WO2019165702A1 - Double-layer coordinated robust optimized scheduling method for multi-microgrids system - Google Patents

Double-layer coordinated robust optimized scheduling method for multi-microgrids system Download PDF

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WO2019165702A1
WO2019165702A1 PCT/CN2018/084940 CN2018084940W WO2019165702A1 WO 2019165702 A1 WO2019165702 A1 WO 2019165702A1 CN 2018084940 W CN2018084940 W CN 2018084940W WO 2019165702 A1 WO2019165702 A1 WO 2019165702A1
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power
sub
layer
microgrid
power supply
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顾伟
邱海峰
吴志
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东南大学
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Definitions

  • 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.
  • microgrid 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.
  • 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.
  • a two-layer coordinated robust optimization scheduling method for a multi-micro network system includes the following steps:
  • 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;
  • 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;
  • 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.
  • 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:
  • the objective function of the user layer robust optimization scheduling model is:
  • 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
  • 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
  • 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.
  • ⁇ ES+i and ⁇ ES-i are the discharge and charging efficiency limits for 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
  • 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.
  • equation (14) is the power balance constraint of the i-th sub-microgrid
  • equations (15)-(16) are the power of the regenerative generator and load in the i-th sub-microgrid Uncertainty set constraint
  • 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 ⁇ -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.
  • ⁇ -it load power uncertainty on the parameter bias is introduced and the introduction of bias parameters, Time period budget parameters for load power uncertainty.
  • 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:
  • the objective function of the robust optimization scheduling model for the power supply layer is:
  • 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;
  • 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;
  • 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.
  • k indicates the start period of the diesel generator startup state, shutdown state, and operating state;
  • 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
  • Equation (33) is the power balance constraint of the power supply layer
  • ⁇ CL+ij and ⁇ 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
  • the operating power constraints of the grid connection line and the commutation tie line, ⁇ r and ⁇ 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
  • the specific content of the step 30) includes:
  • 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:
  • 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.
  • 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.
  • 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.
  • the present invention has the following technical effects:
  • 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.
  • Figure 1 is a flow chart of an embodiment of the present invention
  • FIG. 2 is a topological structural diagram of a multi-micro network system according to an embodiment of the present invention.
  • 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;
  • 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.
  • 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:
  • 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;
  • 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;
  • 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.
  • 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:
  • the objective function of the user layer robust optimization scheduling model is:
  • 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
  • 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
  • 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.
  • ⁇ ES+i and ⁇ ES-i are the discharge and charging efficiency limits for 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
  • 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.
  • equation (14) is the power balance constraint of the i-th sub-microgrid
  • equations (15)-(16) are the power of the regenerative generator and load in the i-th sub-microgrid Uncertainty set constraint
  • 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 ⁇ -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.
  • ⁇ -it load power uncertainty on the parameter bias is introduced and the introduction of bias parameters, Time period budget parameters for load power uncertainty.
  • 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:
  • the objective function of the robust optimization scheduling model for the power supply layer is:
  • 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;
  • 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;
  • 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.
  • k indicates the start period of the diesel generator startup state, shutdown state, and operating state;
  • 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
  • Equation (33) is the power balance constraint of the power supply layer
  • ⁇ CL+ij and ⁇ 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
  • the operating power constraints of the grid connection line and the commutation tie line, ⁇ r and ⁇ 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
  • the specific content of the step 30) includes:
  • 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:
  • 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.
  • 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.
  • 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.
  • 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.

Abstract

Disclosed is a double-layer coordinated robust optimized scheduling method for a multi-microgrids system, comprising the following steps: 10) obtaining operation cost coefficients and operation limit parameters of different devices in a user layer in the multi-microgrids system, and constructing a robust optimized scheduling model of the user layer; 20) obtaining operation cost coefficients and operation limit parameters of different devices in a power supply layer in the multi-microgrids system, and constructing a robust optimized scheduling model of the power supply layer; and 30) solving a double-layer coordinated robust optimization model of the multi-microgrids system: iteratively solving robust optimization problems of the user layer and the power supply layer by using a column constraint generation algorithm, and obtaining a robust coordinated operation plan of the multi-microgrids system is obtained. The method considers the power interaction characteristic and multi-level nondeterminacy of the user layer and the power supply layer in the multi-microgrids system, can realize double-layer coordinated robust optimized scheduling of the multi-microgrids system, and provides guidance and support for making the operation plan of the multi-microgrids system.

Description

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:
Figure PCTCN2018084940-appb-000001
Figure PCTCN2018084940-appb-000001
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:
Figure PCTCN2018084940-appb-000002
Figure PCTCN2018084940-appb-000002
Figure PCTCN2018084940-appb-000003
Figure PCTCN2018084940-appb-000003
Figure PCTCN2018084940-appb-000004
Figure PCTCN2018084940-appb-000004
Figure PCTCN2018084940-appb-000005
Figure PCTCN2018084940-appb-000005
式中，C REi、
Figure PCTCN2018084940-appb-000006
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；
Figure PCTCN2018084940-appb-000007
和
Figure PCTCN2018084940-appb-000008
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、
Figure PCTCN2018084940-appb-000009
Figure PCTCN2018084940-appb-000010
和
Figure PCTCN2018084940-appb-000011
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
Figure PCTCN2018084940-appb-000012
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 ,
Figure PCTCN2018084940-appb-000006
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;
Figure PCTCN2018084940-appb-000007
with
Figure PCTCN2018084940-appb-000008
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 ,
Figure PCTCN2018084940-appb-000009
Figure PCTCN2018084940-appb-000010
with
Figure PCTCN2018084940-appb-000011
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
Figure PCTCN2018084940-appb-000012
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:
Figure PCTCN2018084940-appb-000013
Figure PCTCN2018084940-appb-000013
Figure PCTCN2018084940-appb-000014
Figure PCTCN2018084940-appb-000014
Figure PCTCN2018084940-appb-000015
Figure PCTCN2018084940-appb-000015
Figure PCTCN2018084940-appb-000016
Figure PCTCN2018084940-appb-000016
Figure PCTCN2018084940-appb-000017
Figure PCTCN2018084940-appb-000017
Figure PCTCN2018084940-appb-000018
Figure PCTCN2018084940-appb-000018
Figure PCTCN2018084940-appb-000019
Figure PCTCN2018084940-appb-000019
Figure PCTCN2018084940-appb-000020
Figure PCTCN2018084940-appb-000020
Figure PCTCN2018084940-appb-000021
Figure PCTCN2018084940-appb-000021
Figure PCTCN2018084940-appb-000022
Figure PCTCN2018084940-appb-000022
Figure PCTCN2018084940-appb-000023
Figure PCTCN2018084940-appb-000023
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，
Figure PCTCN2018084940-appb-000024
和
Figure PCTCN2018084940-appb-000025
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，
Figure PCTCN2018084940-appb-000026
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，
Figure PCTCN2018084940-appb-000027
和
Figure PCTCN2018084940-appb-000028
为交互联络线的购电和售电功率限值，
Figure PCTCN2018084940-appb-000029
和
Figure PCTCN2018084940-appb-000030
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，
Figure PCTCN2018084940-appb-000031
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，
Figure PCTCN2018084940-appb-000032
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，
Figure PCTCN2018084940-appb-000033
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，
Figure PCTCN2018084940-appb-000034
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，
Figure PCTCN2018084940-appb-000035
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，
Figure PCTCN2018084940-appb-000036
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，
Figure PCTCN2018084940-appb-000037
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,
Figure PCTCN2018084940-appb-000024
with
Figure PCTCN2018084940-appb-000025
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,
Figure PCTCN2018084940-appb-000026
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,
Figure PCTCN2018084940-appb-000027
with
Figure PCTCN2018084940-appb-000028
For the purchase and sale power limits of the interactive tie line,
Figure PCTCN2018084940-appb-000029
with
Figure PCTCN2018084940-appb-000030
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.
Figure PCTCN2018084940-appb-000031
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
Figure PCTCN2018084940-appb-000032
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.
Figure PCTCN2018084940-appb-000033
And ξ -it are renewable power generator on the uncertainty introduced into the bias parameters and the parameter bias is introduced,
Figure PCTCN2018084940-appb-000034
Time period budget parameters for power uncertainty of renewable generators; for the power uncertainty set L i of the load,
Figure PCTCN2018084940-appb-000035
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.
Figure PCTCN2018084940-appb-000036
Respectively, and κ -it load power uncertainty on the parameter bias is introduced and the introduction of bias parameters,
Figure PCTCN2018084940-appb-000037
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:
Figure PCTCN2018084940-appb-000038
Figure PCTCN2018084940-appb-000038
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:
Figure PCTCN2018084940-appb-000039
Figure PCTCN2018084940-appb-000039
Figure PCTCN2018084940-appb-000040
Figure PCTCN2018084940-appb-000040
Figure PCTCN2018084940-appb-000041
Figure PCTCN2018084940-appb-000041
Figure PCTCN2018084940-appb-000042
Figure PCTCN2018084940-appb-000042
Figure PCTCN2018084940-appb-000043
Figure PCTCN2018084940-appb-000043
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
Figure PCTCN2018084940-appb-000044
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
Figure PCTCN2018084940-appb-000045
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
Figure PCTCN2018084940-appb-000046
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
Figure PCTCN2018084940-appb-000047
表示并网联络线在t时段的购电和售电运行状态；
Figure PCTCN2018084940-appb-000048
和
Figure PCTCN2018084940-appb-000049
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；
Figure PCTCN2018084940-appb-000050
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
Figure PCTCN2018084940-appb-000051
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
Figure PCTCN2018084940-appb-000052
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
Figure PCTCN2018084940-appb-000053
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；
Figure PCTCN2018084940-appb-000054
和
Figure PCTCN2018084940-appb-000055
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
Figure PCTCN2018084940-appb-000044
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
Figure PCTCN2018084940-appb-000045
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
Figure PCTCN2018084940-appb-000046
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
Figure PCTCN2018084940-appb-000047
Indicates the state of purchase and sale of the grid connection line during the t period;
Figure PCTCN2018084940-appb-000048
with
Figure PCTCN2018084940-appb-000049
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;
Figure PCTCN2018084940-appb-000050
For the operating power of the diesel generator; W DE, R represents the rated power of the diesel generator; W CL + ijt and
Figure PCTCN2018084940-appb-000051
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
Figure PCTCN2018084940-appb-000052
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
Figure PCTCN2018084940-appb-000053
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;
Figure PCTCN2018084940-appb-000054
with
Figure PCTCN2018084940-appb-000055
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:
Figure PCTCN2018084940-appb-000056
Figure PCTCN2018084940-appb-000056
Figure PCTCN2018084940-appb-000057
Figure PCTCN2018084940-appb-000057
Figure PCTCN2018084940-appb-000058
Figure PCTCN2018084940-appb-000058
Figure PCTCN2018084940-appb-000059
Figure PCTCN2018084940-appb-000059
Figure PCTCN2018084940-appb-000060
Figure PCTCN2018084940-appb-000060
Figure PCTCN2018084940-appb-000061
Figure PCTCN2018084940-appb-000061
Figure PCTCN2018084940-appb-000062
Figure PCTCN2018084940-appb-000062
Figure PCTCN2018084940-appb-000063
Figure PCTCN2018084940-appb-000063
Figure PCTCN2018084940-appb-000064
Figure PCTCN2018084940-appb-000064
Figure PCTCN2018084940-appb-000065
Figure PCTCN2018084940-appb-000065
Figure PCTCN2018084940-appb-000066
Figure PCTCN2018084940-appb-000066
Figure PCTCN2018084940-appb-000067
Figure PCTCN2018084940-appb-000067
Figure PCTCN2018084940-appb-000068
Figure PCTCN2018084940-appb-000068
Figure PCTCN2018084940-appb-000069
Figure PCTCN2018084940-appb-000069
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
Figure PCTCN2018084940-appb-000070
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
Figure PCTCN2018084940-appb-000071
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
Figure PCTCN2018084940-appb-000072
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
Figure PCTCN2018084940-appb-000070
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
Figure PCTCN2018084940-appb-000071
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
Figure PCTCN2018084940-appb-000072
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:
Figure PCTCN2018084940-appb-000073
Figure PCTCN2018084940-appb-000073
式中，N i为多微网系统中子微网的总数；
Figure PCTCN2018084940-appb-000074
表示用户层模型中的优化结果
Figure PCTCN2018084940-appb-000075
和
Figure PCTCN2018084940-appb-000076
作为已知变量代入供电层模型；
Figure PCTCN2018084940-appb-000077
表示供电层模型中的优化结果*W IL+it和
Figure PCTCN2018084940-appb-000078
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;
Figure PCTCN2018084940-appb-000074
Represents optimization results in the user layer model
Figure PCTCN2018084940-appb-000075
with
Figure PCTCN2018084940-appb-000076
Substituting a known variable into the power supply layer model;
Figure PCTCN2018084940-appb-000077
Indicates the optimization result in the power supply layer model *W IL+it and
Figure PCTCN2018084940-appb-000078
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:
Figure PCTCN2018084940-appb-000079
Figure PCTCN2018084940-appb-000079
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:
Figure PCTCN2018084940-appb-000080
Figure PCTCN2018084940-appb-000080
Figure PCTCN2018084940-appb-000081
Figure PCTCN2018084940-appb-000081
Figure PCTCN2018084940-appb-000082
Figure PCTCN2018084940-appb-000082
Figure PCTCN2018084940-appb-000083
Figure PCTCN2018084940-appb-000083
式中，C REi、
Figure PCTCN2018084940-appb-000084
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；
Figure PCTCN2018084940-appb-000085
和
Figure PCTCN2018084940-appb-000086
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、
Figure PCTCN2018084940-appb-000087
Figure PCTCN2018084940-appb-000088
和
Figure PCTCN2018084940-appb-000089
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
Figure PCTCN2018084940-appb-000090
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 ,
Figure PCTCN2018084940-appb-000084
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;
Figure PCTCN2018084940-appb-000085
with
Figure PCTCN2018084940-appb-000086
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 ,
Figure PCTCN2018084940-appb-000087
Figure PCTCN2018084940-appb-000088
with
Figure PCTCN2018084940-appb-000089
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
Figure PCTCN2018084940-appb-000090
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:
Figure PCTCN2018084940-appb-000091
Figure PCTCN2018084940-appb-000091
Figure PCTCN2018084940-appb-000092
Figure PCTCN2018084940-appb-000092
Figure PCTCN2018084940-appb-000093
Figure PCTCN2018084940-appb-000093
Figure PCTCN2018084940-appb-000094
Figure PCTCN2018084940-appb-000094
Figure PCTCN2018084940-appb-000095
Figure PCTCN2018084940-appb-000095
Figure PCTCN2018084940-appb-000096
Figure PCTCN2018084940-appb-000096
Figure PCTCN2018084940-appb-000097
Figure PCTCN2018084940-appb-000097
Figure PCTCN2018084940-appb-000098
Figure PCTCN2018084940-appb-000098
Figure PCTCN2018084940-appb-000099
Figure PCTCN2018084940-appb-000099
Figure PCTCN2018084940-appb-000100
Figure PCTCN2018084940-appb-000100
Figure PCTCN2018084940-appb-000101
Figure PCTCN2018084940-appb-000101
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，
Figure PCTCN2018084940-appb-000102
和
Figure PCTCN2018084940-appb-000103
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，
Figure PCTCN2018084940-appb-000104
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，
Figure PCTCN2018084940-appb-000105
和
Figure PCTCN2018084940-appb-000106
为交互联络线的购电和售电功率限值，
Figure PCTCN2018084940-appb-000107
和
Figure PCTCN2018084940-appb-000108
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，
Figure PCTCN2018084940-appb-000109
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，
Figure PCTCN2018084940-appb-000110
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，
Figure PCTCN2018084940-appb-000111
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，
Figure PCTCN2018084940-appb-000112
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，
Figure PCTCN2018084940-appb-000113
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，
Figure PCTCN2018084940-appb-000114
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，
Figure PCTCN2018084940-appb-000115
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,
Figure PCTCN2018084940-appb-000102
with
Figure PCTCN2018084940-appb-000103
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,
Figure PCTCN2018084940-appb-000104
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,
Figure PCTCN2018084940-appb-000105
with
Figure PCTCN2018084940-appb-000106
For the purchase and sale power limits of the interactive tie line,
Figure PCTCN2018084940-appb-000107
with
Figure PCTCN2018084940-appb-000108
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.
Figure PCTCN2018084940-appb-000109
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
Figure PCTCN2018084940-appb-000110
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.
Figure PCTCN2018084940-appb-000111
And ξ -it are renewable power generator on the uncertainty introduced into the bias parameters and the parameter bias is introduced,
Figure PCTCN2018084940-appb-000112
Time period budget parameters for power uncertainty of renewable generators; for the power uncertainty set L i of the load,
Figure PCTCN2018084940-appb-000113
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.
Figure PCTCN2018084940-appb-000114
Respectively, and κ -it load power uncertainty on the parameter bias is introduced and the introduction of bias parameters,
Figure PCTCN2018084940-appb-000115
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:
Figure PCTCN2018084940-appb-000116
Figure PCTCN2018084940-appb-000116
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:
Figure PCTCN2018084940-appb-000117
Figure PCTCN2018084940-appb-000117
Figure PCTCN2018084940-appb-000118
Figure PCTCN2018084940-appb-000118
Figure PCTCN2018084940-appb-000119
Figure PCTCN2018084940-appb-000119
Figure PCTCN2018084940-appb-000120
Figure PCTCN2018084940-appb-000120
Figure PCTCN2018084940-appb-000121
Figure PCTCN2018084940-appb-000121
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
Figure PCTCN2018084940-appb-000122
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
Figure PCTCN2018084940-appb-000123
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
Figure PCTCN2018084940-appb-000124
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
Figure PCTCN2018084940-appb-000125
表示并网联络线在t时段的购电和售电运行状态；
Figure PCTCN2018084940-appb-000126
和
Figure PCTCN2018084940-appb-000127
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；
Figure PCTCN2018084940-appb-000128
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
Figure PCTCN2018084940-appb-000129
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
Figure PCTCN2018084940-appb-000130
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
Figure PCTCN2018084940-appb-000131
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；
Figure PCTCN2018084940-appb-000132
和
Figure PCTCN2018084940-appb-000133
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
Figure PCTCN2018084940-appb-000122
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
Figure PCTCN2018084940-appb-000123
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
Figure PCTCN2018084940-appb-000124
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
Figure PCTCN2018084940-appb-000125
Indicates the state of purchase and sale of the grid connection line during the t period;
Figure PCTCN2018084940-appb-000126
with
Figure PCTCN2018084940-appb-000127
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;
Figure PCTCN2018084940-appb-000128
For the operating power of the diesel generator; W DE, R represents the rated power of the diesel generator; W CL + ijt and
Figure PCTCN2018084940-appb-000129
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
Figure PCTCN2018084940-appb-000130
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
Figure PCTCN2018084940-appb-000131
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;
Figure PCTCN2018084940-appb-000132
with
Figure PCTCN2018084940-appb-000133
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:
Figure PCTCN2018084940-appb-000134
Figure PCTCN2018084940-appb-000134
Figure PCTCN2018084940-appb-000135
Figure PCTCN2018084940-appb-000135
Figure PCTCN2018084940-appb-000136
Figure PCTCN2018084940-appb-000136
Figure PCTCN2018084940-appb-000137
Figure PCTCN2018084940-appb-000137
Figure PCTCN2018084940-appb-000138
Figure PCTCN2018084940-appb-000138
Figure PCTCN2018084940-appb-000139
Figure PCTCN2018084940-appb-000139
Figure PCTCN2018084940-appb-000140
Figure PCTCN2018084940-appb-000140
Figure PCTCN2018084940-appb-000141
Figure PCTCN2018084940-appb-000141
Figure PCTCN2018084940-appb-000142
Figure PCTCN2018084940-appb-000142
Figure PCTCN2018084940-appb-000143
Figure PCTCN2018084940-appb-000143
Figure PCTCN2018084940-appb-000144
Figure PCTCN2018084940-appb-000144
Figure PCTCN2018084940-appb-000145
Figure PCTCN2018084940-appb-000145
Figure PCTCN2018084940-appb-000146
Figure PCTCN2018084940-appb-000146
Figure PCTCN2018084940-appb-000147
Figure PCTCN2018084940-appb-000147
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
Figure PCTCN2018084940-appb-000148
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
Figure PCTCN2018084940-appb-000149
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
Figure PCTCN2018084940-appb-000150
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
Figure PCTCN2018084940-appb-000148
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
Figure PCTCN2018084940-appb-000149
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
Figure PCTCN2018084940-appb-000150
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:
Figure PCTCN2018084940-appb-000151
Figure PCTCN2018084940-appb-000151
式中，N i为多微网系统中子微网的总数；
Figure PCTCN2018084940-appb-000152
表示用户层模型中的优化结果
Figure PCTCN2018084940-appb-000153
和
Figure PCTCN2018084940-appb-000154
作为已知变量代入供电层模型；
Figure PCTCN2018084940-appb-000155
表示供电层模型中的优化结果*W IL+it和
Figure PCTCN2018084940-appb-000156
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;
Figure PCTCN2018084940-appb-000152
Represents optimization results in the user layer model
Figure PCTCN2018084940-appb-000153
with
Figure PCTCN2018084940-appb-000154
Substituting a known variable into the power supply layer model;
Figure PCTCN2018084940-appb-000155
Indicates the optimization result in the power supply layer model *W IL+it and
Figure PCTCN2018084940-appb-000156
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.

Claims (5)

  1. 一种多微网系统的双层协调鲁棒优化调度方法，其特征在于，包括以下步骤：A two-layer coordinated robust optimization scheduling method for a multi-micro network system, comprising the steps of:
    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.
  2. 根据权利要求1所述的一种多微网系统的双层协调鲁棒优化调度方法，其特征在于，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：The two-layer coordinated robust optimization scheduling method for a multi-micro network system according to claim 1, wherein in the step 10), the operating cost coefficient and the running limit parameter of each device of the user layer include each sub- All operating cost factors and operating limit parameters associated with renewable generators, energy storage, interactive tie lines and loads in the microgrid, taking into account the power uncertainty of the renewable generators and loads, and the operating cost factors obtained And the operation limit parameter is substituted into the following formula to establish a user-layer robust optimization 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:
    Figure PCTCN2018084940-appb-100001
    Figure PCTCN2018084940-appb-100001
    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:
    Figure PCTCN2018084940-appb-100002
    Figure PCTCN2018084940-appb-100002
    Figure PCTCN2018084940-appb-100003
    Figure PCTCN2018084940-appb-100003
    Figure PCTCN2018084940-appb-100004
    Figure PCTCN2018084940-appb-100004
    Figure PCTCN2018084940-appb-100005
    Figure PCTCN2018084940-appb-100005
    式中，C REi、
    Figure PCTCN2018084940-appb-100006
    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；
    Figure PCTCN2018084940-appb-100007
    和
    Figure PCTCN2018084940-appb-100008
    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、
    Figure PCTCN2018084940-appb-100009
    Figure PCTCN2018084940-appb-100010
    和
    Figure PCTCN2018084940-appb-100011
    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
    Figure PCTCN2018084940-appb-100012
    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 ,
    Figure PCTCN2018084940-appb-100006
    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;
    Figure PCTCN2018084940-appb-100007
    with
    Figure PCTCN2018084940-appb-100008
    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 ,
    Figure PCTCN2018084940-appb-100009
    Figure PCTCN2018084940-appb-100010
    with
    Figure PCTCN2018084940-appb-100011
    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
    Figure PCTCN2018084940-appb-100012
    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:
    Figure PCTCN2018084940-appb-100013
    Figure PCTCN2018084940-appb-100013
    Figure PCTCN2018084940-appb-100014
    Figure PCTCN2018084940-appb-100014
    Figure PCTCN2018084940-appb-100015
    Figure PCTCN2018084940-appb-100015
    Figure PCTCN2018084940-appb-100016
    Figure PCTCN2018084940-appb-100016
    Figure PCTCN2018084940-appb-100017
    Figure PCTCN2018084940-appb-100017
    Figure PCTCN2018084940-appb-100018
    Figure PCTCN2018084940-appb-100018
    Figure PCTCN2018084940-appb-100019
    Figure PCTCN2018084940-appb-100019
    Figure PCTCN2018084940-appb-100020
    Figure PCTCN2018084940-appb-100020
    Figure PCTCN2018084940-appb-100021
    Figure PCTCN2018084940-appb-100021
    Figure PCTCN2018084940-appb-100022
    Figure PCTCN2018084940-appb-100022
    Figure PCTCN2018084940-appb-100023
    Figure PCTCN2018084940-appb-100023
    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，
    Figure PCTCN2018084940-appb-100024
    和
    Figure PCTCN2018084940-appb-100025
    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，
    Figure PCTCN2018084940-appb-100026
    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，
    Figure PCTCN2018084940-appb-100027
    和
    Figure PCTCN2018084940-appb-100028
    为交互联络线的购电和售电功率限值，
    Figure PCTCN2018084940-appb-100029
    和
    Figure PCTCN2018084940-appb-100030
    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，
    Figure PCTCN2018084940-appb-100031
    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，
    Figure PCTCN2018084940-appb-100032
    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，
    Figure PCTCN2018084940-appb-100033
    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，
    Figure PCTCN2018084940-appb-100034
    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，
    Figure PCTCN2018084940-appb-100035
    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，
    Figure PCTCN2018084940-appb-100036
    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，
    Figure PCTCN2018084940-appb-100037
    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,
    Figure PCTCN2018084940-appb-100024
    with
    Figure PCTCN2018084940-appb-100025
    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,
    Figure PCTCN2018084940-appb-100026
    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,
    Figure PCTCN2018084940-appb-100027
    with
    Figure PCTCN2018084940-appb-100028
    For the purchase and sale power limits of the interactive tie line,
    Figure PCTCN2018084940-appb-100029
    with
    Figure PCTCN2018084940-appb-100030
    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.
    Figure PCTCN2018084940-appb-100031
    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
    Figure PCTCN2018084940-appb-100032
    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.
    Figure PCTCN2018084940-appb-100033
    And ξ -it are renewable power generator on the uncertainty introduced into the bias parameters and the parameter bias is introduced,
    Figure PCTCN2018084940-appb-100034
    Time period budget parameters for power uncertainty of renewable generators; for the power uncertainty set L i of the load,
    Figure PCTCN2018084940-appb-100035
    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.
    Figure PCTCN2018084940-appb-100036
    Respectively, and κ -it load power uncertainty on the parameter bias is introduced and the introduction of bias parameters,
    Figure PCTCN2018084940-appb-100037
    Time period budget parameters for load power uncertainty.
  3. 根据权利要求2所述的一种多微网系统的双层协调鲁棒优化调度方法，其特征在于，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：The two-layer coordinated robust optimization scheduling method for a multi-microgrid system according to claim 2, wherein in the step 20), the operating cost coefficient and the operating limit parameter of each device of the power supply layer include the diesel All operating cost factors and operating limit parameters related to generators, interactive tie lines, commutated tie lines and grid-connected lines, taking into account the disconnection uncertainty of the commutating tie line and the grid connection line, the operating cost factor And the operation limit parameter is substituted into the following formula to establish a robust optimization scheduling model of 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:
    Figure PCTCN2018084940-appb-100038
    Figure PCTCN2018084940-appb-100038
    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:
    Figure PCTCN2018084940-appb-100039
    Figure PCTCN2018084940-appb-100039
    Figure PCTCN2018084940-appb-100040
    Figure PCTCN2018084940-appb-100040
    Figure PCTCN2018084940-appb-100041
    Figure PCTCN2018084940-appb-100041
    Figure PCTCN2018084940-appb-100042
    Figure PCTCN2018084940-appb-100042
    Figure PCTCN2018084940-appb-100043
    Figure PCTCN2018084940-appb-100043
    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
    Figure PCTCN2018084940-appb-100044
    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
    Figure PCTCN2018084940-appb-100045
    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
    Figure PCTCN2018084940-appb-100046
    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
    Figure PCTCN2018084940-appb-100047
    表示并网联络线在t时段的购电和售电运行状态；
    Figure PCTCN2018084940-appb-100048
    和
    Figure PCTCN2018084940-appb-100049
    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；
    Figure PCTCN2018084940-appb-100050
    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
    Figure PCTCN2018084940-appb-100051
    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
    Figure PCTCN2018084940-appb-100052
    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
    Figure PCTCN2018084940-appb-100053
    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；
    Figure PCTCN2018084940-appb-100054
    和
    Figure PCTCN2018084940-appb-100055
    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
    Figure PCTCN2018084940-appb-100044
    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
    Figure PCTCN2018084940-appb-100045
    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
    Figure PCTCN2018084940-appb-100046
    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
    Figure PCTCN2018084940-appb-100047
    Indicates the state of purchase and sale of the grid connection line during the t period;
    Figure PCTCN2018084940-appb-100048
    with
    Figure PCTCN2018084940-appb-100049
    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;
    Figure PCTCN2018084940-appb-100050
    For the operating power of the diesel generator; W DE, R represents the rated power of the diesel generator; W CL + ijt and
    Figure PCTCN2018084940-appb-100051
    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
    Figure PCTCN2018084940-appb-100052
    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
    Figure PCTCN2018084940-appb-100053
    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;
    Figure PCTCN2018084940-appb-100054
    with
    Figure PCTCN2018084940-appb-100055
    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:
    Figure PCTCN2018084940-appb-100056
    Figure PCTCN2018084940-appb-100056
    Figure PCTCN2018084940-appb-100057
    Figure PCTCN2018084940-appb-100057
    Figure PCTCN2018084940-appb-100058
    Figure PCTCN2018084940-appb-100058
    Figure PCTCN2018084940-appb-100059
    Figure PCTCN2018084940-appb-100059
    Figure PCTCN2018084940-appb-100060
    Figure PCTCN2018084940-appb-100060
    Figure PCTCN2018084940-appb-100061
    Figure PCTCN2018084940-appb-100061
    Figure PCTCN2018084940-appb-100062
    Figure PCTCN2018084940-appb-100062
    Figure PCTCN2018084940-appb-100063
    Figure PCTCN2018084940-appb-100063
    Figure PCTCN2018084940-appb-100064
    Figure PCTCN2018084940-appb-100064
    Figure PCTCN2018084940-appb-100065
    Figure PCTCN2018084940-appb-100065
    Figure PCTCN2018084940-appb-100066
    Figure PCTCN2018084940-appb-100066
    Figure PCTCN2018084940-appb-100067
    Figure PCTCN2018084940-appb-100067
    Figure PCTCN2018084940-appb-100068
    Figure PCTCN2018084940-appb-100068
    Figure PCTCN2018084940-appb-100069
    Figure PCTCN2018084940-appb-100069
    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
    Figure PCTCN2018084940-appb-100070
    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
    Figure PCTCN2018084940-appb-100071
    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
    Figure PCTCN2018084940-appb-100072
    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
    Figure PCTCN2018084940-appb-100070
    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
    Figure PCTCN2018084940-appb-100071
    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
    Figure PCTCN2018084940-appb-100072
    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.
  4. 根据权利要求3所述的一种多微网系统的双层协调鲁棒优化调度方法，其特征在于，所述步骤30)的具体内容包括：The two-layer coordinated robust optimization scheduling method for a multi-micro network system according to claim 3, wherein the specific content of the step 30) comprises:
    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:
    Figure PCTCN2018084940-appb-100073
    Figure PCTCN2018084940-appb-100073
    式中，N i为多微网系统中子微网的总数；
    Figure PCTCN2018084940-appb-100074
    表示用户层模型中的优化结果
    Figure PCTCN2018084940-appb-100075
    和
    Figure PCTCN2018084940-appb-100076
    作为已知变量代入供电层模型；
    Figure PCTCN2018084940-appb-100077
    表示供电层模型中的优化结果*W IL+it和
    Figure PCTCN2018084940-appb-100078
    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;
    Figure PCTCN2018084940-appb-100074
    Represents optimization results in the user layer model
    Figure PCTCN2018084940-appb-100075
    with
    Figure PCTCN2018084940-appb-100076
    Substituting a known variable into the power supply layer model;
    Figure PCTCN2018084940-appb-100077
    Indicates the optimization result in the power supply layer model *W IL+it and
    Figure PCTCN2018084940-appb-100078
    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.
  5. 根据权利要求4所述的一种多微网系统的双层协调鲁棒优化调度方法，其特征在于，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。The two-layer coordinated robust optimization scheduling method for a multi-micro network system according to claim 4, wherein in step 302), the min-max-min form of the user layer and the system layer is performed by using a column constraint generation algorithm. The robust optimal scheduling model is transformed into a two-stage mixed integer linear programming problem.
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