CN108599373B - Cascade analysis method for transmission and distribution coordination scheduling target of high-proportion renewable energy power system - Google Patents

Cascade analysis method for transmission and distribution coordination scheduling target of high-proportion renewable energy power system Download PDF

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CN108599373B
CN108599373B CN201810465163.5A CN201810465163A CN108599373B CN 108599373 B CN108599373 B CN 108599373B CN 201810465163 A CN201810465163 A CN 201810465163A CN 108599373 B CN108599373 B CN 108599373B
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transmission
distribution network
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network
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CN108599373A (en
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王洪涛
张旭
唐亮
孙辰军
王卓然
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State Grid Corp of China SGCC
Shandong University
State Grid Hebei Electric Power Co Ltd
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Shandong University
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    • H02J13/0017—
    • 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
    • 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]
    • 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
    • Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
    • Y04S10/123—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation the energy generation units being or involving renewable energy sources
    • 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

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Abstract

The invention discloses a high-proportion renewable energy power system transmission and distribution coordination scheduling target cascade analysis method, which comprises the following steps: the local scheduling layer is a scheduling unit which is used for performing combined output optimization on distributed renewable energy sources inside a certain power distribution network; the method comprises the steps that a power transmission network and a power distribution network are respectively used as autonomous main bodies, decoupling is carried out through boundary power, resources in areas are optimized independently, and power transmission plans are arranged in a mutual cooperation mode, wherein a power transmission network scheduling layer builds uncertain modeling on renewable energy sources based on an improved interval method, an energy and standby coordination optimization model is constructed, a power distribution network scheduling layer sets a local scheduling layer aiming at the distributed renewable energy sources, joint output optimization is carried out by combining an energy storage system and a scene method, and therefore sub-problems of the power distribution network layer are constructed into a dynamic economic scheduling model which aims at optimizing economy and eliminating the distributed renewable energy source joint output in the local scheduling layer.

Description

Cascade analysis method for transmission and distribution coordination scheduling target of high-proportion renewable energy power system
Technical Field
The invention relates to the technical field of power grids, in particular to a high-proportion renewable energy power system transmission and distribution coordination scheduling target cascade analysis method.
Background
With the fact that a large number of distributed power sources are connected into a power Distribution system, the operation of a power Distribution Network tends to be more flexible and changeable, the traditional power Distribution Network gradually changes into an Active Distribution Network (ADN), and the interaction between a power transmission Network and the power Distribution Network is increasingly close. Meanwhile, the proportion of renewable energy sources such as wind power and the like connected into the power grid is continuously increased, and the renewable energy sources in a centralized manner or a distributed manner are difficult to absorb. Under the new situation, the dispatching of a high-proportion renewable energy power grid is changed from regulation and control with a power transmission network as a key point to the coordinated operation of each level of power grid on the basis of multi-level extensive intellectualization, and the implementation of layered transmission and distribution cooperative optimization dispatching is a necessary trend of a future dispatching form.
At present, the dispatching operation of the power transmission network and the power distribution network is independent. The optimization of the power Transmission network level is decided by a Transmission System Operator (TSO), and a power distribution network is regarded as a load; optimization of the power Distribution network level is decided by a Distribution System Operator (DSO), and the transmission network is equivalent to a power supply. In an active power distribution network, equivalent load containing renewable energy power generation has strong uncertainty and volatility, accurate prediction is difficult, the problem of power unbalance of a transmission and distribution common node is easily caused by adopting an original transmission and distribution splitting operation mode, the potential of schedulable resources of each layer of system cannot be fully utilized, and the problem of double consumption of centralized renewable energy and distributed renewable energy is difficult to solve. With the advance of electric power market construction, pricing and transaction mechanisms among power transmission and distribution networks are gradually improved, concepts such as multi-agent systems and virtual power plants are developed, and dependable mechanisms and means are provided for optimal scheduling of cooperation of TSO and DSO.
Theoretically, global cooperative optimization of power transmission and distribution network scheduling is realized, a centralized optimization method is more direct, but in an actual power system, the power transmission and distribution network belongs to different scheduling jurisdiction ranges, and the privacy of information among benefit agents in a market environment makes the centralized optimization difficult to realize; secondly, the centralized optimization needs the scheduling center to obtain global data for optimization, and the distribution network data has the characteristics of wide distribution, large quantity and low magnitude, so that the scale of the optimization model is greatly increased, and the calculation cost is high. Therefore, the distributed optimization method is suitable for the optimized scheduling of transmission and distribution coordination, and the problem of transmission and distribution network coordination is researched by the scholars by using the distributed optimization method.
The document "Sun H, Guo Q, Zhang B, et al, Master-Slave-split Global Power Flow Method for Integrated Transmission and distribution Analysis [ J ]. IEEE Transactions on Smart Grid, 2015, 6(3): 1484-.
The literature "Kargarian A, Yong F. System of Systems Based Security-compliance Assembly Incorporating Active Distribution groups [ J ]. IEEE Transactionson Power Systems, 2014, 29(5):2489 and 2498" studies the problem of transmission and Distribution cooperative unit combination, and decomposes the original problem into a transmission grid combination sub-problem and a plurality of Distribution network distributed generation scheduling sub-problems by SoS (system of Systems) theory.
The documents "Zhengshuo L i, Qinglai Guo, Hongbin Sun, et al, coordinated economic dispatch of coupled Transmission and Distribution Systems Using heterogeneous decomposition [ J ]. IEEE Transactions on Power Systems, 2016, 31(6): 4817-4830" and "L i Z, Guo Q, Sun H, et al, coordinated Transmission and Distribution AC OptimalPower Flow [ J ]. IEEE Transactions on Smart group to published.
The document "L in C, Wu W, Chen X, et al. decentralized Dynamic Economic dispatch for Integrated Transmission and Active Distribution Networks Using Multi-parameter Programming [ J ]. IEEE Transactions on Smart grid to be published" proposes to solve the problem of Transmission and Distribution cooperative Dynamic Economic dispatch by Using an improved Multi-parameter Programming method, and is applicable to network structures with interconnection among Distribution Networks.
The document "L in C, Wu W, Zhang B, et al. decentralized Reactive Power optimization Method for Transmission and Distribution Networks Integration L area-Scale DG Integration [ J ]. IEEE Transactions on Stationable Energy Energy, 2017, 8(1): 363:373" utilizes the benders decomposition Method to research the Transmission and Distribution cooperative Reactive power optimization problem, and can solve the Distribution network overvoltage problem caused by distributed power access.
The methods disclosed by the existing documents are all deterministic optimization problems, and under the background that large-scale renewable energy sources are accessed to a power transmission network and a distribution network in a centralized manner, the uncertainty of how to reach the renewable energy sources in the transmission and distribution cooperative optimization scheduling is a subject worthy of deep research.
The optimal scheduling considering the uncertainty of renewable energy is a hot problem in the current research, and according to different methods for processing uncertain variables, the model can be divided into: random optimization based on scenes, robust optimization, opportunity constrained planning and interval optimization. A single optimization method has advantages and disadvantages, and many documents try to combine a plurality of uncertain optimization methods to complement the advantages. And a plurality of researches are combined with distributed optimization and uncertain optimization to solve the scheduling problems of multiple areas of the transmission network, the distribution network and the multiple micro-networks, and the good effect is shown.
However, in the prior art, an effective solution is still lacking for the technical problem of coordinated operation of the transmission network and the active power distribution network in the high-proportion renewable energy power system.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a high-proportion renewable energy power system transmission and distribution coordination scheduling target cascade analysis method, provides a transmission and distribution network coordinated scheduling framework, and provides a processing method for uncertainty of centralized and distributed renewable energy. On the basis of fully respecting and considering the autonomous operation characteristics of all benefit agents, optimization of various types of power generation resources and power utilization plans in a power transmission and distribution network is realized by using hierarchical scheduling and a distributed optimization technology based on an Analytical Target Cascading (ATC), and the overall operation economy and the renewable energy consumption capacity of the power grid are improved.
A high-proportion renewable energy power system transmission and distribution coordination scheduling target cascade analysis method comprises the following steps:
the network structure that links to each other a plurality of initiative distribution networks and transmission network divides into on the whole: the system comprises a power transmission network scheduling layer, a power distribution network scheduling layer and a local scheduling layer, wherein the local scheduling layer is a scheduling unit which is used for performing joint output optimization on distributed renewable energy sources inside a certain power distribution network;
the method comprises the steps that a power transmission network and a power distribution network are respectively used as autonomous main bodies, decoupling is carried out through boundary power, resources in areas are optimized independently, and power transmission plans are arranged in a mutual cooperation mode, wherein a power transmission network scheduling layer builds uncertain modeling on renewable energy sources based on an improved interval method, an energy and standby coordination optimization model is constructed, a power distribution network scheduling layer sets a local scheduling layer aiming at the distributed renewable energy sources, joint output optimization is carried out by combining an energy storage system and a scene method, and therefore sub-problems of the power distribution network layer are constructed into a dynamic economic scheduling model which aims at optimizing economy and eliminating the distributed renewable energy source joint output in the local scheduling layer.
In a further preferred technical scheme, the boundary power of the power transmission network scheduling layer is equivalent to a virtual load; the boundary power of the power distribution network scheduling layer is equivalent to a virtual generator, the decomposed transmission and distribution subsystem independently solves a power generation scheduling scheme meeting the operation constraint of the region, only boundary power information needs to be transmitted to an adjacent system, and the following consistency constraint needs to be met:
Figure GDA0002465705640000031
in the formula:
Figure GDA0002465705640000032
the virtual active load of a power distribution network k on the power transmission network layer surface is obtained;
Figure GDA0002465705640000033
the equivalent active power injection of the k-layer power transmission network of the power distribution network is realized, and the positive power direction is that the power transmission network flows to the power distribution network. trans represents the power transmission network level, and D represents the power distribution network level.
In a further preferred technical solution, there are two optimization objectives of the local scheduling layer: the method has the advantages that firstly, the income of the combined power generation, namely the utilization of renewable energy sources, is maximized; secondly, the fluctuation of renewable energy output in a dispatching period is reduced, and the objective function corresponding to the local dispatching layer based on the optimization objective is as follows:
Figure GDA0002465705640000034
Figure GDA0002465705640000035
in the formula: p is a radical ofsProbability of a wind power scene s; rhosell，tThe price is the combined electricity selling price at the time of R-DG and ESS t;
Figure GDA0002465705640000036
the joint output at the time of R-DG and ESS t under the scene s; pjoi，tThe combined output plan value at the time t of the system is the lowest penalty term No. 3 in the formula (3), Pjoi，tIn the different scenes of mathematics trend
Figure GDA0002465705640000041
αtAnd βtFor a penalty coefficient at the moment of output fluctuation t, the first two terms of the formula (3) ensure the maximum utilization of the R-DG and the suppression of output fluctuation in each wind power scene; item 3 ensures that the total output has minimal fluctuations.
In a further preferred embodiment, the constraint condition of the local scheduling layer objective function is:
Figure GDA0002465705640000042
Figure GDA0002465705640000043
Figure GDA0002465705640000044
Figure GDA0002465705640000045
Figure GDA0002465705640000046
Figure GDA0002465705640000047
Figure GDA0002465705640000048
Figure GDA0002465705640000049
Figure GDA00024657056400000410
wherein: constraints (4) - (6) limit
Figure GDA00024657056400000411
And Pjoi，t(iv) the range of output; constraints (7) - (12) are charging and discharging constraints of the ESS under each wind power scene,
Figure GDA00024657056400000412
the dispatching value and the predicted value of the RDG under the scene s at the moment t are obtained;
Figure GDA00024657056400000413
respectively charging/discharging power and zone bit of ESS in scene s at time t ηch、ηdisRespectively charge/discharge efficiency values;
Figure GDA00024657056400000414
E、
Figure GDA00024657056400000415
respectively the maximum value of charging/discharging power and the upper/lower limit of electric quantity of the ESS,
Figure GDA00024657056400000416
and respectively at the time t, the ESS electric quantity at the scheduling ending time and the scheduling starting time.
In a further preferred technical solution, the distribution network scheduling layer is configured to: the method comprises the following steps of purchasing power to a superior transmission network, generating power in a controllable distributed mode in the region, consuming combined output of an R-DG and an ESS, and enabling a corresponding target function of a distribution network scheduling layer to be as follows:
the optimization target of the power distribution network k is as follows:
Figure GDA00024657056400000417
in the formula:
Figure GDA00024657056400000418
the power generation cost of the C-DG in the distribution network k is calculated;
Figure GDA00024657056400000419
for the cost of purchasing electricity from the distribution network to the transmission network, the expressions are as follows:
Figure GDA00024657056400000420
Figure GDA00024657056400000421
wherein:
Figure GDA0002465705640000051
the output of a generator i in a distribution network k at the moment t is obtained;
Figure GDA0002465705640000052
the cost coefficient of the unit i is obtained;
Figure GDA0002465705640000053
the node electricity price of the transmission and distribution boundary is obtained;
Figure GDA0002465705640000054
the power of the generator is virtualized for the transmission and distribution boundary.
In a further preferred technical solution, the constraint conditions of the objective function corresponding to the power distribution network scheduling layer are as follows:
and (3) restraining the output range of the unit:
Figure GDA0002465705640000055
active power balance constraint:
Figure GDA0002465705640000056
a boundary power transmission constraint to reflect a planned amount of power between the power transmission and distribution networks as determined by advance agreements or market trading;
Figure GDA0002465705640000057
Figure GDA0002465705640000058
line safety constraints;
in the above constraint, Gdist,k、Rdist,k、NLdist,kRespectively collecting C-DG units, R-DG units and load nodes in a power distribution network k;
Figure GDA0002465705640000059
the active output upper and lower limits of the unit i are set;
Figure GDA00024657056400000510
the predicted value of the load j at the time t is obtained;
Figure GDA00024657056400000511
Pb dist,kupper and lower limits of boundary transmission power; qkTransmitting electric quantity for plan between the transmission network and the distribution network k; rho is an allowable deviation proportion, and the climbing constraint is ignored because the installed capacity of the C-DG in the power distribution network is small and the climbing speed is high.
In a further preferred technical scheme, the power transmission network scheduling layer is optimized and established as an energy and standby coordination optimization model, and the model is divided into two stages: step one, deciding a power output reference value and transmission and distribution boundary power of a generator in a prediction scene; determining a standby demand and standby economic distribution among conventional units according to a climbing scene, and mutually influencing, coordinating and optimizing the two stages to find an optimal power generation base point and power exchange plan, wherein the optimal power generation base point and power exchange plan is optimal in a prediction scene and meets the adjustment demand in the climbing scene, and the upper standard 'trans' is saved for simplifying expression of related variables of the power transmission network;
in the power transmission network, decision variables of the TSO are output and standby of a conventional unit, wind power output and transmission and distribution boundary power, and an objective function is as follows:
Figure GDA00024657056400000512
in the formula: cGAnd CRThe power generation cost and the standby cost of the conventional unit are respectively;
Figure GDA00024657056400000513
punishing cost for the abandoned wind under the scene s; csellFor the income of selling electricity to the distribution network by the transmission network, the expressions are as follows:
Figure GDA0002465705640000061
Figure GDA0002465705640000062
Figure GDA0002465705640000063
Figure GDA0002465705640000064
wherein: pgi,t、
Figure GDA0002465705640000065
The active output and the positive and negative rotation standby values of the unit i in the power transmission network at the moment t are obtained;
Figure GDA0002465705640000066
respectively providing positive and negative standby prices for the unit i;
Figure GDA0002465705640000067
punishing cost for wind abandonment of the wind power plant m;
Figure GDA0002465705640000068
the wind power station m is the air volume discarded under the climbing scene s at the time t;
Figure GDA0002465705640000069
and (4) virtual loads of k boundaries of the power transmission network layer and the power distribution network.
In a further preferred technical scheme, the predicted scene constraint in the objective function of the power transmission network scheduling layer is as follows:
Figure GDA00024657056400000610
Figure GDA00024657056400000611
Figure GDA00024657056400000612
Figure GDA00024657056400000613
Figure GDA00024657056400000614
Figure GDA00024657056400000615
Figure GDA00024657056400000616
Figure GDA00024657056400000617
in the formula: gtrans、Nw、NLtrans、Ndist、LtransRespectively integrating a generator, a wind power plant, a load, a transmission and distribution boundary node and a transmission network line; the physical meaning of partial variables of the generator is consistent with the variables in the optimization model of the power distribution network,
Figure GDA00024657056400000618
the maximum positive standby and the maximum negative standby which can be provided by the unit i in the time period t are respectively provided;
Figure GDA00024657056400000619
and
Figure GDA00024657056400000620
is a standby requirement at the moment t; RU (RU)i、RDiThe climbing capacity of the unit i is the climbing capacity;
Figure GDA00024657056400000621
wind power predicted value and planned air abandoning amount under the prediction scene; gl-bThe distribution factor is transferred for node b to line l,
Figure GDA00024657056400000622
Tlthe upper limit and the lower limit of the transmission power of the line l are respectively defined, the formula (25) is the output range constraint of the generator, the formula (26) is the power balance constraint, the formula (27) is the climbing constraint of the generator, the formulas (28) to (29) are standby constraints, the formula (30) is the line safety constraint, and the formulas (31) to (32) are the boundary power constraints.
It should be noted that, traditional IO and IIO require that wind curtailment is not allowed in a reference scene, but there may be a case where the model has no feasible solution, so that the predicted scene wind curtailment is also added to the model.
In a further preferred technical scheme, the hill climbing scene constraint in the objective function of the power transmission network scheduling layer is as follows:
Figure GDA0002465705640000071
Figure GDA0002465705640000072
Figure GDA0002465705640000073
Figure GDA0002465705640000074
Figure GDA0002465705640000075
Figure GDA0002465705640000076
Figure GDA0002465705640000077
Figure GDA0002465705640000078
Figure GDA0002465705640000079
in the formula:
Figure GDA00024657056400000710
outputting power for the unit i in the time period t under the scene s;
Figure GDA00024657056400000711
adjusting the output of the unit;
Figure GDA00024657056400000712
the predicted value of the wind power under the scene s is shown, and the expressions (33) - (35) are the output limit of the unit under the climbing scene; equations (36) - (39) are set climbing constraints under four climbing scenes respectively, wherein the wind power climbing scene corresponds to the set climbing capability constraint; the wind power down-grade climbing scene corresponds to the unit up-grade climbing capacity constraint; equation (40) is the power balance constraint under scenario s; the formula (41) is the air abandoning amount constraint; the safety constraints in a climbing scenario are similar to equation (30).
The method comprises the steps that before solving, a local scheduling layer carries out combined optimization according to an R-DG prediction curve and electricity selling price, and reports a result to a DSO (distributed service center) as an input condition so as to optimize a power transmission and distribution network;
and relaxing the consistency constraint (1) serving as a penalty function into an objective function to enable the boundary power to tend to be consistent to obtain a transmission network objective function and a distribution network k objective function, wherein the solving step is as follows:
step 1: initialization algorithm multiplier, virtual active injection of power distribution network
Figure GDA00024657056400000713
Setting an iteration flag i to be 0;
step 2: setting i to i +1, solving the optimal scheduling subproblem of each DSO according to the formula (42) and the formulas (13) to (19), and obtaining the virtual injection power of the transmission and distribution boundary
Figure GDA00024657056400000714
The DSOs can be solved in parallel to accelerate the calculation speed;
and step 3: after receiving the data of the distribution network, the TSO synthesizes wind power and load prediction information, solves the sub-problem of the power transmission network scheduling layer according to the formula (43) and the formulas (20) - (41), and obtains the virtual load value of the transmission and distribution boundary
Figure GDA0002465705640000081
And transmitting the calculation result to each DSO;
and 4, step 4: judging whether the circulation is converged by using a formula (44), if so, outputting a calculation result, and otherwise, continuing the step 5;
Figure GDA0002465705640000082
in the formula:1、2is the convergence factor; f. ofa(x) Is the objective function value for region a; trans and D respectively refer to a transmission network and a distribution network.
And 5: updating the multiplier v, w of the algorithm by using the formula (45), and returning to the step 2 to start a new iteration;
Figure GDA0002465705640000083
Figure GDA0002465705640000084
in the formula: gamma is a constant, and the value of gamma is generally more than or equal to 1 and less than or equal to 3; the initial values of v and w are typically taken to be relatively small constants.
Compared with the prior art, the invention has the beneficial effects that:
in the background of a high-proportion renewable energy power system, aiming at the problem of insufficient coordination of power generation and power utilization plan formulation between a transmission network and an active power distribution network, a transmission and distribution cooperative scheduling framework is provided based on the ideas of layered scheduling and distributed optimization, and power grid scheduling is decomposed into a transmission network scheduling layer, a power distribution network scheduling layer and a local scheduling layer. The dispatching framework fully respects and considers the autonomous characteristics of the power transmission and distribution networks and the access scene of independent power vendors in the power distribution side market. The independent optimization of each scheduling layer is realized, the coordination of power generation resources between the power transmission and distribution networks is considered, and the overall optimal scheduling scheme is formulated. The power transmission network layer utilizes an improved interval method to express the uncertainty of renewable energy sources in a prediction scene and a climbing scene, the fluctuation characteristic of the renewable energy sources can be considered to reserve for use, so that a day-ahead plan is reasonably made, and the scale of problems is simplified. The local dispatching layer can maximize the utilization rate of the R-DG and the economic benefit of a generator, reduce output fluctuation and meet the requirement of future market development of the power distribution side. The dispatching method provided by the invention improves the overall operation economy and renewable energy consumption capability of the power grid, and research results provide a feasible idea for active cooperative dispatching of the transmission and distribution network.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a framework for cooperative scheduling for transmission and distribution according to the present application;
FIG. 2 is an exploded view of the transmission and distribution network;
FIG. 3 wind power scene modeling;
FIG. 4T 6D1 system;
FIG. 5 wind power and load prediction values;
FIG. 6 local scheduling layer optimization results;
FIG. 7 boundary power comparison;
FIG. 8(a) -FIG. 8(b) spare capacity comparison;
FIG. 9 Total cost convergence curve;
fig. 10 shows comparison of different wind power access ratios.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the present application, detailed descriptions will be given to a transmission and distribution cooperative scheduling framework and decomposition mechanism, a local scheduling layer optimization model, a distribution network scheduling layer optimization model, a transmission and distribution cooperative optimization solution flow, and an example analysis.
Regarding the transmission and distribution cooperative scheduling framework and decomposition mechanism: the transmission and distribution network layered collaborative scheduling framework proposed herein is oriented to a network structure in which a plurality of active distribution networks are connected to a transmission network, and the scheduling framework is shown in fig. 1. The scheduling architecture is generally divided into 3 layers: the system comprises a power transmission network scheduling layer, a power distribution network scheduling layer and a local scheduling layer. The local scheduling layer is a scheduling unit for performing joint output optimization on a Distributed Generation (R-DG) in combination with an Energy Storage System (ESS) in a certain power distribution network. Under a layered cooperation scheduling framework, the TSO and each DSO have autonomous capacity, power generation equipment in a jurisdiction area of the TSO and each DSO is independently scheduled based on a distributed optimization technology, power transmission plans are arranged in a mutual cooperation mode according to power rates of transmission and distribution boundaries, the power generation capacity in a transmission network and a power distribution network needs to be considered when the power generation plans are made, integral operation constraints are met, and the safe and economic operation of the whole system is guaranteed. Because one TSO needs to be oriented to a plurality of DSOs, the TSO can naturally serve as a coordinator to coordinate transmission power among the power transmission and distribution networks to meet consistency constraints. In addition, a good two-way communication network must be established between the dispatch centers to transmit the necessary coordination information.
The key to breaking down a transmission and distribution global system into a transmission subsystem and a distribution subsystem is to find suitable inter-area coupling constraints. The system decomposition mechanism shown in fig. 2 is adopted based on the ATC technology. The active power transmitted by the boundary of the power transmission and distribution network is decomposed, the information of the phase angle of the boundary node is not required to be transmitted, and the modeling of the line tide of the power transmission and distribution network is convenient. At the power transmission network level, the boundary power is equivalent to a virtual load; and on the power distribution network layer, the boundary power is equivalent to a virtual generator. The decomposed transmission and distribution subsystem independently solves the power generation scheduling scheme meeting the operation constraint of the region, only boundary power information needs to be transmitted to an adjacent system, and the following consistency constraint needs to be met:
Figure GDA0002465705640000101
in the formula:
Figure GDA0002465705640000102
the virtual active load of a power distribution network k on the power transmission network layer surface is obtained;
Figure GDA0002465705640000103
and equivalent active power injection is performed on the k-layer power transmission network of the power distribution network. The positive power direction is the transmission network flow to the distribution network.
Regarding the local scheduling layer optimization model: the local scheduling layer is the minimum scheduling unit layer under the layered collaborative scheduling framework, and the reason for constructing the scheduling layer is as follows: 1) in general, the number of R-DGs is small, the positions are scattered, the smoothing effect of large-scale centralized renewable energy power generation is lacked, the output force has strong uncertainty and volatility, and accurate prediction is difficult. With the development of energy storage technology, the ESS is more flexible to apply, and the capacity is easily matched with the R-DG. Therefore, the output of the R-DG and the ESS is often jointly scheduled in the operation of the power distribution network and the microgrid. 2) The local scheduling layer aims at more and more electricity selling main bodies in the future electricity selling side market, the electricity selling main bodies possibly exist in the form of intelligent bodies or virtual power plants, and internal power generation resources are optimized according to the electricity price of a power distribution network to achieve benefit maximization. The optimization goals of the local scheduling layer are two: the method has the advantages that firstly, the income of the combined power generation, namely the utilization of renewable energy sources, is maximized; and secondly, the fluctuation of the output of the renewable energy sources in the dispatching period is reduced. On the basis of establishing a combined optimization model of the distributed wind turbine generator and the ESS, uncertainty of distributed wind power output is described by using a scene generation technology. The model is easy to adjust to a joint optimization scheduling model of the distributed photovoltaic generator set and the ESS, and is not described in any more detail.
An objective function:
Figure GDA0002465705640000104
Figure GDA0002465705640000105
in the formula: p is a radical ofsProbability of a wind power scene s; rhosell，tThe price is the combined electricity selling price at the time of R-DG and ESS t;
Figure GDA0002465705640000106
the joint output at the time of R-DG and ESS t under the scene s; pjoi，tThe combined output plan value at the time t of the system is the lowest penalty term No. 3 in the formula (3), Pjoi，tIn the different scenes of mathematics trend
Figure GDA0002465705640000107
αtAnd βtAnd the penalty coefficient is the punishment coefficient of the moment t of the output fluctuation. The first two terms of the formula (3) ensure the maximum utilization of the R-DG and the suppression of output fluctuation in each wind power scene; item 3 ensures that the total output has minimal fluctuations.
Constraint conditions are as follows:
Figure GDA0002465705640000108
Figure GDA0002465705640000109
Figure GDA0002465705640000111
Figure GDA0002465705640000112
Figure GDA0002465705640000113
Figure GDA0002465705640000114
Figure GDA0002465705640000115
Figure GDA0002465705640000116
Figure GDA0002465705640000117
wherein: constraints (4) - (6) limit
Figure GDA0002465705640000118
And Pjoi，t(iv) the range of output; constraints (7) - (12) are charging and discharging constraints of the ESS under each wind power scene.
Figure GDA0002465705640000119
The dispatching value and the predicted value of the RDG under the scene s at the moment t are obtained;
Figure GDA00024657056400001110
respectively charging/discharging power and zone bit of ESS in scene s at time t ηch、ηdisRespectively charge/discharge efficiency values;
Figure GDA00024657056400001111
Figure GDA00024657056400001112
E、
Figure GDA00024657056400001113
the maximum value of the charging/discharging power of the ESS and the upper/lower limit of the electric quantity are respectively.
Figure GDA00024657056400001114
And respectively at the time t, the ESS electric quantity at the scheduling ending time and the scheduling starting time.
Local scheduling layer obtains combined power generation scheduling plan Pjoi，tAnd then directly reporting to the corresponding DSO. The local dispatch layer may set optimization objectives to match different electricity selling entities,the method can be easily expanded into a combined electricity selling unit considering various resources, and the actual requirements of future ADN development are better met.
The optimization model of the power distribution network scheduling layer is as follows: the resources that can be scheduled by the power distribution network scheduling layer include: 1) purchasing power to a superior power transmission network; 2) controllable Distribution Generation (C-DG) in the region; 3) the combined contribution of the R-DG and ESS is taken up. In order to fully consume the distributed renewable energy, the power generation cost of the distributed renewable energy is not taken into account in the model.
The optimization target of the power distribution network k is as follows:
Figure GDA00024657056400001115
in the formula:
Figure GDA00024657056400001116
the power generation cost of the C-DG in the distribution network k is calculated;
Figure GDA00024657056400001117
for the cost of purchasing electricity from the distribution network to the transmission network, the expressions are as follows:
Figure GDA00024657056400001118
Figure GDA00024657056400001119
wherein:
Figure GDA00024657056400001120
the output of a generator i in a distribution network k at the moment t is obtained;
Figure GDA00024657056400001121
the cost coefficient of the unit i is obtained;
Figure GDA00024657056400001122
the node electricity price of the transmission and distribution boundary is obtained;
Figure GDA00024657056400001123
the power of the generator is virtualized for the transmission and distribution boundary.
Constraint conditions are as follows:
1) and (3) restraining the output range of the unit:
Figure GDA0002465705640000121
2) active power balance constraint:
Figure GDA0002465705640000122
3) boundary power transmission constraints to reflect planned power between power transmission and distribution networks as determined by advance agreements or market trading.
Figure GDA0002465705640000123
Figure GDA0002465705640000124
4) And (3) line safety restraint:
the method adopts the linearized AC power flow of the distribution network.
In the constraint, Gdist,k、Rdist,k、NLdist,kRespectively collecting C-DG units, R-DG units and load nodes in a power distribution network k;
Figure GDA0002465705640000125
Figure GDA0002465705640000126
the active output upper and lower limits of the unit i are set;
Figure GDA0002465705640000127
the predicted value of the load j at the time t is obtained;
Figure GDA0002465705640000128
Pb dist,kis an edgeUpper and lower limits of boundary transmission power; qkTransmitting electric quantity for plan between the transmission network and the distribution network k; ρ is the allowable deviation ratio. The C-DG in the power distribution network has small installed capacity and high climbing speed, so the climbing constraint is ignored.
Regarding the optimization model of the power transmission network scheduling layer: wind power scene modeling based on an improved interval method is characterized in that in an optimal scheduling method considering intermittent renewable energy, an Interval Optimization (IO) is high in calculation efficiency, a random optimization (SO) based on a scene can reflect the random characteristic of intermittent energy power generation more accurately, and an improved interval method (IIO) combining the two methods is adopted. The wind power is taken as an example for explanation, and in order to achieve the purposes of fully or partially absorbing the wind power and meeting the real-time power balance, the scheduling capability of the system for responding to wind power uncertainty and volatility is decomposed into two parts: 1) in each time period, the system spare capacity needs to meet the limit of a wind power prediction interval so as to deal with uncertainty caused by a wind power prediction error; 2) during the time period, the conventional unit needs to have enough climbing capacity to deal with the maximum wind power fluctuation in a large number of wind power prediction scenes.
For ease of analysis, fig. 3 shows only 3 time periods, where abc is the wind power predicted expected value,
Figure GDA0002465705640000129
the upper boundary of the wind power output is set,abcthe lower force boundary. To meet the requirement 1, from the time section t1Analyzing, when a scheduling plan is made according to the point a, in order to deal with the possible output of wind power, the thermal power generating unit in the system at least provides
Figure GDA00024657056400001210
To be used in the down-regulation of
Figure GDA00024657056400001211
The wind power generation system is used for up-regulation to deal with possible fluctuation of wind power in real-time operation; for the same reason t2、t3The time and so on. To satisfy requirement 2, transverse analysis is carried out with time as axis, t1～t2In time period, the wind power maximum climbing scene is as s1Shown with its tail at t2The upper boundary of the time interval corresponds to the maximum climbing requirement of the conventional unit; maximum wind-power climb-down scenario such as s3Shown with its tail at t2And the lower boundary of the time interval corresponds to the maximum climbing requirement of the conventional unit. In the same way, t2～t3In a climbing scene such as s2、s4As shown. It can be seen that at t2The scene of climbing the slope at a moment corresponds to two operating points, and a single scene cannot be represented. Therefore IIO models wind power as 5 typical scenarios: s0-a wind power prediction scenario; s1-uphill scenes between odd and even periods; s2-an uphill scene between even and odd time periods; s3-a downhill scene between odd and even periods; s4And a downhill scene between even and odd time periods. In addition, only s need to be2，s4The missing power values at t-1 are replaced by upper and lower boundary values, respectively, so that the backup constraints generated by the system to satisfy requirement 1 at each time point are naturally formed by the hill climbing scene limitation. Compared with a large number of wind power scenes of SO, IIO can keep higher calculation efficiency and does not seriously deteriorate the objective function; compared with IO, IIO replaces the limit climbing scene with the typical climbing scene, and conservativeness of the model is improved. The IIO may be applicable to various scene generation methods, and the research on the scene generation methods is not discussed in the scope of this discussion and will not be described again.
Power transmission network energy and backup coordination optimization model: the optimization of a power transmission network layer is established into an energy and standby coordination optimization model, and the model is divided into two stages: a first stage decides a power output reference value and a transmission and distribution boundary power of a generator in a prediction scene; and in the second stage, the standby requirement and the standby economic allocation among the conventional units are determined according to the climbing scene. The two stages mutually influence and coordinate optimization, and the basic idea is to find a group of optimal power generation base points and power exchange plans, optimize the power generation base points and the power exchange plans in a prediction scene and meet the adjustment requirements in a climbing scene. For simplicity of presentation, the related variables of the grid are left out below with the superscript "trans".
In the power transmission network, decision variables of the TSO are output and standby of a conventional unit, wind power output and transmission and distribution boundary power, and an objective function is as follows:
Figure GDA0002465705640000131
in the formula: cGAnd CRThe power generation cost and the standby cost of the conventional unit are respectively;
Figure GDA0002465705640000132
punishing cost for the abandoned wind under the scene s; csellAnd selling the power to the power distribution network for the power transmission network. The expressions are as follows:
Figure GDA0002465705640000133
Figure GDA0002465705640000134
Figure GDA0002465705640000135
Figure GDA0002465705640000136
wherein: pgi,t、
Figure GDA0002465705640000137
The active output and the positive and negative rotation standby values of the unit i in the power transmission network at the moment t are obtained;
Figure GDA0002465705640000138
Figure GDA0002465705640000139
respectively providing positive and negative standby prices for the unit i;
Figure GDA00024657056400001310
punishing cost for wind abandonment of the wind power plant m;
Figure GDA00024657056400001311
the wind power station m is the air volume discarded under the climbing scene s at the time t;
Figure GDA00024657056400001312
and (4) virtual loads of k boundaries of the power transmission network layer and the power distribution network.
Predicting scene constraints:
Figure GDA0002465705640000141
Figure GDA0002465705640000142
Figure GDA0002465705640000143
Figure GDA0002465705640000144
Figure GDA0002465705640000145
Figure GDA0002465705640000146
Figure GDA0002465705640000147
Figure GDA0002465705640000148
in the formula: gtrans、Nw、NLtrans、Ndist、LtransRespectively integrating a generator, a wind power plant, a load, a transmission and distribution boundary node and a transmission network line; generator partial variable physical significance and variable in power distribution network optimization modelAnd (5) the consistency is achieved.
Figure GDA0002465705640000149
The maximum positive standby and the maximum negative standby which can be provided by the unit i in the time period t are respectively provided;
Figure GDA00024657056400001410
and
Figure GDA00024657056400001411
is a standby requirement at the moment t; RU (RU)i、RDiThe climbing capacity of the unit i is the climbing capacity;
Figure GDA00024657056400001412
wind power predicted value and planned air abandoning amount under the prediction scene; gl-bThe distribution factor is transferred for node b to line l,
Figure GDA00024657056400001413
Tlupper and lower limits of the transmitted power for line l, respectively. Equation (25) is a generator output range constraint, equation (26) is a power balance constraint, equation (27) is a generator climbing constraint, equations (28) - (29) are standby constraints, equation (30) is a line safety constraint, and equations (31) - (32) are boundary power constraints. It should be noted that, traditional IO and IIO require that wind curtailment is not allowed in a reference scene, but there may be a case where the model has no feasible solution, so that the predicted scene wind curtailment is also added to the model.
And (3) climbing scene constraint:
Figure GDA00024657056400001414
Figure GDA00024657056400001415
Figure GDA00024657056400001416
Figure GDA00024657056400001417
Figure GDA00024657056400001418
Figure GDA00024657056400001419
Figure GDA00024657056400001420
Figure GDA0002465705640000151
Figure GDA0002465705640000152
in the formula:
Figure GDA0002465705640000153
outputting power for the unit i in the time period t under the scene s;
Figure GDA0002465705640000154
adjusting the output of the unit;
Figure GDA0002465705640000155
and the predicted value of the wind power under the scene s is obtained. Equations (33) - (35) are set output limits in a climbing scene; equations (36) - (39) are set climbing constraints under four climbing scenes respectively, wherein the wind power climbing scene corresponds to the set climbing capability constraint; the wind power down-grade climbing scene corresponds to the unit up-grade climbing capacity constraint; equation (40) is the power balance constraint under scenario s; the formula (41) is the air abandoning amount constraint; the safety constraints in a climbing scenario are similar to equation (30).
A transmission and distribution collaborative optimization solving process: in order to achieve the optimization goal of the cooperative consistency of the exchange power plan and the individual benefit between the power transmission and distribution networks, the transmission and distribution cooperative optimization solving method based on the ATC technology is provided. Before solving, the local scheduling layer firstly carries out combined optimization according to the R-DG prediction curve and the electricity selling price, and reports the result to the DSO as an input condition so as to carry out optimization of the power transmission and distribution network.
The ATC technology is mainly used for solving the multi-level and multi-subject coordination optimization problem, allows each optimization subject in the hierarchical structure to make an autonomous decision, and simultaneously coordinates and optimizes to obtain the overall optimal solution of the problem. The method has the advantages that the number of stages is not limited, the same-stage subproblems can have different optimization forms, parameters are easy to select and the like, and the phenomenon that the traditional dual decomposition algorithm based on Lagrangian relaxation is easy to oscillate repeatedly in iteration is overcome, so that the method is often applied to solving the optimization problem of a large-scale system.
Under the ATC algorithm framework, the TSO and the DSO independently solve the optimization sub-problem, and the coordination of the shared parameter and the adjacent region is considered, so that the consistency constraint (1) is used as a penalty function to be relaxed into the objective function, and the boundary power tends to be consistent.
The objective function of the power transmission network is as follows:
Figure GDA0002465705640000156
the k objective function of the power distribution network is as follows:
Figure GDA0002465705640000157
in the formula:
Figure GDA0002465705640000158
is an algorithm multiplier;
Figure GDA0002465705640000159
the value of the coupling variable, which is transferred for the neighboring region, is a known constant.
The ATC-based algorithm flow comprises the following specific steps:
step 1: initialization algorithm multiplier, virtual active injection of power distribution network
Figure GDA00024657056400001510
Setting an iteration flag i to be 0;
step 2: setting i to i +1, solving the optimal scheduling subproblem of each DSO according to the formula (42) and the formulas (13) to (19), and obtaining the virtual injection power of the transmission and distribution boundary
Figure GDA00024657056400001511
The DSOs can be solved in parallel to accelerate the calculation speed;
and step 3: after receiving the data of the distribution network, the TSO synthesizes wind power and load prediction information, solves the sub-problem of the power transmission network scheduling layer according to the formula (43) and the formulas (20) - (41), and obtains the virtual load value of the transmission and distribution boundary
Figure GDA0002465705640000161
And transmitting the calculation result to each DSO;
and 4, step 4: judging whether the circulation is converged by using a formula (44), if so, outputting a calculation result, and otherwise, continuing the step 5;
Figure GDA0002465705640000162
in the formula:1、2is the convergence factor; f. ofa(x) Is the objective function value for region a; trans and D respectively refer to a transmission network and a distribution network.
And 5: updating the multiplier v, w of the algorithm by using the formula (45), and returning to the step 2 to start a new iteration;
Figure GDA0002465705640000163
Figure GDA0002465705640000164
in the formula: gamma is a constant, and the value of gamma is generally more than or equal to 1 and less than or equal to 3; the initial values of v and w are typically taken to be relatively small constants.
Analysis by calculation example: the effectiveness of the proposed distributed transmission and distribution cooperative scheduling strategy is verified by taking the day-ahead scheduling of 24 periods as an example. And (3) solving by adopting Yalmip programming based on Matlab, selecting Cplex by a solver, and using IntelCore i53.2GHz and 8GB memory as a CPU in a test environment.
First, take a 6-node transmission network and 1 ADN interconnected system as an example, wherein a wind farm is located at node B of the transmission network2ADN located at node B4The R-DG and ESS combined power generation quotient is positioned at a power distribution network node 3, as shown in figure 4. The capacity of the R-DG accounts for 25 percent of the power distribution network. The real-time power price of transmission and distribution refers to appendix C, wind power and load prediction data are shown in figure 5, the centralized wind power generation amount exceeds 30%, and the requirement of high proportion is met. The daily planned exchange capacity between the power transmission and distribution networks is 1100MWh, and the allowable deviation ρ is 0. The wind curtailment penalty cost is 100$/MW, and the algorithm convergence coefficient1＝20.001, multiplier initial value vk，t＝wk，tShared variables for distributed computing 0.5, γ 1.5
Figure GDA0002465705640000165
The initial value is set to 0.
The optimization result of a local scheduling layer is that the R-DG in the ADN is assumed to be a wind driven generator, the day-ahead power prediction curve, the combined electricity selling price and the ESS system parameters are shown in appendix C, and the penalty coefficient α in the objective functiontAnd βtRespectively set to 1.2 times of the real-time electricity price. 1000 wind power scenes are generated by adopting a Monte Carlo sampling method, and the scenes are reduced to 20 by adopting a scene reduction technology based on K-means clustering.
The results of the optimization according to the models of equations (2) to (12) are shown in fig. 6, and it can be seen that: the expected output value of the R-DG is basically consistent with the predicted curve, and the local scheduling layer can fully utilize the distributed renewable energy because the wind power scene simulates the possible output of the wind power in the real-time operation process; secondly, the output characteristics of the R-DG and the ESS cells are smooth on the whole, the influence of large-range output fluctuation of the R-DG on the power distribution network is reduced, and the controllability is shown on the whole; meanwhile, the combined output is larger when the electricity selling price is high than when the electricity selling price is low, and the economic optimization is realized. It should be noted that the local scheduling layer gives day-ahead plans on an hourly scale in order to keep consistent with the grid timescale. And the appendix E provides a method for coordinating the operation in the day and the plan before the day, so that the time scale is more flexible and is referred by readers.
Optimizing results of the power transmission and distribution network: in order to analyze the effectiveness and superiority of the distributed cooperative optimization of the transmission and distribution network, the following operation scenes are set. Model 1, independently optimizing a transmission network and a distribution network; model 2, carrying out centralized optimization on a transmission network and a distribution network; model 3, distributed cooperative optimization of transmission and distribution network.
The Mode1 means that the distribution network calculates equivalent boundary power according to (13) - (19) and submits the equivalent boundary power to the transmission network, and the fixed boundary power of the transmission network layer is optimized. And generating a wind power scene by utilizing Monte Carlo sampling in all modes, and constructing a climbing scene according to a 4.1-section method. The calculation results are shown in table 1, where trans refers to transmission network and D refers to distribution network. Ctrans、CR、CWS、CWS0、Csell、CTARespectively calculating the power transmission network power generation cost, the standby cost, the wind abandoning cost of the climbing scene, the wind abandoning cost of the reference scene, the power selling income and the total cost (no income); cD、CbuyRespectively the power generation cost and the electricity purchasing cost of the power distribution network; ctotalWhich is the total cost.
TABLE 1 comparison of the calculated results
Figure GDA0002465705640000171
As can be seen from the calculation results in table 1, in the traditional Mode1 scenario, the distribution network considers its internal power generation resources to perform boundary power prediction, and directly reports the boundary power prediction to the transmission network to execute a power plan, without considering coordination of power generation capacity between the transmission network and the distribution network, which increases the overall operating cost by 33.5% compared with the Mode3, and mainly reflects a large increase in the wind curtailment cost.
In the prediction scene, the wind abandoning cost C in the Mode1WS0The increase indicates that the wind abandon phenomenon occurs when the day-ahead plan is made, and the wind abandon phenomenon does not occur in the Mode 3. Fig. 7 shows the comparison curve of the transmission and distribution boundary power in the two modes, and it can be seen that the boundary power in the Mode3 is consistent under the premise that the daily exchanged electric quantity of the transmission and distribution networkThe peak value of the rate curve is transferred, namely, the distribution network can actively respond to the power output plan of reducing C-DG so as to more consume large-scale centralized wind power in the power transmission network in the period of 4-10 times when the predicted wind power output is higher, and therefore the wind power consumption level is improved.
In a climbing scene, the cost C of the abandoned wind in the Mode1WSThe wind power fluctuation is greatly increased, which indicates that the transmission grid unit cannot provide enough reserve to cope with the wind power fluctuation which may occur in the climbing scene, and only a large amount of wind is abandoned to meet the requirement of power balance. Fig. 8(a) -8 (b) show the spare capacity of the conventional unit in two modes, and it can be seen that during the period of 4-9, the conventional unit in Mode1 cannot provide enough negative spare to cope with the upward fluctuation of wind power, while through the global coordination between the transmission and distribution networks in Mode3, more spare can be provided, and the cost of wind abandon in a climbing scene is reduced. Obviously, the Mode1 greatly increases the risk of power unbalance of the system in real-time operation, and the Mode of transmission and distribution coordination is beneficial to improving the overall operation economy and wind power consumption capacity of the power grid.
For Mode2 and Mode3, the calculation results in table 1 show that the total cost of Mode3 is almost consistent with the total cost of global centralized optimization of Mode2 transmission and distribution network, which indicates the effectiveness of the calculation accuracy of the method proposed herein. Fig. 9 shows a convergence curve of the total cost of the power transmission and distribution network, and the result shows that the optimal solution is converged by the 9-time iterative distributed optimization method, and the total solution time of the solver is 0.3 s. From the point of view of the game: on one hand, the power transmission and distribution network has to pursue the lowest power generation cost per se, and simultaneously considers the power transfer requirements of adjacent regions to further meet consistency constraints, and the iterative process is the process of compromise between region optimization and overall optimization of each main body.
Different wind power access proportion results: in order to study the adaptability of the method to different wind power access ratios, the wind curtailment rates of the Mode1 and the Mode3 planned before the day obtained by changing the centralized wind power access ratio in the power transmission network are shown in fig. 10. It can be seen from fig. 10 that as the wind power connection proportion increases, when the proportion exceeds 25%, the wind curtailment rate of the Mode1 gradually increases, and when the wind power proportion exceeds 40%, the Mode3 obviously increases. It is clear that the Mode3 is still advantageous in dealing with high-proportion wind power access.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (2)

1. The high-proportion renewable energy power system transmission and distribution coordination scheduling target cascade analysis method is characterized by comprising the following steps:
the network structure that links to each other a plurality of initiative distribution networks and transmission network divides into on the whole: the system comprises a power transmission network scheduling layer, a power distribution network scheduling layer and a local scheduling layer, wherein the local scheduling layer is a scheduling unit which is used for performing joint output optimization on distributed renewable energy sources inside a certain power distribution network;
the method comprises the following steps that a power transmission network and a power distribution network are respectively used as autonomous main bodies, decoupling is carried out through boundary power, resources in areas are optimized independently, and power transmission plans are arranged in a mutual cooperation mode, wherein a power transmission network scheduling layer builds uncertainty modeling on renewable energy sources based on an improved interval method, constructs an energy and standby coordination optimization model, sets a local scheduling layer aiming at distributed renewable energy sources, and carries out joint output optimization by combining an energy storage system and utilizing a scene method, so that sub-problems of the power distribution network layer are constructed into a dynamic economic scheduling model which aims at optimizing economy and consuming distributed renewable energy sources in the local scheduling layer;
the boundary power of the power transmission network scheduling layer is equivalent to a virtual load; the boundary power of the power distribution network scheduling layer is equivalent to a virtual generator, the decomposed transmission and distribution subsystem independently solves a power generation scheduling scheme meeting the operation constraint of the region, only boundary power information needs to be transmitted to an adjacent system, and the following consistency constraint needs to be met:
Figure FDA0002489922310000011
in the formula:
Figure FDA0002489922310000012
the virtual active load of a power distribution network k on the power transmission network layer surface is obtained;
Figure FDA0002489922310000013
equivalent active power injection is performed on a k-layer power transmission network of a power distribution network, the positive power direction is that the power transmission network flows to the power distribution network, trans represents a power transmission network layer, and D represents a power distribution network layer;
the optimization goals of the local scheduling layer are two: the benefit of the combined power generation is maximized; secondly, the fluctuation of renewable energy output in a dispatching period is reduced, and the objective function corresponding to the local dispatching layer based on the optimization objective is as follows:
Figure FDA0002489922310000014
Figure FDA0002489922310000015
in the formula: p is a radical ofsProbability of a wind power scene s; rhosell，tThe price is the combined electricity selling price at the time of R-DG and ESS t;
Figure FDA0002489922310000016
the joint output at the time of R-DG and ESS t under the scene s; pjoi，tThe combined output plan value at the time t of the system is the lowest penalty term No. 3 in the formula (3), Pjoi，tIn the different scenes of mathematics trend
Figure FDA0002489922310000017
αtAnd βtFor a penalty coefficient at the moment of output fluctuation t, the first two terms of the formula (3) ensure the maximum utilization of the R-DG and the suppression of output fluctuation in each wind power scene; item 3 ensures that the total output fluctuates minimally;
wherein, R-DG is distributed renewable energy, ESS is energy storage system;
the distribution network scheduling layer is used for: the combined output of power purchasing to a superior transmission network, controllable distributed power generation in the region, R-DG consumption and ESS consumption is obtained, and the corresponding objective function of a power distribution network scheduling layer is as follows:
optimization goals of the distribution network k, i.e.
Figure FDA0002489922310000021
In the formula:
Figure FDA0002489922310000022
the power generation cost of the C-DG in the power distribution network k is calculated;
Figure FDA0002489922310000023
for the cost of purchasing electricity from a power distribution network to a transmission network, the expressions are as follows:
Figure FDA0002489922310000024
Figure FDA0002489922310000025
wherein:
Figure FDA0002489922310000026
the output of a generator i in a power distribution network k at the moment t is obtained;
Figure FDA0002489922310000027
the cost coefficient of the unit i is obtained;
Figure FDA0002489922310000028
the node electricity price of the transmission and distribution boundary is obtained;
Figure FDA0002489922310000029
to distribute the power of the boundary virtual generator,
Figure FDA00024899223100000210
the cost of purchasing electricity from a distribution network to a transmission network is realized, C-DG is controllable distributed generation, Gdist,kA C-DG set in a power distribution network k;
the constraint conditions of the objective function corresponding to the power distribution network scheduling layer are as follows:
and (3) restraining the output range of the unit:
Figure FDA00024899223100000211
active power balance constraint:
Figure FDA00024899223100000212
a boundary power transmission constraint to reflect a planned amount of power between the power transmission and distribution networks as determined by advance agreements or market trading;
Figure FDA00024899223100000213
Figure FDA00024899223100000214
line safety constraints;
in the above constraint, Gdist,k、Rdist,k、NLdist,kRespectively collecting C-DG units, R-DG units and load nodes in a power distribution network k;
Figure FDA00024899223100000215
the active output upper and lower limits of the unit i are set;
Figure FDA00024899223100000216
the predicted value of the load j at the time t is obtained;
Figure FDA00024899223100000217
Pb dist,kupper and lower limits of boundary transmission power; qkTransmitting electric quantity for plan between the transmission network and the distribution network k; rho is an allowable deviation proportion, and the climbing constraint is ignored because the installed capacity of the C-DG in the power distribution network is small and the climbing speed is high;
the optimization of the power transmission network scheduling layer is established as an energy and standby coordination optimization model, and the model is divided into two stages: step one, deciding a power output reference value and transmission and distribution boundary power of a generator in a prediction scene; determining a standby demand and standby economic distribution among conventional units according to a climbing scene, and mutually influencing, coordinating and optimizing the two stages to find an optimal power generation base point and power exchange plan, wherein the optimal power generation base point and power exchange plan is optimal in a prediction scene and meets the adjustment demand in the climbing scene;
in the power transmission network, decision variables of the TSO are output and standby of a conventional unit, wind power output and transmission and distribution boundary power, and an objective function is as follows:
Figure FDA0002489922310000031
in the formula: cGAnd CRThe power generation cost and the standby cost of the conventional unit are respectively;
Figure FDA0002489922310000032
punishing cost for the abandoned wind under the scene s; csellFor the income of selling electricity to the distribution network by the transmission network, the expressions are as follows:
Figure FDA0002489922310000033
Figure FDA0002489922310000034
Figure FDA0002489922310000035
Figure FDA0002489922310000036
wherein: pgi,t、
Figure FDA0002489922310000037
The active output and the positive and negative rotation standby values of the unit i in the power transmission network at the moment t are obtained;
Figure FDA0002489922310000038
respectively providing positive and negative rotation standby prices for the unit i;
Figure FDA0002489922310000039
punishing cost for wind abandonment of the wind power plant m;
Figure FDA00024899223100000310
the wind power station m is the air volume discarded under the climbing scene s at the time t;
Figure FDA00024899223100000311
for virtual loads at the k-boundary of the transmission and distribution network, TSO is the transmission system operator, Gtrans、Nw、NdistRespectively a generator, a wind power plant and a transmission and distribution boundary node set;
predicting scene constraints in a power transmission network scheduling layer objective function:
Figure FDA00024899223100000312
Figure FDA00024899223100000313
Figure FDA00024899223100000314
Figure FDA00024899223100000315
Figure FDA00024899223100000316
Figure FDA00024899223100000317
Figure FDA00024899223100000318
Figure FDA00024899223100000319
in the formula, N Ltrans、LtransRespectively a load and a transmission network line set; the physical meaning of partial variables of the generator is consistent with the variables in the optimization model of the power distribution network,
Figure FDA0002489922310000041
the maximum positive standby and the maximum negative standby which can be provided by the unit i in the time period t are respectively provided; RU (RU)i、RDiThe climbing capacity of the unit i is the climbing capacity;
Figure FDA0002489922310000042
wind power predicted value and planned air abandoning amount under the prediction scene; gl-bThe distribution factor is transferred for node b to line l,
Figure FDA0002489922310000043
T lrespectively transmitting upper and lower limits of work for a line l, wherein a formula (25) is generator output range constraint, a formula (26) is power balance constraint, a formula (27) is generator climbing constraint, formulas (28) to (29) are standby constraint, a formula (30) is line safety constraint, and formulas (31) to (32) are boundary power constraint;
and (3) climbing scene constraint in a power transmission network scheduling layer objective function:
Figure FDA0002489922310000044
Figure FDA0002489922310000045
Figure FDA0002489922310000046
Figure FDA0002489922310000047
Figure FDA0002489922310000048
Figure FDA0002489922310000049
Figure FDA00024899223100000410
Figure FDA00024899223100000411
Figure FDA00024899223100000412
in the formula:
Figure FDA00024899223100000413
outputting power for the unit i in the time period t under the scene s;
Figure FDA00024899223100000414
adjusting the output of the unit;
Figure FDA00024899223100000415
the predicted value of the wind power under the scene s is shown, and the expressions (33) - (35) are the output limit of the unit under the climbing scene; equations (36) - (39) are set climbing constraints under four climbing scenes respectively, wherein the wind power climbing scene corresponds to the set climbing capability constraint; the wind power down-grade climbing scene corresponds to the unit up-grade climbing capacity constraint; equation (40) is the power balance constraint under scenario s; the formula (41) is the air abandoning amount constraint; the safety constraint in a climbing scene is similar to equation (30);
before solving, a local scheduling layer firstly carries out combined optimization according to an R-DG prediction curve and electricity selling price, reports the result to a DSO (distributed generation) unit as an input condition, and carries out optimization of a transmission and distribution network;
relaxing consistency constraint as a penalty function into an objective function to enable boundary power to tend to be consistent, and obtaining a modified transmission network objective function and a modified distribution network k objective function, wherein ATC is a target cascade analysis method, and DSO is a distribution system operator;
the modified transmission grid objective function is as follows:
Figure FDA0002489922310000051
the modified k objective function of the power distribution network is as follows:
Figure FDA0002489922310000052
in the formula: v. ofk,t、wk,tIs an algorithm multiplier;
Figure FDA0002489922310000053
the value of the coupling variable transferred for the adjacent area is a known constant;
the ATC-based algorithm flow comprises the following specific steps:
step 1: initialization algorithm multiplier, virtual active injection of power distribution network
Figure FDA0002489922310000054
Setting an iteration flag i to be 0;
step 2: setting i to i +1, solving the optimal scheduling subproblem of each DSO according to the formula (43) and the formulae (13) to (19), and virtually injecting the obtained transmission and distribution boundary power
Figure FDA0002489922310000055
The data is transmitted to the upper TSO, and each DSO is solved in parallel to accelerate the calculation speed;
and step 3: after receiving the data of the power distribution network, the TSO synthesizes wind power and load prediction information, solves the sub-problem of the power transmission network scheduling layer according to the formula (42) and the formulas (20) - (41), and obtains the virtual load value of the transmission and distribution boundary
Figure FDA0002489922310000056
And transmitting the calculation result to each DSO;
and 4, step 4: judging whether the circulation is converged by using a formula (44), if so, outputting a calculation result, and otherwise, continuing the step 5;
Figure FDA0002489922310000057
in the formula:1、2is the convergence factor; f. ofa(x) Is the objective function value for region a; trans and D respectively refer to a transmission network and a distribution network;
and 5: updating the Algorithm multiplier v with equation (45)k,t、wk,tReturning to the step 2 to start a new iteration;
Figure FDA0002489922310000058
Figure FDA0002489922310000059
in the formula: gamma is a constant, and the value of gamma is more than or equal to 1 and less than or equal to 3; v. ofk,tAnd wk,tThe initial value of (c) takes a small constant.
2. The method according to claim 1, wherein the constraint conditions of the local scheduling layer objective function are:
Figure FDA00024899223100000510
Figure FDA00024899223100000511
Figure FDA00024899223100000512
Figure FDA00024899223100000513
Figure FDA0002489922310000061
Figure FDA0002489922310000062
Figure FDA0002489922310000063
Figure FDA0002489922310000064
Figure FDA0002489922310000065
wherein: constraints (4) - (6) limit
Figure FDA0002489922310000066
And Pjoi，t(iv) the range of output; constraints (7) - (12) are charging and discharging constraints of the ESS under each wind power scene,
Figure FDA0002489922310000067
and
Figure FDA00024899223100000612
the modulation value and the predicted value of the R-DG under the scene s at the moment t are obtained;
Figure FDA0002489922310000068
respectively, discharge/charge power and flag bit of ESS in scene s at time t ηch、ηdisRespectively charge/discharge efficiency values;
Figure FDA0002489922310000069
E、
Figure FDA00024899223100000610
respectively the maximum value of the charging/discharging power of the ESS and the lower/upper limit of the electric quantity,
Figure FDA00024899223100000611
the ESS power at time t, scheduling end and start time, respectively.
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