CN113595158A - Power supply capacity evaluation method for regional power distribution network under power distribution and sales competition situation - Google Patents

Power supply capacity evaluation method for regional power distribution network under power distribution and sales competition situation Download PDF

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CN113595158A
CN113595158A CN202110889597.XA CN202110889597A CN113595158A CN 113595158 A CN113595158 A CN 113595158A CN 202110889597 A CN202110889597 A CN 202110889597A CN 113595158 A CN113595158 A CN 113595158A
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power
load
power supply
constraint
line
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CN113595158B (en
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钱仲豪
茅雷
张骏
袁松
吴茜
李伟伦
毛艳芳
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Nantong Power Supply Co Of State Grid Jiangsu Electric Power Co
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Nantong Power Supply Co Of State Grid Jiangsu Electric Power Co
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • 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
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a method for evaluating the power supply capacity of a regional power distribution network under a power distribution and sale competition situation, which comprises the following steps of: step 1) constructing a multi-objective optimization model comprehensively considering the maximum power supply capacity and the running economy of the power distribution network; step 2), constructing a model constraint condition comprehensively considering the participation of the distributed power supply and the flexible load in the electric power market transaction; and 3) introducing a Pareto front edge to convert the multi-objective function into a series of single objective functions, and solving a Pareto optimal solution for the multi-objective nonlinear optimization model. The method realizes the maximum power supply capacity and the running economy of the power distribution network, and provides a solution algorithm based on Pareto frontier, which can effectively solve the model, while providing a multi-objective optimization model.

Description

Power supply capacity evaluation method for regional power distribution network under power distribution and sales competition situation
Technical Field
The invention belongs to the field of optimized scheduling of power distribution networks, and particularly relates to a power supply capacity evaluation method for a regional power distribution network under a power distribution and sale competition situation.
Background
In recent years, as a power grid company builds widely-existing sensing communication equipment and realizes multi-directional shared data service, a foundation is provided for fine management and interaction of loads. Flexible loads will become a new element that must be dealt with in power distribution network planning. Under the trend of rapid increase of load, the optimization of the power distribution network usually takes guarantee of power utilization reliability as a core, but neglects the improvement of the utilization efficiency of power distribution equipment and the overall economic and social benefits. The effective utilization of the flexible load can obviously improve the utilization rate, the economy and the maximum power supply capacity of the power distribution network equipment. Therefore, whether the current power distribution network can meet the supply of the load at the current stage under the condition of considering N-1 of each main transformer and each feeder line can be judged by calculating the maximum power supply capacity, and a certain flexibility margin is provided to meet the growth trend of the future load, so that the method is worthy of further research. The method aims to comprehensively coordinate various types of scheduling resources in the power market environment by taking the maximum power supply capacity of a system as a target on the basis of ensuring the operating economy of a power distribution network.
At present, for a solving method of the maximum power supply capacity of an active power distribution network, a linear programming method and a mixed integer second-order cone programming method are commonly used, wherein a linear programming algorithm is to use the main transformer constraint of the power distribution network, the feeder line capacity constraint and the like as constraint conditions, to use the maximum load of each main transformer of the power distribution network as an optimization target, and to adopt linear programming software (such as lingo) to solve; the mixed integer second-order cone planning method considers load flow calculation, establishes a more accurate model for the power distribution network, relaxes the constraint condition of non-convex nonlinearity in the model, converts the original problem into a mixed integer second-order cone planning problem to solve, and is more accurate in result and higher in calculation efficiency. The method adopts a mixed integer second-order cone programming method to establish a solving model of the maximum power supply capacity in the power market environment.
Disclosure of Invention
The invention aims to provide a power supply capacity evaluation method of a regional power distribution network under a power distribution and sale competition situation, which can realize the maximum power supply capacity of the power distribution network and the operation economy of the power distribution network.
The technical solution of the invention is as follows: the invention relates to a method for evaluating the power supply capacity of a regional power distribution network under a power distribution and sale competition situation, which comprises the following steps of:
step 1) constructing a multi-objective optimization model comprehensively considering the maximum power supply capacity and the running economy of the power distribution network;
step 2), constructing a model constraint condition comprehensively considering the participation of the distributed power supply and the flexible load in the electric power market transaction;
and 3) introducing a Pareto front edge to convert the multi-objective function into a series of single objective functions, and solving a Pareto optimal solution for the multi-objective nonlinear optimization model.
Further, in the step 1), under an application environment of various resources of the active power distribution network taking power market trading as a guide, in a certain power supply area, the situations of system safe operation and a transformer N-1 are considered, and the maximum load capacity which can be borne by the power distribution network when the power distribution network is reconstructed through system topology is defined as the maximum power supply capacity. The maximum power capacity of the distribution network and the operating economy of the distribution network in the model are considered simultaneously,
(101) the established multi-objective optimization model is specifically as follows:
Figure BDA0003195444890000021
maxf2＝CSub+CDG+CES+CFL+CLoss (0.2)
the equation (0.1) is the maximum objective function considering the power supply capacity of the distribution network, GtThe maximum amplification factor of the load of the power distribution network at the moment t; equation (0.2) is an objective function that takes into account the minimum operating cost of the distribution networkSpecifically including the substation power supply cost CSubDistributed power supply operation cost CDGEnergy storage operation cost CESFlexible load scheduling cost CFLAnd loss cost CLossRespectively correspond to the following:
Figure BDA0003195444890000022
Figure BDA0003195444890000023
Figure BDA0003195444890000024
Figure BDA0003195444890000025
Figure BDA0003195444890000026
in the formula: n is a radical oft、NSub、NPV、NWT、NES、NLA、NCL、NTL、NbranchRespectively representing a set of a dispatching time interval, a transformer substation, a photovoltaic power station (PV), a wind power plant (WT), an energy storage device (ES), a dispatchable residential Load (LA), an interruptible load (CL), a Transferable Load (TL) and a line;
Figure BDA0003195444890000031
respectively representing unit costs of substation power supply, micro gas turbine production, photovoltaic distributed power generation, wind power distributed power generation, energy storage charging, energy storage discharging, schedulable resident load up-regulation, schedulable resident load down-regulation, load interruption, transferable load up-regulation and transferable load down-regulation at the time t;
Figure BDA0003195444890000032
Figure BDA0003195444890000033
respectively representing active power of an i-node transformer substation, a micro gas turbine, a photovoltaic distributed power supply, a wind power distributed power supply, energy storage charging, energy storage discharging, schedulable resident load up-regulation, schedulable resident load down-regulation, load interruption, transferable load up-regulation and transferable load down-regulation at the time t; i isij,tIndicates the magnitude of the current, r, flowing through the line ij at time tijRepresenting the resistance of branch ij.
(102) In the objective function of simply calculating the maximum power supply capacity, it can be found that the network loss in the solved result is large, which is unreasonable in actual operation. Therefore, the network loss is added into the objective function, and the model is embossed by using a second-order cone relaxation technology, so that the model can be efficiently solved. The modified objective function is as follows:
Figure BDA0003195444890000034
in the formula: n is a radical ofnodeRepresents a collection of nodes in the power distribution network,
Figure BDA0003195444890000035
and the active load of the i node at the time t is shown, and phi is a weight coefficient.
In the step 2), main constraint conditions in the model are detailed, wherein the main constraint conditions comprise power flow constraint, line second-order cone constraint, system operation safety constraint, operation variable upper and lower limit constraint, radiation constraint, adjustable and controllable resource constraint and flexible load constraint.
(201) Flow restraint
The power flow constraints comprise node power balance constraints, line voltage drop constraints and line power balance constraints which are respectively expressed by formulas (0.4) to (0.6).
Figure BDA0003195444890000036
Figure BDA0003195444890000041
Figure BDA0003195444890000042
In the formula: u (i), v (i) respectively represent a set with the node i as a head end node and a tail end node; pji,t、Qji,tRespectively representing the active and reactive power flowing in the line ji,
Figure BDA0003195444890000043
respectively representing the active load power and the reactive load power of the node i;
Figure BDA0003195444890000044
respectively representing the reactive power of a photovoltaic distributed power supply and a wind power distributed power supply of a node i; vi,t、Vj,tRespectively representing the voltage amplitudes, Δ V, of the first and last ends of the line ijij,tFor intermediate variables of voltage difference of line ij, Y when line is put into operationij+,t+Yij-,tWhen 1, then Δ Vij,tWhen the line is equal to 0, the line meets the voltage balance constraint; when the line is not in operation, Yij+,t+Yij-,tWhen the voltage is equal to 0, the voltage across the line is not limited by the voltage balance constraint.
(202) Line second order cone constraint
0-1 reconstruction variable Y of line is introduced into modelij+,t、Yij-,tIt indicates whether the line is in forward and reverse operation, respectively. Then introducing variables
Figure BDA0003195444890000045
Instead of the square terms of the voltage and the current, as shown in equation (0.7), further, equation (0.8) is converted into a second order cone equation as shown in equation (0.9):
Figure BDA0003195444890000046
Figure BDA0003195444890000047
Figure BDA0003195444890000048
(203) system operational safety constraints
Equations (0.10) to (0.12) represent upper and lower limit constraints of the node voltage and the branch current, respectively.
Figure BDA0003195444890000049
Figure BDA0003195444890000051
Figure BDA0003195444890000052
In the formula: vmax、VminRespectively representing the maximum and minimum values of the node voltage, ImaxRepresenting the maximum value of the line current.
(204) Upper and lower limit constraints on operating variables
The active power and the reactive power of the operating line should not exceed the rated power of the line, and as shown in a formula (0.13), the active power and the reactive power of the transformer substation should not exceed the rated power of the transformer substation; as shown in equation (0.14), in addition, the active and reactive should satisfy equation (0.15); the charging and discharging power of the stored energy should not exceed the rated power thereof, as shown in the formula (0.16); the active power range, the wind curtailment range and the reactive power range of the photovoltaic distributed power supply and the wind power distributed power supply are shown in formulas (0.17) to (0.18).
Figure BDA0003195444890000053
Figure BDA0003195444890000054
Figure BDA0003195444890000055
Figure BDA0003195444890000056
Figure BDA0003195444890000057
Figure BDA0003195444890000058
In the formula:
Figure BDA0003195444890000059
represents the upper power limit of the substation,
Figure BDA00031954448900000510
represents the upper limit of the charging and discharging power of the energy storage device,
Figure BDA00031954448900000511
respectively represents the power of the abandoned wind and the abandoned light,
Figure BDA00031954448900000512
respectively represents the upper limit of active power of the photovoltaic distributed power supply and the wind power distributed power supply,
Figure BDA0003195444890000061
respectively represents the output percentages of the photovoltaic distributed power supply and the wind power distributed power supply at the moment t,
Figure BDA0003195444890000062
respectively represents the maximum power factors of the photovoltaic distributed power supply and the wind power distributed power supply.
(205) Radiation confinement
The formula (0.19) represents that the load node has only one power inflow path, and the substation node has no power inflow path; because the active power distribution network comprises active devices such as a distributed power supply and energy storage, virtual power constraint is further introduced to ensure a radial structure of the system, and the formula (0.20) represents virtual power balance constraint; equations (0.21) to (0.23) represent upper and lower limit ranges of the line virtual power, the load virtual power, and the substation virtual power.
Figure BDA0003195444890000063
Figure BDA0003195444890000064
Figure BDA0003195444890000065
Figure BDA0003195444890000066
Figure BDA0003195444890000067
In the formula: n is a radical ofLDRepresenting a set of load nodes;
Figure BDA0003195444890000068
and the virtual power of the line, the load node and the substation node is respectively represented.
(206) Adjustable controllable resource constraints
The formula (0.24) represents that the access power and the abandoned light power of the photovoltaic distributed power supply are equal to the output power of the photovoltaic distributed power supply; the formula (0.25) represents that the access power and the abandoned wind power of the wind power distributed generator are equal to the output power of the wind power distributed generator; the formula (0.26) shows that the battery state of charge of the stored energy at the initial time and the end time of the scheduling period is 0.5, and the conversion relation between the battery state of charge at the time t and the battery state of charge and discharge at the previous time is represented.
Figure BDA0003195444890000069
Figure BDA0003195444890000071
Figure BDA0003195444890000072
In the formula: SOCi,tRepresenting the battery nuclear power state of the node i at the time t; etaCH、ηDISRespectively representing the charge and discharge efficiency of the energy storage equipment; deltatIndicating the length of time of each scheduling period.
(207) Flexible load restraint
The equation (0.27) represents the load power at time t of the dispatchable resident dispatchable load i taking into account the demand-side response; the formula (0.28) represents the upper and lower limits of the up-regulated power and the down-regulated power of the dispatchable resident load; equation (0.29) represents the load power at time t for an interruptible load i that accounts for demand side response; the formula (0.30) represents the upper and lower limits of the interruptible load interruption power; equation (0.31) represents the load power at time t for the transferable load i taking into account the demand-side response; the formula (0.32) represents the upper and lower limits of the power to be adjusted up and down for the transferable load, and the total energy to be adjusted up in the scheduling period is equal to the total energy to be adjusted down.
Figure BDA0003195444890000073
Figure BDA0003195444890000074
Figure BDA0003195444890000075
Figure BDA0003195444890000076
Figure BDA0003195444890000077
Figure BDA0003195444890000078
In the formula: xiLA、ξCL、ξTLRepresenting upper limits for dispatchable residential load, interruptible load, transferable load adjustment percentage;
Figure BDA0003195444890000079
the variable is 0-1, which respectively indicates whether the dispatchable resident load adjusts power upwards and downwards;
Figure BDA0003195444890000081
the variable is 0-1, indicating whether the transferable load is adjusting power up and down, respectively.
(208) N-1 constraint
After the main transformer has an N-1 fault, the line connected with the main transformer is in an open circuit state, as shown in the formula (0.33):
Figure BDA0003195444890000082
in the formula: n is a radical offaultIndicating connection to faulty transformerThe line set of (2).
In the step 3), the described maximum power supply capacity model of the power distribution network in the power market environment is a multi-objective nonlinear optimization model. Meanwhile, the maximum power supply capacities and the economical efficiency of the two objective functions are mutually conflicting, the economical efficiency is relatively poor when the maximum power supply capacity is large, the economical efficiency is relatively good when the maximum power supply capacity is small, and the simultaneous optimal solution of the two objective functions is difficult to find. For this reason, a Pareto front edge is introduced into the model, and a Pareto optimal solution of the model is solved.
(301) The core of Pareto frontier is mainly to convert a multi-objective function into a series of single objective functions for solving. For convenience of describing the detailed transformation process, the above model is written here in matrix form as follows:
Figure BDA0003195444890000083
in the formula EkIs the objective function of the model, where k is 2; thetai(x) 0 or less and taui(x) 0 is respectively an inequality constraint and an equality constraint in the model; x is the number ofeMore than or equal to 0 is a variable constraint in the model; x is the number of3∈ΩconeIs a second order cone constraint in the model.
(302) Respectively and independently calculating minimum value and maximum value E of two objective functionsmin1,Emax1And Emin2,Emax2。
(303) The normalization process for the two objective functions is as follows:
Figure BDA0003195444890000084
at this point standardization can be achieved
Figure BDA0003195444890000085
And
Figure BDA0003195444890000086
defining the Uptopont line vector asShown below:
Figure BDA0003195444890000091
(304) the segmentation processing is performed on the utopia line, and the specific node constraint generation is as follows:
Figure BDA0003195444890000092
in the formula: s is the number of segmented nodes on the Utto line; eta1,pAnd η2,pRespectively, are a number between 0 and 1.
(305) Taking the maximum power supply capacity as the main objective function, the modified model is generated as follows.
Figure BDA0003195444890000093
The constraint U (E-P) added to the equation (0.38) is compared to the original objective functionp)TNot less than 0 and U (E-P)p+1)TAnd the area constraint of two adjacent nodes on the Ulto line is less than or equal to 0, and the model is used for solving the optimal model in the area.
Under the application condition of various resources of the active power distribution network oriented to the electric power market transaction, the situation of safe system operation and the situation of a transformer N-1 are considered, and the reconstruction of the power distribution network is realized through system topology. The maximum power supply capacity and the running economy of the power distribution network are realized, and the Pareto frontier-based solving algorithm capable of effectively solving the model is provided while the multi-objective optimization model is provided.
Drawings
FIG. 1 is an algorithmic flow chart of model solution;
FIG. 2 is a diagram of an improved 94-node topology for model testing;
FIG. 3 is a maximum power supply force diagram of a power distribution system under different scenarios;
FIG. 4, FIG. 5, FIG. 6 and FIG. 7 are reconstructed node diagrams of the power distribution system under different scenarios;
fig. 4, scenario 2, fig. 5, scenario 3, fig. 6, scenario 4, and fig. 7, scenario 5;
FIGS. 8, 9 and 10 are schematic diagrams illustrating the adjustment ratios of the 3 flexible loads in the scheduling period;
wherein: FIG. 8 is a diagram illustrating a scheduling scenario for a schedulable load during a scheduling period; FIG. 9 is a reduction scale within an interruptible load scheduling period; FIG. 10 may transfer the transfer case within a load scheduling cycle;
fig. 11, fig. 12, fig. 13, fig. 14, and fig. 15 are schematic front-edge diagrams of pareto with maximum power supply capability in different scenarios, respectively;
wherein: scene 1 of fig. 11, scene 2 of fig. 12, scene 3 of fig. 13, and scene 4 of fig. 14; fig. 15 scenario 5.
Detailed description of the preferred embodiments
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a method for evaluating power supply capacity of a regional distribution network in a competitive situation of power distribution and sale. The test is performed based on the system shown in fig. 2, and specifically includes the following steps:
detailed description of the invention
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
A power supply capacity evaluation method for a regional power distribution network under a distribution and sale power competition situation comprises the following steps:
step 1: constructing a multi-objective optimization model comprehensively considering the maximum power supply capacity and the running economy of the power distribution network;
step 2: constructing a model constraint condition comprehensively considering the participation of the distributed power supply and the flexible load in the electric power market transaction;
and step 3: and introducing a Pareto front edge to convert the multi-objective function into a series of single objective functions, and solving a Pareto optimal solution of the multi-objective nonlinear optimization model.
In this embodiment, step 1 can be implemented by the following process:
step 101: establishing a multi-objective optimization model as follows:
Figure BDA0003195444890000101
maxf2＝CSub+CDG+CES+CFL+CLoss (0.40)
equation (0.39) is the objective function that takes into account the maximum power supply capacity of the distribution network, GtThe maximum amplification factor of the load of the power distribution network at the moment t; the formula (0.40) is an objective function considering the minimum operation cost of the power distribution network, and particularly comprises the power supply cost C of the transformer substationSubDistributed power supply operation cost CDGEnergy storage operation cost CESFlexible load scheduling cost CFLAnd loss cost CLossRespectively correspond to the following:
Figure BDA0003195444890000111
Figure BDA0003195444890000112
Figure BDA0003195444890000113
Figure BDA0003195444890000114
Figure BDA0003195444890000115
in the formula: n is a radical oft、NSub、NPV、NWT、NES、NLA、NCL、NTL、NbranchRespectively representing a dispatching time interval, a transformer substation, a photovoltaic power station (PV), a wind power plant (WT), an energy storage device (ES), a dispatchable residential Load (LA) and an interruptible load (CL)) Transferable Load (TL), collection of lines;
Figure BDA0003195444890000116
respectively representing unit costs of substation power supply, micro gas turbine production, photovoltaic distributed power generation, wind power distributed power generation, energy storage charging, energy storage discharging, schedulable resident load up-regulation, schedulable resident load down-regulation, load interruption, transferable load up-regulation and transferable load down-regulation at the time t;
Figure BDA0003195444890000117
Figure BDA0003195444890000118
respectively representing active power of an i-node transformer substation, a micro gas turbine, a photovoltaic distributed power supply, a wind power distributed power supply, energy storage charging, energy storage discharging, schedulable resident load up-regulation, schedulable resident load down-regulation, load interruption, transferable load up-regulation and transferable load down-regulation at the time t; i isij,tIndicates the magnitude of the current, r, flowing through the line ij at time tijRepresenting the resistance of branch ij.
Step 102: in the objective function of simply calculating the maximum power supply capacity, it can be found that the network loss in the solved result is large, which is unreasonable in actual operation. Therefore, the network loss is added into the objective function, and the model is embossed by using a second-order cone relaxation technology, so that the model can be efficiently solved. The modified objective function is as follows:
Figure BDA0003195444890000119
in the formula: n is a radical ofnodeRepresents a collection of nodes in the power distribution network,
Figure BDA00031954448900001110
and the active load of the i node at the time t is shown, and phi is a weight coefficient.
In this embodiment, step 2 can be implemented by the following process:
step 201: and giving power flow constraints including node power balance constraint, line voltage drop constraint and line power balance constraint, which are respectively expressed by formulas (0.42) to (0.44).
Figure BDA0003195444890000121
Figure BDA0003195444890000122
Figure BDA0003195444890000123
In the formula: u (i), v (i) respectively represent a set with the node i as a head end node and a tail end node; pji,t、Qji,tRespectively representing the active and reactive power flowing in the line ji,
Figure BDA0003195444890000124
respectively representing the active load power and the reactive load power of the node i;
Figure BDA0003195444890000125
respectively representing the reactive power of a photovoltaic distributed power supply and a wind power distributed power supply of a node i; vi,t、Vj,tRespectively representing the voltage amplitudes, Δ V, of the first and last ends of the line ijij,tFor intermediate variables of voltage difference of line ij, Y when line is put into operationij+,t+Yij-,tWhen 1, then Δ Vij,tWhen the line is equal to 0, the line meets the voltage balance constraint; when the line is not in operation, Yij+,t+Yij-,tWhen the voltage is equal to 0, the voltage across the line is not limited by the voltage balance constraint.
Step 202: line second order cone constraint
0-1 reconstruction variable Y of line is introduced into modelij+,t、Yij-,tIt indicates whether the line is in forward and reverse operation, respectively. Then introducing variables
Figure BDA0003195444890000126
Instead of the square terms of the voltage and the current, as shown in equation (0.45), further, equation (0.46) is converted into a second order cone equation as shown in equation (0.47):
Figure BDA0003195444890000127
Figure BDA0003195444890000131
Figure BDA0003195444890000132
step 203: system operational safety constraints
Equations (0.48) to (0.50) represent upper and lower limit constraints of the node voltage and the branch current, respectively.
Figure BDA0003195444890000133
Figure BDA0003195444890000134
Figure BDA0003195444890000135
In the formula: vmax、VminRespectively representing the maximum and minimum values of the node voltage, ImaxRepresenting the maximum value of the line current.
Step 204: upper and lower limit constraints on operating variables
The active power and the reactive power of the operating line should not exceed the rated power of the line, and as shown in a formula (0.51), the active power and the reactive power of the transformer substation should not exceed the rated power of the transformer substation; as shown in equation (0.52), in addition, the active and reactive should satisfy equation (0.53); the charging and discharging power of the stored energy should not exceed the rated power thereof, as shown in the formula (0.54); the active power range, the wind curtailment range and the reactive power range of the photovoltaic distributed power supply and the wind power distributed power supply are shown in formulas (0.55) to (0.56).
Figure BDA0003195444890000136
Figure BDA0003195444890000137
Figure BDA0003195444890000138
Figure BDA0003195444890000141
Figure BDA0003195444890000142
Figure BDA0003195444890000143
In the formula:
Figure BDA0003195444890000144
represents the upper power limit of the substation,
Figure BDA0003195444890000145
represents the upper limit of the charging and discharging power of the energy storage device,
Figure BDA0003195444890000146
respectively represents the power of the abandoned wind and the abandoned light,
Figure BDA0003195444890000147
respectively represents the upper limit of active power of the photovoltaic distributed power supply and the wind power distributed power supply,
Figure BDA0003195444890000148
respectively represents the output percentages of the photovoltaic distributed power supply and the wind power distributed power supply at the moment t,
Figure BDA0003195444890000149
respectively represents the maximum power factors of the photovoltaic distributed power supply and the wind power distributed power supply.
Step 205: radiation confinement
The formula (0.57) represents that the load node has only one power inflow path, and the substation node has no power inflow path; because the active power distribution network comprises active devices such as a distributed power supply and energy storage, virtual power constraint is further introduced to ensure a radial structure of the system, and the formula (0.58) represents virtual power balance constraint; equations (0.59) to (0.60) represent upper and lower limit ranges of the line virtual power, the load virtual power, and the substation virtual power.
Figure BDA00031954448900001410
Figure BDA00031954448900001411
Figure BDA00031954448900001412
Figure BDA00031954448900001413
Figure BDA00031954448900001414
In the formula: n is a radical ofLDRepresenting a set of load nodes;
Figure BDA0003195444890000151
and the virtual power of the line, the load node and the substation node is respectively represented.
Step 206: adjustable controllable resource constraints
The formula (0.62) represents that the photovoltaic distributed power access power and the abandoned light power are equal to the photovoltaic distributed power output power; the formula (0.63) represents that the access power and the abandoned wind power of the wind power distributed generator are equal to the output power of the wind power distributed generator; the formula (0.64) shows that the battery charge states of the stored energy at the initial time and the end time of the scheduling period are both 0.5, and the conversion relation between the battery charge state at the time t and the battery charge state and the charging and discharging behaviors at the previous time is realized.
Figure BDA0003195444890000152
Figure BDA0003195444890000153
Figure BDA0003195444890000154
In the formula: SOCi,tRepresenting the battery nuclear power state of the node i at the time t; etaCH、ηDISRespectively representing the charge and discharge efficiency of the energy storage equipment; deltatIndicating the length of time of each scheduling period.
Step 207: flexible load restraint
The equation (0.65) represents the load power at time t of the dispatchable resident dispatchable load i taking into account the demand-side response; the formula (0.66) represents the upper and lower limits of the up-regulated power and the down-regulated power of the dispatchable resident load; equation (0.67) represents the load power at time t for an interruptible load i that accounts for demand side response; the formula (0.68) represents the upper and lower limits of the interruptible load interruption power; equation (0.69) represents the load power at time t for the transferable load i taking into account the demand-side response; the formula (0.70) represents the upper and lower limits of the power to be adjusted up and down for the transferable load, and the total energy to be adjusted up in the scheduling period is equal to the total energy to be adjusted down.
Figure BDA0003195444890000155
Figure BDA0003195444890000156
Figure BDA0003195444890000161
Figure BDA0003195444890000162
Figure BDA0003195444890000163
Figure BDA0003195444890000164
In the formula: xiLA、ξCL、ξTLRepresenting upper limits for dispatchable residential load, interruptible load, transferable load adjustment percentage;
Figure BDA0003195444890000165
the variable is 0-1, which respectively indicates whether the dispatchable resident load adjusts power upwards and downwards;
Figure BDA0003195444890000166
the variable is 0-1, indicating whether the transferable load is adjusting power up and down, respectively.
Step 208: n-1 constraint
After the main transformer has an N-1 fault, the line connected with the main transformer is in an open circuit state, as shown in the formula (0.71):
Figure BDA0003195444890000167
in the formula: n is a radical offaultRepresenting the set of lines connected to the fault transformer.
In this embodiment, step 3 can be implemented by the following process:
under the condition of considering the power market environment, the maximum power supply capacity model of the power distribution network is a multi-objective nonlinear optimization model. The two objective functions of the maximum power supply capacity and the economy are mutually conflicting, the economy is relatively poor when the maximum power supply capacity is large, the economy is relatively good when the maximum power supply capacity is small, and the simultaneous optimal solution of the two objective functions is difficult to find.
For two objective functions of maximum power supply capacity and economy, assuming that a solution a exists, no other solution can be found in the variable space, which can be better than the value of the maximum power supply capacity in the solution a (note that the value of the solution a is better than the function value corresponding to a in both objective function values), and then the solution a is an optimal solution in the pareto frontier. A solution model of a multi-objective function is established by combining with a Uutopia line, so that the pareto frontier of the maximum power supply capacity in the power market environment is drawn.
Step 301: converting the multi-objective function into a series of single objective functions, and writing the model into a matrix form as follows:
Figure BDA0003195444890000171
in the formula EkIs the objective function of the model, where k is 2; thetai(x) 0 or less and taui(x) 0 is respectively an inequality constraint and an equality constraint in the model; x is the number ofeMore than or equal to 0 is a variable constraint in the model; x is the number ofe∈ΩconeIs a second order cone constraint in the model.
Step 302: separately determining two objective functionsNumerical minimum and maximum values Emin1,Emax1And Emin2,Emax2；
Step 303: the normalization process for the two objective functions is as follows:
Figure BDA0003195444890000172
at this point standardization can be achieved
Figure BDA0003195444890000173
And
Figure BDA0003195444890000174
the Uptopont line vector is defined as follows:
Figure BDA0003195444890000175
step 304: the segmentation processing is performed on the utopia line, and the specific node constraint generation is as follows:
Figure BDA0003195444890000176
in the formula: s is the number of segmented nodes on the Utto line; eta1,pAnd η2,pRespectively, are a number between 0 and 1.
Step 305: taking the maximum power supply capacity as the main objective function, the modified model is generated as follows.
Figure BDA0003195444890000181
The constraint U (E-P) added to the equation (0.76) is compared to the original objective functionp)TNot less than 0 and U (E-P)p+1)TAnd the area constraint of two adjacent nodes on the Ulto line is less than or equal to 0, and the model is used for solving the optimal model in the area.
The following takes an improved 94-node power distribution network test system as an example, as shown in fig. 2.
The maximum power supply capacity under the normal operation condition of the power distribution network and the maximum power supply capacity under the fault condition under the power market environment can be better analyzed. Here, 5 scenes are defined for detailed analysis, and scene 1 is a normal operation condition; scene 2 is A, B, C feeder fault operation under the transformer T1; scene 3 is D, E, F feeder fault operation under transformer T2; scenario 4 is G, H, I feeder fault operation under transformer T3 and scenario 5 is J, K feeder fault operation under transformer T4.
1) The maximum power supply capacity of the power distribution network under 5 scenes is calculated respectively and is shown in fig. 3:
the maximum power supply capacity of the power distribution network is high in a normal operation scene, the power supply capacity at any moment is larger than 1.5, the load margin of the power distribution network is large, and the safety is high. The maximum power supply capacity of the power distribution network is reduced along with the increase of the load, the power load at 0:00 o 'clock at night is small, the maximum power supply capacity is very high, the power load at 18:00-20:00 o' clock at night is large, and the maximum power supply capacity is low. When a transformer N-1 fault occurs, the maximum power supply capacity of the distribution network is significantly reduced compared to normal, but also remains above 1.2.
When scenes 2 to 5 are given at the same time, the reconstruction results of the 94-node system are shown in fig. 4:
as can be seen from the analysis of fig. 4, 5, 6, and 7, when an N-1 fault occurs in scenes 2 to 5, the system transfers the load of the fault area through reconstruction, so as to ensure the safe and stable operation of the system, and the conditions of radial operation of the power distribution network can be satisfied in all scenes. The network topological diagram reconstructed when the transformer substation T1 exits due to faults is selected as shown in FIG. 4, the load carried by the feeder line A is transferred to the feeder line G and the feeder line H through the connecting lines 84 and 85 and the section switches 5-6 due to the fact that the main transformer T1 exits due to faults, and the load is supplied by the main transformer T3; the load on the feeder B is transferred to the feeder F through the connecting line 86 and supplied by the main transformer T2; the load on the feeder C is transferred to the feeder D through the connecting line 89, and is supplied from the main transformer T2.
2) In this case, three flexible loads, namely a schedulable load, an interruptible load and a transferable load, are considered. Taking an example of scenario 5 as an example, fig. 8 to 10 represent load scheduling situations of node 3 (schedulable load), node 6 (interruptible load), and node 4 (transferable load) in a scheduling cycle, respectively. For schedulable load, the maximum scheduling proportion of each time interval is 20%, and the total amount of upward scheduling load in the scheduling period needs to be greater than 50% of the total amount of downward scheduling load, from fig. 8, the load scheduling amount in the scheduling period is consistent with the expectation; for interruptible loads, the amount of the interrupted load in the scheduling period is not more than 45% of the total amount of the loads, so that the interruption condition of the interruptible loads is expected from fig. 9, and in order to increase the maximum power supply capacity of the power distribution network, the total amount of the interrupted loads is larger in a period with a higher load amount; for transferable loads, the maximum load transfer amount per time period is 20%, and the total load up-regulation amount is equal to the total load down-regulation amount in the scheduling cycle, fig. 10 is in accordance with the expectation, and the transferable loads comprehensively consider the requirements of economy and power supply capacity, and perform down-regulation in the time period with higher load amount and higher electricity price, and perform up-regulation in the time period with lower load amount and lower electricity price.
3) For two objective functions of maximum power supply capacity and economy, assuming that a solution a exists, no other solution can be found in the variable space, which can be better than the value of the maximum power supply capacity in the solution a (note that the value of the solution a is better than the function value corresponding to a in both objective function values), and then the solution a is an optimal solution in the pareto frontier. The subject is combined with the Utobond line to establish a solution model of a multi-objective function, so that the pareto frontier of the maximum power supply capacity in the power market environment is drawn.
First, the minimum and maximum values for the two objective functions were separately determined, as shown in the following table. A larger value for the maximum power supply capability objective SC indicates a better maximum power supply capability, and a smaller total cost for the economic objective indicates a better economic efficiency. When the two objective functions simultaneously use the minimum value or the maximum value as the optimum, the pareto frontier is more intuitive, so that the subject carries out certain treatment on economic indexes, and the two objective functions with the maximum power supply capacity and the economic efficiency simultaneously use the maximum value as the optimum.
TABLE 1 maximum power supply capability and economic Range
Figure BDA0003195444890000191
Figure BDA0003195444890000201
As can be seen from table 1, in scenario 1, each transformer can operate normally, and in this scenario, the maximum value of the maximum power supply capacity is higher than in scenarios 2 to 5 considering the substation N-1, which is consistent with the expectation, because the load in the fault area needs to be supplied by load transfer in the other four scenarios, thereby increasing the power supply pressure of the transfer substation. In addition, by comparing the maximum value of the maximum power supply capacity under scenes 2-5, the maximum value of the TSC under scene 2 (N-1 occurs in the transformer substation T1) is the largest, the maximum value of the TSC under scene 5 (N-1 occurs in the transformer substation T4) is the smallest, the influence of the transformer substation N-1 on the maximum power supply capacity can be reflected from the maximum value of the TSC, in the case of the scheme, the sequence of the transformer substations with larger influences on the maximum power supply capacity is the transformer substation T1, the transformer substation T2, the transformer substation T3 and the transformer substation T4, and the weak power supply links in the power distribution network can be identified by combining the maximum power supply capacity theory, so that the weak power supply links in the power distribution network can be reinforced in a targeted manner. Further, it can be seen that the enhancement of the maximum power supply capacity of the power distribution network increases the operation cost in the scheduling period, that is, the maximum power supply capacity and the economy cannot be optimized at the same time.
As can be seen from fig. 11 to fig. 15, the trend of the pareto frontier considering the maximum power supply capability of the power market environment is consistent under five scenarios: the maximum power supply capacity needs to be improved by reducing the economical efficiency of the operation of the power distribution network, when the TSC value is increased, the scheduling cost of the power distribution network tends to rise, and the relationship between the TSC value and the scheduling cost is not a simple linear relationship. The maximum power supply capacity of the system can be judged by combining the economic requirement of the operation of the power distribution network through the pareto frontier, and the overall power supply performance of the power distribution network is comprehensively judged. In the formula: n is a radical ofLDRepresenting a set of load nodes;
Figure BDA0003195444890000202
and the virtual power of the line, the load node and the substation node is respectively represented.
(206) Adjustable controllable resource constraints
The formula (0.24) represents that the access power and the abandoned light power of the photovoltaic distributed power supply are equal to the output power of the photovoltaic distributed power supply; the formula (0.25) represents that the access power and the abandoned wind power of the wind power distributed generator are equal to the output power of the wind power distributed generator; the formula (0.26) shows that the battery state of charge of the stored energy at the initial time and the end time of the scheduling period is 0.5, and the conversion relation between the battery state of charge at the time t and the battery state of charge and discharge at the previous time is represented.
Figure BDA0003195444890000203
Figure BDA0003195444890000211
Figure BDA0003195444890000212
In the formula: SOCi,tRepresenting the battery nuclear power state of the node i at the time t; etaCH、ηDISRespectively representing the charge and discharge efficiency of the energy storage equipment; deltatIndicating the length of time of each scheduling period.
(207) Flexible load restraint
The equation (0.27) represents the load power at time t of the dispatchable resident dispatchable load i taking into account the demand-side response; the formula (0.28) represents the upper and lower limits of the up-regulated power and the down-regulated power of the dispatchable resident load; equation (0.29) represents the load power at time t for an interruptible load i that accounts for demand side response; the formula (0.30) represents the upper and lower limits of the interruptible load interruption power; equation (0.31) represents the load power at time t for the transferable load i taking into account the demand-side response; the formula (0.32) represents the upper and lower limits of the power to be adjusted up and down for the transferable load, and the total energy to be adjusted up in the scheduling period is equal to the total energy to be adjusted down.
Figure BDA0003195444890000213
Figure BDA0003195444890000214
Figure BDA0003195444890000215
Figure BDA0003195444890000216
Figure BDA0003195444890000217
Figure BDA0003195444890000218
In the formula: xiLA、ξCL、ξTLRepresenting upper limits for dispatchable residential load, interruptible load, transferable load adjustment percentage;
Figure BDA0003195444890000219
the variable is 0-1, which respectively indicates whether the dispatchable resident load adjusts power upwards and downwards;
Figure BDA0003195444890000221
the variable is 0-1, indicating whether the transferable load is adjusting power up and down, respectively.
(208) N-1 constraint
After the main transformer has an N-1 fault, the line connected with the main transformer is in an open circuit state, as shown in the formula (0.33):
Figure BDA0003195444890000222
in the formula: n is a radical offaultRepresenting the set of lines connected to the fault transformer.
In the step 3), the described maximum power supply capacity model of the power distribution network in the power market environment is a multi-objective nonlinear optimization model. Meanwhile, the maximum power supply capacities and the economical efficiency of the two objective functions are mutually conflicting, the economical efficiency is relatively poor when the maximum power supply capacity is large, the economical efficiency is relatively good when the maximum power supply capacity is small, and the simultaneous optimal solution of the two objective functions is difficult to find. For this reason, a Pareto front edge is introduced into the model, and a Pareto optimal solution of the model is solved.
(301) The core of Pareto frontier is mainly to convert a multi-objective function into a series of single objective functions for solving. For convenience of describing the detailed transformation process, the above model is written here in matrix form as follows:
Figure BDA0003195444890000223
in the formula EkIs the objective function of the model, where k is 2; thetai(x) 0 or less and taui(x) 0 is respectively an inequality constraint and an equality constraint in the model; x is the number ofeMore than or equal to 0 is a variable constraint in the model; x is the number of3∈ΩconeIs a second order cone constraint in the model.
(302) Respectively and independently calculating minimum value and maximum value E of two objective functionsmin1,Emax1And Emin2,Emax2。
(303) The normalization process for the two objective functions is as follows:
Figure BDA0003195444890000224
at this time canTo be standardized
Figure BDA0003195444890000225
And
Figure BDA0003195444890000226
the Uptopont line vector is defined as follows:
Figure BDA0003195444890000231
(304) the segmentation processing is performed on the utopia line, and the specific node constraint generation is as follows:
Figure BDA0003195444890000232
in the formula: s is the number of segmented nodes on the Utto line; eta1,pAnd η2,pRespectively, are a number between 0 and 1.
(305) Taking the maximum power supply capacity as the main objective function, the modified model is generated as follows.
Figure BDA0003195444890000233
The constraint U (E-P) added to the equation (0.38) is compared to the original objective functionp)TNot less than 0 and U (E-P)p+1)TAnd the area constraint of two adjacent nodes on the Ulto line is less than or equal to 0, and the model is used for solving the optimal model in the area.
As can be seen from fig. 11 to 15, the trend of the pareto frontier considering the maximum power supply capability of the power market environment is consistent under five scenarios: the maximum power supply capacity needs to be improved by reducing the economical efficiency of the operation of the power distribution network, when the TSC value is increased, the scheduling cost of the power distribution network tends to rise, and the relationship between the TSC value and the scheduling cost is not a simple linear relationship. The maximum power supply capacity of the system can be judged by combining the economic requirement of the operation of the power distribution network through the pareto frontier, and the overall power supply performance of the power distribution network is comprehensively judged.

Claims (4)

1. A power supply capacity evaluation method for a regional power distribution network under a distribution and sale power competition situation comprises the following steps:
step 1) constructing a multi-objective optimization model comprehensively considering the maximum power supply capacity and the running economy of the power distribution network;
step 2), constructing a model constraint condition comprehensively considering the participation of the distributed power supply and the flexible load in the electric power market transaction;
and 3) introducing a Pareto front edge to convert the multi-objective function into a series of single objective functions, and solving a Pareto optimal solution for the multi-objective nonlinear optimization model.
2. The method for evaluating the power supply capacity of the regional power distribution network under the competitive situation of power distribution and sale as claimed in claim 1, wherein in the step 1), under the application environment of various types of resources of the active power distribution network taking into account the market trading of power as a guide, the power supply capacity evaluation model is constructed in a nonlinear multi-target form, and the maximum power supply capacity and the operating economy of the power distribution network are considered at the same time:
(101) the established multi-objective optimization model is specifically as follows:
Figure FDA0003195444880000011
max f2＝CSub+CDG+CES+CFL+CLoss (0.2)
the equation (0.1) is the maximum objective function considering the power supply capacity of the distribution network, GtThe maximum amplification factor of the load of the power distribution network at the moment t; the formula (0.2) is an objective function considering the minimum operation cost of the power distribution network, and specifically comprises the power supply cost C of the transformer substationSubDistributed power supply operation cost CDGEnergy storage operation cost CESFlexible load scheduling cost CFLAnd loss cost CLossRespectively correspond to the following:
Figure FDA0003195444880000021
Figure FDA0003195444880000022
Figure FDA0003195444880000023
Figure FDA0003195444880000024
Figure FDA0003195444880000025
in the formula: n is a radical oft、NSub、NPV、NWT、NES、NLA、NCL、NTL、NbranchRespectively representing a set of a dispatching time interval, a transformer substation, a photovoltaic power station (PV), a wind power plant (WT), an energy storage device (ES), a dispatchable residential Load (LA), an interruptible load (CL), a Transferable Load (TL) and a line;
Figure FDA0003195444880000026
respectively representing unit costs of substation power supply, micro gas turbine production, photovoltaic distributed power generation, wind power distributed power generation, energy storage charging, energy storage discharging, schedulable resident load up-regulation, schedulable resident load down-regulation, load interruption, transferable load up-regulation and transferable load down-regulation at the time t;
Figure FDA0003195444880000027
Figure FDA0003195444880000028
respectively representing active power of an i-node transformer substation, a micro gas turbine, a photovoltaic distributed power supply, a wind power distributed power supply, energy storage charging, energy storage discharging, schedulable resident load up-regulation, schedulable resident load down-regulation, load interruption, transferable load up-regulation and transferable load down-regulation at the time t; i isij,tIndicates the magnitude of the current, r, flowing through the line ij at time tijRepresents the resistance of branch ij;
(102) in the objective function of simply calculating the maximum power supply capacity, the fact that the network loss is large in the solved result can be found, and the fact is unreasonable in actual operation; therefore, the network loss is added into the objective function, and the model is embossed by using a second-order cone relaxation technology, so that the model can be efficiently solved; the modified objective function is as follows:
Figure FDA0003195444880000029
in the formula: n is a radical ofnodeRepresents a collection of nodes in the power distribution network,
Figure FDA00031954448800000210
and the active load of the i node at the time t is shown, and phi is a weight coefficient.
3. The method for evaluating the power supply capacity of the regional power distribution network under the competitive situation of the power distribution and sale as recited in claim 1, wherein in the step 2), main constraint conditions are given firstly, wherein the main constraint conditions comprise a power flow constraint, a line second-order cone constraint, a system operation safety constraint, an operation variable upper and lower limit constraint, a radiation constraint, an adjustable and controllable resource constraint and a flexible load constraint;
(201) flow restraint
The power flow constraint comprises a node power balance constraint, a line voltage drop constraint and a line power balance constraint which are respectively expressed by formulas (0.4) - (0.6);
Figure FDA0003195444880000031
Figure FDA0003195444880000032
Figure FDA0003195444880000033
in the formula: u (i), v (i) respectively represent a set with the node i as a head end node and a tail end node; pji,t、Qji,tRespectively representing the active and reactive power flowing in the line ji,
Figure FDA0003195444880000034
respectively representing the active load power and the reactive load power of the node i;
Figure FDA0003195444880000035
respectively representing the reactive power of a photovoltaic distributed power supply and a wind power distributed power supply of a node i; vi,t、Vj,tRespectively representing the voltage amplitudes, Δ V, of the first and last ends of the line ijij,tFor intermediate variables of voltage difference of line ij, Y when line is put into operationij+,t+Yij-,tWhen 1, then Δ Vij,tWhen the line is equal to 0, the line meets the voltage balance constraint; when the line is not in operation, Yij+,t+Yij-,tWhen the voltage is equal to 0, the voltage at the two ends of the line is not limited by the voltage balance constraint;
(202) line second order cone constraint
0-1 reconstruction variable Y of line is introduced into modelij+,t、Yij-,tRespectively indicating whether the line is put into operation in the forward direction and the reverse direction; then introducing variables
Figure FDA0003195444880000036
Instead of the square terms of voltage and current, as shown in equation (0.7), equation (0.8) is further converted into a second order tapered equation(0.9) shown in the specification:
Figure FDA0003195444880000041
Figure FDA0003195444880000042
Figure FDA0003195444880000043
(203) system operational safety constraints
Formulas (0.10) to (0.12) respectively represent upper and lower limit constraints of the node voltage and the branch current;
Figure FDA0003195444880000044
Figure FDA0003195444880000045
Figure FDA0003195444880000046
in the formula: vmax、VminRespectively representing the maximum and minimum values of the node voltage, ImaxRepresents the maximum value of the line current;
(204) upper and lower limit constraints on operating variables
The active power and the reactive power of the operating line should not exceed the rated power of the line, and as shown in a formula (0.13), the active power and the reactive power of the transformer substation should not exceed the rated power of the transformer substation; as shown in equation (0.14), in addition, the active and reactive should satisfy equation (0.15); the charging and discharging power of the stored energy should not exceed the rated power thereof, as shown in the formula (0.16); the active power range, the wind curtailment range and the reactive power range of the photovoltaic distributed power supply and the wind power distributed power supply are shown in formulas (0.17) to (0.18);
Figure FDA0003195444880000047
Figure FDA0003195444880000048
Figure FDA0003195444880000051
Figure FDA0003195444880000052
Figure FDA0003195444880000053
Figure FDA0003195444880000054
in the formula:
Figure FDA0003195444880000055
represents the upper power limit of the substation,
Figure FDA0003195444880000056
represents the upper limit of the charging and discharging power of the energy storage device,
Figure FDA0003195444880000057
respectively represents the power of the abandoned wind and the abandoned light,
Figure FDA0003195444880000058
respectively represents the upper limit of active power of the photovoltaic distributed power supply and the wind power distributed power supply,
Figure FDA0003195444880000059
respectively represents the output percentages of the photovoltaic distributed power supply and the wind power distributed power supply at the moment t,
Figure FDA00031954448800000510
respectively representing maximum power factors of the photovoltaic distributed power supply and the wind power distributed power supply;
(205) radiation confinement
The formula (0.19) represents that the load node has only one power inflow path, and the substation node has no power inflow path; because the active power distribution network comprises active devices such as a distributed power supply and energy storage, virtual power constraint is further introduced to ensure a radial structure of the system, and the formula (0.20) represents virtual power balance constraint; equations (0.21) to (0.23) represent upper and lower limit ranges of line virtual power, load virtual power, and substation virtual power;
Figure FDA0003195444880000061
Figure FDA0003195444880000062
Figure FDA0003195444880000063
Figure FDA0003195444880000064
Figure FDA0003195444880000065
in the formula: n is a radical ofLDRepresenting a set of load nodes;
Figure FDA0003195444880000066
respectively representing the virtual power of a line, a load node and a transformer substation node;
(206) adjustable controllable resource constraints
The formula (0.19) represents that the access power and the abandoned light power of the photovoltaic distributed power supply are equal to the output power of the photovoltaic distributed power supply; the formula (0.20) represents that the access power and the abandoned wind power of the wind power distributed generator are equal to the output power of the wind power distributed generator; the formula (0.21) shows that the battery charge states of the stored energy at the initial time and the end time of the scheduling period are both 0.5, and the conversion relation between the battery charge state at the time t and the battery charge state and the charging and discharging behaviors at the previous time is formed;
Figure FDA0003195444880000067
Figure FDA0003195444880000068
Figure FDA0003195444880000069
in the formula: SOCi,tRepresenting the battery nuclear power state of the node i at the time t; etaCH、ηDISRespectively representing the charge and discharge efficiency of the energy storage equipment; deltatIndicating the time length of each scheduling period;
(207) flexible load restraint
Equation (0.22) represents the load power at time t for the dispatchable load i taking into account the demand-side response; the formula (0.23) represents the upper limit and the lower limit of the flexible load up-regulation power and the down-regulation power; equation (0.24) represents the load power at time t for an interruptible load i that accounts for demand side response; the formula (0.25) represents the upper and lower limits of the interruptible load interruption power; equation (0.26) represents the load power at time t for the transferable load i taking into account the demand-side response; the formula (0.27) represents the upper and lower limits of the adjustable power and the adjustable power of the transferable load, and the total energy adjusted in the dispatching cycle is equal to the total energy adjusted in the dispatching cycle;
Figure FDA0003195444880000071
Figure FDA0003195444880000072
Figure FDA0003195444880000073
Figure FDA0003195444880000074
Figure FDA0003195444880000075
Figure FDA0003195444880000076
in the formula: xiLA、ξCL、ξTLRepresenting upper limits for dispatchable residential load, interruptible load, transferable load adjustment percentage;
Figure FDA0003195444880000077
the variable is 0-1, which respectively indicates whether the dispatchable resident load adjusts power upwards and downwards;
Figure FDA0003195444880000078
a variable of 0-1, which indicates whether the transferable load adjusts power up and down respectively;
(208) n-1 constraint
After the main transformer has an N-1 fault, the line connected with the main transformer is in an open circuit state, as shown in the formula (0.28):
Figure FDA0003195444880000079
in the formula: n is a radical offaultRepresenting the set of lines connected to the fault transformer.
4. The method for evaluating the power supply capacity of the regional power distribution network under the competitive situation of the power distribution and sale as claimed in claim 1, wherein in the step 3), the described model of the maximum power supply capacity of the power distribution network under the power market environment is a multi-objective nonlinear optimization model; meanwhile, the maximum power supply capacities and the economical efficiency of the two objective functions are mutually conflicting, the economical efficiency is relatively poor when the maximum power supply capacity is large, the economical efficiency is relatively good when the maximum power supply capacity is small, and the simultaneous optimal solution of the two objective functions is difficult to find; therefore, introducing a Pareto front edge into the model, and solving a Pareto optimal solution of the model;
(301) the core of the Pareto frontier is mainly to convert a multi-objective function into a series of single objective functions for solving; for convenience of describing the detailed transformation process, the above model is written here in matrix form as follows:
Figure FDA0003195444880000081
in the formula EkK is the number of objective functions of the model, where k is 2; thetai(x) 0 or less and taui(x) 0 is respectively an inequality constraint and an equality constraint in the model; x is the number ofeMore than or equal to 0 is a variable constraint in the model; x is the number ofe∈ΩconeA second order cone constraint in the model;
(302) respectively and independently calculating the minimum value and the maximum value of the two objective functions
Figure FDA0003195444880000082
And Emin 2,Emax 2；
(303) The normalization process for the two objective functions is as follows:
Figure FDA0003195444880000083
at this point standardization can be achieved
Figure FDA0003195444880000084
And
Figure FDA0003195444880000085
the Uptopont line vector is defined as follows:
Figure FDA0003195444880000086
(304) the segmentation processing is performed on the utopia line, and the specific node constraint generation is as follows:
Figure FDA0003195444880000087
in the formula: s is the number of segmented nodes on the Utto line; eta1,pAnd η2,pAre each a number between 0 and 1;
(305) taking the maximum power supply capacity as a main objective function, and generating a modified model as shown below;
Figure FDA0003195444880000091
compared with the original objective functionNumber, newly added constraint U (E-P) in equation (0.33)p)TNot less than 0 and U (E-P)p+1)TAnd the area constraint of two adjacent nodes on the Ulto line is less than or equal to 0, and the model is used for solving the optimal model in the area.
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