标函数如下:In step 2, an optimal operation model of the distribution network with distributed energy storage is established, with the optimization goal of minimizing the total operation cost of the distribution network; the operation cost of the distribution network includes: the cost of purchasing electricity from the upper power grid, the cost of generating electricity from thermal power units, the penalty cost of wind turbines for abandoning wind, the operation cost of energy storage devices, and the cost of load loss. The objective function of the optimal operation model of the distribution network is as follows:

Figure SMS_79
Figure SMS_79
;

其中:

Figure SMS_89
为配电网从上级电网购电费用,
Figure SMS_82
为火电机组运行成本,
Figure SMS_93
为弃风惩罚成本,
Figure SMS_84
为储能装置运行成本,
Figure SMS_95
为失负荷成本;
Figure SMS_87
t时段配电网从上级电网的购电成本,
Figure SMS_88
t时段配电网从上级电网的购电有功功率;A、B、C为火电机组发电成本系数;
Figure SMS_81
表示火电机组gt时段的出力;
Figure SMS_90
风电机组
Figure SMS_80
的弃风惩罚成本;
Figure SMS_91
表示风电场wt时段的预测出力;
Figure SMS_86
表示风电场wt时段的出力;
Figure SMS_92
为储能使用成本,
Figure SMS_85
表示储能dt时段的放电量;
Figure SMS_94
为配电网切负荷成本,
Figure SMS_83
表示在shed处在t时段的切负荷电量。in:
Figure SMS_89
The cost of electricity purchased by the distribution network from the upper power grid.
Figure SMS_82
is the operating cost of thermal power units,
Figure SMS_93
Penalty costs for wind curtailment,
Figure SMS_84
is the operating cost of the energy storage device,
Figure SMS_95
is the loss of load cost;
Figure SMS_87
is the cost of electricity purchased by the distribution network from the upper power grid during period t ,
Figure SMS_88
is the active power purchased by the distribution network from the upper power grid during period t ; A, B, C are the power generation cost coefficients of thermal power units;
Figure SMS_81
It represents the output of thermal power unit g in period t ;
Figure SMS_90
Wind turbines
Figure SMS_80
The penalty cost of wind curtailment;
Figure SMS_91
represents the predicted output of wind farm w in period t ;
Figure SMS_86
represents the output of wind farm w in period t ;
Figure SMS_92
is the cost of energy storage,
Figure SMS_85
It represents the discharge amount of energy storage d in time period t ;
Figure SMS_94
is the load shedding cost of the distribution network,
Figure SMS_83
It represents the load shedding power in the shed during the period t .

约束条件Constraints

1)利用DistFlow模型来描述配电网节点功率平衡,系统运行约束条件表示为:1) The DistFlow model is used to describe the power balance of distribution network nodes, and the system operation constraints are expressed as:

节点功率平衡约束:Node power balance constraints:

Figure SMS_96
Figure SMS_96
;

其中,集合

Figure SMS_113
为连接到配电网节点j的设备集合;
Figure SMS_103
为配电网中以j为首端节点的支路末端节点集合;
Figure SMS_107
为配电线路ij的电阻,
Figure SMS_112
为配电线路ij的电抗;
Figure SMS_119
t时段配电线路ij的电流;
Figure SMS_114
t时段节点i的电压大小;
Figure SMS_118
t时段第g台火电机组的有功发电功率;
Figure SMS_115
为第w台风电机组在t时段的有功发电功率;
Figure SMS_117
表示分布式储能装置dt时段的有功放电量,
Figure SMS_101
表示储能dt时段的有功充电量;
Figure SMS_108
t时段上级电网向配电网输送的有功功率;
Figure SMS_99
t时段上级电网向配电网输送的无功功率;
Figure SMS_111
t时刻失电负荷功率,
Figure SMS_104
t时段负荷d的功率因数;
Figure SMS_110
t时段配电线路ij段的有功功率;
Figure SMS_98
t时段配电线路ij段的无功功率;
Figure SMS_105
t时段配电线路ij段的有功功率;
Figure SMS_100
t时段配电线路ij段的无功功率;
Figure SMS_109
分别为t时段负荷d的有功负荷值和无功负荷值;
Figure SMS_97
Figure SMS_106
为输电线路ij段的电流大小限制;
Figure SMS_102
Figure SMS_116
为节点i的电压大小限制。Among them, the collection
Figure SMS_113
is the set of devices connected to the distribution network node j ;
Figure SMS_103
is the set of branch end nodes with j as the head end node in the distribution network;
Figure SMS_107
is the resistance of the distribution line ij ,
Figure SMS_112
is the reactance of the distribution line ij ;
Figure SMS_119
is the current of the distribution line ij during period t ;
Figure SMS_114
is the voltage of node i at time period t ;
Figure SMS_118
is the active power generation of the g- th thermal power unit in period t ;
Figure SMS_115
is the active power generated by the w- th wind turbine in period t ;
Figure SMS_117
represents the active discharge of distributed energy storage device d in period t ,
Figure SMS_101
It represents the active charge of energy storage d in period t ;
Figure SMS_108
is the active power transmitted from the upper power grid to the distribution network during period t ;
Figure SMS_99
is the reactive power transmitted from the upper power grid to the distribution network during period t ;
Figure SMS_111
is the power of the power-off load at time t ,
Figure SMS_104
is the power factor of load d in period t ;
Figure SMS_110
Refers to the active power of the ij section of the distribution line during time period t ;
Figure SMS_98
Refers to the reactive power of the ij section of the distribution line during time period t ;
Figure SMS_105
Refers to the active power of the ij section of the distribution line during time period t ;
Figure SMS_100
Refers to the reactive power of the ij section of the distribution line during time period t ;
Figure SMS_109
are respectively the active load value and reactive load value of load d in period t ;
Figure SMS_97
and
Figure SMS_106
is the current limit of the ij section of the transmission line;
Figure SMS_102
and
Figure SMS_116
is the voltage limit of node i .

2)由于火电机组性能与数量是确定的,其出力有一定的限制,此外,火电机组还有启停约束与爬坡约束,火电机组具体运行约束条件表示为:2) Since the performance and quantity of thermal power units are fixed, their output is limited. In addition, thermal power units are subject to start-stop constraints and ramp constraints. The specific operating constraints of thermal power units are expressed as:

火电机组出力约束:

Figure SMS_120
;Output constraints of thermal power units:
Figure SMS_120
;

火电机组爬坡约束:

Figure SMS_121
;Thermal power unit climbing constraints:
Figure SMS_121
;

火电机组启停约束:

Figure SMS_122
;Constraints on starting and stopping thermal power units:
Figure SMS_122
;

其中:

Figure SMS_125
为状态变量(0、1),表示火电机组的工作状态;
Figure SMS_126
火电机组出力的最小限制;
Figure SMS_130
火电机组提供出力的最大限制;
Figure SMS_124
Figure SMS_127
分别为火电机组g的上、下爬坡率;
Figure SMS_129
Figure SMS_131
分别指火电机组g的开机和停机时间计数器;
Figure SMS_123
Figure SMS_128
分别指燃气机组g的最小开机和停机时间。in:
Figure SMS_125
is a state variable (0, 1), indicating the working state of the thermal power unit;
Figure SMS_126
Minimum limit on the output of thermal power units;
Figure SMS_130
The maximum limit of the output provided by thermal power units;
Figure SMS_124
and
Figure SMS_127
are the up and down climbing rates of thermal power unit g respectively;
Figure SMS_129
and
Figure SMS_131
They refer to the start-up and shutdown time counters of thermal power unit g respectively;
Figure SMS_123
and
Figure SMS_128
They respectively refer to the minimum startup and shutdown time of gas generator set g .

3)由于风电机组性能与数量确定,其出力有一定的限制,从风电机组输送到配电网的出力有上下界限制,风电机组具体运行约束条件表示为:3) Since the performance and quantity of wind turbines are determined, their output is limited to a certain extent. The output transmitted from wind turbines to the distribution network has upper and lower limits. The specific operating constraints of wind turbines are expressed as:

风电机组出力约束:

Figure SMS_132
;Wind turbine output constraints:
Figure SMS_132
;

风电机组实际出力、弃风大小、风电机组预测出力的关系:

Figure SMS_133
;The relationship between the actual output of wind turbines, the amount of wind abandonment, and the predicted output of wind turbines:
Figure SMS_133
;

其中:

Figure SMS_134
为第w台风电机组在t时段的预测发电功率,
Figure SMS_135
为第w台风电机组在t时段的弃风量。in:
Figure SMS_134
is the predicted power generation of the w- th wind turbine in period t ,
Figure SMS_135
is the wind abandonment volume of the w- th wind turbine in period t .

4)由于储能装置受到本身制造工艺和系统中变流器的限制,存在电池充放电功率最大、最小值,因此储能装置充放电功率约束;每次充放电前后满足电量的约束为;考虑充放电功率上限,引入

Figure SMS_136
表示任一时刻分布式储能装置只能处于充电、放电、不充不放3种状态之一,而不存在既充电又放电的物理不可行现象,得出每次充放电功率限制;且储能装置在24小时充放电后的电量与当天初始时刻的电量相等。储能装置具体约束条件表示为:4) Since the energy storage device is limited by its own manufacturing process and the converter in the system, there are maximum and minimum values of battery charging and discharging power, so the energy storage device charging and discharging power constraints; the power constraints before and after each charge and discharge are; considering the upper limit of charging and discharging power, introduce
Figure SMS_136
It means that at any time, the distributed energy storage device can only be in one of the three states of charging, discharging, and neither charging nor discharging. There is no physically impossible phenomenon of both charging and discharging, and the power limit of each charge and discharge is obtained; and the power of the energy storage device after 24 hours of charging and discharging is equal to the power at the beginning of the day. The specific constraints of the energy storage device are expressed as:

储能装置充放电功率约束:Energy storage device charging and discharging power constraints:

Figure SMS_137
Figure SMS_137
;

每次充放电前后电量约束:

Figure SMS_138
;Capacity limit before and after each charge and discharge:
Figure SMS_138
;

每次充放电功率限制:

Figure SMS_139
;Each charge and discharge power limit:
Figure SMS_139
;

24小时充放电后电量与当天初始时刻的电量相等约束:

Figure SMS_140
;The power after 24 hours of charging and discharging is equal to the power at the beginning of the day:
Figure SMS_140
;

其中:

Figure SMS_142
Figure SMS_144
为储能装置充放电功率限制的最小值与最大值;
Figure SMS_146
是储能装置t时段的电量;
Figure SMS_143
是储能装置初始时刻的电量,
Figure SMS_148
是储能装置一天结束时刻的电量;
Figure SMS_149
Figure SMS_150
是储能装置的在t时段的充放电功率;
Figure SMS_141
是储能装置的充放电效率;
Figure SMS_145
为储能装置在t时段充放电的状态变量,取值为0或1;
Figure SMS_147
为储能装置充放电功率的最大值限制。in:
Figure SMS_142
,
Figure SMS_144
The minimum and maximum values of the charging and discharging power limits of the energy storage device;
Figure SMS_146
is the amount of electricity in the energy storage device during period t ;
Figure SMS_143
is the initial charge of the energy storage device,
Figure SMS_148
is the amount of electricity in the energy storage device at the end of the day;
Figure SMS_149
,
Figure SMS_150
is the charging and discharging power of the energy storage device in period t ;
Figure SMS_141
is the charging and discharging efficiency of the energy storage device;
Figure SMS_145
is the state variable of the energy storage device charging and discharging during period t , and its value is 0 or 1;
Figure SMS_147
It is the maximum value limit of charging and discharging power of energy storage device.

电压降与支路电流约束表示的配电网节点电流方程是线性非凸的,所以对节点平衡非线性约束进行一定程度的松弛,将其转化为二阶锥优化模型进行求解:The node current equation of the distribution network represented by the voltage drop and branch current constraints is linear and non-convex, so the node balance nonlinear constraints are relaxed to a certain extent and converted into a second-order cone optimization model for solution:

首先定义

Figure SMS_151
Figure SMS_152
这两个中间变量,结合支路电流与功率关系式:First define
Figure SMS_151
and
Figure SMS_152
These two intermediate variables, combined with the branch current and power relationship:

Figure SMS_153
Figure SMS_153
;

对接点电压降落方程进行松弛处理得到:Relaxing the junction voltage drop equation yields:

Figure SMS_154
Figure SMS_154
;

进一步转换得到二阶锥方程:Further transformation yields the second-order cone equation:

Figure SMS_155
Figure SMS_155
;

二阶锥松弛后的电流和电压约束为:

Figure SMS_156
;The current and voltage constraints after the second-order cone relaxation are:
Figure SMS_156
;

步骤3中,将系统参数、目标函数与约束条件转换成Matlab软件中的语句进行编译,并在Matlab软件中调用Gurobi商业求解器对模型进行优化求解,得出风电机组、火电机组的出力计划,以及分布式储能装置在参与系统调峰填谷的作用与对系统经济效应的提高情况。In step 3, the system parameters, objective function and constraints are converted into statements in Matlab software for compilation, and the Gurobi commercial solver is called in Matlab software to optimize and solve the model, and the output plan of wind turbines and thermal power units is obtained, as well as the role of distributed energy storage devices in participating in the system's peak-loading and valley-filling and the improvement of the system's economic effects.

本实施例提供了一种含分布式储能的电力系统优化运行系统,其采用上述的一种含分布式储能的电力系统优化运行方法,并包括:This embodiment provides a power system optimization operation system including distributed energy storage, which adopts the above-mentioned power system optimization operation method including distributed energy storage, and includes:

数据获取模块,用于获取一日内配电网结构参数、风电场预测出力、储能装置运行参数、向上级电网购电费用、弃风惩罚成本、火电机组运行成本、储能装置运行成本、切负荷成本;The data acquisition module is used to obtain the distribution network structure parameters, wind farm forecast output, energy storage device operating parameters, electricity purchase costs from the superior power grid, wind abandonment penalty costs, thermal power unit operating costs, energy storage device operating costs, and load shedding costs within a day;

建模模块,用于基于日前配电网各机组出力与运行状态、储能装置运行状态数据构建风电并网的分布式储能装置参与配电网优化调度模型;Modeling module, used to build a model for distributed energy storage devices connected to the wind power grid to participate in the distribution network optimization dispatching based on the output and operating status of each unit in the distribution network and the operating status data of the energy storage device;

优化计算模块,用于将调度模型构建为易于求解的混合整数规划模型,引入0-1二进制状态变量对部分变量进行约束,采用混合整数二阶锥规划法对配电网节点的非线性潮流约束进行改写处理,并通过设置实例分析求解结果;The optimization calculation module is used to construct the dispatch model into an easy-to-solve mixed integer programming model, introduce 0-1 binary state variables to constrain some variables, use mixed integer second-order cone programming to rewrite the nonlinear power flow constraints of distribution network nodes, and analyze the solution results by setting examples;

输出出力计划模块,用于输出优化计算模块的优化计算结果,并且得出系统各机组出力日前计划。The output plan module is used to output the optimization calculation results of the optimization calculation module and obtain the day-ahead output plan of each unit in the system.

本实施例提供了一种含分布式储能的电力系统优化运行设备,即计算机设备300(也是图2中计算机设备104),计算机设备300可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备,计算机设备示意框图参阅图3,但图3仅仅是计算机设备300的示例,并不构成对计算机设备300的限定,可以包括比图示更多或更少的部件,或者组合某些部件,或者不同的部件,例如终端还可以包括输入输出设备、网络接入设备、总线等。计算机设备300包括处理器301、电源302、有线或无线接口308、输入输出接口309、存储器307,以及在存储器307中并可在所述处理器301上的操作系统303、计算机程序304、数据库305、非易失性存储介质306。处理器301执行计算机程序304时实现如上述的含分布式储能的电力系统优化运行方法。This embodiment provides a device for optimizing the operation of a power system including distributed energy storage, namely, a computer device 300 (also the computer device 104 in FIG. 2 ). The computer device 300 may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The schematic block diagram of the computer device is shown in FIG. 3 , but FIG. 3 is only an example of the computer device 300 and does not constitute a limitation on the computer device 300 . The computer device 300 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc. The computer device 300 includes a processor 301 , a power supply 302 , a wired or wireless interface 308 , an input and output interface 309 , a memory 307 , and an operating system 303 , a computer program 304 , a database 305 , and a non-volatile storage medium 306 in the memory 307 and on the processor 301 . When the processor 301 executes the computer program 304 , the above-mentioned method for optimizing the operation of a power system including distributed energy storage is implemented.

1)处理器301是内置于计算机设备300超大规模集成电路,它包括运算逻辑部件、寄存器部件和控制部件等。它可以从存储器中取出指令,放入指令寄存器,并对指令译码,它把指令分解成一系列的微操作,然后发出各种控制命令,执行微操作系列,从而完成一条指令的执行。处理器301可以是中央处理单元(Central Processing Unit,CPU),还可以是其他通用处理器、数字信号处理器(Digital Signal Processor,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现场可编程门阵列(Field-Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等,在此不作限定。1) The processor 301 is a very large scale integrated circuit built into the computer device 300, which includes an arithmetic logic unit, a register unit, and a control unit. It can fetch instructions from the memory, put them into the instruction register, and decode the instructions. It decomposes the instructions into a series of micro-operations, and then issues various control commands to execute the micro-operation series, thereby completing the execution of an instruction. The processor 301 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are not limited here.

2)电源302是为计算机设备300中所有单元或模块正常运行提供功率的装置。2) The power supply 302 is a device that provides power for all units or modules in the computer device 300 to operate normally.

3)存储器307,以及在所述存储器中并可在所述处理器上的操作系统303、计算机程序304、数据库305可以作为一个集成的模块存储于计算机的非易失性存储介质306中。3) Memory 307, and the operating system 303, computer program 304, and database 305 in the memory and on the processor can be stored as an integrated module in the non-volatile storage medium 306 of the computer.

操作系统303是方便用户、管理和控制计算机软硬件资源的系统软件(或程序集合),同时也是计算机系统的核心与基石,充当软件和硬件之间的媒介。它可以为用户提供交互式命令接口、批处理命令接口、程序接口等。操作系统303可以是Windows、Maxos x、Linux等,在此不作限定。The operating system 303 is a system software (or a collection of programs) that facilitates users, manages and controls computer hardware and software resources. It is also the core and cornerstone of the computer system, acting as a medium between software and hardware. It can provide users with interactive command interfaces, batch command interfaces, program interfaces, etc. The operating system 303 can be Windows, Maxos x, Linux, etc., which are not limited here.

计算机程序304是指一组指示计算机或其他具有消息处理能力装置,通常用某种程序设计语言编写,运行于某种目标体系结构上。其中,计算机程序304包括计算机程序代码,计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等,在本专利讨论的实例中,计算机程序304将第一方面建立的配电网数学模型将以Matlab软件中代码的形式存储,并根据优化算法进行编译。The computer program 304 refers to a set of instructions for a computer or other device with message processing capability, which is usually written in a certain programming language and runs on a certain target architecture. The computer program 304 includes computer program code, which can be in source code form, object code form, executable file or some intermediate form, etc. In the example discussed in this patent, the computer program 304 stores the mathematical model of the distribution network established in the first aspect in the form of code in Matlab software and compiles it according to the optimization algorithm.

数据库305是指结构化信息或数据的有序集合,一般以电子形式存储在计算机系统中,通常由数据库管理系统 (DBMS) 来控制。其基本结构分为物理数据层、 概念数据层和用户数据层这三个层次。数据库305使用 SQL 编程语言来查询、操作和定义数据,进行数据访问控制。Database 305 refers to an ordered collection of structured information or data, generally stored in electronic form in a computer system and usually controlled by a database management system (DBMS). Its basic structure is divided into three levels: physical data layer, conceptual data layer and user data layer. Database 305 uses SQL programming language to query, operate and define data and perform data access control.

存储器307的主要功能是存放程序和数据。程序是计算机操作的依据,数据是计算机操作的对象。不管是程序还是数据,在存储器中都是用二进制的形式来表示的,并统称信息。存储器主要分为主存储器(内存)和辅助存储器(外存)。存储器307可以是随机存取存储器(RAM)、主存储器(内存)、只读存储器(ROM)、存储器、硬盘、辅助存储器(外存)软盘、光盘等,在此不作限定。The main function of the memory 307 is to store programs and data. Programs are the basis for computer operations, and data are the objects of computer operations. Whether it is a program or data, it is represented in binary form in the memory and is collectively referred to as information. The memory is mainly divided into a main memory (internal memory) and an auxiliary memory (external memory). The memory 307 can be a random access memory (RAM), a main memory (internal memory), a read-only memory (ROM), a memory, a hard disk, an auxiliary memory (external memory), a floppy disk, an optical disk, etc., which are not limited here.

4)非易失性存储介质306是计算机程序304的存储介质,其特点是在关闭计算机或者突然性、意外性关闭计算机的时候数据也不会丢失。在很多的存储系统的写操作程序中,内存作为控制器和硬盘之间的重要桥梁,提供更快速的性能,但是如果发生突然间断电的情况,非易失性存储介质可以有效保护内存中的数据不丢失。非易失性存储介质306可是任何包含或存储程序的有形介质,其可以是电、磁、光、电磁、红外线、半导体的系统、装置、设备,更具体的例子包括但不限于:具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、光纤、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件,或它们任意合适的组合。4) Non-volatile storage medium 306 is the storage medium of computer program 304, and its characteristic is that data will not be lost when the computer is turned off or suddenly or accidentally turned off. In many storage system write operation programs, memory is an important bridge between the controller and the hard disk, providing faster performance, but if a sudden power outage occurs, non-volatile storage medium can effectively protect the data in the memory from being lost. Non-volatile storage medium 306 can be any tangible medium containing or storing programs, which can be an electrical, magnetic, optical, electromagnetic, infrared, semiconductor system, device, equipment, more specific examples include but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, an optical fiber, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

5)有线或无线接口308负责处理从终端用户设备到无线介质之间的数字通信,保证电力数据采集设备101~103、计算机设备、调度中心之间通过线路进行数据交互,还可以通过网络或蓝牙等方式进行数据交互,在此不做限定。输入输出接口309是计算机设备300中处理器301与外部设备之间交换信息的连接电路,它们通过总线与处理器301相连。输入输出接口分为总线接口和通信接口两类,一般做成电路插卡的形式,输入输出接口309可以是软盘驱动器适配卡、硬盘驱动器适配卡、并行打印机适配卡等形式,在此不作限定。5) The wired or wireless interface 308 is responsible for processing the digital communication from the terminal user device to the wireless medium, ensuring data exchange between the power data acquisition equipment 101~103, the computer equipment, and the dispatching center through the line. Data exchange can also be carried out through the network or Bluetooth, etc., which is not limited here. The input and output interface 309 is a connection circuit for exchanging information between the processor 301 in the computer device 300 and the external device. They are connected to the processor 301 through the bus. The input and output interface is divided into two categories: bus interface and communication interface. It is generally made in the form of a circuit card. The input and output interface 309 can be in the form of a floppy disk drive adapter card, a hard disk drive adapter card, a parallel printer adapter card, etc., which is not limited here.

6)上述计算机设备300各模块的设计与划分,是根据系统优化调度需求确定之后需要对计算机设备系统进行整体分析和设计的。在实际工程应用中,还需根据实际需求调用计算机设备中的不同的功能单元、模块完成目标。上述单元或模块的存在形式是可以灵活变化的,可以将所有单元或模块集成在同一个处理单元中,也可以根据计算机设备设计将任意个单元集成在一个单元中。上述单元或模块的功能实现形式可以是软件也可以是硬件形式,具体根据实际工程需求进行选择即可,在此不作限定。6) The design and division of each module of the above-mentioned computer device 300 is determined according to the system optimization scheduling requirements, and then the computer device system needs to be analyzed and designed as a whole. In actual engineering applications, it is also necessary to call different functional units and modules in the computer device according to actual needs to achieve the goal. The existence form of the above-mentioned units or modules can be flexibly changed. All units or modules can be integrated in the same processing unit, or any units can be integrated into one unit according to the design of the computer device. The functional implementation form of the above-mentioned units or modules can be software or hardware, which can be selected according to the actual engineering needs and is not limited here.

实施例2:Embodiment 2:

本实施例提供了一种分布式储能参与含风电并网的配电网调峰填谷优化运行策略方法,通过在系统中加入多个分布式储能装置优化风电的消纳情况,降低弃风情况发生概率,通过分布式储能装置调峰填谷的作用,增加系统经济效益。建立了以优化全网一日运行经济效益最高为目标函数,考虑了节点功率平衡约束、电压降与支路电流约束、火电机组出力约束、火电机组爬坡率约束、火电机组启停约束、风电出力约束、分布式储能装置容量约束、分布式储能装置充放电运行约束,再对节点平衡非线性约束进行一定程度的松弛,将其转化为混合整数二阶锥优化模型进行求解。This embodiment provides a distributed energy storage participating in the peak-shaving and valley-filling optimization operation strategy method of the distribution network including wind power grid connection, which optimizes the consumption of wind power by adding multiple distributed energy storage devices in the system, reduces the probability of wind power abandonment, and increases the economic benefits of the system through the peak-shaving and valley-filling effect of distributed energy storage devices. An objective function is established to optimize the highest economic benefits of the entire network's daily operation, taking into account node power balance constraints, voltage drop and branch current constraints, thermal power unit output constraints, thermal power unit climbing rate constraints, thermal power unit start-stop constraints, wind power output constraints, distributed energy storage device capacity constraints, and distributed energy storage device charging and discharging operation constraints. Then, the node balance nonlinear constraints are relaxed to a certain extent and converted into a mixed integer second-order cone optimization model for solution.

在风电出力建模方面,考虑风电出力为确定性出力方式,具体出力情况参照某风电场某日实际出力情况。为促进分布式储能装置对风电的消纳,将弃风惩罚成本调高,使系统在优化过程中尽量通过消纳风电减少弃风。在分布式储能装置建模方面,对储能装置的容量、充放电状态以及充放电功率进行了合理的限制。In terms of wind power output modeling, wind power output is considered to be a deterministic output mode, and the specific output situation refers to the actual output situation of a certain wind farm on a certain day. In order to promote the consumption of wind power by distributed energy storage devices, the penalty cost for wind abandonment is increased so that the system can reduce wind abandonment as much as possible by absorbing wind power during the optimization process. In terms of distributed energy storage device modeling, reasonable restrictions are placed on the capacity, charging and discharging state, and charging and discharging power of the energy storage device.

通过以下实例的技术效果说明本发明的技术思想,实例中的数据均采用标幺值形式表示。The technical idea of the present invention is explained through the technical effects of the following examples, and the data in the examples are all expressed in per-unit form.

实例以含分布式储能的配电网系统以标准IEEE33节点配电网系统为基础,节点1处为变电站节点,即配电网向上级电网购电节点,该节点没有电力负荷,其余节点皆有电力负荷,另外,在系统中接入了一台火电机组、三台风电机组和四台分布式储能装置,将风电场位置设置在节点12、节点19、节点27处,将火电机组节点设置在节点2处,将分布式储能装置位置设置在节点13、节点14、节点20、节点29处,配电网系统结构图如图4所示。The example is based on the distribution network system with distributed energy storage and the standard IEEE33-node distribution network system. Node 1 is the substation node, that is, the node where the distribution network purchases electricity from the superior power grid. This node has no power load, and the other nodes all have power load. In addition, a thermal power unit, three wind turbines and four distributed energy storage devices are connected to the system. The wind farm is set at nodes 12, 19 and 27, the thermal power unit node is set at node 2, and the distributed energy storage device is set at nodes 13, 14, 20 and 29. The structure diagram of the distribution network system is shown in Figure 4.

通过设置如下实例分析储能装置在系统中调峰填谷以及对增强系统对风电的消纳作用,实例1:系统接入风电机组但无储能装置;实例2:系统接入风电机组且接入储能装置;实例3:储能装置接入点变化对系统运行产生的影响;实例4:储能装置充放电功率变化对系统运行产生的影响。The following examples are set up to analyze the role of energy storage devices in peak load shifting and valley filling in the system and in enhancing the system's absorption of wind power. Example 1: The system is connected to wind turbines but has no energy storage devices; Example 2: The system is connected to wind turbines and energy storage devices; Example 3: The impact of changes in the access point of the energy storage device on the system operation; Example 4: The impact of changes in the charging and discharging power of the energy storage device on the system operation.

得出了实例1与实例2的一日运行经济效益情况,如表1。The economic benefits of one-day operation of Example 1 and Example 2 are obtained, as shown in Table 1.

表1实例1与实例2的一日运行经济效益情况Table 1 Economic benefits of one-day operation of Example 1 and Example 2

Figure SMS_157
Figure SMS_157

从图5(a)、图5(b)中系统各机组出力情况可以看出,在用电负荷的低谷时段,如0点-8点时间段,由于绝大多数居民都在休息,生活活动少,用电负荷很低,风电机组产生的电量存在消纳不足的问题,此时储能装置可以储存部分的过剩风电;到了用电高峰时段,如11点-14点时间段、17点-20点时间段,此时居民生活活动较多,用电负荷增加,储能装置的通过释放储存电量对系统进行供电。上述措施一定程度上减小了系统的购电成本,降低了系统的供电压力,运行经济性得到提高。From the output of each unit in the system in Figure 5 (a) and Figure 5 (b), it can be seen that during the low-power load period, such as the 0:00-8:00 period, since most residents are resting and have few daily activities, the power load is very low, and the power generated by the wind turbines is insufficiently absorbed. At this time, the energy storage device can store some of the excess wind power; during the peak power consumption period, such as the 11:00-14:00 period and the 17:00-20:00 period, when residents have more daily activities and the power load increases, the energy storage device releases the stored power to supply power to the system. The above measures have reduced the system's power purchase cost to a certain extent, reduced the system's power supply pressure, and improved the economic efficiency of operation.

从表1两个实例的一日运行经济效益情况可以看出,由于储能装置接入系统,促进了系统对风电的消纳,购电量与火电机组出力均下降,系统的经济效益提高。From the economic benefits of one day's operation of the two examples in Table 1, it can be seen that the connection of the energy storage device to the system promotes the system's consumption of wind power, the amount of electricity purchased and the output of thermal power units both decrease, and the economic benefits of the system improve.

本实施例相比现有技术有如下优点:Compared with the prior art, this embodiment has the following advantages:

实例3通过改变四个储能装置相对于风力发电机组的距离提出了三种不同的系统运行方式(方式3.1、方式3.2、方式3.3),三种运行方式的系统结构如图6所示,储能装置接入点发生变化的原则是距离所在线路的风力发电机组越来越远。以上三个方式设置的系统通过本发明的优化技术后得出的储能装置内部电量

Figure SMS_158
的变化情况如图4所示,此外,得出了方式3.1、方式3.2、方式3.3的一日运行经济效益情况,如表2。Example 3 proposes three different system operation modes (mode 3.1, mode 3.2, mode 3.3) by changing the distances of the four energy storage devices relative to the wind turbine generator set. The system structures of the three operation modes are shown in Figure 6. The principle of changing the access point of the energy storage device is that the distance from the wind turbine generator set on the line is getting farther and farther. The internal power of the energy storage device obtained by the optimization technology of the present invention in the above three systems is
Figure SMS_158
The changes in are shown in Figure 4. In addition, the daily operating economic benefits of Mode 3.1, Mode 3.2, and Mode 3.3 are obtained, as shown in Table 2.

表2方式3.1、方式3.2、方式3.3的一日运行经济效益情况Table 2 Economic benefits of one-day operation of Mode 3.1, Mode 3.2 and Mode 3.3

Figure SMS_159
Figure SMS_159

从表2中可以看出,随着储能装置接入点距离风力发电机组位置的增大,系统购电成本不断升高,储能装置使用成本不断降低,系统一日运行总成本不断升高,这说明,当储能装置距离风电机组的位置越近时,储能装置对于风电的消纳情况更好,储能装置的使用更充足,对于系统调峰填谷的最用更明显,可以在一定程度上提高系统运行的经济性。It can be seen from Table 2 that as the distance between the access point of the energy storage device and the location of the wind turbine increases, the system's electricity purchase cost continues to increase, the cost of using the energy storage device continues to decrease, and the total daily operating cost of the system continues to increase. This shows that when the energy storage device is closer to the wind turbine, the energy storage device can better absorb wind power, the energy storage device can be used more fully, and the peak-shaving and valley-filling function of the system can be more obvious, which can improve the economy of the system operation to a certain extent.

图7通过储能装置内部电量

Figure SMS_160
的变化情况,说明了当储能装置相对于风电机组位置变化时,储能装置的使用情况,从方式3.1方式3.3,储能装置一日运行中总充电量不断降,总放电量也不断降低,总体上即可说明储能装置的使用是在降低的,对于系统调峰填谷的作用也降低了。Figure 7: The internal charge of the energy storage device
Figure SMS_160
The changes in the energy storage device illustrate the use of the energy storage device when the position of the energy storage device changes relative to the wind turbine. From mode 3.1 to mode 3.3, the total charge amount of the energy storage device continues to decrease during one day's operation, and the total discharge amount also continues to decrease. In general, it can be explained that the use of the energy storage device is decreasing, and its role in peak-loading and valley-filling of the system is also reduced.

实例4通过改变储能装置的充、放电功率上限

Figure SMS_161
变化提出了三种不同的系统运行方式(方式4.1、方式4.2、方式4.3、方式4.4、方式4.5),这五种运行方式的变化规律是将
Figure SMS_162
的值不断提高。以上五个方式设置的系统通过本发明的优化技术后得出的各机组出力结果如图8所示,此外,得出了方式4.1、方式4.2、方式4.3、方式4.4、方式4.5的一日运行经济效益情况,如表3。Example 4: By changing the upper limit of the charging and discharging power of the energy storage device
Figure SMS_161
The changes proposed three different system operation modes (mode 4.1, mode 4.2, mode 4.3, mode 4.4, mode 4.5). The change rules of these five operation modes are to
Figure SMS_162
The output results of each unit obtained by the optimization technology of the present invention for the above five systems are shown in FIG8 . In addition, the economic benefits of one-day operation of mode 4.1, mode 4.2, mode 4.3, mode 4.4 and mode 4.5 are obtained, as shown in Table 3 .

表3方式4.1、方式4.2、方式4.3、方式4.4、方式4.5的一日运行经济效益情况Table 3 Economic benefits of one-day operation of Mode 4.1, Mode 4.2, Mode 4.3, Mode 4.4 and Mode 4.5

Figure SMS_163
Figure SMS_163

图8描绘了五种运行方式下储能装置电量

Figure SMS_164
的变化情况,从图中可以看出,从方式4.1到方式4.5,随着储能装置的充、放电功率上限
Figure SMS_165
不断升高,储能装置电量
Figure SMS_166
变化幅度更大,即储能装置使用量不断提升,方式4.4与方式4.5的储能装置电量
Figure SMS_167
变化曲线完全重合,这说明在
Figure SMS_168
后,
Figure SMS_169
不再是限制储能装置使用的因素。Figure 8 depicts the energy storage device capacity under five operating modes.
Figure SMS_164
As can be seen from the figure, from mode 4.1 to mode 4.5, as the upper limit of the charging and discharging power of the energy storage device increases,
Figure SMS_165
The energy storage device is constantly increasing.
Figure SMS_166
The change is greater, that is, the use of energy storage devices continues to increase, and the energy storage device power of method 4.4 and method 4.5 is
Figure SMS_167
The change curves completely overlap, which shows that
Figure SMS_168
back,
Figure SMS_169
It is no longer a factor limiting the use of energy storage devices.

表3是实例4中五种运行方式下的各出力成本与总成本比较,从表中可以看出,从方式4.1到方式4.5,储能装置的充、放电功率上限

Figure SMS_170
不断提高时,系统成本显著降低,火电机组成本有微小的降低,储能装置使用成本不断上升,系统一日运行总成本不断升高,这说明,
Figure SMS_171
的限制影响了储能装置的使用,当
Figure SMS_172
的值提高,储能装置可以更大程度的投入系统能量的调度。值得注意的是,方式4.4与方式4.5的各部分成本相同,说明此时
Figure SMS_173
已经不是限制储能装置使用的参数,在此系统下,如果不对
Figure SMS_174
进行限制,储能装置的充、放电功率最大在0.5-0.6之间。Table 3 is a comparison of the output costs and total costs under the five operating modes in Example 4. It can be seen from the table that from mode 4.1 to mode 4.5, the upper limit of the charging and discharging power of the energy storage device
Figure SMS_170
When the system cost is continuously improved, the system cost is significantly reduced, the cost of thermal power units is slightly reduced, the cost of energy storage devices is continuously increased, and the total cost of system operation per day is continuously increased. This shows that
Figure SMS_171
The limitation of
Figure SMS_172
As the value of increases, the energy storage device can be put into the system energy dispatch to a greater extent. It is worth noting that the costs of each part of method 4.4 and method 4.5 are the same, indicating that at this time
Figure SMS_173
It is no longer a parameter that limits the use of energy storage devices. In this system, if
Figure SMS_174
The maximum charging and discharging power of the energy storage device is limited to 0.5-0.6.

此外,类比实例4,可以用相似的控制变量的方法证明储能装置容量也是限制储能装置参与系统调峰填谷的一个重要参数,储能装置的容量上限

Figure SMS_175
不断提高时,系统成本显著降低,火电机组成本有微小的降低,储能装置使用成本不断上升,系统一日运行总成本不断升高,这说明,
Figure SMS_176
的限制影响了储能装置的使用,当
Figure SMS_177
的值提高,储能装置可以更大程度的投入系统能量的调度。In addition, by analogy with Example 4, a similar control variable method can be used to prove that the capacity of the energy storage device is also an important parameter that limits the energy storage device from participating in the peak-shaving and valley-filling system.
Figure SMS_175
When the system cost is continuously improved, the system cost is significantly reduced, the cost of thermal power units is slightly reduced, the cost of energy storage devices is continuously increased, and the total cost of system operation per day is continuously increased. This shows that
Figure SMS_176
The limitation of
Figure SMS_177
As the value of increases, the energy storage device can be used to dispatch system energy to a greater extent.

以上示意性的对本发明及其实施方式进行了描述,该描述没有限制性,附图中所示的也只是本发明的实施方式之一,实际的结构并不局限于此。所以,如果本领域的普通技术人员受其启示,在不脱离本发明创造宗旨的情况下,不经创造性的设计出与该技术方案相似的结构方式及实施例,均应属于本发明的保护范围。The above is a schematic description of the present invention and its implementation methods, which is not restrictive. The drawings show only one implementation method of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and does not deviate from the purpose of the invention, he or she can design a structure and an embodiment similar to the technical solution without creativity, which should fall within the protection scope of the present invention.

Claims (10)

1. The optimal operation method of the power system containing distributed energy storage is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring initial parameters of the system, including power distribution network structure parameters, predicted output of a wind power plant, operating parameters of an energy storage device, electricity purchasing cost to an upper-level power grid, wind abandoning penalty cost, operating cost of a thermal power generating unit, operating cost of the energy storage device and load shedding cost;
step 2: establishing an optimized operation model of a power distribution network containing distributed energy storage, wherein the model comprises the following steps: the system comprises a thermal power generating unit, a wind generating set, an energy storage device and a power load; determining constraint conditions of each part of system operation by taking the lowest one-day operation cost of the power distribution network as an optimization target, and solving the established day-ahead economic dispatching model of the power distribution network by using a mixed integer programming method and a second-order conic linear programming method;
and step 3: calling a Gurobi solver in Matlab software to carry out optimization solution on the model, and forming a day-ahead electricity purchasing plan, a wind power generation unit and a thermal power generation unit output plan, a distributed energy storage device charging and discharging plan, a distributed energy storage device participating in system peak regulation and valley filling and a system economic benefit improvement condition;
and 4, step 4: the influence of the position of the energy storage device connected to the power distribution network or the charge and discharge power parameters on the wind power consumption and the system operation economic condition is researched by changing the position of the energy storage device connected to the power distribution network or the charge and discharge power parameters of the energy storage device.
2. The method for optimizing the operation of a power system including distributed energy storage according to claim 1, wherein: step 2, establishing a power distribution network optimization operation model containing distributed energy storage, and taking the total operation cost of the minimized power distribution network as an optimization target; the operating cost of the power distribution network comprises: the power distribution network obtains the objective function of the power distribution network optimization operation model from the electricity purchasing cost of a superior power grid, the power generation cost of a thermal power generating unit, the wind abandoning penalty cost of a wind power generating unit, the operation cost of an energy storage device and the load loss cost as follows:
Figure QLYQS_1
;
wherein:
Figure QLYQS_10
charging fee for purchasing electricity from higher-level power grid for power distribution network>
Figure QLYQS_7
For the operating costs of a thermal power plant>
Figure QLYQS_13
Penalty cost for wind curtailment>
Figure QLYQS_4
For the operating cost of the energy storage device, the device is switched on>
Figure QLYQS_11
The cost for losing load;
Figure QLYQS_9
Is composed oftBased on the electricity purchase cost of the superior power grid, the time-interval distribution network>
Figure QLYQS_14
Is composed oftThe active power of electricity purchase of a time-interval power distribution network from a superior power grid; A. b and C are power generation cost coefficients of the thermal power generating unit;
Figure QLYQS_5
indicating thermal power generating unitgIn thattThe output of the time period;
Figure QLYQS_15
Wind turbine generator system->
Figure QLYQS_3
The wind abandon penalty cost;
Figure QLYQS_16
Representing wind farmswIn thattPredicted outcomes for a time period;
Figure QLYQS_2
Representing wind farmswIn thattThe output of the time period;
Figure QLYQS_17
For the cost of energy storage, the device is used>
Figure QLYQS_8
Indicating stored energydIn thattThe amount of discharge over a period of time;
Figure QLYQS_12
For the load-shedding cost of the distribution network, the system>
Figure QLYQS_6
Is shown inshedIs located intLoad shedding power of a time period. />
3. The method for optimizing the operation of a power system comprising distributed energy storage according to claim 2, wherein: in step 2, a DistFlow model is used for describing power balance of the power distribution network nodes, and system operation constraint conditions are expressed as follows:
node power balance constraint:
Figure QLYQS_18
wherein, set
Figure QLYQS_38
For connection to nodes of the distribution networkjA set of devices of;
Figure QLYQS_24
In a power distribution networkjA set of branch end nodes that are head-end nodes;
Figure QLYQS_32
Is a distribution lineijIn combination with a resistor>
Figure QLYQS_23
Is a distribution lineijA reactance of (d);
Figure QLYQS_33
Is composed oftTime interval distribution lineijThe current of (a);
Figure QLYQS_36
Is composed oftTime interval nodeiVoltage magnitude of (d);
Figure QLYQS_40
Is composed oftIn the first periodgThe active power generation power of the thermal power generating unit;
Figure QLYQS_37
Is as followswThe typhoon generator is arranged attActive generated power of a time period;
Figure QLYQS_41
Representing distributed energy storage devicesdIn thattActive discharge amount of a time period>
Figure QLYQS_19
Indicating stored energydIn thattAn active charge amount for the time period;
Figure QLYQS_29
Is composed oftActive power transmitted to a power distribution network by a superior power grid in a time period;
Figure QLYQS_25
Is composed oftThe reactive power transmitted to the power distribution network by the superior power grid in the time period;
Figure QLYQS_34
Is composed oftPower of load on power-off at any moment>
Figure QLYQS_26
Is composed oftTime interval loaddThe power factor of (c);
Figure QLYQS_31
FingertTime interval distribution lineijActive power of the segment;
Figure QLYQS_20
Finger-shapedtTime interval distribution lineijReactive power of the segment;
Figure QLYQS_27
Finger-shapedtTime interval distribution lineijActive power of the segment;
Figure QLYQS_35
FingertTime interval distribution lineijReactive power of the segment;
Figure QLYQS_39
Are respectively astTime interval loaddThe active load value and the reactive load value of (2);
Figure QLYQS_21
And &>
Figure QLYQS_30
For transmission linesijCurrent magnitude limits of the segments;
Figure QLYQS_22
And &>
Figure QLYQS_28
Is a nodeiIs limited by the voltage level of (c).
4. The method for optimizing operation of a power system comprising distributed energy storage according to claim 3, wherein: in step 2, the specific operation constraint conditions of the thermal power generating unit are represented as follows:
output restraint of the thermal power generating unit:
Figure QLYQS_42
output restraint of the thermal power generating unit:
Figure QLYQS_43
thermal power unit opens and stops restraint:
Figure QLYQS_44
wherein:
Figure QLYQS_47
the state variable represents the working state of the thermal power generating unit;
Figure QLYQS_50
Minimum limit of thermal power unit output;
Figure QLYQS_52
The maximum limit of the output provided by the thermal power generating unit;
Figure QLYQS_45
And &>
Figure QLYQS_48
Are respectively thermal power generating unitsgUp and down hill climbing rate of->
Figure QLYQS_51
And
Figure QLYQS_53
respectively indicating a starting time counter and a stopping time counter of the thermal power generating unit g;
Figure QLYQS_46
And &>
Figure QLYQS_49
Respectively refer to gas unitsgMinimum boot and downtime.
5. The method for optimizing operation of a power system comprising distributed energy storage according to claim 4, wherein: in step 2, the specific operation constraint conditions of the wind turbine generator are expressed as follows:
output restraint of the wind turbine generator:
Figure QLYQS_54
;
the relation between the actual output of the wind turbine generator, the size of the abandoned wind and the predicted output of the wind turbine generator is as follows:
Figure QLYQS_55
;
wherein:
Figure QLYQS_56
is as followswThe typhoon generator is arranged attPredicted generation power of a time period->
Figure QLYQS_57
Is as followswThe typhoon generator is arranged attThe air abandon amount of time slot.
6. The method for optimizing the operation of a power system comprising distributed energy storage according to claim 5, wherein: in step 2, the specific constraint conditions of the energy storage device are expressed as follows:
energy storage device chargerAnd (3) discharge power constraint:
Figure QLYQS_58
;
electric quantity constraint before and after each charge and discharge:
Figure QLYQS_59
;
power limitation of each charge and discharge:
Figure QLYQS_60
;
and after 24 hours of charge and discharge, the electric quantity is equal to the electric quantity at the initial moment of the day, and the restraint is as follows:
Figure QLYQS_61
;
wherein:
Figure QLYQS_63
Figure QLYQS_67
limiting the minimum value and the maximum value of the charging and discharging power of the energy storage device;
Figure QLYQS_69
Is an energy storage devicetThe amount of electricity over a period of time;
Figure QLYQS_62
Is the electric quantity at the initial moment of the energy storage device>
Figure QLYQS_66
Is the electrical quantity of the energy storage device at the end of a day;
Figure QLYQS_68
is an energy storage devicetCharging and discharging power of a time period;
Figure QLYQS_70
Is the charge-discharge efficiency of the energy storage device;
Figure QLYQS_64
Is an energy storage devicetThe value of the state variable of time interval charge and discharge is 0 or 1;
Figure QLYQS_65
And limiting the maximum value of the charging and discharging power of the energy storage device.
7. The method for optimizing operation of a power system comprising distributed energy storage according to claim 6, wherein: in the step 2, a power distribution network node current equation represented by voltage drop and branch current constraint is linear and non-convex, so that node balance non-linear constraint is loosened to a certain degree, and the node balance non-linear constraint is converted into a second-order cone optimization model to be solved:
first, define
Figure QLYQS_71
And &>
Figure QLYQS_72
These two intermediate variables, in combination with the branch current versus power relationship:
Figure QLYQS_73
and (3) carrying out relaxation treatment on the voltage drop equation of the contact point to obtain:
Figure QLYQS_74
;/>
further conversion yields a second order cone equation:
Figure QLYQS_75
the current and voltage constraints after the second order cone relaxation are:
Figure QLYQS_76
8. the method for optimizing operation of a power system comprising distributed energy storage according to claim 7, wherein: and step 3, converting system parameters, target functions and constraint conditions into statements in Matlab software for compiling, calling a Gurobi commercial solver in the Matlab software to carry out optimization solution on the model, and obtaining output plans of the wind power generation unit and the thermal power generation unit and improvement conditions of the distributed energy storage device in participating in system peak load regulation and valley filling and on the system economic effect.
9. The power system that contains distributed energy storage optimizes the operation system, its characterized in that: which adopts a method for optimizing the operation of a power system comprising distributed energy storage according to any of claims 1-8 and comprises:
the data acquisition module is used for acquiring structural parameters of a distribution network in one day, predicted output of a wind power plant, operating parameters of an energy storage device, electricity purchasing cost for an upper-level power grid, wind abandoning penalty cost, operating cost of a thermal power generating unit, operating cost of the energy storage device and load shedding cost;
the modeling module is used for constructing a wind power grid-connected distributed energy storage device participation power distribution network optimized scheduling model based on the output and running state of each unit of the power distribution network and the running state data of the energy storage device at the day before;
the optimization calculation module is used for constructing the scheduling model into a mixed integer programming model easy to solve, introducing 0-1 binary state variables to constrain partial variables, adopting a mixed integer second-order cone programming method to rewrite nonlinear power flow constraints of the power distribution network nodes, and analyzing and solving results by setting examples;
and the output plan module is used for outputting the optimized calculation result of the optimized calculation module and obtaining the day-ahead output plan of each unit of the system.
10. Contain distributed energy storage's electric power system optimization operation equipment, its characterized in that: comprising a processor which, when executing a computer program, carries out a method for optimized operation of a power system comprising distributed energy storage according to any of claims 1-8.
CN202310081216.4A 2023-02-08 2023-02-08 Power system optimization operation method, system and equipment including distributed energy storage Pending CN115882523A (en)

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