CN112257274B - Quantitative evaluation method and system for operation flexibility of power distribution system - Google Patents

Quantitative evaluation method and system for operation flexibility of power distribution system Download PDF

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CN112257274B
CN112257274B CN202011157891.3A CN202011157891A CN112257274B CN 112257274 B CN112257274 B CN 112257274B CN 202011157891 A CN202011157891 A CN 202011157891A CN 112257274 B CN112257274 B CN 112257274B
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planning
power
distribution system
feeder
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CN112257274A (en
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陈一铭
王承民
谢宁
王智冬
李晖
王帅
朱承治
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
Shanghai Jiaotong University
State Grid Zhejiang Electric Power Co Ltd
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
Shanghai Jiaotong University
State Grid Zhejiang Electric Power Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides a quantitative evaluation method and a quantitative evaluation system for operation flexibility of a power distribution system, wherein the quantitative evaluation method comprises the following steps: step M1: inputting initial planning operation data; step M2: generating an initial payload planning scenario based on the initial planning operational data; step M3: solving a planning layer optimization model based on the initial net load planning scene, determining an extension scheme S1, and calculating total investment; step M4: correcting the power distribution system network frame and the constraint according to an extension scheme S1, solving an operation layer optimization model, and determining the operation state of each device in the power distribution system; step M5: correcting according to the running state of each device in the power distribution system to obtain a new net load planning scene, solving a planning layer optimization model, determining an extension scheme S2, and calculating the total investment; step M6: and judging whether the error between the total investment of the scheme S1 and the total investment of the scheme S2 is smaller than a preset value or not, when the error is larger than the preset value, setting the extension scheme S2 as the extension scheme S1, and repeatedly executing the steps M4 to M6 until the planning result is smaller than or equal to the preset value.

Description

Quantitative evaluation method and system for operation flexibility of power distribution system
Technical Field
The invention relates to the field of planning and operation of a power distribution network of a power system, in particular to a quantitative evaluation method and system for operation flexibility of the power distribution system, and more particularly relates to a calculation and double-layer extension planning model for the operation flexibility of the power distribution network.
Background
The conventional planning method of the power distribution network at present comprises the steps of firstly conducting long-term prediction on loads and installed capacities of planning years in a target area to form a plurality of typical planning scenes such as spring, summer, autumn and winter, then taking the minimum expansion cost of the power distribution network as a target function, and obtaining an optimal planning result by using a commercial solver to calculate under the constraints of conventional power flow, voltage, investment, net racks and the like.
However, with the increasing national load demand and the increasing requirement of the proportion of clean energy, it is expected that a large amount of clean and green new energy such as wind power, photovoltaic and the like will be accessed in a power system in the future, and the new energy such as wind and light is greatly influenced by external environmental factors, so that the uncertainty and the volatility are very strong. The power distribution network serves as the tail end of a power system, uncertainty and volatility caused by access of a large number of Distributed Generation (DG) seriously affect stable operation of the system and power utilization quality of users, new challenges and requirements are provided for planning of the power distribution network, and a conventional deterministic planning method cannot deal with future strong source load uncertainty.
Flexibility was first introduced in the design study of process systems, and was later introduced by power system trainees as a property of the grid to deal with uncertainties in planning operations. The power distribution network can adapt to the fluctuation of source load uncertainty by calling various flexible resources in the system, and the purposes of improving the robustness of the system and slowing down investment are achieved. Therefore, the planning method suitable for the future intelligent power distribution network is bound to be combined with the operation scheduling of the system, the uncertainty of the power distribution system is processed, and a more accurate planning result is obtained.
Patent document CN110707681A (application number: 201910795661.0) discloses an interconnected power distribution system with flexible multi-state switches and a reliable operation evaluation method. The interconnected power distribution system with the flexible multi-state switch mainly comprises a flexible multi-state switch FMSS formed by fully-controlled power electronic devices, an MMC is used as a current conversion port to form a multi-port FMSS device, and the multi-port FMSS device comprises a central control system and three ports: the port MMC1, the port MMC2 and the port MMC3 comprise 4 sub-modules; the sub-module MMC adopts FBSM and HBSM series-parallel configuration to form a topological structure of the sub-module MMC; therefore, the electric energy quality of the multi-end flexible interconnection power distribution system is improved, and the loss of the current converter is reduced; the reliability of the interconnected power distribution system is evaluated by using a sequential Monte Carlo method, and the positive and negative benefits of the flexible switch device access are quantitatively analyzed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a quantitative evaluation method and system for the operation flexibility of a power distribution system.
The quantitative evaluation method for the operation flexibility of the power distribution system provided by the invention comprises the following steps:
step M1: inputting initial planning operation data including example data, planning period and planning operation scene, and initializing;
step M2: generating an initial payload planning scenario based on the initial planning operational data;
step M3: solving a planning layer optimization model based on an initial net load planning scene, determining a power distribution system extension scheme S1 in a planning year, and calculating total investment according to the power distribution system extension scheme S1 in the planning year;
step M4: correcting the power distribution system network frame and the constraint according to a power distribution system extension scheme S1 in a planned year to obtain a corrected power distribution system network frame, solving an operation layer optimization model, and determining the operation state of each device in the power distribution system;
step M5: correcting according to the running state of each device in the power distribution system to obtain a new net load planning scene; solving a planning layer optimization model according to the obtained new net load planning scene, determining a power distribution system extension scheme S2 in a planning year, and calculating total investment according to the power distribution system extension scheme S2 in the planning year;
step M6: judging whether the total investment calculated by the power distribution system extension scheme S1 in the planned year and the total investment error calculated by the power distribution system extension scheme S2 in the planned year are smaller than a preset value or not, when the errors are larger than the preset value, setting the power distribution system extension scheme S2 in the planned year as the power distribution system extension scheme S1 in the planned year, and repeatedly executing the step M4 to the step M6 until the planning result is smaller than or equal to the preset value;
the planning layer optimization model is that the upper layer model is an extension planning layer, and the objective function is that the comprehensive extension investment cost and the flexible cost are minimum;
the operation layer optimization model is characterized in that a lower layer model is an operation scheduling layer, and an objective function is the operation flexibility of the maximized system under a given net rack.
Preferably, the example data in the step M1 includes rack topology parameters, types of nodes, active loads of load nodes, power generation capacity of power generation nodes, and line transmission capacity parameters.
Preferably, the planning layer optimization model comprises a planning layer optimization model objective function and a planning layer optimization model constrained optimization model;
the planning layer optimization model objective function comprises minimum comprehensive expansion investment cost and minimum flexible cost;
and the planning layer optimization model constraints are power flow constraints, safety constraints and investment constraints.
Preferably, the operation layer optimization model comprises an operation layer optimization model objective function and an operation layer optimization model constrained optimization model;
the operational layer optimization model objective function comprises maximizing operational flexibility of the power distribution system under a given power distribution system grid;
the operation layer optimization model constraints comprise power flow constraints, safety constraints, upper-level power grid feed-in power constraints, flexible resource constraints, and correction constraints of the distributed power supply and the load actual value considering uncertainty.
Preferably, the planning layer optimization model objective function includes:
Figure BDA0002743359350000031
Figure BDA0002743359350000032
wherein, CifTo expand the sum of investment cost and flexibility cost; kappaLA cost recovery factor for the feeder; c. CLInvestment cost per unit length of feeder line;
Figure BDA0002743359350000033
is the line length of the feeder ij; y isijA binary investment variable representing the feeder line when yijIf the value is 1, the investment capacity expansion is shown in the planning year, and when y isijIf the value is 0, the circuit is unchanged; c. CflAn average net value representing a change in compliance requirement; psiLRepresenting a feeder set; subscript L has no special meaning, only as a naming label; r isinThe discount rate is obtained; t isLRepresenting a planning cycle; kappaflThe flexibility benefit coefficient ensures that the flexibility and the economy are in the same dimension;
the power flow constraint in the planning layer optimization model constraint comprises the following steps:
Figure BDA0002743359350000034
Figure BDA0002743359350000035
Figure BDA0002743359350000036
wherein the content of the first and second substances,
Figure BDA0002743359350000037
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isijAnd xijResistance and reactance of the feeder ij;
Figure BDA0002743359350000038
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure BDA0002743359350000039
the interruption capacity of the interruptible load on the node i at the time t;
Figure BDA0002743359350000041
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure BDA0002743359350000042
the active load and the reactive load on a node i at the moment t are shown;Ω iis a set of feeders in communication with node i;
Figure BDA0002743359350000043
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; u shapei,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure BDA0002743359350000044
is a reference voltage amplitude;
the safety constraints include:
Figure BDA0002743359350000045
Figure BDA0002743359350000046
wherein the content of the first and second substances,
Figure BDA0002743359350000047
the original line capacity of the feeder line is obtained; p'ijThe line capacity after the feeder line is expanded; pij,tRepresents, Ui,tmax、Ui,tminThe maximum value and the minimum value of the node voltage of the power distribution system are respectively;
the investment constraints include:
yij≤1 ij∈ψL (8)。
preferably, the run-level optimization model objective function includes:
Figure BDA0002743359350000048
wherein, cflThe average net value of the flexible demand change is shown, delta T is a time interval, and T is the operation scheduling scene duration; psiNA collection of power distribution system nodes is represented,
Figure BDA0002743359350000049
and
Figure BDA00027433593500000410
representing the flexible requirements of the distributed power supply and the load on a node i at the time t for the uncertain change values of the distributed power supply and the load, and correcting the actual network-on values of the distributed power supply and the load;
the power flow constraint based on the second-order cone relaxation comprises the following steps:
Figure BDA00027433593500000411
Figure BDA00027433593500000412
Figure BDA00027433593500000413
Figure BDA00027433593500000414
wherein the content of the first and second substances,
Figure BDA00027433593500000415
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isikAnd xikResistance and reactance of the feeder ik;
Figure BDA0002743359350000051
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure BDA0002743359350000052
the interruption capacity of the interruptible load on the node i at the time t;
Figure BDA0002743359350000053
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure BDA0002743359350000054
the active load and the reactive load on a node i at the moment t are shown;Ω iis a set of feeders in communication with node i;
Figure BDA0002743359350000055
for communication with node i and with branch direction outgoingA feeder set; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; v. ofj,t＝|Uj,t|2Instead of node voltage amplitude, vi,t＝|Ui,t|2Instead of node voltage amplitude, Ui,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; r isijAnd xijResistance and reactance of the feeder ij; to be provided with
Figure BDA0002743359350000056
Replacing the current amplitude of the feeder; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure BDA0002743359350000057
is a reference voltage amplitude;
the safety constraints include:
Figure BDA0002743359350000058
Figure BDA0002743359350000059
in the formula (I), the compound is shown in the specification,
Figure BDA00027433593500000510
Ui,tminrespectively representing the maximum value and the minimum value of the node voltage of the power distribution system;
Figure BDA00027433593500000511
transforming the maximum current value allowed to flow on the front feeder ij for capacity expansion; i'ijFor the maximum current value, l, allowed to flow on the feeder ij after capacity expansionij,tRepresenting the current amplitude of the feeder;
the superior grid feed-in power constraint comprises:
Figure BDA00027433593500000512
Figure BDA00027433593500000513
wherein the content of the first and second substances,
Figure BDA00027433593500000514
representing the active power fed into the upper grid,
Figure BDA00027433593500000515
represents the maximum active power fed into the upper grid,
Figure BDA00027433593500000516
representing the reactive power fed into the upper level grid,
Figure BDA00027433593500000517
representing the maximum reactive power fed into the upper level grid,
the distributed power output constraints include:
Figure BDA00027433593500000518
Figure BDA00027433593500000519
in the formula (I), the compound is shown in the specification,
Figure BDA00027433593500000520
the upper limit and the lower limit of the distributed power supply output at the node i are set;
Figure BDA00027433593500000521
is the power angle; psiDGIs a distributed power supply set;
interruptible load constraint:
Figure BDA0002743359350000061
Figure BDA0002743359350000062
in the formula (I), the compound is shown in the specification,
Figure BDA0002743359350000063
maximum interrupt capacity, which is the interruptible load at node i; t isi ILThe maximum calling time of the interruptible load in the running period;
Figure BDA0002743359350000064
is identified for a binary call that can interrupt the load,
Figure BDA0002743359350000065
representing a call; psiILIs an interruptible load set;
and (4) energy storage system constraint:
Figure BDA0002743359350000066
Figure BDA0002743359350000067
Figure BDA0002743359350000068
Figure BDA0002743359350000069
Figure BDA00027433593500000610
wherein the content of the first and second substances,
Figure BDA00027433593500000611
the maximum discharge and charge power of the energy storage system at node i;
Figure BDA00027433593500000612
the state of charge value of the energy storage equipment at the node i at the time t;
Figure BDA00027433593500000613
the minimum value and the maximum value of the charge of the energy storage equipment at the node i are obtained; beta is aesdThe energy storage self-discharge loss rate is obtained; etaesc，ηesdCharging and discharging efficiency coefficients of the energy storage device;
Figure BDA00027433593500000614
is the energy storage device capacity;
Figure BDA00027433593500000615
charging and discharging binary variables for the energy storage equipment; psiesIs an energy storage device set.
The invention provides a quantitative evaluation system for the operation flexibility of a power distribution system, which comprises:
module M1: inputting initial planning operation data including example data, planning period and planning operation scene, and initializing;
module M2: generating an initial payload planning scenario based on the initial planning operational data;
module M3: solving a planning layer optimization model based on an initial net load planning scene, determining a power distribution system extension scheme S1 in a planning year, and calculating total investment according to the power distribution system extension scheme S1 in the planning year;
module M4: correcting the power distribution system network frame and the constraint according to a power distribution system extension scheme S1 in a planned year to obtain a corrected power distribution system network frame, solving an operation layer optimization model, and determining the operation state of each device in the power distribution system;
module M5: correcting according to the running state of each device in the power distribution system to obtain a new net load planning scene; solving a planning layer optimization model according to the obtained new net load planning scene, determining a power distribution system extension scheme S2 in a planning year, and calculating total investment according to the power distribution system extension scheme S2 in the planning year;
module M6: judging whether the total investment calculated by the power distribution system extension scheme S1 in the planned year and the total investment error calculated by the power distribution system extension scheme S2 in the planned year are smaller than a preset value or not, when the errors are larger than the preset value, setting the power distribution system extension scheme S2 in the planned year as the power distribution system extension scheme S1 in the planned year, and repeatedly triggering the execution of the modules M4 to M6 until the planning result is smaller than or equal to the preset value;
the planning layer optimization model is that the upper layer model is an extension planning layer, and the objective function is that the comprehensive extension investment cost and the flexible cost are minimum;
the operation layer optimization model is characterized in that a lower layer model is an operation scheduling layer, and an objective function is the operation flexibility of the maximized system under a given net rack.
Preferably, the example data in the module M1 includes rack topology parameters, types of nodes, active loads of load nodes, power generation capacity of power generation nodes, and line transmission capacity parameters.
Preferably, the planning layer optimization model comprises a planning layer optimization model objective function and a planning layer optimization model constrained optimization model;
the planning layer optimization model objective function comprises minimum comprehensive expansion investment cost and minimum flexible cost;
the planning layer optimization model constraints are power flow constraints, safety constraints and investment constraints;
the operation layer optimization model comprises an operation layer optimization model objective function and an operation layer optimization model constrained optimization model;
the operational layer optimization model objective function comprises maximizing operational flexibility of the power distribution system under a given power distribution system grid;
the operation layer optimization model constraints comprise power flow constraints, safety constraints, upper-level power grid feed-in power constraints, flexible resource constraints, and correction constraints of the distributed power supply and the load actual value considering uncertainty.
Preferably, the planning layer optimization model objective function includes:
Figure BDA0002743359350000071
Figure BDA0002743359350000072
wherein, CifTo expand the sum of investment cost and flexibility cost; kappaLA cost recovery factor for the feeder; c. CLInvestment cost per unit length of feeder line;
Figure BDA0002743359350000073
is the line length of the feeder ij; y isijA binary investment variable representing the feeder line when yijIf the value is 1, the investment capacity expansion is shown in the planning year, and when y isijIf the value is 0, the circuit is unchanged; c. CflAn average net value representing a change in compliance requirement; psiLRepresenting a feeder set; subscript L has no special meaning, only as a naming label; r isinThe discount rate is obtained; t isLRepresenting a planning cycle; kappaflThe flexibility benefit coefficient ensures that the flexibility and the economy are in the same dimension;
the power flow constraint in the planning layer optimization model constraint comprises the following steps:
Figure BDA0002743359350000074
Figure BDA0002743359350000081
Figure BDA0002743359350000082
wherein the content of the first and second substances,
Figure BDA0002743359350000083
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isijAnd xijResistance and reactance of the feeder ij;
Figure BDA0002743359350000084
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure BDA0002743359350000085
the interruption capacity of the interruptible load on the node i at the time t;
Figure BDA0002743359350000086
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure BDA0002743359350000087
the active load and the reactive load on a node i at the moment t are shown;Ω iis a set of feeders in communication with node i;
Figure BDA0002743359350000088
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; u shapei,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure BDA0002743359350000089
is a reference voltage amplitude;
the safety constraints include:
Figure BDA00027433593500000810
Figure BDA00027433593500000811
wherein the content of the first and second substances,
Figure BDA00027433593500000812
the original line capacity of the feeder line is obtained; p'ijThe line capacity after the feeder line is expanded; pij,tRepresents, Ui,tmax、Ui,tminThe maximum value and the minimum value of the node voltage of the power distribution system are respectively;
the investment constraints include:
yij≤1 ij∈ψL (8)；
the run-level optimization model objective function comprises:
Figure BDA00027433593500000813
wherein, cflThe average net value of the flexible demand change is shown, delta T is a time interval, and T is the operation scheduling scene duration; psiNA collection of power distribution system nodes is represented,
Figure BDA00027433593500000814
and
Figure BDA00027433593500000815
representing the flexible requirements of the distributed power supply and the load on a node i at the time t for the uncertain change values of the distributed power supply and the load, and correcting the actual network-on values of the distributed power supply and the load;
the power flow constraint based on the second-order cone relaxation comprises the following steps:
Figure BDA0002743359350000091
Figure BDA0002743359350000092
Figure BDA0002743359350000093
Figure BDA0002743359350000094
wherein the content of the first and second substances,
Figure BDA0002743359350000095
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isikAnd xikResistance and reactance of the feeder ik;
Figure BDA0002743359350000096
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure BDA0002743359350000097
the interruption capacity of the interruptible load on the node i at the time t;
Figure BDA0002743359350000098
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure BDA0002743359350000099
the active load and the reactive load on a node i at the moment t are shown;Ω iis a set of feeders in communication with node i;
Figure BDA00027433593500000910
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LTo be fedA set of lines; v. ofj,t＝|Uj,t|2Instead of node voltage amplitude, vi,t＝|Ui,t|2Instead of node voltage amplitude, Ui,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; r isijAnd xijResistance and reactance of the feeder ij; to be provided with
Figure BDA00027433593500000911
Replacing the current amplitude of the feeder; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure BDA00027433593500000912
is a reference voltage amplitude;
the safety constraints include:
Figure BDA00027433593500000913
Figure BDA00027433593500000914
in the formula (I), the compound is shown in the specification,
Figure BDA00027433593500000915
Ui,tminrespectively representing the maximum value and the minimum value of the node voltage of the power distribution system;
Figure BDA00027433593500000916
transforming the maximum current value allowed to flow on the front feeder ij for capacity expansion; i'ijFor the maximum current value, l, allowed to flow on the feeder ij after capacity expansionij,tRepresenting the current amplitude of the feeder;
the superior grid feed-in power constraint comprises:
Figure BDA00027433593500000917
Figure BDA0002743359350000101
wherein the content of the first and second substances,
Figure BDA0002743359350000102
representing the active power fed into the upper grid,
Figure BDA0002743359350000103
represents the maximum active power fed into the upper grid,
Figure BDA0002743359350000104
representing the reactive power fed into the upper level grid,
Figure BDA0002743359350000105
representing the maximum reactive power fed into the upper level grid,
the distributed power output constraints include:
Figure BDA0002743359350000106
Figure BDA0002743359350000107
in the formula (I), the compound is shown in the specification,
Figure BDA0002743359350000108
the upper limit and the lower limit of the distributed power supply output at the node i are set;
Figure BDA0002743359350000109
is the power angle; psiDGIs a distributed power supply set;
interruptible load constraint:
Figure BDA00027433593500001010
Figure BDA00027433593500001011
in the formula (I), the compound is shown in the specification,
Figure BDA00027433593500001012
maximum interrupt capacity, which is the interruptible load at node i; t isi ILThe maximum calling time of the interruptible load in the running period;
Figure BDA00027433593500001013
is identified for a binary call that can interrupt the load,
Figure BDA00027433593500001014
representing a call; psiILIs an interruptible load set;
and (4) energy storage system constraint:
Figure BDA00027433593500001015
Figure BDA00027433593500001016
Figure BDA00027433593500001017
Figure BDA00027433593500001018
Figure BDA00027433593500001019
wherein the content of the first and second substances,
Figure BDA00027433593500001020
for maximum discharge and charge work of the energy storage system at node iRate;
Figure BDA00027433593500001021
the state of charge value of the energy storage equipment at the node i at the time t;
Figure BDA00027433593500001022
the minimum value and the maximum value of the charge of the energy storage equipment at the node i are obtained; beta is aesdThe energy storage self-discharge loss rate is obtained; etaesc，ηesdCharging and discharging efficiency coefficients of the energy storage device;
Figure BDA00027433593500001023
is the energy storage device capacity;
Figure BDA00027433593500001024
charging and discharging binary variables for the energy storage equipment; psiesIs an energy storage device set.
Compared with the prior art, the invention has the following beneficial effects:
1. the method comprehensively considers planning and operation processes to further define operation flexibility, realizes accurate quantification of the operation flexibility of the power distribution network based on an optimization model by combining the definition, and is more intuitive compared with other power distribution network flexibility evaluation indexes.
2. The planning method of the invention fully considers the uncertainty in the power distribution system, and the obtained planning result can effectively cope with the uncertain fluctuation in the system by responding to the uncertainty through the flexibility of the power distribution system.
3. The method of the invention combines the active control means in the operation of the distribution system in the planning process, solves the problem of uncertainty of source load by dispatching the flexible resources in the distribution network, and effectively slows down the extension investment.
4. The double-layer programming model used by the method is a mixed integer linear optimization model after mathematical treatment, and the optimal solution can be resolved by a mathematical method without falling into the local optimal problem.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is an algorithm flow chart of a power distribution network flexibility planning method proposed by the present invention;
FIG. 2 is a diagram of an improved IEEE33 node power distribution network topology;
fig. 3 is a graph of initial load and distributed power output.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1
Aiming at the problems that the traditional planning method cannot handle uncertainty and a large amount of standby resources need to be provided to deal with new energy access, so that excessive investment is caused, the invention provides a power distribution network flexible planning method considering the operation scheduling process, and the power distribution system has higher robustness and achieves the aim of reducing the investment by dealing with the source load uncertainty through flexible resources in the scheduling system.
The technical scheme adopted by the invention is as follows: the flexibility in the operational scheduling process is first quantified by building an optimization model that aims at maximizing the net flexibility fluctuations. On the basis, a power distribution network double-layer extension plan combining planning and operation is provided, the lower layer operation layer enables the system to have the maximum operation flexibility by scheduling various flexible resources, the upper layer planning layer plans the network frame under a worse net load scene to minimize investment cost and flexibility cost, the two layers are alternately and iteratively solved, and finally when a planning result meets a certain error, an economic and flexible extension planning result is obtained.
The operation flexibility means that under a given scene, the errors of the load, the DG actual value and the predicted value at a certain moment influenced by the uncertain parameters enable the system to operate in a state deviating from a preset normal state, and a net flexibility requirement change value enabling the system to reach a critical stable state is obtained.
The quantitative evaluation method for the operation flexibility of the power distribution system provided by the invention comprises the following steps:
step M1: inputting initial planning operation data including example data, planning period and planning operation scene, and initializing;
step M2: generating an initial payload planning scenario based on the initial planning operational data;
step M3: solving a planning layer optimization model based on an initial net load planning scene, determining a power distribution system extension scheme S1 in a planning year, and calculating total investment according to the power distribution system extension scheme S1 in the planning year;
step M4: correcting the power distribution system network frame and the constraint according to a power distribution system extension scheme S1 in a planned year to obtain a corrected power distribution system network frame, solving an operation layer optimization model, and determining the operation state of each device in the power distribution system;
step M5: correcting according to the running state of each device in the power distribution system to obtain a new net load planning scene; solving a planning layer optimization model according to the obtained new net load planning scene, determining a power distribution system extension scheme S2 in a planning year, and calculating total investment according to the power distribution system extension scheme S2 in the planning year;
step M6: judging whether the total investment calculated by the power distribution system extension scheme S1 in the planned year and the total investment error calculated by the power distribution system extension scheme S2 in the planned year are smaller than a preset value or not, when the errors are larger than the preset value, setting the power distribution system extension scheme S2 in the planned year as the power distribution system extension scheme S1 in the planned year, and repeatedly executing the step M4 to the step M6 until the planning result is smaller than or equal to the preset value;
the planning layer optimization model is that the upper layer model is an extension planning layer, and the objective function is that the comprehensive extension investment cost and the flexible cost are minimum;
the operation layer optimization model is characterized in that a lower layer model is an operation scheduling layer, and an objective function is the operation flexibility of the maximized system under a given net rack.
A quantitative evaluation method for the operation flexibility of a power distribution system comprehensively considers two angles of planning and operation, and uses an optimization mathematical method to search the maximum source load fluctuation value adaptable by using various flexible resources in the system under the safety constraint of the power distribution system, thereby realizing the direct quantitative calculation of the flexibility of the power system.
Planning and operation are combined, source load uncertainty is adapted through flexibility of a power distribution system, a double-layer optimization model is constructed, an upper layer model transmits an optimized grid structure to a lower layer model, and the lower layer model transmits an operation scheduling scene with a larger flexibility requirement to the upper layer model; the upper layer model changes an expansion scheme based on a harsher scene, the lower layer dispatches flexible resources according to a new net rack to obtain higher operation flexibility, and the flexible resources and the new net rack are alternately solved to meet a certain calculation error and then obtain a final planning and operation scheme.
Specifically, the example data in the step M1 includes rack topology parameters, types of nodes, active loads of load nodes, power generation capacity of power generation nodes, and line transmission capacity parameters.
Specifically, the planning layer optimization model comprises a planning layer optimization model objective function and a planning layer optimization model constrained optimization model;
the planning layer optimization model objective function comprises minimum comprehensive expansion investment cost and minimum flexible cost;
and the planning layer optimization model constraints are power flow constraints, safety constraints and investment constraints.
Specifically, the running layer optimization model comprises a running layer optimization model objective function and a running layer optimization model constrained optimization model;
the operational layer optimization model objective function comprises maximizing operational flexibility of the power distribution system under a given power distribution system grid;
the operation layer optimization model constraints comprise power flow constraints, safety constraints, upper-level power grid feed-in power constraints, flexible resource constraints, and correction constraints of the distributed power supply and the load actual value considering uncertainty.
Specifically, the planning layer optimization model objective function includes:
Figure BDA0002743359350000131
Figure BDA0002743359350000132
wherein, CifTo expand the sum of investment cost and flexibility cost; kappaLA cost recovery factor for the feeder; c. CLInvestment cost per unit length of feeder line;
Figure BDA0002743359350000133
is the line length of the feeder ij; y isijA binary investment variable representing the feeder line when yijIf the value is 1, the investment capacity expansion is shown in the planning year, and when y isijIf the value is 0, the circuit is unchanged; c. CflAn average net value representing a change in compliance requirement; psiLRepresenting a feeder set; subscript L has no special meaning, only as a naming label; r isinThe discount rate is obtained; t isLRepresenting a planning cycle; kappaflThe flexibility benefit coefficient ensures that the flexibility and the economy are in the same dimension;
the power flow constraint in the planning layer optimization model constraint comprises the following steps:
Figure BDA0002743359350000134
Figure BDA0002743359350000135
Figure BDA0002743359350000136
wherein the content of the first and second substances,
Figure BDA0002743359350000141
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isijAnd xijResistance and reactance of the feeder ij;
Figure BDA0002743359350000142
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure BDA0002743359350000143
the interruption capacity of the interruptible load on the node i at the time t;
Figure BDA0002743359350000144
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure BDA0002743359350000145
the active load and the reactive load on a node i at the moment t are shown;Ω iis a set of feeders in communication with node i;
Figure BDA0002743359350000146
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; u shapei,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure BDA0002743359350000147
is a reference voltage amplitude;
the safety constraints include:
Figure BDA0002743359350000148
Figure BDA0002743359350000149
wherein the content of the first and second substances,
Figure BDA00027433593500001410
the original line capacity of the feeder line is obtained; p'ijThe line capacity after the feeder line is expanded; pij,tRepresents, Ui,tmax、Ui,tminThe maximum value and the minimum value of the node voltage of the power distribution system are respectively;
the investment constraints include:
yij≤1 ij∈ψL (8)。
specifically, the run-level optimization model objective function includes:
Figure BDA00027433593500001411
wherein, cflThe average net value of the flexible demand change is shown, delta T is a time interval, and T is the operation scheduling scene duration; psiNA collection of power distribution system nodes is represented,
Figure BDA00027433593500001412
and
Figure BDA00027433593500001413
representing the flexible requirements of the distributed power supply and the load on a node i at the time t for the uncertain change values of the distributed power supply and the load, and correcting the actual network-on values of the distributed power supply and the load;
the power flow constraint based on the second-order cone relaxation comprises the following steps:
Figure BDA00027433593500001414
Figure BDA00027433593500001415
Figure BDA0002743359350000151
Figure BDA0002743359350000152
wherein the content of the first and second substances,
Figure BDA0002743359350000153
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isikAnd xikResistance and reactance of the feeder ik;
Figure BDA0002743359350000154
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure BDA0002743359350000155
the interruption capacity of the interruptible load on the node i at the time t;
Figure BDA0002743359350000156
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure BDA0002743359350000157
the active load and the reactive load on a node i at the moment t are shown;Ω iis a set of feeders in communication with node i;
Figure BDA0002743359350000158
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor power distribution systemsSet of nodes, psiLIs a feeder line set; v. ofj,t＝|Uj,t|2Instead of node voltage amplitude, vi,t＝|Ui,t|2Instead of node voltage amplitude, Ui,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; r isijAnd xijResistance and reactance of the feeder ij; to be provided with
Figure BDA0002743359350000159
Replacing the current amplitude of the feeder; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure BDA00027433593500001510
is a reference voltage amplitude;
the safety constraints include:
Figure BDA00027433593500001511
Figure BDA00027433593500001512
in the formula (I), the compound is shown in the specification,
Figure BDA00027433593500001513
Ui,tminrespectively representing the maximum value and the minimum value of the node voltage of the power distribution system;
Figure BDA00027433593500001514
transforming the maximum current value allowed to flow on the front feeder ij for capacity expansion; i'ijFor the maximum current value, l, allowed to flow on the feeder ij after capacity expansionij,tRepresenting the current amplitude of the feeder;
the superior grid feed-in power constraint comprises:
Figure BDA00027433593500001515
Figure BDA00027433593500001516
wherein the content of the first and second substances,
Figure BDA00027433593500001517
representing the active power fed into the upper grid,
Figure BDA00027433593500001518
represents the maximum active power fed into the upper grid,
Figure BDA00027433593500001519
representing the reactive power fed into the upper level grid,
Figure BDA00027433593500001520
representing the maximum reactive power fed into the upper level grid,
the distributed power output constraints include:
Figure BDA0002743359350000161
Figure BDA0002743359350000162
in the formula (I), the compound is shown in the specification,
Figure BDA0002743359350000163
the upper limit and the lower limit of the distributed power supply output at the node i are set;
Figure BDA0002743359350000164
is the power angle; psiDGIs a distributed power supply set;
interruptible load constraint:
Figure BDA0002743359350000165
Figure BDA0002743359350000166
in the formula (I), the compound is shown in the specification,
Figure BDA0002743359350000167
maximum interrupt capacity, which is the interruptible load at node i; t isi ILThe maximum calling time of the interruptible load in the running period;
Figure BDA0002743359350000168
is identified for a binary call that can interrupt the load,
Figure BDA0002743359350000169
representing a call; psiILIs an interruptible load set;
and (4) energy storage system constraint:
Figure BDA00027433593500001610
Figure BDA00027433593500001611
Figure BDA00027433593500001612
Figure BDA00027433593500001613
Figure BDA00027433593500001614
wherein the content of the first and second substances,
Figure BDA00027433593500001615
for energy storage at node iMaximum discharge and charge power of the system;
Figure BDA00027433593500001616
the state of charge value of the energy storage equipment at the node i at the time t;
Figure BDA00027433593500001617
the minimum value and the maximum value of the charge of the energy storage equipment at the node i are obtained; beta is aesdThe energy storage self-discharge loss rate is obtained; etaesc，ηesdCharging and discharging efficiency coefficients of the energy storage device;
Figure BDA00027433593500001618
is the energy storage device capacity;
Figure BDA00027433593500001619
charging and discharging binary variables for the energy storage equipment; psiesIs an energy storage device set.
The invention provides a quantitative evaluation system for the operation flexibility of a power distribution system, which comprises:
module M1: inputting initial planning operation data including example data, planning period and planning operation scene, and initializing;
module M2: generating an initial payload planning scenario based on the initial planning operational data;
module M3: solving a planning layer optimization model based on an initial net load planning scene, determining a power distribution system extension scheme S1 in a planning year, and calculating total investment according to the power distribution system extension scheme S1 in the planning year;
module M4: correcting the power distribution system network frame and the constraint according to a power distribution system extension scheme S1 in a planned year to obtain a corrected power distribution system network frame, solving an operation layer optimization model, and determining the operation state of each device in the power distribution system;
module M5: correcting according to the running state of each device in the power distribution system to obtain a new net load planning scene; solving a planning layer optimization model according to the obtained new net load planning scene, determining a power distribution system extension scheme S2 in a planning year, and calculating total investment according to the power distribution system extension scheme S2 in the planning year;
module M6: judging whether the total investment calculated by the power distribution system extension scheme S1 in the planned year and the total investment error calculated by the power distribution system extension scheme S2 in the planned year are smaller than a preset value or not, when the errors are larger than the preset value, setting the power distribution system extension scheme S2 in the planned year as the power distribution system extension scheme S1 in the planned year, and repeatedly triggering the execution of the modules M4 to M6 until the planning result is smaller than or equal to the preset value;
the planning layer optimization model is that the upper layer model is an extension planning layer, and the objective function is that the comprehensive extension investment cost and the flexible cost are minimum;
the operation layer optimization model is characterized in that a lower layer model is an operation scheduling layer, and an objective function is the operation flexibility of the maximized system under a given net rack.
A quantitative evaluation method for the operation flexibility of a power distribution system comprehensively considers two angles of planning and operation, and uses an optimization mathematical method to search the maximum source load fluctuation value adaptable by using various flexible resources in the system under the safety constraint of the power distribution system, thereby realizing the direct quantitative calculation of the flexibility of the power system.
Planning and operation are combined, source load uncertainty is adapted through flexibility of a power distribution system, a double-layer optimization model is constructed, an upper layer model transmits an optimized grid structure to a lower layer model, and the lower layer model transmits an operation scheduling scene with a larger flexibility requirement to the upper layer model; the upper layer model changes an expansion scheme based on a harsher scene, the lower layer dispatches flexible resources according to a new net rack to obtain higher operation flexibility, and the flexible resources and the new net rack are alternately solved to meet a certain calculation error and then obtain a final planning and operation scheme.
Specifically, the example data in the module M1 includes rack topology parameters, types of nodes, active loads of load nodes, power generation capacity of power generation nodes, and line transmission capacity parameters.
Specifically, the planning layer optimization model comprises a planning layer optimization model objective function and a planning layer optimization model constrained optimization model;
the planning layer optimization model objective function comprises minimum comprehensive expansion investment cost and minimum flexible cost;
and the planning layer optimization model constraints are power flow constraints, safety constraints and investment constraints.
Specifically, the running layer optimization model comprises a running layer optimization model objective function and a running layer optimization model constrained optimization model;
the operational layer optimization model objective function comprises maximizing operational flexibility of the power distribution system under a given power distribution system grid;
the operation layer optimization model constraints comprise power flow constraints, safety constraints, upper-level power grid feed-in power constraints, flexible resource constraints, and correction constraints of the distributed power supply and the load actual value considering uncertainty.
Specifically, the planning layer optimization model objective function includes:
Figure BDA0002743359350000181
Figure BDA0002743359350000182
wherein, CifTo expand the sum of investment cost and flexibility cost; kappaLA cost recovery factor for the feeder; c. CLInvestment cost per unit length of feeder line;
Figure BDA0002743359350000183
is the line length of the feeder ij; y isijA binary investment variable representing the feeder line when yijIf the value is 1, the investment capacity expansion is shown in the planning year, and when y isijIf the value is 0, the circuit is unchanged; c. CflAn average net value representing a change in compliance requirement; psiLRepresenting a feeder set; subscript L has no special meaning, only as a naming label; r isinThe discount rate is obtained; t isLRepresenting a planning cycle; kappaflThe flexibility benefit coefficient ensures that the flexibility and the economy are in the same dimension;
the power flow constraint in the planning layer optimization model constraint comprises the following steps:
Figure BDA0002743359350000184
Figure BDA0002743359350000185
Figure BDA0002743359350000186
wherein the content of the first and second substances,
Figure BDA0002743359350000187
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isijAnd xijResistance and reactance of the feeder ij;
Figure BDA0002743359350000188
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure BDA0002743359350000189
the interruption capacity of the interruptible load on the node i at the time t;
Figure BDA00027433593500001810
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure BDA00027433593500001811
the active load and the reactive load on a node i at the moment t are shown;Ω iis a set of feeders in communication with node i;
Figure BDA00027433593500001812
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; u shapei,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure BDA00027433593500001813
is a reference voltage amplitude;
the safety constraints include:
Figure BDA0002743359350000191
Figure BDA0002743359350000192
wherein the content of the first and second substances,
Figure BDA0002743359350000193
the original line capacity of the feeder line is obtained; p'ijThe line capacity after the feeder line is expanded; pij,tRepresents, Ui,tmax、Ui,tminThe maximum value and the minimum value of the node voltage of the power distribution system are respectively;
the investment constraints include:
yij≤1 ij∈ψL (8)。
specifically, the run-level optimization model objective function includes:
Figure BDA0002743359350000194
wherein, cflThe average net value of the flexible demand change is shown, delta T is a time interval, and T is the operation scheduling scene duration; psiNA collection of power distribution system nodes is represented,
Figure BDA0002743359350000195
and
Figure BDA0002743359350000196
representing the flexible requirements of the distributed power supply and the load on a node i at the time t for the uncertain change values of the distributed power supply and the load, and correcting the actual network-on values of the distributed power supply and the load;
the power flow constraint based on the second-order cone relaxation comprises the following steps:
Figure BDA0002743359350000197
Figure BDA0002743359350000198
Figure BDA0002743359350000199
Figure BDA00027433593500001910
wherein the content of the first and second substances,
Figure BDA00027433593500001911
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isikAnd xikResistance and reactance of the feeder ik;
Figure BDA00027433593500001912
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure BDA00027433593500001913
the interruption capacity of the interruptible load on the node i at the time t;
Figure BDA00027433593500001914
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure BDA00027433593500001915
the active load and the reactive load on a node i at the moment t are shown;Ω iis a set of feeders in communication with node i;
Figure BDA0002743359350000201
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; v. ofj,t＝|Uj,t|2Instead of node voltage amplitude, vi,t＝|Ui,t|2Instead of node voltage amplitude, Ui,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; r isijAnd xijResistance and reactance of the feeder ij; to be provided with
Figure BDA0002743359350000202
Replacing the current amplitude of the feeder; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure BDA0002743359350000203
is a reference voltage amplitude;
the safety constraints include:
Figure BDA0002743359350000204
Figure BDA0002743359350000205
in the formula (I), the compound is shown in the specification,
Figure BDA0002743359350000206
Ui,tminrespectively representing the maximum value and the minimum value of the node voltage of the power distribution system;
Figure BDA0002743359350000207
transforming the maximum current value allowed to flow on the front feeder ij for capacity expansion; i'ijFor the maximum current value, l, allowed to flow on the feeder ij after capacity expansionij,tRepresenting the current amplitude of the feeder;
the superior grid feed-in power constraint comprises:
Figure BDA0002743359350000208
Figure BDA0002743359350000209
wherein the content of the first and second substances,
Figure BDA00027433593500002010
representing the active power fed into the upper grid,
Figure BDA00027433593500002011
represents the maximum active power fed into the upper grid,
Figure BDA00027433593500002012
representing the reactive power fed into the upper level grid,
Figure BDA00027433593500002013
representing the maximum reactive power fed into the upper level grid,
the distributed power output constraints include:
Figure BDA00027433593500002014
Figure BDA00027433593500002015
in the formula (I), the compound is shown in the specification,
Figure BDA00027433593500002016
the upper limit and the lower limit of the distributed power supply output at the node i are set;
Figure BDA00027433593500002017
is the power angle; psiDGIs a distributed power supply set;
interruptible load constraint:
Figure BDA00027433593500002018
Figure BDA00027433593500002019
in the formula (I), the compound is shown in the specification,
Figure BDA00027433593500002020
maximum interrupt capacity, which is the interruptible load at node i; t isi ILThe maximum calling time of the interruptible load in the running period;
Figure BDA00027433593500002021
is identified for a binary call that can interrupt the load,
Figure BDA00027433593500002022
representing a call; psiILIs an interruptible load set;
and (4) energy storage system constraint:
Figure BDA0002743359350000211
Figure BDA0002743359350000212
Figure BDA0002743359350000213
Figure BDA0002743359350000214
Figure BDA0002743359350000215
wherein the content of the first and second substances,
Figure BDA0002743359350000216
the maximum discharge and charge power of the energy storage system at node i;
Figure BDA0002743359350000217
the state of charge value of the energy storage equipment at the node i at the time t;
Figure BDA0002743359350000218
the minimum value and the maximum value of the charge of the energy storage equipment at the node i are obtained; beta is aesdThe energy storage self-discharge loss rate is obtained; etaesc，ηesdCharging and discharging efficiency coefficients of the energy storage device;
Figure BDA0002743359350000219
is the energy storage device capacity;
Figure BDA00027433593500002110
charging and discharging binary variables for the energy storage equipment; psiesIs an energy storage device set.
Example 2
Example 2 is a modification of example 1
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a process are given, but the scope of the present invention is not limited to the following embodiments.
The embodiment is used for carrying out extension planning on an IEEE standard power distribution system, and a topological diagram is shown in figure 2.
As shown in fig. 1, the present embodiment includes: and inputting example data, forming a power distribution network flexible double-layer extension planning model, performing iterative solution, and outputting a planning result.
The input sample data comprises a voltage level of 12.66kV and a reference capacity of 100 MVA. A controllable gas turbine set is connected into the nodes 8, 16 and 28, and the installed capacity is 2 MW; the nodes 3, 18 and 22 are connected to an uncontrollable wind turbine generator, and the installed capacity is 2 MW; the nodes 5 and 7 are connected into an energy storage power station, and the capacity of energy storage equipment is 2 MW; the load of the nodes 25 and 33 is interruptible load, and the maximum interruption hour is 10 h; the upper feed-in maximum power is set to be 5 MW; the original maximum current-carrying capacity of the distribution line is 200A, and the original maximum current-carrying capacity after expansion is 500A; the planning period is 10 years, and table 1 shows the corresponding parameters required by the planning method provided by the invention.
TABLE 1
Figure BDA00027433593500002111
Figure BDA0002743359350000221
The initial load and distributed power output curves are shown in fig. 3.
According to the invention, a planning model is formed, and the result after iterative solution is as follows: lines 5-6,8-9.9-10,12-13,16-17,17-18,23-24 and 28-29 are expanded, and the expansion cost is 2186.2 ten thousand yuan; and according to the flexibility calculation method, the flexibility of the power distribution system of the embodiment before the extension is 3.808, and the flexibility after the extension is 8.731, which improves 129%.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (10)

1. A quantitative evaluation method for operation flexibility of a power distribution system is characterized by comprising the following steps:
step M1: inputting initial planning operation data including example data, planning period and planning operation scene, and initializing;
step M2: generating an initial payload planning scenario based on the initial planning operational data;
step M3: solving a planning layer optimization model based on an initial net load planning scene, determining a power distribution system extension scheme S1 in a planning year, and calculating total investment according to the power distribution system extension scheme S1 in the planning year;
step M4: correcting the power distribution system network frame and the constraint according to a power distribution system extension scheme S1 in a planned year to obtain a corrected power distribution system network frame, solving an operation layer optimization model, and determining the operation state of each device in the power distribution system;
step M5: correcting according to the running state of each device in the power distribution system to obtain a new net load planning scene; solving a planning layer optimization model according to the obtained new net load planning scene, determining a power distribution system extension scheme S2 in a planning year, and calculating total investment according to the power distribution system extension scheme S2 in the planning year;
step M6: judging whether the total investment calculated by the power distribution system extension scheme S1 in the planned year and the total investment error calculated by the power distribution system extension scheme S2 in the planned year are smaller than a preset value or not, when the errors are larger than the preset value, setting the power distribution system extension scheme S2 in the planned year as the power distribution system extension scheme S1 in the planned year, and repeatedly executing the step M4 to the step M6 until the planning result is smaller than or equal to the preset value;
the planning layer optimization model is that the upper layer model is an extension planning layer, and the objective function is that the comprehensive extension investment cost and the flexible cost are minimum;
the operation layer optimization model is characterized in that a lower layer model is an operation scheduling layer, and an objective function is the operation flexibility of the maximized system under a given net rack.
2. The quantitative evaluation method for the operation flexibility of the power distribution system according to claim 1, wherein the example data in the step M1 comprises grid topology parameters, node types, active loads of load nodes, power generation capacity of power generation nodes and line transmission capacity parameters.
3. The quantitative assessment method of power distribution system operational flexibility according to claim 1, wherein the planning layer optimization model comprises a planning layer optimization model objective function, a planning layer optimization model constrained optimization model;
the planning layer optimization model objective function comprises minimum comprehensive expansion investment cost and minimum flexible cost;
and the planning layer optimization model constraints are power flow constraints, safety constraints and investment constraints.
4. The quantitative assessment method of power distribution system operational flexibility according to claim 1, wherein the operational layer optimization model comprises an operational layer optimization model objective function, an operational layer optimization model constrained optimization model;
the operational layer optimization model objective function comprises maximizing operational flexibility of the power distribution system under a given power distribution system grid;
the operation layer optimization model constraints comprise power flow constraints, safety constraints, upper-level power grid feed-in power constraints, flexible resource constraints, and correction constraints of the distributed power supply and the load actual value considering uncertainty.
5. The method of claim 3, wherein the planning layer optimization model objective function comprises:
Figure FDA0002743359340000021
Figure FDA0002743359340000022
wherein, CifTo expand the sum of investment cost and flexibility cost; kappaLA cost recovery factor for the feeder; c. CLInvestment cost per unit length of feeder line;
Figure FDA0002743359340000023
is the line length of the feeder ij; y isijA binary investment variable representing the feeder line when yijIf the value is 1, the investment capacity expansion is shown in the planning year, and when y isijIf the value is 0, the circuit is unchanged; c. CflAn average net value representing a change in compliance requirement; psiLRepresenting a feeder set; subscript L has no special meaning, only as a naming label; r isinThe discount rate is obtained; t isLRepresenting a planning cycle; kappaflThe flexibility benefit coefficient ensures that the flexibility and the economy are in the same dimension;
the power flow constraint in the planning layer optimization model constraint comprises the following steps:
Figure FDA0002743359340000024
Figure FDA0002743359340000025
Figure FDA0002743359340000026
wherein the content of the first and second substances,
Figure FDA0002743359340000027
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isijAnd xijResistance and reactance of the feeder ij;
Figure FDA0002743359340000028
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure FDA0002743359340000029
the interruption capacity of the interruptible load on the node i at the time t;
Figure FDA00027433593400000210
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure FDA00027433593400000211
the active load and the reactive load on a node i at the moment t are shown; omegaiIs a set of feeders in communication with node i;
Figure FDA00027433593400000212
the feeder set is communicated with the node i and the branch direction is outflow;ψNfor a collection of nodes of the distribution system, #LIs a feeder line set; u shapei,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure FDA0002743359340000031
is a reference voltage amplitude;
the safety constraints include:
Figure FDA0002743359340000032
Figure FDA0002743359340000033
wherein the content of the first and second substances,
Figure FDA0002743359340000034
the original line capacity of the feeder line is obtained; p'ijThe line capacity after the feeder line is expanded; pij,tRepresents, Ui,tmax、Ui,tminThe maximum value and the minimum value of the node voltage of the power distribution system are respectively;
the investment constraints include:
yij≤1 ij∈ψL (8)。
6. the method of claim 4, wherein the operational layer optimization model objective function comprises:
Figure FDA0002743359340000035
wherein, cflThe average net value of the flexible demand change is shown, delta T is a time interval, and T is the operation scheduling scene duration; psiNA collection of power distribution system nodes is represented,
Figure FDA0002743359340000036
and
Figure FDA0002743359340000037
representing the flexible requirements of the distributed power supply and the load on a node i at the time t for the uncertain change values of the distributed power supply and the load, and correcting the actual network-on values of the distributed power supply and the load;
the power flow constraint based on the second-order cone relaxation comprises the following steps:
Figure FDA0002743359340000038
Figure FDA0002743359340000039
Figure FDA00027433593400000310
Figure FDA00027433593400000311
wherein the content of the first and second substances,
Figure FDA00027433593400000312
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isikAnd xikResistance and reactance of the feeder ik;
Figure FDA00027433593400000313
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure FDA00027433593400000314
the interruption capacity of the interruptible load on the node i at the time t;
Figure FDA0002743359340000041
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure FDA0002743359340000042
the active load and the reactive load on a node i at the moment t are shown; omegaiIs a set of feeders in communication with node i;
Figure FDA0002743359340000043
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; v. ofj,t＝|Uj,t|2Instead of node voltage amplitude, vi,t＝|Ui,t|2Instead of node voltage amplitude, Ui,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; r isijAnd xijResistance and reactance of the feeder ij; to be provided with
Figure FDA0002743359340000044
Replacing the current amplitude of the feeder; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure FDA0002743359340000045
is a reference voltage amplitude;
the safety constraints include:
Figure FDA0002743359340000046
Figure FDA0002743359340000047
in the formula (I), the compound is shown in the specification,
Figure FDA0002743359340000048
Ui,tminrespectively representing the maximum value and the minimum value of the node voltage of the power distribution system;
Figure FDA0002743359340000049
transforming the maximum current value allowed to flow on the front feeder ij for capacity expansion; i'ijFor the maximum current value, l, allowed to flow on the feeder ij after capacity expansionij,tRepresenting the current amplitude of the feeder;
the superior grid feed-in power constraint comprises:
Figure FDA00027433593400000410
Figure FDA00027433593400000411
wherein the content of the first and second substances,
Figure FDA00027433593400000412
representing the active power fed into the upper grid,
Figure FDA00027433593400000413
represents the maximum active power fed into the upper grid,
Figure FDA00027433593400000414
representing the reactive power fed into the upper level grid,
Figure FDA00027433593400000415
representing maximum reactive power fed into the upper-level network，
The distributed power output constraints include:
Figure FDA00027433593400000416
Figure FDA00027433593400000417
in the formula (I), the compound is shown in the specification,
Figure FDA00027433593400000418
the upper limit and the lower limit of the distributed power supply output at the node i are set;
Figure FDA00027433593400000419
is the power angle; psiDGIs a distributed power supply set;
interruptible load constraint:
Figure FDA00027433593400000420
Figure FDA00027433593400000421
in the formula (I), the compound is shown in the specification,
Figure FDA0002743359340000051
maximum interrupt capacity, which is the interruptible load at node i; t isi ILThe maximum calling time of the interruptible load in the running period;
Figure FDA0002743359340000052
is identified for a binary call that can interrupt the load,
Figure FDA0002743359340000053
representing a call; psiILIs an interruptible load set;
and (4) energy storage system constraint:
Figure FDA0002743359340000054
Figure FDA0002743359340000055
Figure FDA0002743359340000056
Figure FDA0002743359340000057
Figure FDA0002743359340000058
wherein the content of the first and second substances,
Figure FDA0002743359340000059
the maximum discharge and charge power of the energy storage system at node i;
Figure FDA00027433593400000510
the state of charge value of the energy storage equipment at the node i at the time t;
Figure FDA00027433593400000511
the minimum value and the maximum value of the charge of the energy storage equipment at the node i are obtained; beta is aesdThe energy storage self-discharge loss rate is obtained; etaesc，ηesdCharging and discharging efficiency coefficients of the energy storage device;
Figure FDA00027433593400000512
is the energy storage device capacity;
Figure FDA00027433593400000513
charging and discharging binary variables for the energy storage equipment; psiesIs an energy storage device set.
7. A system for quantitatively evaluating the operational flexibility of an electrical distribution system, comprising:
module M1: inputting initial planning operation data including example data, planning period and planning operation scene, and initializing;
module M2: generating an initial payload planning scenario based on the initial planning operational data;
module M3: solving a planning layer optimization model based on an initial net load planning scene, determining a power distribution system extension scheme S1 in a planning year, and calculating total investment according to the power distribution system extension scheme S1 in the planning year;
module M4: correcting the power distribution system network frame and the constraint according to a power distribution system extension scheme S1 in a planned year to obtain a corrected power distribution system network frame, solving an operation layer optimization model, and determining the operation state of each device in the power distribution system;
module M5: correcting according to the running state of each device in the power distribution system to obtain a new net load planning scene; solving a planning layer optimization model according to the obtained new net load planning scene, determining a power distribution system extension scheme S2 in a planning year, and calculating total investment according to the power distribution system extension scheme S2 in the planning year;
module M6: judging whether the total investment calculated by the power distribution system extension scheme S1 in the planned year and the total investment error calculated by the power distribution system extension scheme S2 in the planned year are smaller than a preset value or not, when the errors are larger than the preset value, setting the power distribution system extension scheme S2 in the planned year as the power distribution system extension scheme S1 in the planned year, and repeatedly triggering the execution of the modules M4 to M6 until the planning result is smaller than or equal to the preset value;
the planning layer optimization model is that the upper layer model is an extension planning layer, and the objective function is that the comprehensive extension investment cost and the flexible cost are minimum;
the operation layer optimization model is characterized in that a lower layer model is an operation scheduling layer, and an objective function is the operation flexibility of the maximized system under a given net rack.
8. The system for quantitatively evaluating the operation flexibility of the power distribution system as claimed in claim 7, wherein the example data in the module M1 comprises grid topology parameters, node types, load node active loads, power generation node power generation capacity and line transmission capacity parameters.
9. The system for quantitative assessment of power distribution system operational flexibility according to claim 7, wherein said planning layer optimization model comprises a planning layer optimization model objective function, a planning layer optimization model constrained optimization model;
the planning layer optimization model objective function comprises minimum comprehensive expansion investment cost and minimum flexible cost;
the planning layer optimization model constraints are power flow constraints, safety constraints and investment constraints;
the operation layer optimization model comprises an operation layer optimization model objective function and an operation layer optimization model constrained optimization model;
the operational layer optimization model objective function comprises maximizing operational flexibility of the power distribution system under a given power distribution system grid;
the operation layer optimization model constraints comprise power flow constraints, safety constraints, upper-level power grid feed-in power constraints, flexible resource constraints, and correction constraints of the distributed power supply and the load actual value considering uncertainty.
10. The system of claim 9, wherein the planning layer optimization model objective function comprises:
Figure FDA0002743359340000061
Figure FDA0002743359340000062
wherein, CifTo expand the sum of investment cost and flexibility cost; kappaLA cost recovery factor for the feeder; c. CLInvestment cost per unit length of feeder line;
Figure FDA0002743359340000063
is the line length of the feeder ij; y isijA binary investment variable representing the feeder line when yijIf the value is 1, the investment capacity expansion is shown in the planning year, and when y isijIf the value is 0, the circuit is unchanged; c. CflAn average net value representing a change in compliance requirement; psiLRepresenting a feeder set; subscript L has no special meaning, only as a naming label; r isinThe discount rate is obtained; t isLRepresenting a planning cycle; kappaflThe flexibility benefit coefficient ensures that the flexibility and the economy are in the same dimension;
the power flow constraint in the planning layer optimization model constraint comprises the following steps:
Figure FDA0002743359340000064
Figure FDA0002743359340000071
Figure FDA0002743359340000072
wherein the content of the first and second substances,
Figure FDA0002743359340000073
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is a correlation matrix for characterizingThe topological structure of the system is that the element is preset to be in the positive direction with 1, namely if AijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isijAnd xijResistance and reactance of the feeder ij;
Figure FDA0002743359340000074
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure FDA0002743359340000075
the interruption capacity of the interruptible load on the node i at the time t;
Figure FDA0002743359340000076
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure FDA0002743359340000077
the active load and the reactive load on a node i at the moment t are shown; omegaiIs a set of feeders in communication with node i;
Figure FDA0002743359340000078
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; u shapei,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure FDA0002743359340000079
is a reference voltage amplitude;
the safety constraints include:
Figure FDA00027433593400000710
Figure FDA00027433593400000711
wherein the content of the first and second substances,
Figure FDA00027433593400000712
the original line capacity of the feeder line is obtained; p'ijThe line capacity after the feeder line is expanded; pij,tRepresents, Ui,tmax、Ui,tminThe maximum value and the minimum value of the node voltage of the power distribution system are respectively;
the investment constraints include:
yij≤1 ij∈ψL (8)；
the run-level optimization model objective function comprises:
Figure FDA00027433593400000713
wherein, cflThe average net value of the flexible demand change is shown, delta T is a time interval, and T is the operation scheduling scene duration; psiNA collection of power distribution system nodes is represented,
Figure FDA00027433593400000714
and
Figure FDA00027433593400000715
representing the flexible requirements of the distributed power supply and the load on a node i at the time t for the uncertain change values of the distributed power supply and the load, and correcting the actual network-on values of the distributed power supply and the load;
the power flow constraint based on the second-order cone relaxation comprises the following steps:
Figure FDA0002743359340000081
Figure FDA0002743359340000082
Figure FDA0002743359340000083
Figure FDA0002743359340000084
wherein the content of the first and second substances,
Figure FDA0002743359340000085
respectively feeding active power and reactive power into a superior power grid on a node i at the time t; a is incidence matrix for representing topological structure of system, and the element is preset to be positive direction with 1, namely if A isijIf the value is 1, the feeder ij flows out of the node i; pij,t，Qij,tIs the current on the feeder ij at the time t; r isikAnd xikResistance and reactance of the feeder ik;
Figure FDA0002743359340000086
the active and reactive power output of the distributed power supply on the node i at the time t is realized;
Figure FDA0002743359340000087
the interruption capacity of the interruptible load on the node i at the time t;
Figure FDA0002743359340000088
the discharge power and the charge power of the energy storage system on a node i at the moment t;
Figure FDA0002743359340000089
the active load and the reactive load on a node i at the moment t are shown; omegaiIs a set of feeders in communication with node i;
Figure FDA00027433593400000810
the feeder set is communicated with the node i and the branch direction is outflow; psiNFor a collection of nodes of the distribution system, #LIs a feeder line set; v. ofj,t＝|Uj,t|2Instead of node voltage amplitude, vi,t＝|Ui,t|2Instead of node voltage amplitude, Ui,tAnd Uj,tRepresenting the node voltages of the node i and the node j at the moment t; r isijAnd xijResistance and reactance of the feeder ij; to be provided with
Figure FDA00027433593400000811
Replacing the current amplitude of the feeder; t', which represents a scheduling period of a new scene delivered by a lower layer,
Figure FDA00027433593400000812
is a reference voltage amplitude;
the safety constraints include:
Figure FDA00027433593400000813
Figure FDA00027433593400000814
in the formula (I), the compound is shown in the specification,
Figure FDA00027433593400000815
Ui,tminrespectively representing the maximum value and the minimum value of the node voltage of the power distribution system;
Figure FDA00027433593400000816
transforming the maximum current value allowed to flow on the front feeder ij for capacity expansion; i'ijFor the maximum current value, l, allowed to flow on the feeder ij after capacity expansionij,tRepresenting the current amplitude of the feeder;
the superior grid feed-in power constraint comprises:
Figure FDA00027433593400000817
Figure FDA0002743359340000091
wherein the content of the first and second substances,
Figure FDA0002743359340000092
representing the active power fed into the upper grid,
Figure FDA0002743359340000093
represents the maximum active power fed into the upper grid,
Figure FDA0002743359340000094
representing the reactive power fed into the upper level grid,
Figure FDA0002743359340000095
representing the maximum reactive power fed into the upper level grid,
the distributed power output constraints include:
Figure FDA0002743359340000096
Figure FDA0002743359340000097
in the formula (I), the compound is shown in the specification,
Figure FDA0002743359340000098
the upper limit and the lower limit of the distributed power supply output at the node i are set;
Figure FDA0002743359340000099
is the power angle; psiDGIs a distributed power supply set;
interruptible load constraint:
Figure FDA00027433593400000910
Figure FDA00027433593400000911
in the formula (I), the compound is shown in the specification,
Figure FDA00027433593400000912
maximum interrupt capacity, which is the interruptible load at node i; t isi ILThe maximum calling time of the interruptible load in the running period;
Figure FDA00027433593400000913
is identified for a binary call that can interrupt the load,
Figure FDA00027433593400000914
representing a call; psiILIs an interruptible load set;
and (4) energy storage system constraint:
Figure FDA00027433593400000915
Figure FDA00027433593400000916
Figure FDA00027433593400000917
Figure FDA00027433593400000918
Figure FDA00027433593400000919
wherein the content of the first and second substances,
Figure FDA00027433593400000920
the maximum discharge and charge power of the energy storage system at node i;
Figure FDA00027433593400000921
the state of charge value of the energy storage equipment at the node i at the time t;
Figure FDA00027433593400000922
the minimum value and the maximum value of the charge of the energy storage equipment at the node i are obtained; beta is aesdThe energy storage self-discharge loss rate is obtained; etaesc，ηesdCharging and discharging efficiency coefficients of the energy storage device;
Figure FDA00027433593400000923
is the energy storage device capacity;
Figure FDA00027433593400000924
charging and discharging binary variables for the energy storage equipment; psiesIs an energy storage device set.
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