CN113937781A - Virtual power plant rolling scheduling technology considering temperature control load polymer - Google Patents

Virtual power plant rolling scheduling technology considering temperature control load polymer Download PDF

Info

Publication number
CN113937781A
CN113937781A CN202111142934.5A CN202111142934A CN113937781A CN 113937781 A CN113937781 A CN 113937781A CN 202111142934 A CN202111142934 A CN 202111142934A CN 113937781 A CN113937781 A CN 113937781A
Authority
CN
China
Prior art keywords
temperature control
control load
power
node
power plant
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111142934.5A
Other languages
Chinese (zh)
Inventor
李江南
祝宇翔
史军
张炀
卢非凡
刘傲
车诒颖
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Power Supply Bureau Co Ltd
Original Assignee
Shenzhen Power Supply Bureau Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Power Supply Bureau Co Ltd filed Critical Shenzhen Power Supply Bureau Co Ltd
Priority to CN202111142934.5A priority Critical patent/CN113937781A/en
Publication of CN113937781A publication Critical patent/CN113937781A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J3/38—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
    • H02J2203/20—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
    • 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
    • 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

Landscapes

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

Abstract

The invention provides a virtual power plant rolling scheduling method considering mass temperature control load aggregation, which comprises the following steps: counting the temperature control loads on each bus of the virtual power plant, clustering the temperature control loads according to the demand information of each temperature control load, and calculating the parameters of aggregators; then according to the temperature control load aggregation result, the prediction information of renewable energy sources and various electricity prices, a virtual power plant optimization scheduling model considering the temperature control load aggregators is established, a pre-optimization result of rolling scheduling is obtained through solving, and the reference power of each aggregator is obtained; decomposing and optimizing the reference power by each aggregator and distributing the reference power to each temperature control load in the aggregator to obtain the actual power requirement of each temperature control load in the aggregator; and (4) formulating a final rolling scheduling plan according to the actual power requirements of all temperature control loads. By implementing the method, the coordination scheduling of massive temperature control loads in the virtual power plant can be realized, and the method has the advantages of high calculation efficiency, wide compatibility and strong practicability.

Description

Virtual power plant rolling scheduling technology considering temperature control load polymer
Technical Field
The invention relates to the technical field of virtual power plant scheduling control, in particular to a virtual power plant rolling scheduling method considering a temperature control load aggregator.
Background
The temperature control load has great potential for improving the operation efficiency and the economy of the power system. However, the temperature control loads are large in number, small in scale and various, and challenges are brought to centralized management and optimal scheduling of the power system. The virtual power plant technology can be used for carrying out aggregation management on the massive temperature control loads, the scheduling potential of the massive temperature control loads in the power system is fully excavated, and the problem of 'dimensional disaster' brought to a power system manager by the massive temperature control loads is solved. In view of this, the invention provides a virtual power plant rolling scheduling method considering mass temperature control load cluster aggregation, which can be used for making a rolling scheduling plan of a virtual power plant to internal controllable devices.
Currently, the academics have conducted a great deal of research on the problem of optimal scheduling of virtual power plants. For example, in one prior art, a virtual power plant two-stage optimization scheduling model considering dynamic characteristics of a heat supply link is provided, and the flexibility of a heat load is utilized to improve the regulation capability of a power system. In addition, in another prior art, a virtual power plant stochastic scheduling optimization model considering the operation risk is provided by considering the energy storage system and the excitation type demand response and considering the uncertainty of the distributed energy power generation. However, the existing research and technology mainly have the following disadvantages:
in the prior art, only a small amount of flexible devices are modeled one by one, however, in an actual system, the temperature control loads have the characteristics of large quantity, small capacity and various parameters, and if the temperature control loads are managed one by one, a virtual power plant manager faces the problem of 'dimension disaster'. Therefore, the virtual power plant scheduling method needs to be combined with an effective cluster aggregation management method, and the optimized operation efficiency of the system is improved.
In the prior art, the problem of optimized scheduling inside the virtual power plant is mainly focused, however, with the development of the power market technology, the virtual power plant agent needs to consider interaction with the superior power market, and therefore, the internal scheduling plan and the external interaction scheme of the virtual power plant need to be optimized cooperatively.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides the virtual power plant rolling scheduling method considering the temperature control load aggregator, can realize the coordination scheduling of mass temperature control loads in the virtual power plant, and has the advantages of high calculation efficiency, wide compatibility and strong practicability.
In order to solve the above technical problem, an aspect of the present invention provides a virtual power plant rolling scheduling method considering a temperature control load polymer, including:
step S1, clustering temperature control load data of each node on each bus in the virtual power plant according to the demand information of each temperature control load to form a plurality of temperature control load aggregators, and calculating the parameters of each temperature control load aggregator;
step S2, at each scheduling moment, according to the temperature control load clustering aggregation result, the renewable energy sources and the prediction information of various electricity prices, establishing a virtual power plant rolling scheduling optimization model and solving to obtain a pre-optimization result of rolling scheduling, and according to the pre-optimization result, obtaining the reference power of each temperature control load aggregator;
step S3, performing depolymerization on the reference power of each aggregator, decomposing the reference power into each temperature control load in the aggregator, and obtaining the actual power requirement of each temperature control load;
and step S4, finishing the final optimization of the rolling scheduling of the virtual power plant according to the actual power requirements of all the temperature control loads to obtain a final rolling scheduling plan scheme.
Preferably, the step S1 further includes:
step S10, determining temperature control load parameters of all nodes;
step S11, clustering and dividing temperature control load nodes with similar characteristics into the same group according to the characteristics of the temperature control load by adopting an NJW spectral clustering algorithm to form a plurality of temperature control load aggregators;
and step S12, obtaining the equivalent demand parameter information of each temperature control load aggregator according to all the temperature control load parameters in each temperature control load aggregator according to the temperature control load clustering result.
Preferably, the step S10 is specifically:
for each temperature control load, determining a key parameter thereof comprises
Figure BDA0003284408680000021
Figure BDA0003284408680000022
To reflect the demand information;
a general model of the temperature controlled load was established according to the following equation:
Figure BDA0003284408680000023
Figure BDA0003284408680000024
Figure BDA0003284408680000025
wherein the content of the first and second substances,
Figure BDA0003284408680000026
and
Figure BDA0003284408680000027
respectively, a lower power demand limit and an upper power demand limit of the temperature control load j;
Figure BDA0003284408680000028
and
Figure BDA0003284408680000029
respectively, an energy demand lower limit and an energy demand upper limit of the temperature control load j;
Figure BDA00032844086800000210
and
Figure BDA00032844086800000211
is the conversion factor of the temperature-controlled load j;
Figure BDA00032844086800000212
is the initial temperature of the temperature controlled load j;
Figure BDA00032844086800000213
the temperature of the external environment at the moment t;
Figure BDA00032844086800000214
and
Figure BDA00032844086800000215
respectively the active power demand and the temperature of the temperature control load j at the moment t;
Figure BDA0003284408680000031
the temperature of the temperature controlled load j at time t-1; the delta T is the scheduling time interval,
Figure BDA0003284408680000032
the temperature of the external environment at the moment t.
Preferably, the step S11 further includes:
step S110, inputting each temperature control load parameter and the number of aggregators expected to be obtained by each node of the virtual power plant, and recording the number of aggregators at the node i as NAGG,i；
Step S111, carrying out extremum normalization on each temperature control load parameter, wherein the normalization method comprises the following steps:
Figure BDA0003284408680000033
wherein N isL,iThe number of temperature control loads at node i;
Figure BDA0003284408680000034
Figure BDA0003284408680000035
is the original demand parameter of the temperature control load j;
Figure BDA0003284408680000036
Figure BDA0003284408680000037
is the demand parameter of the temperature control load j after normalization;
step S112, the Euclidean distance is adopted to measure the difference between different temperature control loads under each node, and the difference matrix D between the temperature control loads l and j under each node is obtained by the following definition:
Figure BDA0003284408680000038
wherein, the element of the ith row and the jth column in the D (l, j) matrix D; alpha is alpha1,α2,α3,α4,α5,α6,α7,α8Is a weight coefficient with a value range of [0, 1%]The reference values are set to: alpha is alpha1＝α2＝0.2，α3＝α4＝0.5，α5＝α6＝0.8，α7＝α8＝0.95；
Figure BDA0003284408680000039
Are respectively as
Figure BDA00032844086800000310
Figure BDA00032844086800000311
Carrying out normalization processing on the parameters;
step S113, converting the difference matrix D into the adjacency matrix K by using a gaussian kernel function as follows:
Figure BDA00032844086800000312
wherein, the element of the ith row and the jth column in the K (l, j) matrix K;
Figure BDA00032844086800000315
is a Gaussian kernel function parameter;
step S114, normalizing the adjacent matrix K, and calculating the matrix K front N after normalizationAGG,iThe maximum eigenvalue and the corresponding eigenvector are respectively recorded as u1,u2,…,
Figure BDA00032844086800000313
Constructing a feature vector space matrix using the feature vectors
Figure BDA00032844086800000314
And S115, clustering the eigenvector space matrix U by using a K-means algorithm to obtain a temperature control load clustering result under each node.
Preferably, the step S12 is specifically:
according to the temperature control load clustering result, for each temperature control load aggregator, according to all temperature control load parameters in the aggregator, acquiring equivalent demand parameter information of the aggregator as follows:
Figure BDA0003284408680000041
Figure BDA0003284408680000042
Figure BDA0003284408680000043
wherein N isL,k,iThe number of temperature control loads in the aggregation quotient k at the node i; the equivalent parameter of the aggregators is
Figure BDA0003284408680000044
Figure BDA0003284408680000045
Figure BDA0003284408680000046
And
Figure BDA0003284408680000047
respectively setting the lower limit and the upper limit of the equivalent power requirement of the aggregation quotient k at the node i;
Figure BDA0003284408680000048
and
Figure BDA0003284408680000049
respectively setting the lower limit and the upper limit of the equivalent energy requirement of the aggregation quotient k at the node i;
Figure BDA00032844086800000410
and
Figure BDA00032844086800000411
the equivalent conversion coefficient of the aggregation quotient k at the node i;
Figure BDA00032844086800000412
is the equivalent initial temperature of the aggregation quotient k at node i.
Preferably, the step S2 further includes:
step S20, for each scheduling time, modeling renewable energy and electricity price uncertainty in virtual power plant operation optimization by adopting a random optimization method based on a typical scene set, and establishing a rolling scheduling pre-optimization model, wherein an optimization objective function of the rolling scheduling pre-optimization model is as follows:
Figure BDA00032844086800000413
wherein N issAnd ρsRespectively representing the number of scenes and the weight of each scene; t is the total number of operating time periods of the virtual power plant; tau is the current running time point;
Figure BDA00032844086800000414
predicting the electricity price for the energy market under the scene s at the moment t;
Figure BDA00032844086800000415
predicting the electricity price for the rotating standby market under the scene s at the moment t;
Figure BDA00032844086800000416
the interactive active power of the virtual power plant and the energy market under the scene s at the moment t is obtained;
Figure BDA00032844086800000417
the interactive standby capacity of the virtual power plant and the auxiliary service market under the scene s at the moment t;
Figure BDA00032844086800000418
clearing active power for a superior energy market at time t;
Figure BDA00032844086800000419
spare capacity cleared for a superior auxiliary service market at time t;
Figure BDA00032844086800000420
the active output of the generator set i in a scene s at the moment t;
Figure BDA00032844086800000421
the standby capacity of the generator set i in a scene s at the moment t;
Figure BDA00032844086800000422
and
Figure BDA00032844086800000423
respectively outputting active power and inputting active power of the energy storage device i in a scene s at the moment t;
Figure BDA00032844086800000424
the standby capacity of the energy storage device i in a scene s at the moment t;
Figure BDA00032844086800000425
a deviation penalty coefficient corresponding to the deviation of the polymer active power from the energy market scheduling plan;
Figure BDA00032844086800000426
a deviation penalty coefficient corresponding to the deviation of the aggregate reserve capacity from the reserve market scheduling plan; n is a radical ofDGAnd NESSThe number of distributed generator sets and the number of energy storage devices in the polymer are respectively;
wherein the content of the first and second substances,
Figure BDA00032844086800000427
and
Figure BDA00032844086800000428
the operation costs of the generator set i and the energy storage device i are respectively expressed by the following formulas:
Figure BDA0003284408680000051
Figure BDA0003284408680000052
wherein the content of the first and second substances,
Figure BDA0003284408680000053
rotating the standby calling probability for the time t; a isi、biAnd ciIs the operating cost factor of the generator; AP (Access Point)iIs the operating cost coefficient of the energy storage device;
decision variable packet in the optimization model
Figure BDA0003284408680000054
Figure BDA0003284408680000055
Step S21, adopting power flow equation model constraint, distributed generator set model constraint, renewable energy output constraint, energy storage model constraint, system rotation standby balance constraint model and temperature control load aggregator model constraint to constrain the rolling scheduling pre-optimization model, solving the rolling scheduling pre-optimization model, and obtaining the reference power of each temperature control load aggregator
Figure BDA0003284408680000056
Preferably, in the step S21, the method further includes:
step S210, realizing the power flow equation model constraint by adopting the following formula, wherein a linear power flow model is adopted to describe the active power flow balance and the reactive power flow balance in the virtual power plant:
Figure BDA0003284408680000057
Figure BDA0003284408680000058
Figure BDA0003284408680000059
Figure BDA00032844086800000510
Figure BDA00032844086800000511
Figure BDA00032844086800000512
wherein the content of the first and second substances,
Figure BDA00032844086800000513
NBthe total number of nodes in the virtual power plant; r isijAnd xijResistance and reactance between line i and line j, respectively; thetai、Vi、Pi、QiRespectively phase position, voltage amplitude value, injection active power and injection reactive power at a node i; thetaj、VjPhase and voltage amplitude at node j, respectively;
Figure BDA00032844086800000514
and
Figure BDA00032844086800000515
respectively representing the active power output of the wind power plant i and the photovoltaic power plant i in a scene s at the moment t;
Figure BDA00032844086800000516
and
Figure BDA00032844086800000517
respectively providing reactive power output of a wind power plant i, a photovoltaic power plant i and a generator set i in a scene s at the moment t;
Figure BDA00032844086800000518
the reference power in the scene s at the time t for the aggregation quotient k at the node i;
Figure BDA00032844086800000519
and
Figure BDA00032844086800000520
respectively an active load and a reactive load at the t moment at the node i; pijAnd QijBranch flow active power and branch flow reactive power between the node i and the node j are respectively;
Figure BDA0003284408680000061
the reference active power of the aggregation quotient k at the node i in a scene s at the moment t is obtained;
and the power flow and the node voltage of each branch are limited within a certain range:
Figure BDA0003284408680000062
wherein, PijAnd QijRespectively an active power flow and a reactive power flow between the node i and the node j; sij,NRated capacity for the transmission line between node i and node j; vi,maxAnd Vi,minRespectively an upper limit and a lower limit of the node voltage amplitude;
step S211, implementing distributed generator set model constraint by using the following formula, which at least includes: the unit is exerted power, is climbed and is exerted power, start and stop time duration:
Figure BDA0003284408680000063
Figure BDA0003284408680000064
Figure BDA0003284408680000065
Figure BDA0003284408680000066
wherein the content of the first and second substances,
Figure BDA0003284408680000067
and
Figure BDA0003284408680000068
respectively representing the upper and lower output limits of the generator set i;
Figure BDA0003284408680000069
the maximum climbing speed of the generator set i;
Figure BDA00032844086800000610
the rated capacity of the generator set i;
step S212, realizing the output constraint of the renewable energy source by adopting the following formula:
Figure BDA00032844086800000611
Figure BDA00032844086800000612
wherein the content of the first and second substances,
Figure BDA00032844086800000613
and
Figure BDA00032844086800000614
rated capacities of a wind power plant i and a photovoltaic power plant i are respectively set;
Figure BDA00032844086800000615
the predicted value of the wind power plant i at the moment t in the scene s is obtained;
Figure BDA00032844086800000616
the predicted value of the photovoltaic power station i at the moment t in the scene s is obtained;
step S213, implementing the energy storage model constraint by using the following formula:
Figure BDA00032844086800000617
Figure BDA00032844086800000618
Figure BDA00032844086800000619
Figure BDA00032844086800000620
wherein the content of the first and second substances,
Figure BDA00032844086800000621
and
Figure BDA00032844086800000622
the maximum input/output power and the minimum input/output power of the energy storage device i are respectively;
Figure BDA00032844086800000623
and
Figure BDA00032844086800000624
respectively an energy storage upper limit and an energy storage lower limit of the energy storage device i; etainAnd ηoutPower input and output energy conversion coefficients, respectively; λ is the energy dissipation coefficient of the stored energy;
step S214, implementing system rotation standby balance constraint by adopting the following formula:
Figure BDA00032844086800000625
wherein, | Δ RL|、
Figure BDA0003284408680000071
And
Figure BDA0003284408680000072
respectively the standby requirements of the polymer internal load, the wind power plant i and the photovoltaic power plant i;
step S215, realizing temperature control load aggregator model constraint by adopting the following formula:
Figure BDA0003284408680000073
Figure BDA0003284408680000074
Figure BDA0003284408680000075
wherein the content of the first and second substances,
Figure BDA0003284408680000076
is the reference indoor temperature state in scenario s at time t for the aggregate quotient k at node i.
Preferably, the step S3 further includes:
reference power obtained by each aggregator according to virtual power plant pre-optimization steps
Figure BDA0003284408680000077
Making an operation plan of the internal temperature control load;
for a specific aggregation quotient k, the target of the de-aggregation enables the overall output power to follow the reference power track obtained in the pre-optimization step, and the number of start-stop times of each temperature control load is reduced, wherein a specific target function is as follows;
Figure BDA0003284408680000078
the weight between different optimization targets is adjusted by setting the value of the coefficient M in the objective function;
the power consumption of each temperature control load at each moment needs to meet the following requirement model, specifically the following requirement model;
Figure BDA0003284408680000079
Figure BDA00032844086800000710
Figure BDA00032844086800000711
solving the disaggregation model, and acquiring the actual power demand of the temperature control load according to the following formula:
Figure BDA00032844086800000712
wherein the content of the first and second substances,
Figure BDA00032844086800000713
the actual power requirement of the aggregation quotient k at the node i at the moment t is obtained; n is a radical ofAGG,iThe number of aggregators at node i.
Preferably, the step S4 further includes:
decision variables in rolling scheduling optimization model of virtual power plant
Figure BDA00032844086800000714
Is replaced by a fixed value
Figure BDA00032844086800000715
Updating decision variables of the model to
Figure BDA00032844086800000716
Solving the rolling scheduling optimization model of the virtual power plant again to obtain the optimal solution of each decision variable, and calculating according to the weight of each scene
Figure BDA00032844086800000717
And finishing the final optimization of the rolling scheduling of the virtual power plant to obtain a final rolling scheduling plan scheme, wherein the final rolling scheduling plan scheme comprises a time-sharing scheduling plan of the renewable energy source unit, the distributed generator set and the energy storage equipment and a bidding plan for an upper-level power market.
The embodiment of the invention has the following beneficial effects:
the invention provides a virtual power plant rolling scheduling method considering temperature control load aggregators, which can realize the coordination scheduling of the mass temperature control loads in a virtual power plant by considering the aggregation and deaggregation processes of the temperature control loads, has high-efficiency calculation efficiency, can fully excavate the flexibility of a load side, and solves the problem of 'dimension disaster' caused by the mass small-scale temperature control loads to the system optimization scheduling;
the virtual power plant optimization scheduling model provided by the invention considers the interaction between the virtual power plant and the superior power market, can realize that the internal scheduling plan and the external interaction scheme need to be optimized in a cooperative way urgently, improves the universality of the method in the power market background, and has high practical value.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of one embodiment of a virtual power plant rolling scheduling technique in accordance with the present invention that considers a temperature controlled load aggregate.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, a main flow diagram of an embodiment of a virtual plant rolling scheduling method considering a temperature-controlled load aggregate according to the present invention is shown. In this embodiment, the method includes the steps of:
step S1, clustering temperature control load data of each node on each bus in the virtual power plant according to the demand information of each temperature control load to form a plurality of temperature control load aggregators, and calculating the parameters of each temperature control load aggregator;
in a specific example, the step S1 further includes:
step S10, determining temperature control load parameters of all nodes;
specifically, the step S10 specifically includes:
for each temperature control load, determining a key parameter thereof comprises
Figure BDA0003284408680000091
Figure BDA0003284408680000092
To reflect the demand information;
a general model of the temperature controlled load was established according to the following equation:
Figure BDA0003284408680000093
Figure BDA0003284408680000094
Figure BDA0003284408680000095
wherein the content of the first and second substances,
Figure BDA0003284408680000096
and
Figure BDA0003284408680000097
respectively, a lower power demand limit and an upper power demand limit of the temperature control load j;
Figure BDA0003284408680000098
and
Figure BDA0003284408680000099
respectively, an energy demand lower limit and an energy demand upper limit of the temperature control load j;
Figure BDA00032844086800000910
and
Figure BDA00032844086800000911
is the conversion factor of the temperature-controlled load j;
Figure BDA00032844086800000912
is the initial temperature of the temperature controlled load j;
Figure BDA00032844086800000913
the temperature of the external environment at the moment t;
Figure BDA00032844086800000914
and
Figure BDA00032844086800000915
respectively the active power demand and the temperature of the temperature control load j at the moment t;
Figure BDA00032844086800000916
the temperature of the temperature controlled load j at time t-1; Δ T is a scheduling time interval;
Figure BDA00032844086800000917
the temperature of the external environment at the moment t.
Step S11, clustering and dividing temperature control load nodes with similar characteristics into the same group according to the characteristics of the temperature control load by adopting an NJW spectral clustering algorithm to form a plurality of temperature control load aggregators; the NJW (Ng-Jordan-Weiss) spectral clustering algorithm is a classic spectral clustering algorithm, and in the invention, temperature control load clusters with similar characteristics can be divided into the same group by the NJW spectral clustering algorithm to form a aggregator for unified management, so that the upper-level virtual power plant dispatching can be more conveniently accepted.
Specifically, the step S11 further includes:
step S110, inputting each temperature control load parameter and the number of aggregators expected to be obtained by each node of the virtual power plant, and recording the number of aggregators at the node i as NAGG,i；
Step S111, carrying out extremum normalization on each temperature control load parameter, wherein the normalization method comprises the following steps:
Figure BDA00032844086800000918
wherein N isL,iThe number of temperature control loads at node i;
Figure BDA00032844086800000919
Figure BDA00032844086800000920
is the original demand parameter of the temperature control load j;
Figure BDA00032844086800000921
Figure BDA00032844086800000922
is the demand parameter of the temperature control load j after normalization;
step S112, the Euclidean distance is adopted to measure the difference between different temperature control loads under each node, and the difference matrix D between the temperature control loads l and j under each node is obtained by the following definition:
Figure BDA0003284408680000101
wherein, the element of the ith row and the jth column in the D (l, j) matrix D; alpha is alpha1,α2,α3,α4,α5,α6,α7,α8Is a weight coefficient with a value range of [0, 1%]The reference values are set to: alpha is alpha1＝α2＝0.2，α3＝α4＝0.5，α5＝α6＝0.8，α7＝α8＝0.95；
Figure BDA0003284408680000102
Are respectively as
Figure BDA0003284408680000103
Figure BDA0003284408680000104
Carrying out normalization processing on the parameters;
step S113, converting the difference matrix D into the adjacency matrix K by using a gaussian kernel function as follows:
Figure BDA0003284408680000105
wherein, the element of the ith row and the jth column in the K (l, j) matrix K;
Figure BDA00032844086800001020
is a Gaussian kernel function parameter;
step S114, normalizing the adjacent matrix K, and calculating the matrix K front N after normalizationAGG,iThe maximum eigenvalue and the corresponding eigenvector are respectively recorded as u1,u2,…,
Figure BDA0003284408680000106
Constructing a feature vector space matrix using the feature vectors
Figure BDA0003284408680000107
And S115, clustering the eigenvector space matrix U by using a K-means algorithm to obtain a temperature control load clustering result under each node.
And step S12, obtaining the equivalent demand parameter information of each temperature control load aggregator according to all the temperature control load parameters in each temperature control load aggregator according to the temperature control load clustering result.
Specifically, the step S12 specifically includes:
according to the temperature control load clustering result, for each temperature control load aggregator, according to all temperature control load parameters in the aggregator, acquiring equivalent demand parameter information of the aggregator as follows:
Figure BDA0003284408680000108
Figure BDA0003284408680000109
Figure BDA00032844086800001010
wherein N isL,k,iThe number of temperature control loads in the aggregation quotient k at the node i; the equivalent parameter of the aggregators is
Figure BDA00032844086800001011
Figure BDA00032844086800001012
Figure BDA00032844086800001013
And
Figure BDA00032844086800001014
respectively setting the lower limit and the upper limit of the equivalent power requirement of the aggregation quotient k at the node i;
Figure BDA00032844086800001015
and
Figure BDA00032844086800001016
respectively setting the lower limit and the upper limit of the equivalent energy requirement of the aggregation quotient k at the node i;
Figure BDA00032844086800001017
and
Figure BDA00032844086800001018
the equivalent conversion coefficient of the aggregation quotient k at the node i;
Figure BDA00032844086800001019
is the equivalent initial temperature of the aggregation quotient k at node i.
It should be noted that, in the rolling scheduling process, since the key parameters and current state of the temperature control load are changed continuously, the cluster aggregation process should be performed again at each scheduling time. Therefore, in the virtual power plant rolling scheduling process, the temperature control load aggregation is a time-sharing dynamic updating process.
Step S2, establishing a virtual power plant rolling scheduling optimization model and solving the model according to the temperature control load clustering aggregation result, the renewable energy sources and the prediction information of various electricity prices to obtain a pre-optimization result of rolling scheduling, and obtaining the reference power of each temperature control load aggregator according to the pre-optimization result;
in a specific example, the step S2 further includes:
step S20, for each scheduling time, the virtual power plant agent needs to perform a series of rolling optimizations to obtain a distributed generator set, renewable energy, energy storage device, and temperature control load scheduling plan, and a virtual power plant and power market interaction plan. Modeling renewable energy and electricity price uncertainty in virtual power plant operation optimization by adopting a random optimization method based on a typical scene set, and establishing a rolling scheduling pre-optimization model, wherein the virtual power plant rolling scheduling pre-optimization model takes the lowest total operation cost of a virtual power plant agent as a target, and the operation cost considered in the model comprises the following five items: the virtual power plant is from the electric energy purchase cost in the upper energy market, from the reserve purchase cost in rotatory reserve market, the inside distributed generating set running cost of virtual power plant, and the penalty that the energy and reserve interactive deviation of energy and the higher level electric power market caused, so its optimization objective function is as follows:
Figure BDA0003284408680000111
wherein N issAnd ρsRespectively representing the number of scenes and the weight of each scene; t is the total number of operating time periods of the virtual power plant; tau is the current running time point;
Figure BDA0003284408680000112
predicting the electricity price for the energy market under the scene s at the moment t;
Figure BDA0003284408680000113
predicting the electricity price for the rotating standby market under the scene s at the moment t;
Figure BDA0003284408680000114
the interactive active power of the virtual power plant and the energy market under the scene s at the moment t is obtained;
Figure BDA0003284408680000115
the interactive standby capacity of the virtual power plant and the auxiliary service market under the scene s at the moment t;
Figure BDA0003284408680000116
clearing active power for a superior energy market at time t;
Figure BDA0003284408680000117
spare capacity cleared for a superior auxiliary service market at time t;
Figure BDA0003284408680000118
the active output of the generator set i in a scene s at the moment t;
Figure BDA0003284408680000119
the standby capacity of the generator set i in a scene s at the moment t;
Figure BDA00032844086800001110
and
Figure BDA00032844086800001111
respectively outputting active power and inputting active power of the energy storage device i in a scene s at the moment t;
Figure BDA00032844086800001112
the standby capacity of the energy storage device i in a scene s at the moment t;
Figure BDA00032844086800001113
a deviation penalty coefficient corresponding to the deviation of the polymer active power from the energy market scheduling plan;
Figure BDA00032844086800001114
a deviation penalty coefficient corresponding to the deviation of the aggregate reserve capacity from the reserve market scheduling plan; n is a radical ofDGAnd NESSThe number of distributed generator sets and the number of energy storage devices in the polymer are respectively;
wherein the content of the first and second substances,
Figure BDA00032844086800001115
and
Figure BDA00032844086800001116
the operating costs of the generator set i and the energy storage device i are respectively expressed by the following formulas:
Figure BDA0003284408680000121
Figure BDA0003284408680000122
wherein the content of the first and second substances,
Figure BDA0003284408680000123
rotating the standby calling probability for the time t; a isi，biAnd ciIs the operating cost factor of the generator; AP (Access Point)iIs the operating cost coefficient of the energy storage device;
decision variable packet in the optimization model
Figure BDA0003284408680000124
Figure BDA0003284408680000125
Step S21, adopting power flow equation model constraint, distributed generator set model constraint, renewable energy output constraint, energy storage model constraint, system rotation standby balance constraint model and temperature control load aggregator model constraint to constrain the rolling scheduling pre-optimization model, solving the rolling scheduling pre-optimization model, and obtaining the reference power of each temperature control load aggregator
Figure BDA0003284408680000126
Specifically, in step S21, the method further includes:
step S210, realizing the power flow equation model constraint by adopting the following formula, wherein a linear power flow model is adopted to describe the active power flow balance and the reactive power flow balance in the virtual power plant:
Figure BDA0003284408680000127
Figure BDA0003284408680000128
Figure BDA0003284408680000129
Figure BDA00032844086800001210
Figure BDA00032844086800001211
Figure BDA00032844086800001212
wherein the content of the first and second substances,
Figure BDA00032844086800001213
NBthe total number of nodes in the virtual power plant; r isijAnd xijResistance and reactance between line i and line j, respectively; thetai、Vi、Pi、QiRespectively phase position, voltage amplitude value, injection active power and injection reactive power at a node i; thetaj、VjPhase and voltage amplitude at node j, respectively;
Figure BDA00032844086800001214
and
Figure BDA00032844086800001215
respectively representing the active power output of the wind power plant i and the photovoltaic power plant i in a scene s at the moment t;
Figure BDA00032844086800001216
and
Figure BDA00032844086800001217
respectively providing reactive power output of a wind power plant i, a photovoltaic power plant i and a generator set i in a scene s at the moment t;
Figure BDA00032844086800001218
the reference power in the scene s at the time t for the aggregation quotient k at the node i;
Figure BDA00032844086800001219
and
Figure BDA00032844086800001220
respectively an active load and a reactive load at the t moment at the node i; pijAnd QijBranch flow active power and branch flow reactive power between the node i and the node j are respectively;
Figure BDA0003284408680000131
the reference active power of the aggregation quotient k at the node i in a scene s at the moment t is obtained;
and the power flow and the node voltage of each branch are limited within a certain range:
Figure BDA0003284408680000132
wherein, PijAnd QijRespectively an active power flow and a reactive power flow between the node i and the node j; sij,NRated capacity for the transmission line between node i and node j; vi,maxAnd Vi,minRespectively an upper limit and a lower limit of the node voltage amplitude;
step S211, implementing distributed generator set model constraint by using the following formula, which at least includes: the unit is exerted power, is climbed and is exerted power, start and stop time duration:
Figure BDA0003284408680000133
Figure BDA0003284408680000134
Figure BDA0003284408680000135
Figure BDA0003284408680000136
wherein the content of the first and second substances,
Figure BDA0003284408680000137
and
Figure BDA0003284408680000138
respectively representing the upper and lower output limits of the generator set i;
Figure BDA0003284408680000139
the maximum climbing speed of the generator set i;
Figure BDA00032844086800001310
the rated capacity of the generator set i;
step S212, realizing the output constraint of the renewable energy source by adopting the following formula:
Figure BDA00032844086800001311
Figure BDA00032844086800001312
wherein the content of the first and second substances,
Figure BDA00032844086800001313
and
Figure BDA00032844086800001314
rated capacities of a wind power plant i and a photovoltaic power plant i are respectively set;
Figure BDA00032844086800001315
the predicted value of the wind power plant i at the moment t in the scene s is obtained;
Figure BDA00032844086800001316
the predicted value of the photovoltaic power station i at the moment t in the scene s is obtained;
step S213, implementing the energy storage model constraint by using the following formula:
Figure BDA00032844086800001317
Figure BDA00032844086800001318
Figure BDA00032844086800001319
Figure BDA00032844086800001320
wherein the content of the first and second substances,
Figure BDA00032844086800001321
and
Figure BDA00032844086800001322
and the maximum input/output power and the minimum input/output power of the energy storage device i respectively;
Figure BDA00032844086800001323
and
Figure BDA00032844086800001324
respectively an energy storage upper limit and an energy storage lower limit of the energy storage device i; etainAnd ηoutPower input and output energy conversion coefficients, respectively; λ is the energy dissipation coefficient of the stored energy;
step S214, implementing system rotation standby balance constraint by adopting the following formula:
Figure BDA00032844086800001325
wherein, | Δ RL|，
Figure BDA0003284408680000141
And
Figure BDA0003284408680000142
respectively the standby requirements of the polymer internal load, the wind power plant i and the photovoltaic power plant i;
step S215, realizing temperature control load aggregator model constraint by adopting the following formula:
Figure BDA0003284408680000143
Figure BDA0003284408680000144
Figure BDA0003284408680000145
wherein the content of the first and second substances,
Figure BDA0003284408680000146
is the reference indoor temperature state in scenario s at time t for the aggregate quotient k at node i.
It can be understood that, by solving the equations (10) - (33) in the virtual plant rolling scheduling pre-optimization model of the temperature-controlled load aggregators, reference power of each temperature-controlled load aggregator can be obtained
Figure BDA0003284408680000147
Step S3, performing depolymerization on the reference power of each aggregator, decomposing the reference power into each temperature control load in the aggregator, and obtaining the actual power requirement of each temperature control load;
in a specific example, the step S3 further includes:
reference power obtained by each aggregator according to virtual power plant pre-optimization steps
Figure BDA0003284408680000148
Making an operation plan of the internal temperature control load;
for a specific aggregation quotient k, the target of the de-aggregation enables the overall output power to follow the reference power track obtained in the pre-optimization step, and the number of start-stop times of each temperature control load is reduced, wherein a specific target function is as follows;
Figure BDA0003284408680000149
the weight between different optimization targets is adjusted by setting the value of the coefficient M in the objective function; generally speaking, the decomposition model should preferably ensure the power tracking accuracy of the aggregator, so that M can take a larger value, and the reference value range is 1000-10000;
the power consumption of each temperature control load at each moment needs to meet the following requirement model, specifically the following requirement model;
Figure BDA00032844086800001410
Figure BDA00032844086800001411
Figure BDA00032844086800001412
the solution model is a typical quadratic programming problem, and the solution model is solved, that is, the actual power demand of the temperature control load can be obtained according to the following formula:
Figure BDA00032844086800001413
wherein the content of the first and second substances,
Figure BDA00032844086800001414
the actual power requirement of the aggregation quotient k at the node i at the moment t is obtained; n is a radical ofAGG,iThe number of aggregators at node i.
Notably, the reference power trace for each aggregator
Figure BDA0003284408680000151
And the actual power trajectory
Figure BDA0003284408680000152
There may be a certain deviation between them, so the final optimization process in the subsequent steps is needed to determine the final optimized scheduling scheme.
And step S4, finishing the final optimization of the rolling scheduling of the virtual power plant according to the actual power requirements of all the temperature control loads to obtain a final rolling scheduling plan scheme.
In a specific example, the step S4 further includes:
the mathematical modeling of the virtual plant final optimization and the virtual plant pre-optimization are exactly the same, however, in the final optimization step, the power requirements of all temperature control load aggregators are determined, and therefore, in step S4, the decision variables in the virtual plant rolling scheduling optimization model need to be determined
Figure BDA0003284408680000153
Is replaced by a fixed value
Figure BDA0003284408680000154
Updating decision variables of the model to
Figure BDA0003284408680000155
Solving the rolling scheduling optimization model of the virtual power plant again to obtain the optimal solution of each decision variable, and calculating according to the weight of each scene
Figure BDA0003284408680000156
And finishing the final optimization of the rolling scheduling of the virtual power plant to obtain a final rolling scheduling plan scheme, wherein the final rolling scheduling plan scheme comprises a time-sharing scheduling plan of the renewable energy source unit, the distributed generator set and the energy storage equipment and a bidding plan for an upper-level power market.
It will be appreciated that the method of the invention is generally implemented by a proxy device of a virtual power plant. In practical application, the method comprises the following steps: and the virtual power plant agent counts the quantity of the temperature control loads on each bus, performs cluster clustering on the temperature control loads according to the demand information of the temperature control loads, and calculates the parameters of the aggregators. According to the temperature control load clustering aggregation result, the renewable energy sources and the prediction information of various electricity prices, the virtual power plant agent needs to perform pre-optimization of rolling scheduling, and the optimized scheduling result is issued to the aggregator. After the pre-optimization step is completed, each temperature controlled load aggregator receives a series of reference powers. And each aggregator decomposes the reference power into each temperature control load therein to realize temperature control load depolymerization so as to meet the requirements of each user and report the actual power consumption to the virtual power plant agent. And according to the actual power consumption of all the temperature control loads, the virtual power plant agent formulates a final rolling scheduling final optimization plan.
The embodiment of the invention has the following beneficial effects:
the invention provides a virtual power plant rolling scheduling method considering temperature control load aggregators, which can realize the coordination scheduling of the mass temperature control loads in a virtual power plant by considering the aggregation and deaggregation processes of the temperature control loads, has high-efficiency calculation efficiency, can fully excavate the flexibility of a load side, and solves the problem of 'dimension disaster' caused by the mass small-scale temperature control loads to the system optimization scheduling;
the virtual power plant optimization scheduling model provided by the invention considers the interaction between the virtual power plant and the superior power market, can realize that the internal scheduling plan and the external interaction scheme need to be optimized in a cooperative way urgently, improves the universality of the method in the power market background, and has high practical value.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (9)

1. A virtual power plant rolling scheduling method considering temperature control load polymers is characterized by comprising the following steps:
step S1, clustering temperature control load data of each node on each bus in the virtual power plant according to the demand information of each temperature control load to form a plurality of temperature control load aggregators, and calculating the parameters of each temperature control load aggregator;
step S2, at each scheduling moment, according to the temperature control load clustering aggregation result, the renewable energy sources and the prediction information of various electricity prices, establishing a virtual power plant rolling scheduling optimization model and solving to obtain a pre-optimization result of rolling scheduling, and according to the pre-optimization result, obtaining the reference power of each temperature control load aggregator;
step S3, performing depolymerization on the reference power of each aggregator, decomposing the reference power into each temperature control load in the aggregator, and obtaining the actual power requirement of each temperature control load;
and step S4, finishing the final optimization of the rolling scheduling of the virtual power plant according to the actual power requirements of all the temperature control loads to obtain a final rolling scheduling plan scheme.
2. The method of claim 1, wherein the step S1 further comprises:
step S10, determining temperature control load parameters of all nodes;
step S11, clustering and dividing temperature control load nodes with similar characteristics into the same group according to the characteristics of the temperature control load by adopting an NJW spectral clustering algorithm to form a plurality of temperature control load aggregators;
and step S12, obtaining the equivalent demand parameter information of each temperature control load aggregator according to all the temperature control load parameters in each temperature control load aggregator according to the temperature control load clustering result.
3. The method according to claim 2, wherein the step S10 is specifically:
for each temperature control load, determining a key parameter thereof comprises
Figure FDA0003284408670000011
Figure FDA0003284408670000012
To reflect the demand information;
a general model of the temperature controlled load was established according to the following equation:
Figure FDA0003284408670000013
Figure FDA0003284408670000014
Figure FDA0003284408670000015
wherein the content of the first and second substances,
Figure FDA0003284408670000016
and
Figure FDA0003284408670000017
respectively, a lower power demand limit and an upper power demand limit of the temperature control load j;
Figure FDA0003284408670000018
and
Figure FDA0003284408670000019
respectively, an energy demand lower limit and an energy demand upper limit of the temperature control load j;
Figure FDA00032844086700000110
and
Figure FDA00032844086700000111
is the conversion factor of the temperature-controlled load j;
Figure FDA00032844086700000112
is the initial temperature of the temperature controlled load j;
Figure FDA00032844086700000113
the temperature of the external environment at the moment t;
Figure FDA00032844086700000114
and
Figure FDA00032844086700000115
respectively the active power demand and the temperature of the temperature control load j at the moment t;
Figure FDA0003284408670000021
the temperature of the temperature controlled load j at time t-1; the delta T is the scheduling time interval,
Figure FDA0003284408670000022
the temperature of the external environment at the moment t.
4. The method of claim 3, wherein the step S11 further comprises:
step S110, inputting each temperature control load parameter and the number of aggregators expected to be obtained by each node of the virtual power plant, and recording the number of aggregators at the node i as NAGG,i；
Step S111, carrying out extremum normalization on each temperature control load parameter, wherein the normalization method comprises the following steps:
Figure FDA0003284408670000023
wherein N isL,iThe number of temperature control loads at node i;
Figure FDA0003284408670000024
Figure FDA0003284408670000025
is the original demand parameter of the temperature control load j;
Figure FDA0003284408670000026
Figure FDA0003284408670000027
is the demand parameter of the temperature control load j after normalization;
step S112, the Euclidean distance is adopted to measure the difference between different temperature control loads under each node, and the difference matrix D between the temperature control loads l and j under each node is obtained by the following definition:
Figure FDA0003284408670000028
wherein, the element of the ith row and the jth column in the D (l, j) matrix D; alpha is alpha1,α2,α3,α4,α5,α6,α7,α8Is a weight coefficient with a value range of [0, 1%]；
Figure FDA0003284408670000029
Are respectively as
Figure FDA00032844086700000210
Carrying out normalization processing on the parameters;
step S113, converting the difference matrix D into the adjacency matrix K by using a gaussian kernel function as follows:
Figure FDA00032844086700000211
wherein, the element of the ith row and the jth column in the K (l, j) matrix K;
Figure FDA00032844086700000212
is a Gaussian kernel function parameter;
step S114, normalizing the adjacent matrix K, and calculating the matrix K front N after normalizationAGG,iMaximum eigenvalue and corresponding eigenvector thereofThe feature vectors are respectively recorded as
Figure FDA00032844086700000213
Constructing a feature vector space matrix using the feature vectors
Figure FDA00032844086700000214
And S115, clustering the eigenvector space matrix U by using a K-means algorithm to obtain a temperature control load clustering result under each node.
5. The method according to claim 3, wherein the step S12 is specifically:
according to the temperature control load clustering result, for each temperature control load aggregator, according to all temperature control load parameters in the aggregator, acquiring equivalent demand parameter information of the aggregator as follows:
Figure FDA0003284408670000031
Figure FDA0003284408670000032
Figure FDA0003284408670000033
wherein N isL,k,iThe number of temperature control loads in the aggregation quotient k at the node i; the equivalent parameter of the aggregators is
Figure FDA0003284408670000034
Figure FDA0003284408670000035
Figure FDA0003284408670000036
And
Figure FDA0003284408670000037
respectively setting the lower limit and the upper limit of the equivalent power requirement of the aggregation quotient k at the node i;
Figure FDA0003284408670000038
and
Figure FDA0003284408670000039
respectively setting the lower limit and the upper limit of the equivalent energy requirement of the aggregation quotient k at the node i;
Figure FDA00032844086700000310
and
Figure FDA00032844086700000311
the equivalent conversion coefficient of the aggregation quotient k at the node i;
Figure FDA00032844086700000312
is the equivalent initial temperature of the aggregation quotient k at node i.
6. The method of claim 5, wherein the step S2 further comprises:
step S20, for each scheduling time, modeling renewable energy and electricity price uncertainty in virtual power plant operation optimization by adopting a random optimization method based on a typical scene set, and establishing a rolling scheduling pre-optimization model, wherein an optimization objective function of the rolling scheduling pre-optimization model is as follows:
Figure FDA00032844086700000313
wherein N issAnd ρsRespectively representing the number of scenes and the weight of each scene; t is the total number of operating time periods of the virtual power plant; tau is the current running time point;
Figure FDA00032844086700000314
predicting the electricity price for the energy market under the scene s at the moment t;
Figure FDA00032844086700000315
predicting the electricity price for the rotating standby market under the scene s at the moment t;
Figure FDA00032844086700000316
the interactive active power of the virtual power plant and the energy market under the scene s at the moment t is obtained;
Figure FDA00032844086700000317
the interactive standby capacity of the virtual power plant and the auxiliary service market under the scene s at the moment t;
Figure FDA00032844086700000318
clearing active power for a superior energy market at time t;
Figure FDA00032844086700000319
spare capacity cleared for a superior auxiliary service market at time t;
Figure FDA00032844086700000320
the active output of the generator set i in a scene s at the moment t;
Figure FDA00032844086700000321
the standby capacity of the generator set i in a scene s at the moment t;
Figure FDA00032844086700000322
and
Figure FDA00032844086700000323
respectively outputting active power and inputting active power of the energy storage device i in a scene s at the moment t;
Figure FDA00032844086700000324
the standby capacity of the energy storage device i in a scene s at the moment t;
Figure FDA00032844086700000325
a deviation penalty coefficient corresponding to the deviation of the polymer active power from the energy market scheduling plan;
Figure FDA00032844086700000326
a deviation penalty coefficient corresponding to the deviation of the aggregate reserve capacity from the reserve market scheduling plan; n is a radical ofDGAnd NESSThe number of distributed generator sets and the number of energy storage devices in the polymer are respectively;
wherein, Fi DG(. and F)i ESSThe operating costs of the generator set i and the energy storage device i are respectively expressed by the following formulas:
Figure FDA0003284408670000041
Figure FDA0003284408670000042
wherein the content of the first and second substances,
Figure FDA0003284408670000043
rotating the standby calling probability for the time t; a isi、biAnd ciIs the operating cost factor of the generator; AP (Access Point)iIs the operating cost coefficient of the energy storage device;
decision variable packet in the optimization model
Figure FDA0003284408670000044
Figure FDA0003284408670000045
Step (ii) ofS21, adopting power flow equation model constraint, distributed generator set model constraint, renewable energy output constraint, energy storage model constraint, system rotation standby balance constraint model and temperature control load aggregator model constraint to constrain the rolling scheduling pre-optimization model, solving the rolling scheduling pre-optimization model, and obtaining reference power of each temperature control load aggregator
Figure FDA0003284408670000046
7. The method of claim 6, wherein in the step S21, further comprising:
step S210, realizing the power flow equation model constraint by adopting the following formula, wherein a linear power flow model is adopted to describe the active power flow balance and the reactive power flow balance in the virtual power plant:
Figure FDA0003284408670000047
Figure FDA0003284408670000048
Figure FDA0003284408670000049
Figure FDA00032844086700000410
Figure FDA00032844086700000411
Figure FDA00032844086700000412
wherein the content of the first and second substances,
Figure FDA00032844086700000413
NBthe total number of nodes in the virtual power plant; r isijAnd xijResistance and reactance between line i and line j, respectively; thetai、Vi、Pi、QiRespectively phase position, voltage amplitude value, injection active power and injection reactive power at a node i; thetaj、VjPhase and voltage amplitude at node j, respectively;
Figure FDA00032844086700000414
and
Figure FDA0003284408670000051
respectively representing the active power output of the wind power plant i and the photovoltaic power plant i in a scene s at the moment t;
Figure FDA0003284408670000052
and
Figure FDA0003284408670000053
respectively providing reactive power output of a wind power plant i, a photovoltaic power plant i and a generator set i in a scene s at the moment t;
Figure FDA0003284408670000054
the reference power in the scene s at the time t for the aggregation quotient k at the node i;
Figure FDA0003284408670000055
and
Figure FDA0003284408670000056
respectively an active load and a reactive load at the t moment at the node i; pijAnd QijBranch flow active power and branch flow reactive power between the node i and the node j are respectively;
Figure FDA0003284408670000057
the reference active power of the aggregation quotient k at the node i in a scene s at the moment t is obtained;
and the power flow and the node voltage of each branch are limited within a certain range:
Figure FDA0003284408670000058
wherein, PijAnd QijRespectively an active power flow and a reactive power flow between the node i and the node j; sij,NRated capacity for the transmission line between node i and node j; vi,maxAnd Vi,minRespectively an upper limit and a lower limit of the node voltage amplitude;
step S211, implementing distributed generator set model constraint by using the following formula, which at least includes: the unit is exerted power, is climbed and is exerted power, start and stop time duration:
Figure FDA0003284408670000059
Figure FDA00032844086700000510
Figure FDA00032844086700000511
Figure FDA00032844086700000512
wherein the content of the first and second substances,
Figure FDA00032844086700000513
and
Figure FDA00032844086700000514
respectively representing the upper and lower output limits of the generator set i;
Figure FDA00032844086700000515
the maximum climbing speed of the generator set i;
Figure FDA00032844086700000516
the rated capacity of the generator set i;
step S212, realizing the output constraint of the renewable energy source by adopting the following formula:
Figure FDA00032844086700000517
Figure FDA00032844086700000518
wherein the content of the first and second substances,
Figure FDA00032844086700000519
and
Figure FDA00032844086700000520
rated capacities of a wind power plant i and a photovoltaic power plant i are respectively set;
Figure FDA00032844086700000521
the predicted value of the wind power plant i at the moment t in the scene s is obtained;
Figure FDA00032844086700000522
the predicted value of the photovoltaic power station i at the moment t in the scene s is obtained;
step S213, implementing the energy storage model constraint by using the following formula:
Figure FDA00032844086700000523
Figure FDA00032844086700000524
Figure FDA00032844086700000525
Figure FDA00032844086700000526
wherein the content of the first and second substances,
Figure FDA00032844086700000527
and
Figure FDA00032844086700000528
the maximum input/output power and the minimum input/output power of the energy storage device i are respectively;
Figure FDA0003284408670000061
and
Figure FDA0003284408670000062
respectively an energy storage upper limit and an energy storage lower limit of the energy storage device i; etainAnd ηoutPower input and output energy conversion coefficients, respectively; λ is the energy dissipation coefficient of the stored energy;
step S214, implementing system rotation standby balance constraint by adopting the following formula:
Figure FDA0003284408670000063
wherein, | Δ RL|、
Figure FDA0003284408670000064
And
Figure FDA0003284408670000065
respectively the standby requirements of the polymer internal load, the wind power plant i and the photovoltaic power plant i;
step S215, realizing temperature control load aggregator model constraint by adopting the following formula:
Figure FDA0003284408670000066
Figure FDA0003284408670000067
Figure FDA0003284408670000068
wherein the content of the first and second substances,
Figure FDA0003284408670000069
is the reference indoor temperature state in scenario s at time t for the aggregate quotient k at node i.
8. The method of claim 7, wherein the step S3 further comprises:
reference power obtained by each aggregator according to virtual power plant pre-optimization steps
Figure FDA00032844086700000610
Making an operation plan of the internal temperature control load;
for a specific aggregation quotient k, the target of the de-aggregation enables the overall output power to follow the reference power track obtained in the pre-optimization step, and the number of start-stop times of each temperature control load is reduced, wherein a specific target function is as follows;
Figure FDA00032844086700000611
the weight between different optimization targets is adjusted by setting the value of the coefficient M in the objective function;
the power consumption of each temperature control load at each moment needs to meet the following requirement model, specifically the following requirement model;
Figure FDA00032844086700000612
Figure FDA00032844086700000613
Figure FDA00032844086700000614
solving the disaggregation model, and acquiring the actual power demand of the temperature control load according to the following formula:
Figure FDA00032844086700000615
wherein the content of the first and second substances,
Figure FDA00032844086700000616
the actual power requirement of the aggregation quotient k at the node i at the moment t is obtained; n is a radical ofAGG,iThe number of aggregators at node i.
9. The method of claim 8, wherein the step S4 further comprises:
decision variables in rolling scheduling optimization model of virtual power plant
Figure FDA0003284408670000071
Is replaced by a fixed value
Figure FDA0003284408670000072
Updating decision variables of the model to
Figure FDA0003284408670000073
Solving the rolling scheduling optimization model of the virtual power plant again to obtain the optimal solution of each decision variable, and calculating according to the weight of each scene
Figure FDA0003284408670000074
And finishing the final optimization of the rolling scheduling of the virtual power plant to obtain a final rolling scheduling plan scheme, wherein the final rolling scheduling plan scheme comprises a time-sharing scheduling plan of the renewable energy source unit, the distributed generator set and the energy storage equipment and a bidding plan for an upper-level power market.
CN202111142934.5A 2021-09-28 2021-09-28 Virtual power plant rolling scheduling technology considering temperature control load polymer Pending CN113937781A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111142934.5A CN113937781A (en) 2021-09-28 2021-09-28 Virtual power plant rolling scheduling technology considering temperature control load polymer

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111142934.5A CN113937781A (en) 2021-09-28 2021-09-28 Virtual power plant rolling scheduling technology considering temperature control load polymer

Publications (1)

Publication Number Publication Date
CN113937781A true CN113937781A (en) 2022-01-14

Family

ID=79277152

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111142934.5A Pending CN113937781A (en) 2021-09-28 2021-09-28 Virtual power plant rolling scheduling technology considering temperature control load polymer

Country Status (1)

Country Link
CN (1) CN113937781A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114825371A (en) * 2022-04-08 2022-07-29 四川大学 Aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation
CN117791627A (en) * 2024-02-26 2024-03-29 国网山东省电力公司东营供电公司 Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109902884A (en) * 2019-03-27 2019-06-18 合肥工业大学 A kind of virtual plant Optimization Scheduling based on leader-followers games strategy
CN112465208A (en) * 2020-11-20 2021-03-09 国网江苏省电力有限公司盐城供电分公司 Virtual power plant random self-adaptive robust optimization scheduling method considering block chain technology

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109902884A (en) * 2019-03-27 2019-06-18 合肥工业大学 A kind of virtual plant Optimization Scheduling based on leader-followers games strategy
CN112465208A (en) * 2020-11-20 2021-03-09 国网江苏省电力有限公司盐城供电分公司 Virtual power plant random self-adaptive robust optimization scheduling method considering block chain technology

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ZHONGKAI YI等: "A Multi-Time-Scale Economic Scheduling Strategy for Virtual Power Plant Based on Deferrable Loads Aggregation and Disaggregation", 《IEEE TRANSACTIONS ON SUSTAINABLE ENERGY》, vol. 11, no. 3, pages 1332, XP011794373, DOI: 10.1109/TSTE.2019.2924936 *
ZHONGKAI YI等: "Aggregate Operation Model for Numerous Small-Capacity Distributed Energy Resources Considering Uncertainty", 《IEEE》, pages 4208 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114825371A (en) * 2022-04-08 2022-07-29 四川大学 Aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation
CN114825371B (en) * 2022-04-08 2023-03-31 四川大学 Aggregation temperature control load multi-layer regulation and control method based on node voltage constraints before and after regulation
CN117791627A (en) * 2024-02-26 2024-03-29 国网山东省电力公司东营供电公司 Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant
CN117791627B (en) * 2024-02-26 2024-05-14 国网山东省电力公司东营供电公司 Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant

Similar Documents

Publication Publication Date Title
Yang et al. Day-ahead wind power forecasting based on the clustering of equivalent power curves
Hu et al. A new clustering approach for scenario reduction in multi-stochastic variable programming
CN111695793B (en) Method and system for evaluating energy utilization flexibility of comprehensive energy system
Ghadimi et al. PSO based fuzzy stochastic long-term model for deployment of distributed energy resources in distribution systems with several objectives
CN111709672A (en) Virtual power plant economic dispatching method based on scene and deep reinforcement learning
Fang et al. Deep reinforcement learning for scenario-based robust economic dispatch strategy in internet of energy
CN113937781A (en) Virtual power plant rolling scheduling technology considering temperature control load polymer
CN112131733A (en) Distributed power supply planning method considering influence of charging load of electric automobile
CN110518580A (en) A kind of active distribution network running optimizatin method for considering microgrid and actively optimizing
CN110852565B (en) Grid frame planning method considering different functional attributes
CN113541198A (en) Day-ahead scheduling method and device for active power distribution network, electronic equipment and storage medium
CN113794199A (en) Maximum profit optimization method of wind power energy storage system considering electric power market fluctuation
Guo et al. A short-term load forecasting model of LSTM neural network considering demand response
Elamine et al. Multi-agent system based on fuzzy control and prediction using NN for smart microgrid energy management
Wen et al. Optimal intra-day operations of behind-the-meter battery storage for primary frequency regulation provision: A hybrid lookahead method
CN114638124A (en) Power system optimization method and system and computer readable storage medium
He et al. Biobjective optimization-based frequency regulation of power grids with high-participated renewable energy and energy storage systems
CN114970986A (en) Distributed power supply and energy storage collaborative planning method based on Nash equilibrium
CN114358378A (en) User side energy storage optimal configuration system and method for considering demand management
CN113364043A (en) Micro-grid group optimization method based on condition risk value
Leonori et al. Anfis synthesis by clustering for microgrids ems design
Zhang et al. Research on peak and valley periods partition and distributed energy storage optimal allocation considering load characteristics of industrial park
CN117522177B (en) Smart power grid stability prediction method
Li et al. Federated Multi-agent Deep Reinforcement Learning for Multi-microgrid Energy Management
CN112448403B (en) Decoupling configuration method for energy storage of power distribution network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination