一种多微网系统的双层协调鲁棒优化调度方法Two-layer coordinated robust optimization scheduling method for multi-micro network systems
技术领域Technical field
本发明涉及微网的经济调度和能量管理技术领域,特别是一种多微网系统的双层协调鲁棒优化调度方法。The invention relates to the technical field of economic dispatching and energy management of a piconet, in particular to a two-layer coordinated robust optimal scheduling method for a multi-micro network system.
背景技术Background technique
由于煤炭、石油等化石能源的日渐枯竭及其高污染给生态环境带来的巨大影响,以风能、太阳能等为代表的可再生清洁能源引起了广泛关注。由于可再生能源出力具有较强的间歇性及波动性,微网已经成为电力系统领域接入和利用可再生能源的有效技术和重要途径。为了保证微网稳定高效地运行,有必要对其进行能量调度管理以制定合理的运行计划。而随着可再生能源利用率的逐步提高,多个微网会同时接入电力系统,此外电力电子技术的快速发展使得越来越多直流型电源(如光伏、燃料电池、储能等)及直流型负荷(电动汽车、家用电器等)接入了微网,从而形成交直流混合多微网系统。由于各子微网的源荷特性各不相同,多微网的协调优化调度相比于传统单微网更加复杂。Due to the depletion of fossil energy such as coal and petroleum and the great impact of high pollution on the ecological environment, renewable and clean energy represented by wind and solar energy has attracted wide attention. Due to the strong intermittent and volatility of renewable energy output, microgrid has become an effective technology and an important way to access and utilize renewable energy in the power system field. In order to ensure the stable and efficient operation of the microgrid, it is necessary to manage the energy dispatch to establish a reasonable operation plan. With the gradual increase in the utilization rate of renewable energy, multiple micro-grids will be connected to the power system at the same time. In addition, the rapid development of power electronics technology has led to more and more DC-type power sources (such as photovoltaics, fuel cells, energy storage, etc.) and DC load (electric vehicles, household appliances, etc.) is connected to the microgrid to form an AC/DC hybrid multi-microgrid system. Since the source-source characteristics of each sub-grid are different, the coordinated optimization scheduling of the multi-micro network is more complicated than the traditional single-micro network.
可再生能源受自然条件的影响具有随机性和间歇性,且负荷波动性较强,导致微网中存在较多的不确定性,这给微网的优化调度带来了巨大的挑战。目前鲁棒优化在多微网系统中的应用较少,且已有研究仅考虑子微网中源荷的不确定性,忽略微网中可能出现的并离网切换和线路断线等不确定性因素;已有研究将多微网看成统一整体进行优化调度,而实际中子微网和所接入上层系统属于不同的利益主体,二者之间仅存在功率交互信息,因此其优化调度常常需要划分成两层分别进行;此外,已有多微网双层优化调度模型未考虑双层之间的交互关系,忽略了子微网与上层系统之间的相互影响。Renewable energy is random and intermittent due to the influence of natural conditions, and the load volatility is strong, which leads to more uncertainties in the microgrid, which brings great challenges to the optimal scheduling of the microgrid. At present, the application of robust optimization in multi-microgrid systems is less, and the existing research only considers the uncertainty of the source-load in the sub-microgrid, ignoring the uncertainties that may occur in the micro-grid and off-grid handover and line disconnection. Sexual factors; existing research sees multi-micro network as a unified whole for optimal scheduling, while the actual neutron micro-network and the connected upper-layer system belong to different interest subjects, and there is only power interaction information between them, so its optimal scheduling It is often necessary to divide into two layers separately; in addition, the existing multi-micro network two-layer optimal scheduling model does not consider the interaction between the two layers, ignoring the interaction between the sub-micro network and the upper system.
发明内容Summary of the invention
本发明所要解决的技术问题是克服现有技术的不足而提供一种多微网系统的双层协调鲁棒优化调度方法,该方法考虑到用户层源荷功率不确定性和供电层联络线断线的不确定性,能够实现用户层和供电层的协调鲁棒优化调度,为制定多微网系统的运行计划提供指导和帮助。The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a two-layer coordinated robust optimization scheduling method for a multi-micro network system, which takes into account the user layer source-load power uncertainty and the power supply layer tie line break. The uncertainty of the line can realize the coordinated and robust optimization scheduling of the user layer and the power supply layer, and provide guidance and help for formulating the operation plan of the multi-micro network system.
本发明为解决上述技术问题采用以下技术方案:The present invention adopts the following technical solutions to solve the above technical problems:
根据本发明提出的一种多微网系统的双层协调鲁棒优化调度方法,包括以下步骤:A two-layer coordinated robust optimization scheduling method for a multi-micro network system according to the present invention includes the following steps:
步骤10)、获取多微网系统中用户层各设备的运行成本系数及运行限值参数,构建min-max-min形式的用户层鲁棒优化调度模型;Step 10): Obtain an operating cost coefficient and a running limit parameter of each device in the user layer in the multi-micro network system, and construct a user-layer robust optimal scheduling model in the form of min-max-min;
步骤20)、获取多微网系统中供电层各设备的运行成本系数及运行限值参数,构建min-max-min形式的供电层鲁棒优化调度模型;Step 20): Obtain an operating cost coefficient and a running limit parameter of each device in the power supply layer in the multi-micro network system, and construct a robust optimization scheduling model of the power supply layer in the form of min-max-min;
步骤30)、求解由步骤10)用户层鲁棒优化调度模型和步骤20)供电层鲁棒优化调度模型组成的多微网系统的双层协调鲁棒优化模型,即利用列约束生成算法迭代求解用户层和供电层的鲁棒优化问题,获得多微网系统的鲁棒协调运行计划。Step 30), solving a two-layer coordinated robust optimization model of the multi-microgrid system consisting of the step 10) user layer robust optimization scheduling model and step 20) power supply layer robust optimization scheduling model, that is, iteratively solving by using the column constraint generation algorithm The robust optimization problem of the user layer and the power supply layer obtains a robust coordinated operation plan of the multi-micro network system.
作为本发明所述的一种多微网系统的双层协调鲁棒优化调度方法进一步优化方案,所述步骤10)中,用户层各设备的运行成本系数及运行限值参数包括各子微网中与可再生发电机、储能、交互联络线及负荷相关的所有运行成本系数和运行限值参数,计及可再生发电机和负荷的功率不确定性,将所获取的运行成本系数及运行限值参数代入下式建立min-max-min形式的用户层鲁棒优化调度模型:As a further optimization scheme of the two-layer coordinated robust optimization scheduling method of the multi-micro network system according to the present invention, in the step 10), the operating cost coefficient and the operation limit parameter of each device of the user layer include each sub-micro network. All operating cost factors and operating limit parameters related to renewable generators, energy storage, interactive tie lines and loads, taking into account the power uncertainty of renewable generators and loads, and the operating cost factors and operations obtained The limit parameter is substituted into the following formula to establish a user-layer robust optimal scheduling model in the form of min-max-min:
用户层鲁棒优化调度模型的目标函数为:The objective function of the user layer robust optimization scheduling model is:
式(1)所示目标函数中相关项根据下式计算得到:The correlation term in the objective function shown in equation (1) is calculated according to the following formula:
式中,C
REi、
C
ILi和C
DPi分别为第i个子微网中可再生发电机、可削减负荷、储能、交互联络线和交互联络线功率偏差的运行成本;m
REi、m
LSi、m
ESi、m
Bit、m
Sit和m
DPi为第i个子微网中可再生发电机、可削减负荷、储能、交互联络线购电、交互联络线售电及交互联络线功率偏差的运行成本系数;
和
为第i个子微网中交互联络线在t时段的购电和售电运行状态;p
it和l
it为第i个子微网中可再生发电机和负荷的最大可运行功率;P
i和L
i表示第i个子微网中可再生发电机和负荷的功率不确定性集;P
REit、
和
分别为第i个子微网中可再生发电机、储能充电、储能放电、交互联络线购电、交互联络线售电和可削减负荷在t时段的实际运行功率;N
t为一个调度周期的总时段数,Δt为时段间隔;*W
IL+it和
为供电层模型中第i个子微网的交互联络线购电和售电的功率优化结果;
Where, C REi , C ILi and C DPi are the operating costs of the regenerative generator, load reduction, energy storage, interactive tie line and cross tie line power deviation in the i-th sub-microgrid; m REi , m LSi , m ESi , m Bit , m Sit and m DPi are the operating cost coefficients of renewable generators, load reduction, energy storage, cross-link purchase, cross-link sales, and cross-link power deviations in the i-th sub-microgrid; with The state of power purchase and power sale operation of the interactive tie line in the i-th sub-microgrid during t period; p it and l it are the maximum operable power of the renewable generator and load in the i-th sub-microgrid; P i and L i represents the power uncertainty set of the renewable generator and load in the i-th sub-microgrid; P REit , with Respective generators, energy storage and charging, energy storage and discharge, cross-link line purchase, cross-link sales, and actual operating power of the load during the t-th period; n t is a scheduling period The total number of time periods, Δt is the time interval; *W IL+it and Power optimization results for power purchase and sale of the i-th sub-microgrid in the power supply layer model;
用户层鲁棒优化调度模型的约束条件为:The constraints of the user layer robust optimization scheduling model are:
式(6)为第i个子微网中可再生发电机的发电功率约束;式(7)为第i个子微网中储能的充放电功率约束,
和
为储能的最大放电和充电功率限值,式(8)-(9)为该储能的荷电状态约束,SOC
it和SOC
i(t-1)为t和t-1时段储能的荷电状态,η
ES+i和η
ES-i为储能的放电和充电效率限值,
为储能的额定容量,SOC
mini和SOC
maxi为储能的荷电状态下限值和上限值,SOC
i0为储能的初始荷电状态限值,SOC
iNt为储能在调度周期末的荷电状态限值;式(10)-(12)为第i个子微网中交互联络线的运行功率及功率波动约束,
和
为交互联络线的购电和售电功率限值,
和
为交互联络线功率波动的上下限值;式(13)为第i个子微网中可削减负荷的功率约束,
为t时段可削减负荷的运行功率限值;式(14)为第i个子微网的功率平衡约束;式(15)-(16)为第i个子微网中可再生发电机和负荷的功率不确定性集约束;对于可再生发电机的功率不确定性集P
i,
和p
-it分别是t时段可再生发电机最大可运行功率的预测标称值、预测上偏差值和预测下偏差值,
和ξ
-it分别为可再生发电机功率不确定性的上偏差引入参数和下偏差引入参数,
为可再生发电机功率不确定性的时段预算参数;对于负荷的功率不确定性集L
i,
和l
-it分别是t时段负荷最大可运行功率的预测标称值、预测上偏差值和预测下偏差值,
和κ
-it分别为负荷功率不确定性的上偏差引入参数和下偏差引入参数,
为负荷功率不确定性的时段预算参数。
Equation (6) is the power generation constraint of the renewable generator in the i-th sub-microgrid; Equation (7) is the charge-discharge power constraint of the energy storage in the i-th sub-microgrid, with For the maximum discharge and charging power limits of energy storage, equations (8)-(9) are the state of charge constraints for the energy storage, and SOC it and SOC i(t-1) are energy storage for the t and t-1 periods. State of charge, η ES+i and η ES-i are the discharge and charging efficiency limits for energy storage, For the rated capacity of energy storage, SOC mini and SOC maxi are the lower and upper limits of the state of charge of the energy storage, SOC i0 is the initial state of charge of the energy storage, and SOC iNt is the energy storage at the end of the scheduling period. Charge state limit; equations (10)-(12) are the operating power and power fluctuation constraints of the interactive tie line in the i-th sub-microgrid, with For the purchase and sale power limits of the interactive tie line, with The upper and lower limits of the power fluctuation of the interactive tie line; Equation (13) is the power constraint that can reduce the load in the i-th sub-microgrid. For the t period, the operating power limit of the load can be reduced; equation (14) is the power balance constraint of the i-th sub-microgrid; and equations (15)-(16) are the power of the regenerative generator and load in the i-th sub-microgrid Uncertainty set constraint; for the power uncertainty set P i of the renewable generator And p -it are the predicted nominal value, the predicted upper deviation value and the predicted lower deviation value of the maximum operable power of the regenerative generator in the t period, respectively. And ξ -it are renewable power generator on the uncertainty introduced into the bias parameters and the parameter bias is introduced, Time period budget parameters for power uncertainty of renewable generators; for the power uncertainty set L i of the load, And l -it are the predicted nominal value of the maximum operational power of the t-time load, the predicted upper deviation value, and the predicted lower deviation value, respectively. Respectively, and κ -it load power uncertainty on the parameter bias is introduced and the introduction of bias parameters, Time period budget parameters for load power uncertainty.
作为本发明所述的一种多微网系统的双层协调鲁棒优化调度方法进一步优化方案, 所述步骤20)中,供电层各设备的运行成本系数及运行限值参数包括与柴油发电机、交互联络线、换流联络线及并网联络线相关的所有运行成本系数及运行限值参数,计及换流联络线和并网联络线的断线不确定性,将运行成本系数及运行限值参数代入下式建立min-max-min形式的供电层鲁棒优化调度模型:As a further optimization scheme of the two-layer coordinated robust optimization scheduling method for a multi-micro network system according to the present invention, in the step 20), the operating cost coefficient and the operation limit parameter of each device of the power supply layer include a diesel generator All operating cost factors and operating limit parameters related to the interactive tie line, the commutation tie line and the grid connection line, taking into account the disconnection uncertainty of the commutation tie line and the grid connection line, the running cost coefficient and operation The limit parameter is substituted into the following formula to establish a robust optimization scheduling model for the power supply layer in the form of min-max-min:
供电层鲁棒优化调度模型的目标函数为:The objective function of the robust optimization scheduling model for the power supply layer is:
式(17)目标函数中相关项可根据下式计算得到:The correlation term in the objective function of equation (17) can be calculated according to the following formula:
式中,F
ON、F
OFF和F
FUEL分别为柴油发电机的启动成本、停机成本和燃料成本;F
CL、F
IL和F
DP分别为供电层模型中换流联络线、交互联络线和交互联络线功率偏差的运行成本;m
ON、m
OFF和m
FUEL分别为柴油发电机的启动成本系数、停机成本系数和燃料成本系数;m
CL+ij和
分别表示第i个子微网和第j个子微网之间的换流联络线的功率从第i个子微网流向第j个子微网和从第j个子微网流向第i个子微网时的运行成本系数;S
CL+ijt和
表示第i个子微网和第j个子微网之间的换流联络线在t时段的正向和反向运行状态;S
IL+it和
表示供电层模型中第i个子微网的交互联络线在t时段的购电和售电运行状态;S
GL+t和
表示并网联络线在t时段的购电和售电运行状态;
和
分别为柴油发电机在t时段的启动状态、停机状态和运行状态;r
t和z
t为不确定性集中并网联络线和换流联络线的运行状态;R和Z分别为并网联络线和换流联络线的断线不确定性集;
为柴油发电机的运行功率;W
DE,R表示柴油发电机的额定功率;W
CL+ijt和
为第i个子微网和第j个子微网之间的换流联络线在t时段的正向和反向运行功率;W
IL+it和
为供电层模型中第i个子微网的交互联络线在t时段的购电和售电功率;W
GL+t和
为并网联络线在t时段的购电和售电功率;a
DE和b
DE为柴油发电机的油耗特性系数;
和
为用户层模型中第i个子微网的交互联络线的购电和售电功率优化结果;
Where F ON , F OFF and F FUEL are the starting cost, shutdown cost and fuel cost of the diesel generator respectively; F CL , F IL and F DP are the commutation tie lines, interactive tie lines and interactions in the power supply layer model respectively Operating cost of tie line power deviation; m ON , m OFF and m FUEL are the starting cost coefficient, the stopping cost coefficient and the fuel cost coefficient of the diesel generator respectively; m CL+ij and The operation of the commutation tie line between the i-th child micro-network and the j-th sub-micro network respectively flows from the i-th sub-micro network to the j-th sub-micro network and from the j-th sub-micro network to the ith sub-micro network Cost factor; S CL+ijt and Representing the forward and reverse running states of the commutation tie line between the i-th sub-micronet and the j-th sub-grid during the t period; S IL+it and Indicates the state of power purchase and power sale of the i-th sub-microgrid in the power supply layer model during the t period; S GL+t and Indicates the state of purchase and sale of the grid connection line during the t period; with They are the starting state, shutdown state and running state of the diesel generator in the t period; r t and z t are the operating states of the grid and the commutating line of the uncertainty concentration; R and Z are the grid connection lines respectively. And the disconnection uncertainty set of the commutation tie line; For the operating power of the diesel generator; W DE, R represents the rated power of the diesel generator; W CL + ijt and The forward and reverse running powers of the commutation tie line between the i-th sub-micronet and the j-th sub-grid during the t-period; W IL+it and The power purchase and power sales of the i-th sub-microgrid in the power supply layer model during the t period; W GL+t and The power purchase and sale power of the grid connection line during the t period; a DE and b DE are the fuel consumption characteristic coefficients of the diesel generator; with Optimizing the power purchase and power sales of the i-th sub-micro network's interactive tie line in the user layer model;
供电层鲁棒优化调度模型的约束条件为:The constraints of the robust optimization scheduling model for the power supply layer are:
式(23)-(24)为柴油发电机的最小持续开机时间、最小持续关机时间和最大持续开机时间约束,N
ON,min、N
OFF,min和N
ON,max分别为柴油发电机的最小持续开机时段数限值、最小持续关机时段数限值和最大持续开机时段数限值;k表示柴油发电机启动状态、停机状态和运行状态的开始时段;式(25)为柴油发电机的运行功率及爬坡速度约束,M
DE,min和M
DE,max为柴油发电机开机状态下运行功率的下限值和上限值,RD
DE和RU
DE为柴油发电机的单位时段内下爬坡和上爬坡的速率限值;式(26)-(28)为供电层模型中第i个子微网中交互联络线的运行功率及功率波动约束;式(29)-(30)为第i个子微网和第j个子微网之间的换流联络线运行功率和功率波动约束,M
CL+ij和
为换流联络线的正向和反向功率限值,RD
CLij和RU
CLij为换流联络线功率波动的上下限值;式(31)-(32)为并网联络线运行功率和功率波动约束,M
GL+和M
GL-为并网联络线的购电和售电功率限值,RD
GL和RU
GL为并网联络线功率波动的上下限值;式(33)为供电层的功率平衡约束,η
CL+ij和η
CL-ij为第i个子微网和第j个子微网之间的换流联络线的正向和反向运行效率;式(34)-(35)为考虑了断线不确定性后并网联络线和换流联络线的运行功率约束,Π
r和Π
z分别为并网 联络线和换流联络线的断线时段预算参数,p和q表示供电层模型中所考虑的第p个子微网和第q个子微网之间的换流联络线的断线不确定性,W
CL+pqt和
为第p个子微网和第q个子微网之间的换流联络线在t时段的正向和反向运行功率,M
CL+pq和
为该换流联络线的正向和反向运行功率限值;式(36)为并网联络线和换流联络线的断线不确定性集。
Equations (23)-(24) are the minimum continuous start-up time, minimum continuous shutdown time and maximum continuous start-up time constraint for diesel generators. N ON, min , N OFF, min and N ON,max are the minimum of diesel generators respectively. The number of continuous power-on period limits, the minimum number of continuous shutdown periods, and the maximum number of continuous power-on periods; k indicates the start period of the diesel generator startup state, shutdown state, and operating state; and equation (25) is the operation of the diesel generator Power and climbing speed constraints, M DE, min and M DE,max are the lower and upper limits of the operating power of the diesel generator in the on state, RD DE and RU DE are the downhills in the unit time of the diesel generator And the rate limit of the uphill climb; equations (26)-(28) are the operating power and power fluctuation constraints of the interaction line in the i-th sub-microgrid in the power supply layer model; equations (29)-(30) are the i-th The commutation tie line between the sub-microgrid and the j-th sub-grid runs power and power fluctuation constraints, M CL+ij and For the forward and reverse power limits of the commutation tie line, RD CLij and RU CLij are the upper and lower limits of the power fluctuation of the commutating tie line; equations (31)-(32) are the operating power and power fluctuations of the grid tie line. Constraint, M GL+ and M GL- are the power purchase and power selling power limit of the grid connection line, RD GL and RU GL are the upper and lower limits of the grid connection power fluctuation; Equation (33) is the power balance constraint of the power supply layer , η CL+ij and η CL-ij are the forward and reverse running efficiencies of the commutation tie line between the i-th sub-microgrid and the j-th sub-grid; equations (34)-(35) are considered to be broken After the line uncertainty, the operating power constraints of the grid connection line and the commutation tie line, Π r and Π z are the budget parameters of the disconnection period of the grid connection line and the commutation tie line respectively, p and q represent the power supply layer model The disconnection uncertainty of the commutating tie line between the p-th sub-microgrid and the q-th sub-grid considered, W CL+pqt and For the commutating tie line between the p-th sub-micronet and the q-th sub-grid, the forward and reverse running powers in the t period, M CL+pq and The forward and reverse operating power limits for the commutation tie line; Equation (36) is the disconnection uncertainty set for the grid tie line and the commutated tie line.
作为本发明所述的一种多微网系统的双层协调鲁棒优化调度方法进一步优化方案,所述步骤30)的具体内容包括:As a further optimization scheme of the two-layer coordinated robust optimization scheduling method of the multi-micro network system according to the present invention, the specific content of the step 30) includes:
步骤301):将用户层和供电层的min-max-min形式鲁棒优化调度模型写成以下形式:Step 301): Write the min-max-min form robust optimization scheduling model of the user layer and the power supply layer into the following form:
式中,N
i为多微网系统中子微网的总数;
表示用户层模型中的优化结果
和
作为已知变量代入供电层模型;
表示供电层模型中的优化结果*W
IL+it和
作为已知变量代入用户层模型。
Where N i is the total number of sub-microgrids in the multi-microgrid system; Represents optimization results in the user layer model with Substituting a known variable into the power supply layer model; Indicates the optimization result in the power supply layer model *W IL+it and Substituting the user layer model as a known variable.
步骤302):基于步骤301)所述模型,将用户层和系统层的min-max-min形式鲁棒优化调度模型均转化为两阶段混合整数线性规划问题,利用整数优化建模工具箱YALMIP调用求解器CPLEX迭代求解用户层和供电层的两阶段混合整数线性规划问题,获得多微网系统的双层协调鲁棒优化调度计划。Step 302): based on the model of step 301), transform the mini-max-min form robust optimization scheduling model of the user layer and the system layer into a two-stage mixed integer linear programming problem, and use the integer optimization modeling toolbox YALMIP to call The solver CPLEX iteratively solves the two-stage mixed integer linear programming problem of the user layer and the power supply layer, and obtains the two-layer coordinated robust optimization scheduling scheme of the multi-micro network system.
作为本发明所述的一种多微网系统的双层协调鲁棒优化调度方法进一步优化方案,步骤302)中,利用列约束生成算法将用户层和系统层的min-max-min形式鲁棒优化调度模型均转化为两阶段混合整数线性规划问题。As a further optimization scheme of the two-layer coordinated robust optimization scheduling method of the multi-micro network system according to the present invention, in step 302), the column-constrained generation algorithm is used to robustly the min-max-min form of the user layer and the system layer. The optimal scheduling model is transformed into a two-stage mixed integer linear programming problem.
本发明采用以上技术方案与现有技术相比,具有以下技术效果:Compared with the prior art, the present invention has the following technical effects:
本发明针对多个子微网接入的多微网系统提出一种双层协调鲁棒优化调度方法,该方法中多微网系统被划分为用户层和供电层两个利益主体,计及每层的不确定性因素分 别开展鲁棒优化调度;由于双层之间存在相互影响,以交互联络线功率作为交互变量,在模型中引入功率约束及偏差惩罚以实现双层的协调,采用列约束生成算法快速有效求解各层的min-max-min问题,获取多微网系统的鲁棒优化调度计划。The present invention proposes a two-layer coordinated robust optimal scheduling method for multiple micro-grid accessing multi-micro network systems, in which the multi-micro network system is divided into two stakeholders: user layer and power supply layer, taking into account each layer. The uncertain factors are respectively used to perform robust optimization scheduling. Because of the interaction between the two layers, the interaction line power is used as the interaction variable, and the power constraint and the deviation penalty are introduced into the model to achieve the double-layer coordination. The algorithm quickly and efficiently solves the min-max-min problem of each layer and obtains the robust optimal scheduling plan of the multi-micro network system.
附图说明DRAWINGS
图1为本发明实施例的流程图;Figure 1 is a flow chart of an embodiment of the present invention;
图2为本发明实施例中多微网系统的拓扑结构图。2 is a topological structural diagram of a multi-micro network system according to an embodiment of the present invention.
具体实施方式Detailed ways
下面结合附图,对本发明实施例的技术方案做进一步的说明。The technical solutions of the embodiments of the present invention are further described below with reference to the accompanying drawings.
目前鲁棒优化在多微网系统中的应用较少,且已有研究仅考虑子微网中源荷的不确定性,忽略多微网系统中可能出现的并离网切换和线路断线等不确定性因素;此外,已有的多微网双层优化调度模型将多微网系统看成统一整体进行优化调度,未考虑双层之间的交互关系,忽略了子微网与上层系统之间的相互影响。实际中子微网和所接入上层系统属于不同的利益主体,二者之间仅存在功率交互信息,因此其优化调度需划分成两层分别进行。本发明针对多个子微网接入的多微网系统提出一种双层协调鲁棒优化调度方法,该方法中多微网系统被划分为用户层和供电层两个利益主体,计及每层的不确定性因素分别开展鲁棒优化调度;由于双层之间存在相互影响,以交互联络线功率作为交互变量,在模型中引入功率约束及偏差惩罚以实现双层的协调,采用列约束生成算法快速有效求解各层的min-max-min问题,获取多微网系统的鲁棒优化调度计划。At present, the application of robust optimization in multi-micro network systems is less, and the existing research only considers the uncertainty of the source and the load in the sub-microgrid, ignoring the possible off-grid handover and line disconnection in the multi-microgrid system. Uncertainty factor; In addition, the existing multi-micro network double-layer optimization scheduling model regards the multi-micro network system as a unified whole for optimal scheduling, without considering the interaction between the two layers, ignoring the sub-micro network and the upper system. The mutual influence. The actual neutron micro-network and the access upper-layer system belong to different interest subjects, and there is only power interaction information between them. Therefore, the optimal scheduling needs to be divided into two layers. The present invention proposes a two-layer coordinated robust optimal scheduling method for multiple micro-grid accessing multi-micro network systems, in which the multi-micro network system is divided into two stakeholders: user layer and power supply layer, taking into account each layer. The uncertain factors are respectively used to perform robust optimization scheduling. Because of the interaction between the two layers, the interaction line power is used as the interaction variable, and the power constraint and the deviation penalty are introduced into the model to achieve the double-layer coordination. The algorithm quickly and efficiently solves the min-max-min problem of each layer and obtains the robust optimal scheduling plan of the multi-micro network system.
如图1所示,本发明实施例采用一种多微网系统的双层协调鲁棒优化调度方法,多微网系统的拓扑结构如图2所示。该方法包括以下步骤:As shown in FIG. 1, the embodiment of the present invention adopts a two-layer coordinated robust optimization scheduling method for a multi-micro network system, and the topology structure of the multi-micro network system is as shown in FIG. 2 . The method includes the following steps:
步骤10)、获取多微网系统中用户层各设备的运行成本系数及运行限值参数,构建min-max-min形式的用户层鲁棒优化调度模型;Step 10): Obtain an operating cost coefficient and a running limit parameter of each device in the user layer in the multi-micro network system, and construct a user-layer robust optimal scheduling model in the form of min-max-min;
步骤20)、获取多微网系统中供电层各设备的运行成本系数及运行限值参数,构建min-max-min形式的供电层鲁棒优化调度模型;Step 20): Obtain an operating cost coefficient and a running limit parameter of each device in the power supply layer in the multi-micro network system, and construct a robust optimization scheduling model of the power supply layer in the form of min-max-min;
步骤30)、求解由步骤10)用户层鲁棒优化调度模型和步骤20)供电层鲁棒优化调度模型组成的多微网系统的双层协调鲁棒优化模型,即利用列约束生成算法迭代求解用户层和供电层的鲁棒优化问题,获得多微网系统的鲁棒协调运行计划。Step 30), solving a two-layer coordinated robust optimization model of the multi-microgrid system consisting of the step 10) user layer robust optimization scheduling model and step 20) power supply layer robust optimization scheduling model, that is, iteratively solving by using the column constraint generation algorithm The robust optimization problem of the user layer and the power supply layer obtains a robust coordinated operation plan of the multi-micro network system.
作为本发明所述的一种多微网系统的双层协调鲁棒优化调度方法进一步优化方案,所述步骤10)中,用户层各设备的运行成本系数及运行限值参数包括各子微网中与可再生发电机、储能、交互联络线及负荷相关的所有运行成本系数和运行限值参数,计及 可再生发电机和负荷的功率不确定性,将所获取的运行成本系数及运行限值参数代入下式建立min-max-min形式的用户层鲁棒优化调度模型:As a further optimization scheme of the two-layer coordinated robust optimization scheduling method of the multi-micro network system according to the present invention, in the step 10), the operating cost coefficient and the operation limit parameter of each device of the user layer include each sub-micro network. All operating cost factors and operating limit parameters related to renewable generators, energy storage, interactive tie lines and loads, taking into account the power uncertainty of renewable generators and loads, and the operating cost factors and operations obtained The limit parameter is substituted into the following formula to establish a user-layer robust optimal scheduling model in the form of min-max-min:
用户层鲁棒优化调度模型的目标函数为:The objective function of the user layer robust optimization scheduling model is:
式(1)所示目标函数中相关项根据下式计算得到:The correlation term in the objective function shown in equation (1) is calculated according to the following formula:
式中,C
REi、
C
ILi和C
DPi分别为第i个子微网中可再生发电机、可削减负荷、储能、交互联络线和交互联络线功率偏差的运行成本;m
REi、m
LSi、m
ESi、m
Bit、m
Sit和m
DPi为第i个子微网中可再生发电机、可削减负荷、储能、交互联络线购电、交互联络线售电及交互联络线功率偏差的运行成本系数;
和
为第i个子微网中交互联络线在t时段的购电和售电运行状态;p
it和l
it为第i个子微网中可再生发电机和负荷的最大可运行功率;P
i和L
i表示第i个子微网中可再生发电机和负荷的功率不确定性集;P
REit、
和
分别为第i个子微网中可再生发电机、储能充电、储能放电、交互联络线购电、交互联络线售电和可削减负荷在t时段的实际运行功率;N
t为一个调度周期的总时段数,Δt为时段间隔;*W
IL+it和
为供电层模型中第i个子微网的交互联络线购电和售电的功率优化结果;
Where, C REi , C ILi and C DPi are the operating costs of the regenerative generator, load reduction, energy storage, interactive tie line and cross tie line power deviation in the i-th sub-microgrid; m REi , m LSi , m ESi , m Bit , m Sit and m DPi are the operating cost coefficients of renewable generators, load reduction, energy storage, cross-link purchase, cross-link sales, and cross-link power deviations in the i-th sub-microgrid; with The state of power purchase and power sale operation of the interactive tie line in the i-th sub-microgrid during t period; p it and l it are the maximum operable power of the renewable generator and load in the i-th sub-microgrid; P i and L i represents the power uncertainty set of the renewable generator and load in the i-th sub-microgrid; P REit , with Respective generators, energy storage and charging, energy storage and discharge, cross-link line purchase, cross-link sales, and actual operating power of the load during the t-th period; n t is a scheduling period The total number of time periods, Δt is the time interval; *W IL+it and Power optimization results for power purchase and sale of the i-th sub-microgrid in the power supply layer model;
用户层鲁棒优化调度模型的约束条件为:The constraints of the user layer robust optimization scheduling model are:
式(6)为第i个子微网中可再生发电机的发电功率约束;式(7)为第i个子微网中储能的充放电功率约束,
和
为储能的最大放电和充电功率限值,式(8)-(9)为该储能的荷电状态约束,SOC
it和SOC
i(t-1)为t和t-1时段储能的荷电状态,η
ES+i和η
ES-i为储能的放电和充电效率限值,
为储能的额定容量,SOC
mini和SOC
maxi为储能的荷电状态下限值和上限值,SOC
i0为储能的初始荷电状态限值,SOC
iNt为储能在调度周期末的荷电状态限值;式(10)-(12)为第i个子微网中交互联络线的运行功率及功率波动约束,
和
为交互联络线的购电和售电功率限值,
和
为交互联络线功率波动的上下限值;式(13)为第i个子微网中可削减负荷的功率约束,
为t时段可削减负荷的运行功率限值;式(14)为第i个子微网的功率平衡约束;式(15)-(16)为第i个子微网中可再生发电机和负荷的功率不确定性集约束;对于可再生发电机的功率不确定性集P
i,
和p
-it分别是t时段可再生发电机最大可运行功率的预测标称值、预测上偏差值和预测下偏差值,
和ξ
-it分别为可再生发电机功率不确定性的上偏差引入参数和下偏差引入参数,
为可再生发电机功率不确定性的时段预算参数;对于负荷的功率不确定性集L
i,
和l
-it分别是t时段负荷最大可运行功率的预测标称值、预测上偏差值和预测下偏差值,
和κ
-it分别为负荷功率不确定性的上偏差引入参数和下偏差引入参数,
为负荷功率不确定性的时段预算参数。
Equation (6) is the power generation constraint of the renewable generator in the i-th sub-microgrid; Equation (7) is the charge-discharge power constraint of the energy storage in the i-th sub-microgrid, with For the maximum discharge and charging power limits of energy storage, equations (8)-(9) are the state of charge constraints for the energy storage, and SOC it and SOC i(t-1) are energy storage for the t and t-1 periods. State of charge, η ES+i and η ES-i are the discharge and charging efficiency limits for energy storage, For the rated capacity of energy storage, SOC mini and SOC maxi are the lower and upper limits of the state of charge of the energy storage, SOC i0 is the initial state of charge of the energy storage, and SOC iNt is the energy storage at the end of the scheduling period. Charge state limit; equations (10)-(12) are the operating power and power fluctuation constraints of the interactive tie line in the i-th sub-microgrid, with For the purchase and sale power limits of the interactive tie line, with The upper and lower limits of the power fluctuation of the interactive tie line; Equation (13) is the power constraint that can reduce the load in the i-th sub-microgrid. For the t period, the operating power limit of the load can be reduced; equation (14) is the power balance constraint of the i-th sub-microgrid; and equations (15)-(16) are the power of the regenerative generator and load in the i-th sub-microgrid Uncertainty set constraint; for the power uncertainty set P i of the renewable generator And p -it are the predicted nominal value, the predicted upper deviation value and the predicted lower deviation value of the maximum operable power of the regenerative generator in the t period, respectively. And ξ -it are renewable power generator on the uncertainty introduced into the bias parameters and the parameter bias is introduced, Time period budget parameters for power uncertainty of renewable generators; for the power uncertainty set L i of the load, And l -it are the predicted nominal value of the maximum operational power of the t-time load, the predicted upper deviation value, and the predicted lower deviation value, respectively. Respectively, and κ -it load power uncertainty on the parameter bias is introduced and the introduction of bias parameters, Time period budget parameters for load power uncertainty.
作为本发明所述的一种多微网系统的双层协调鲁棒优化调度方法进一步优化方案,所述步骤20)中,供电层各设备的运行成本系数及运行限值参数包括与柴油发电机、交互联络线、换流联络线及并网联络线相关的所有运行成本系数及运行限值参数,计及换流联络线和并网联络线的断线不确定性,将运行成本系数及运行限值参数代入下式建立min-max-min形式的供电层鲁棒优化调度模型:As a further optimization scheme of the two-layer coordinated robust optimization scheduling method for a multi-micro network system according to the present invention, in the step 20), the operating cost coefficient and the operation limit parameter of each device of the power supply layer include a diesel generator All operating cost factors and operating limit parameters related to the interactive tie line, the commutation tie line and the grid connection line, taking into account the disconnection uncertainty of the commutation tie line and the grid connection line, the running cost coefficient and operation The limit parameter is substituted into the following formula to establish a robust optimization scheduling model for the power supply layer in the form of min-max-min:
供电层鲁棒优化调度模型的目标函数为:The objective function of the robust optimization scheduling model for the power supply layer is:
式(17)目标函数中相关项可根据下式计算得到:The correlation term in the objective function of equation (17) can be calculated according to the following formula:
式中,F
ON、F
OFF和F
FUEL分别为柴油发电机的启动成本、停机成本和燃料成本;F
CL、F
IL和F
DP分别为供电层模型中换流联络线、交互联络线和交互联络线功率偏差的运行成本;m
ON、m
OFF和m
FUEL分别为柴油发电机的启动成本系数、停机成本系数和燃料成本系数;m
CL+ij和
分别表示第i个子微网和第j个子微网之间的换流联络线的功率从第i个子微网流向第j个子微网和从第j个子微网流向第i个子微网时的运行成本系数;S
CL+ijt和
表示第i个子微网和第j个子微网之间的换流联络线在t时段的正向和反向运行状态;S
IL+it和
表示供电层模型中第i个子微网的交互联络线在t时段的购电和售电运行状态;S
GL+t和
表示并网联络线在t时段的购电和售电运行状态;
和
分别为柴油发电机在t时段的启动状态、停机状态和运行状态;r
t和z
t为不确定性集中并网联络线和换流联络线的运行状态;R和Z分别为并网联络线和换流联络线的断线不确定性集;
为柴油发电机的运行功率;W
DE,R表示柴油发电机的额定功率;W
CL+ijt和
为第i个子微网和第j个子微网之间的换流联络线在t时段的正向和反向运行功率;W
IL+it和
为供电层模型中第i个子微网的交互联络线在t时段的购电和售电功率;W
GL+t和
为并网联络线在t时段的购电和售电功率;a
DE和b
DE为柴油发电机的油耗特性系数;
和
为用户层模型中第i个子微网的交互联络线的购电和售电功率优化结果;
Where F ON , F OFF and F FUEL are the starting cost, shutdown cost and fuel cost of the diesel generator respectively; F CL , F IL and F DP are the commutation tie lines, interactive tie lines and interactions in the power supply layer model respectively Operating cost of tie line power deviation; m ON , m OFF and m FUEL are the starting cost coefficient, the stopping cost coefficient and the fuel cost coefficient of the diesel generator respectively; m CL+ij and The operation of the commutation tie line between the i-th child micro-network and the j-th sub-micro network respectively flows from the i-th sub-micro network to the j-th sub-micro network and from the j-th sub-micro network to the ith sub-micro network Cost factor; S CL+ijt and Representing the forward and reverse running states of the commutation tie line between the i-th sub-micronet and the j-th sub-grid during the t period; S IL+it and Indicates the state of power purchase and power sale of the i-th sub-microgrid in the power supply layer model during the t period; S GL+t and Indicates the state of purchase and sale of the grid connection line during the t period; with They are the starting state, shutdown state and running state of the diesel generator in the t period; r t and z t are the operating states of the grid and the commutating line of the uncertainty concentration; R and Z are the grid connection lines respectively. And the disconnection uncertainty set of the commutation tie line; For the operating power of the diesel generator; W DE, R represents the rated power of the diesel generator; W CL + ijt and The forward and reverse running powers of the commutation tie line between the i-th sub-micronet and the j-th sub-grid during the t-period; W IL+it and The power purchase and power sales of the i-th sub-microgrid in the power supply layer model during the t period; W GL+t and The power purchase and sale power of the grid connection line during the t period; a DE and b DE are the fuel consumption characteristic coefficients of the diesel generator; with Optimizing the power purchase and power sales of the i-th sub-micro network's interactive tie line in the user layer model;
供电层鲁棒优化调度模型的约束条件为:The constraints of the robust optimization scheduling model for the power supply layer are:
式(23)-(24)为柴油发电机的最小持续开机时间、最小持续关机时间和最大持续开机时间约束,N
ON,min、N
OFF,min和N
ON,max分别为柴油发电机的最小持续开机时段数限值、最小持续关机时段数限值和最大持续开机时段数限值;k表示柴油发电机启动状态、停机状态和运行状态的开始时段;式(25)为柴油发电机的运行功率及爬坡速度约束,M
DE,min和M
DE,max为柴油发电机开机状态下运行功率的下限值和上限值,RD
DE和RU
DE为柴油发电机的单位时段内下爬坡和上爬坡的速率限值;式(26)-(28)为供电层模型中第i个子微网中交互联络线的运行功率及功率波动约束;式(29)-(30)为第i个子微网和第j个子微网之间的换流联络线运行功率和功率波动约束,M
CL+ij和
为换流联络线的正向和反向功率限值,RD
CLij和RU
CLij为换流联络线功率波动的上下限值;式(31)-(32)为并网联络线运行功率和功率波动约束,M
GL+和M
GL-为并网联络线的购电和售电功率限值,RD
GL和RU
GL为并网联络线功率波动的上下限值;式(33)为供电层的功率平衡约束,η
CL+ij和η
CL-ij为第i个子微网和第j个子微网之间的换流联络线的正向和反向运行效率;式(34)-(35)为考虑了断线不确定性后并网联络线和换流联络线的运行功率约束,Π
r和Π
z分别为并网联络线和换流联络线的断线时段预算参数,p和q表示供电层模型中所考虑的第p个子微网和第q个子微网之间的换流联络线的断线不确定性,W
CL+pqt和
为第p个子微网和第q个子微网之间的换流联络线在t时段的正向和反向运行功率,M
CL+pq和
为该换流联络线的正向和反向运行功率限值;式(36)为并网联络线和换流联络线的断线不确定性集。
Equations (23)-(24) are the minimum continuous start-up time, minimum continuous shutdown time and maximum continuous start-up time constraint for diesel generators. N ON, min , N OFF, min and N ON,max are the minimum of diesel generators respectively. The number of continuous power-on period limits, the minimum number of continuous shutdown periods, and the maximum number of continuous power-on periods; k indicates the start period of the diesel generator startup state, shutdown state, and operating state; and equation (25) is the operation of the diesel generator Power and climbing speed constraints, M DE, min and M DE,max are the lower and upper limits of the operating power of the diesel generator in the on state, RD DE and RU DE are the downhills in the unit time of the diesel generator And the rate limit of the uphill climb; equations (26)-(28) are the operating power and power fluctuation constraints of the interaction line in the i-th sub-microgrid in the power supply layer model; equations (29)-(30) are the i-th The commutation tie line between the sub-microgrid and the j-th sub-grid runs power and power fluctuation constraints, M CL+ij and For the forward and reverse power limits of the commutation tie line, RD CLij and RU CLij are the upper and lower limits of the power fluctuation of the commutating tie line; equations (31)-(32) are the operating power and power fluctuations of the grid tie line. Constraint, M GL+ and M GL- are the power purchase and power selling power limit of the grid connection line, RD GL and RU GL are the upper and lower limits of the grid connection power fluctuation; Equation (33) is the power balance constraint of the power supply layer , η CL+ij and η CL-ij are the forward and reverse running efficiencies of the commutation tie line between the i-th sub-microgrid and the j-th sub-grid; equations (34)-(35) are considered to be broken After the line uncertainty, the operating power constraints of the grid connection line and the commutation tie line, Π r and Π z are the budget parameters of the disconnection period of the grid connection line and the commutation tie line respectively, p and q represent the power supply layer model The disconnection uncertainty of the commutating tie line between the p-th sub-microgrid and the q-th sub-grid considered, W CL+pqt and For the commutating tie line between the p-th sub-micronet and the q-th sub-grid, the forward and reverse running powers in the t period, M CL+pq and The forward and reverse operating power limits for the commutation tie line; Equation (36) is the disconnection uncertainty set for the grid tie line and the commutated tie line.
作为本发明所述的一种多微网系统的双层协调鲁棒优化调度方法进一步优化方案,所述步骤30)的具体内容包括:As a further optimization scheme of the two-layer coordinated robust optimization scheduling method of the multi-micro network system according to the present invention, the specific content of the step 30) includes:
步骤301):将用户层和供电层的min-max-min形式鲁棒优化调度模型写成以下形式:Step 301): Write the min-max-min form robust optimization scheduling model of the user layer and the power supply layer into the following form:
式中,N
i为多微网系统中子微网的总数;
表示用户层模型中的优化结果
和
作为已知变量代入供电层模型;
表示供电层模型中的优化结果*W
IL+it和
作为已知变量代入用户层模型。
Where N i is the total number of sub-microgrids in the multi-microgrid system; Represents optimization results in the user layer model with Substituting a known variable into the power supply layer model; Indicates the optimization result in the power supply layer model *W IL+it and Substituting the user layer model as a known variable.
步骤302):基于步骤301)所述模型,将用户层和系统层的min-max-min形式鲁棒优化调度模型均转化为两阶段混合整数线性规划问题,利用整数优化建模工具箱YALMIP调用求解器CPLEX迭代求解用户层和供电层的两阶段混合整数线性规划问题,获得多微网系统的双层协调鲁棒优化调度计划。Step 302): based on the model of step 301), transform the mini-max-min form robust optimization scheduling model of the user layer and the system layer into a two-stage mixed integer linear programming problem, and use the integer optimization modeling toolbox YALMIP to call The solver CPLEX iteratively solves the two-stage mixed integer linear programming problem of the user layer and the power supply layer, and obtains the two-layer coordinated robust optimization scheduling scheme of the multi-micro network system.
作为本发明所述的一种多微网系统的双层协调鲁棒优化调度方法进一步优化方案,步骤302)中,利用列约束生成算法将用户层和系统层的min-max-min形式鲁棒优化调度模型均转化为两阶段混合整数线性规划问题。As a further optimization scheme of the two-layer coordinated robust optimization scheduling method of the multi-micro network system according to the present invention, in step 302), the column-constrained generation algorithm is used to robustly the min-max-min form of the user layer and the system layer. The optimal scheduling model is transformed into a two-stage mixed integer linear programming problem.
本发明实施例的方法,针对多微网系统提出一种双层协调鲁棒优化调度方法,该方法将多微网系统划分为用户层和供电层两个利益主体,考虑到双层之间的相互影响,把交互联络线功率作为优化变量,在鲁棒模型中引入交互功率约束及偏差惩罚成本以实现双层的协调调度,同时计及每层的不确定性因素分别开展鲁棒优化;采用列约束生成算法快速求解各层的min-max-min问题,获取多微网系统的协调鲁棒优化调度计划。The method of the embodiment of the present invention proposes a two-layer coordinated robust optimization scheduling method for a multi-micro network system, which divides the multi-micro network system into two interest groups, a user layer and a power supply layer, considering Mutual influence, using the interactive tie line power as the optimization variable, introducing the interactive power constraint and the deviation penalty cost in the robust model to achieve the two-layer coordinated scheduling, and taking into account the uncertainty factors of each layer to carry out robust optimization respectively; The column constraint generation algorithm quickly solves the min-max-min problem of each layer and obtains a coordinated and robust optimal scheduling plan for the multi-microgrid system.
以上显示和描述了本发明的基本原理、主要特征和优点。本领域的技术人员应该了解,本发明不受上述具体实施例的限制,上述具体实施例和说明书中的描述只是为了进一步说明本发明的原理,在不脱离本发明精神和范围的前提下,本发明还会有各种变化和改进,这些变化和改进都落入要求保护的本发明范围内。本发明要求保护的范围由权利要求书及其等效物界定。The basic principles, main features and advantages of the present invention have been shown and described above. It should be understood by those skilled in the art that the present invention is not limited by the foregoing embodiments, and the description of the present invention and the description of the present invention are only intended to further illustrate the principles of the present invention without departing from the spirit and scope of the invention. There are various changes and modifications of the invention which fall within the scope of the invention as claimed. The scope of the invention is defined by the claims and their equivalents.