CN112186768A - Method and system for cooperatively scheduling AC/DC power distribution network with joint participation of MG, LA and DNO - Google Patents

Method and system for cooperatively scheduling AC/DC power distribution network with joint participation of MG, LA and DNO Download PDF

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CN112186768A
CN112186768A CN202011143851.3A CN202011143851A CN112186768A CN 112186768 A CN112186768 A CN 112186768A CN 202011143851 A CN202011143851 A CN 202011143851A CN 112186768 A CN112186768 A CN 112186768A
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electricity
distribution network
power
price
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CN112186768B (en
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尹斌鑫
苗世洪
张迪
赵海彭
林毓军
杨志豪
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Huazhong University of Science and Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/25—Design optimisation, verification or simulation using particle-based methods
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/02—Circuit arrangements for ac mains or ac distribution networks using a single network for simultaneous distribution of power at different frequencies; using a single network for simultaneous distribution of ac power and of dc power
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • H02J5/00—Circuit arrangements for transfer of electric power between ac networks and dc networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract

The invention discloses an AC/DC power distribution network cooperative scheduling method and system with MG, LA and DNO participating together, and belongs to the field of power distribution network optimized scheduling. The dynamic game mechanism of the alternating current-direct current power distribution network established by the invention reduces the pricing right of a power Distribution Network Operator (DNO), maintains the scheduling right of the DNO on the power distribution network, and can ensure the safe operation of the power distribution network while considering the economic benefits of other benefit subjects of the power distribution network. The initiative that other benefit subjects except DNO participate in scheduling is improved by the dynamic game mechanism, the consumption rate of the power distribution network to the distributed renewable energy sources is higher, the condition of power transmission to the main network is reduced, and the benefits of the general society of the power distribution network are improved. Under the dynamic game mechanism, all participants have the bargaining power related to the electricity price of the participants, the relevance between the income of each participant and the mastered resource amount of the participant is stronger, and the market fairness is improved.

Description

Method and system for cooperatively scheduling AC/DC power distribution network with joint participation of MG, LA and DNO
Technical Field
The invention belongs to the field of optimal scheduling of a power distribution network, and particularly relates to an AC/DC power distribution network cooperative scheduling method and system with joint participation of MG, LA and DNO.
Background
Compared with the traditional alternating current distribution network, the alternating current-direct current distribution network has the following advantages: the control of the power flow can be realized through the power electronic device; the direct current region has no synchronization problem of phase and frequency, and the power quality is higher; the distributed power supply is more flexible to access, and because rectification and inversion processes are omitted, the installation cost of the converter can be saved, and the energy loss is reduced. Meanwhile, with the maturity of power electronic technology, the advantages are more prominent, and the alternating current-direct current distribution network receives more and more attention.
In order to deal with the severe environmental pollution problem and meet the increasing electric Energy demand and power supply form demand of users, Distributed Renewable Energy Source (DRES) power generation and microgrid technology is rapidly developed, and more DRES-containing micro grids and communities thereof are connected to a power distribution network. The micro-grid technology is beneficial to improving the permeability of DRES and realizing the on-site production and consumption of energy. However, due to the non-schedulability of the DRES, in order to improve the consumption rate, scheduling resources such as power distribution network operator scheduling, demand response in the power distribution network, schedulable distributed power supplies and the like are required.
With the continuous deepening of the power system innovation, the power market in China is further opened, and after load aggregators, micro-grids and DNO of DR participate in the power market, the power distribution network scheduling is complicated: the interests of the participants in the power market are often inconsistent and even conflict with each other, and the traditional scheduling method under a single interest subject is no longer suitable for the power market of the novel power distribution network. The market behavior of the above benefit agents is related to the safety, economy and fairness of the power distribution network. Therefore, research on a joint cooperative scheduling method of multi-benefit main bodies of the alternating current/direct current power distribution network, which can guarantee reasonable benefits of each power market participant and the safety of the power distribution network, is urgently needed.
The 'active power distribution network complete information dynamic game behavior under participation of multi-benefit subjects' discloses an alternating current power distribution network complete information dynamic game strategy under participation of LA. In this document, it is pointed out that in dynamic gaming, the proactive party can first propose a policy favorable to itself, which has a strong proactive advantage, and an excessively strong proactive advantage may cause the result of the gaming to be seriously affected by the proactive advantage or the gaming to be in a state of being rigid. In order to ensure the fair and smooth game, sequential bargaining functions are proposed based on Weber-Fechner Law (W-F) Law in psychophysics to depict the mutual compromise process of participants, so that the coordinated dispatching of the alternating-current power distribution network considering the benefits of all participants is realized.
However, the above scheduling strategy is only suitable for coordinated scheduling of the ac distribution network with LA and DNO participation, and is not suitable for the case where MG, LA and DNO participate in the ac distribution network together. And the system dispatching right is dispersed, so that the dispatching right of the DNO to the AC/DC distribution network cannot be ensured, and the safe operation of the AC/DC distribution network cannot be ensured.
Disclosure of Invention
Aiming at the defects and improvement requirements of the prior art, the invention provides a method and a system for cooperatively scheduling an AC/DC power distribution network with MG, LA and DNO participating together, and aims to solve the problem that the scheduling method cannot be applied to the novel power distribution network power market under the traditional single-benefit main body because the benefits of market participants are inconsistent and even mutually conflict after LA, MG and DNO participate in the power market.
To achieve the above object, according to a first aspect of the present invention, there is provided a method for cooperatively scheduling an ac/dc power distribution network in which MG, LA, and DNO participate together, the method including the steps of:
s1, constructing a profit function and operation constraint of each benefit subject in an AC/DC power distribution network, wherein the types of the benefit subjects comprise: each benefit subject has bargaining right and only DNO has scheduling right;
s2, taking each benefit subject as a game participant, wherein the game participant comprises a bargaining participant and a plurality of decision participants, the bargaining participant has the right of making electricity purchasing/selling prices related to the bargaining participant, the decision participants have the right of negotiating the electricity purchasing/selling prices with the bargaining participant, electricity purchasing/selling price constraint in the DNO and MG interaction process and electricity price constraint in the DNO and LA interaction process are taken as game constraint, each benefit subject maximizes a game target by virtue of a self income function, an AC/DC distribution network complete information dynamic game model is constructed, the game electricity price is represented by the particle position, the income function value of each benefit subject is represented by the fitness of the particles, the game model is solved by adopting a dynamic game particle swarm algorithm, and the electricity price obtained by the game is output;
s3, taking the electricity price obtained by the game as the input of a scheduling model, taking the profit function maximization of DNO as a scheduling target, taking the operation constraint of MG and DNO as a scheduling constraint, establishing the scheduling model, and solving the scheduling model by adopting linear programming to obtain a scheduling scheme and the profit function values of all benefit subjects;
and S4, sequentially changing the bargaining participants, repeating the steps S2-S3 until all game participants participate in bargaining, all decision participants agree to the current power price and scheduling mode, and the income function deviation ratio of each participant between the last two games is smaller than a set threshold value, so that the final power price and scheduling result is obtained.
Preferably, the gaming process is as follows:
firstly, DNO is used as a bargaining participant, and other participants become the bargaining participants in sequence;
in each game, the DNO makes and releases the operation mode of the power distribution network according to the electricity price made by the current bargained participants on the premise of ensuring the power supply and safe operation of all participants; and other participants calculate own income functions according to the issued operation mode of the power distribution network and decide whether to accept the electricity price and the operation mode.
Preferably, the price for buying/selling electricity during the interaction between the DNO and the MG is restricted, including:
1) the electricity price of the DNO for purchasing electricity to each MG is larger than that of the MG for purchasing electricity to the DRES (distributed renewable energy source) thereof
Figure BDA0002737271950000031
To protect the benefit of MG and is less than the price of DNO purchasing electricity from main network
Figure BDA0002737271950000032
To protect the benefits of DNO, the following constraints apply:
Figure BDA0002737271950000033
2) the electricity price for selling electricity from DNO to MG is greater than that for selling electricity to main network
Figure BDA0002737271950000034
To protect the income of DNO and at the same time, the price of electricity is less than that for selling electricity to the conventional load
Figure BDA0002737271950000035
To protect the gain of the MG, there are the following constraints:
Figure BDA0002737271950000036
preferably, the electricity price during DNO and LA interactions is constrained:
the electricity price for selling the electricity from the DNO to the LA is larger than that for selling the electricity to the main network
Figure BDA0002737271950000041
At the same time, the electricity is sold to a conventional loadElectricity price of
Figure BDA0002737271950000042
To protect the revenue of both parties, there are the following constraints:
Figure BDA0002737271950000043
preferably, the benefit agent MG is divided into an alternating current MG and a direct current MG, which are respectively connected to an alternating current region and a direct current region of an alternating current-direct current power distribution network, purchase power for distributed renewable energy sources in the MG, supply power for loads in the MG, and interact with DNO;
the game profit function for the MG is expressed as follows:
Figure BDA0002737271950000044
in the formula, rMG,mAnd cMG,mRespectively obtaining the income of the mth MG and the equivalent operation energy consumption, wherein the income of the mth MG comprises the income of the MG for selling electricity to the DNO and the load in the microgrid, and the equivalent social energy consumption of the MG for purchasing electricity from the DNO and the DRES in the microgrid;
Figure BDA0002737271950000045
and
Figure BDA0002737271950000046
the load size and the electricity price in the mth MG respectively;
Figure BDA0002737271950000047
and
Figure BDA0002737271950000048
and respectively purchasing power and electricity prices from the mth MG to the DRES in the microgrid.
Preferably, the operational constraints of the benefit agent MG comprise: 1) a power balance constraint; 2) and (5) purchasing power to DRES.
Preferably, the gaming benefit function of the DNO is expressed as follows:
BDNO＝rDNO-cDNO
in the formula, rDNOAnd cDNORespectively the income and the operation energy consumption of DNO;
rDNOthe calculation formula of (a) is as follows:
Figure BDA0002737271950000049
wherein t is a time period; NT and delta T are the number of the scheduling time segments and the scheduling time length respectively;
Figure BDA00027372719500000410
and
Figure BDA00027372719500000411
respectively carrying out conventional load size and electricity price;
Figure BDA00027372719500000412
and
Figure BDA00027372719500000413
respectively the load size and the electricity selling price of the load aggregator; NMG is the number of piconets;
Figure BDA00027372719500000414
and
Figure BDA00027372719500000415
the DNO respectively sells electricity quantity and price to the mth MG; pt GsAnd
Figure BDA0002737271950000051
the electric quantity and the price of the sold electricity are respectively sold to the main network by the DNO;
cDNOthe calculation formula of (a) is as follows:
Figure BDA0002737271950000052
in the formula (I), the compound is shown in the specification,
Figure BDA0002737271950000053
and Pt GbRespectively purchasing electric quantity and electricity purchasing price from the main network for DNO;
Figure BDA0002737271950000054
and
Figure BDA0002737271950000055
respectively purchasing electricity quantity and electricity price from the DNO to the mth MG; c (P)t DDG) The power generation energy consumption of the DDG and the output P of the DDGt DDGIt is related.
Preferably, the operational constraints of the benefit agent DNO include: 1) the branch load flow and the node voltage of the alternating-current power distribution network are constrained; 2) the branch power flow and the node voltage of the direct-current power distribution network are constrained; 3) VSC operation constraints; 4) the DNOs exchange power constraints with the primary and micro networks.
Preferably, the game profit function for the LA is expressed as follows:
Figure BDA0002737271950000056
in the formula (I), the compound is shown in the specification,
Figure BDA0002737271950000057
for LA, the flexible load in the t period is
Figure BDA0002737271950000058
The function value of the electricity utilization benefit is the actual electricity consumption LDRThe area of the shaded portion enclosed by the marginal utility.
To achieve the above object, according to a second aspect of the present invention, there is provided a cooperative dispatching system for an ac/dc distribution network in which MG, LA and DNO participate together, including: a computer-readable storage medium and a processor;
the computer-readable storage medium is used for storing executable instructions;
the processor is configured to read an executable instruction stored in the computer-readable storage medium, and execute the method for cooperatively scheduling the ac/dc power distribution network in which the MG, the LA, and the DNO participate together in the first aspect.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
(1) the dynamic game mechanism of the alternating current-direct current power distribution network established by the invention reduces the pricing right of a power Distribution Network Operator (DNO), maintains the scheduling right of the DNO on the power distribution network, and can ensure the safe operation of the power distribution network while considering the economic benefits of other benefit subjects of the power distribution network.
(2) The initiative that other benefit subjects except DNO participate in scheduling is improved through the dynamic game mechanism, the consumption rate of a power distribution network to Distributed Renewable Energy Sources (DRES) is higher, the condition of power transmission to a main network is reduced, and the benefit of a general society of the power distribution network is improved.
(3) Under a dynamic game mechanism, all participants have the bargaining right of the electricity price related to the participants, the relevance of the income of each participant and the mastered resource quantity (such as renewable energy sources of a micro-grid and flexible load of a load aggregator) of each participant is stronger, and the market fairness is improved.
Drawings
FIG. 1 is a schematic diagram of an AC/DC distribution network according to the present invention;
FIG. 2 is an equivalent circuit diagram of a voltage source converter provided by the present invention;
FIG. 3 is a graph of the marginal utility function of the load aggregator provided by the present invention;
FIG. 4 is a flow chart for solving a dynamic gaming problem provided by the present invention;
FIG. 5 is a diagram of a simulation system architecture provided by the present invention;
FIG. 6 is a wind power, photovoltaic, load graph provided by the present invention;
fig. 7(a) is a scheduling result diagram of the fixed electricity price situation provided by the present embodiment;
fig. 7(b) is a scheduling result diagram of the DNO pricing electricity situation provided by the present embodiment;
fig. 7(c) is a scheduling result diagram under the dynamic gaming mechanism provided by the present embodiment;
fig. 8 is a diagram of a distributed renewable energy consumption result under three scheduling mechanisms provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention concept of the invention is as follows: and determining the income function of each benefit subject in the AC/DC power distribution network on the basis of analyzing rights and interests, responsibilities and schedulable resources of each benefit subject in the AC/DC power distribution network. And then, establishing an operation constraint model of the alternating current-direct current power distribution network, which considers scheduling resources such as demand side response, a controllable distributed power source, a voltage source converter and the like. In order to solve the problem of interest relationship of all benefit agents, a complete information dynamic game mechanism which disperses DNO pricing rights into the bargaining rights of all benefit agents and reserves DNO scheduling rights is designed. And finally, solving the dynamic game problem by adopting a method combining a dynamic game particle swarm algorithm and linear programming. The cooperative scheduling mechanism can improve the market fairness, the system economy and the environmental protection of the power distribution network.
The invention provides a method for cooperatively scheduling an AC/DC power distribution network with MG, LA and DNO participating together, which comprises the following steps:
s1, constructing a profit function and operation constraint of each benefit subject in the AC/DC power distribution network, wherein the types of the benefit subjects comprise: each benefit agent of the micro-grid MG, the load aggregation provider LA and the distribution network operator DNO has the bargaining right, and only the DNO has the scheduling right.
As shown in fig. 1, the main market of ac/dc distribution networks is mainly divided into four categories:
a Distribution Network Operator (DNO) is responsible for daily scheduling, operation and maintenance of a distribution Network, supplies power to conventional loads in the jurisdiction of the distribution Network, and interacts with a superior power grid and other main bodies connected to the distribution Network. Schedulable resources for DNO mainly include: the Voltage Source Converter (VSC) can adjust the distributed power source, the exchange power with the upper-level power grid, the power for purchasing power to each micro-grid, and the like.
And a Micro Grid (MG) is divided into an alternating current Micro Grid and a direct current Micro Grid, which are respectively connected to an alternating current area and a direct current area of an alternating current-direct current power distribution network, purchase power for distributed renewable energy sources in the MG, supply power for loads in the MG and interact with DNO.
And thirdly, a Load Aggregator (LA) aggregates a large amount of adjustable flexible Load resources to represent that the Load Aggregator interacts with the DNO to participate in the bidding competition of the power market. Schedulable resources of LA mainly include: polymeric industrial, commercial, etc. types of flexible loads.
And fourthly, the traditional non-dispatchable load (IL) can be divided into a conventional load accessed to the power distribution network and a microgrid load accessed to the MG, and the conventional load and the microgrid load are respectively powered by the DNO and the MG.
Different from the prior art, the invention disperses the pricing rights of the power distribution network operators into the bargaining rights of all benefit subjects, and simultaneously reserves the complete information dynamic game mechanism of the scheduling rights of the power distribution network operators.
Gain function and operational constraints for MG
The MG purchases power from the DRES, supplies power to the loads in the microgrid, and sells excess power to the DNO, or purchases the shortage of power from the DNO when the DRES is under-producing power. The decision variable of the MG is the price of electricity purchased/sold to the DNO. The revenue function of the MG is its net profit, and the game revenue function of the MG is expressed as follows:
Figure BDA0002737271950000081
in the formula, rMG,mAnd cMG,mRespectively obtaining the income of the mth MG and the equivalent operation energy consumption, wherein the income of the mth MG comprises the income of the MG for selling electricity to the DNO and the load in the microgrid, and the equivalent social energy consumption of the MG for purchasing electricity from the DNO and the DRES in the microgrid;
Figure BDA0002737271950000082
and
Figure BDA0002737271950000083
the load size and the electricity price in the mth MG respectively;
Figure BDA0002737271950000084
and
Figure BDA0002737271950000085
and respectively purchasing power and electricity prices from the mth MG to the DRES in the microgrid.
The MG is divided into an alternating current MG and a direct current MG, which are respectively connected to an alternating current area and a direct current area of an alternating current and direct current power distribution network, purchase power for distributed renewable energy sources in the MG, supply power for loads in the MG and interact with DNO. Preferably, the operational constraints of the benefit agent MG comprise: 1) a power balance constraint; 2) the power purchase constraint to DRES is as follows:
Figure BDA0002737271950000086
in the formula (I), the compound is shown in the specification,
Figure BDA0002737271950000087
the maximum output of DRES of the mth MG in the period t.
Gaming function and run constraints for DNO
Preferably, the gaming benefit function of the DNO is expressed as follows:
BDNO＝rDNO-cDNO
in the formula, rDNOAnd cDNORespectively the income and the operation energy consumption of DNO;
rDNOthe method mainly comprises the income of selling electricity to loads in a main network, a micro-grid and a power distribution network, and the calculation formula is as follows:
Figure BDA0002737271950000091
wherein t is a time period; NT and delta T are the number of the scheduling time segments and the scheduling time length respectively;
Figure BDA0002737271950000092
and
Figure BDA0002737271950000093
respectively carrying out conventional load size and electricity price;
Figure BDA0002737271950000094
and
Figure BDA0002737271950000095
respectively the load size and the electricity selling price of the load aggregator; NMG is the number of piconets;
Figure BDA0002737271950000096
and
Figure BDA0002737271950000097
the DNO respectively sells electricity quantity and price to the mth MG; pt GsAnd
Figure BDA0002737271950000098
the electric quantity and the price of the sold electricity are respectively sold to the main network by the DNO;
cDNOthe method mainly comprises equivalent social energy consumption for purchasing electricity from a main network and a micro network and operation energy consumption of DDG, and the calculation formula is as follows:
Figure BDA0002737271950000099
in the formula (I), the compound is shown in the specification,
Figure BDA00027372719500000910
and Pt GbRespectively purchasing electric quantity and electricity purchasing price from the main network for DNO;
Figure BDA00027372719500000911
and
Figure BDA00027372719500000912
respectively DNO toThe electricity purchasing quantity and the electricity purchasing price of the m MGs are obtained; c (P)t DDG) The power generation energy consumption of the DDG and the output P of the DDGt DDGIt is related.
Preferably, the operational constraints of the DNO include: 1) the branch load flow and the node voltage of the alternating-current power distribution network are constrained; 2) the branch power flow and the node voltage of the direct-current power distribution network are constrained; 3) VSC operation constraints; 4) the DNOs exchange power constraints with the primary and micro networks.
(1) AC distribution network branch tide and node voltage constraint
The branch flow and the node voltage are calculated by adopting a linear Disflow flow model, which is shown as the following formula.
Figure BDA0002737271950000101
In the formula, Pt ijAnd
Figure BDA0002737271950000102
the active power and the reactive power of the branch ij are respectively;
Figure BDA0002737271950000103
and
Figure BDA0002737271950000104
respectively the active power and the reactive power of the conventional load at the node j;
Figure BDA0002737271950000105
and
Figure BDA0002737271950000106
respectively the active and reactive power of the LA at node j;
Figure BDA0002737271950000107
and
Figure BDA0002737271950000108
the active power and the reactive power of the DNO selling electricity to the MG at the node j are respectively, and the magnitude of the reactive power and the negative in the MGThe power factor of the charge is related;
Figure BDA0002737271950000109
and
Figure BDA00027372719500001010
respectively the active power and the reactive power of the DNO to purchase electricity to the MG at the node j, wherein the reactive power of the mth MG
Figure BDA00027372719500001011
And its active power
Figure BDA00027372719500001012
And power factor angle of distributed power supply
Figure BDA00027372719500001013
(ii) related;
Figure BDA00027372719500001014
and
Figure BDA00027372719500001015
the active output and the reactive output of the DDG at the joint j are respectively; vt iThe effective value of the line voltage of the alternating current node i; r isijAnd xijThe resistance and reactance of branch ij, respectively; vacAnd 0 is the rated voltage of the alternating-current distribution network.
The current-carrying capacity constraint and the voltage upper and lower limit constraint of the alternating current branch are shown as the following formula.
Figure BDA00027372719500001016
In the formula (I), the compound is shown in the specification,
Figure BDA00027372719500001017
the upper limit of the current-carrying capacity of the alternating current branch ij;
Figure BDA00027372719500001018
and
Figure BDA00027372719500001019
respectively, the upper and lower voltage limits of the ac node i.
The ampacity constraint of formula (la) is the inside of a circle, and the equivalence can be approximated by inscribed regular dodecagons of the circle, thereby converting into the following linear constraint:
Figure BDA00027372719500001020
in the formula, alphad、βdAnddfor the current-carrying capacity constraint linearization coefficient, the value is as follows:
TABLE 1 ampacity constraint linearization coefficients
Figure BDA00027372719500001021
Figure BDA0002737271950000111
(2) Direct current distribution network branch tide and node voltage constraint
The constraints of a direct current distribution network are as follows:
Figure BDA0002737271950000112
in the formula, Pt dc,ijThe active power of the direct current branch ij;
Figure BDA0002737271950000113
the active power of the direct current conventional load at the node j;
Figure BDA0002737271950000114
and
Figure BDA0002737271950000115
power for purchasing and selling electricity from the DNO to the direct current MG at the direct current node j respectively;
Figure BDA0002737271950000116
is the voltage at dc node i; vdc,0The rated voltage of the direct current distribution network.
The current-carrying capacity constraint and the voltage upper and lower limits constraint of the direct current branch are as follows:
Figure BDA0002737271950000117
in the formula (I), the compound is shown in the specification,
Figure BDA0002737271950000118
the upper limit of the current-carrying capacity of the direct current branch ij;
Figure BDA0002737271950000119
and
Figure BDA00027372719500001110
respectively, the upper and lower voltage limits of the dc node i.
(3) VSC model
The VSC model is shown in figure 2. Wherein, Vt iThe voltage of a connection point between the nth VSC and the node i of the alternating current distribution network;
Figure BDA00027372719500001111
is the ac side voltage of the VSC located at node i; zVSC,nIs the equivalent impedance of the VSC for equivalent filters and internal losses;
Figure BDA00027372719500001112
is the complex power flowing into the VSC; pt dc,iIs the dc power from the VSC; vt dc,iIs the dc voltage output by the VSC.
Through the equivalent circuit conversion, the power flow of the VSC can be equivalent to the impedance ZVSC,nThe alternating current branch power flow model does not write branch power flow constraint of the VSC independently.
The loss of the VSC is already at the reactance ZVSC,nEquivalent in terms of above, thus influx of VSCThe dc active power is equal to the dc active power out of the VSC, and thus has the following formula.
Figure BDA0002737271950000121
In the formula (I), the compound is shown in the specification,
Figure BDA0002737271950000122
is the ac active power flowing into the VSC.
The VSC ac side voltage is related to the dc side voltage as follows:
Figure BDA0002737271950000123
wherein mu is the DC voltage utilization rate, and mu is obtained when the modulation mode is SPWM
Figure BDA0002737271950000124
Mn,tFor the modulation degree of the nth VSC, the value range is as follows: m is not less than 0n,t≤1。
The above equation may be equivalent to the following constraint:
Figure BDA0002737271950000125
the upper and lower limits of the power and voltage of the VSC are constrained as follows:
Figure BDA0002737271950000126
in the formula (I), the compound is shown in the specification,
Figure BDA0002737271950000127
and
Figure BDA0002737271950000128
the upper and lower limits of the alternating-current side voltage of the nth VSC are respectively positioned at the node i;
Figure BDA0002737271950000129
and
Figure BDA00027372719500001210
respectively the reactive power flowing into the VSC in the t period and the upper limit and the lower limit of reactive regulation of the VSC;
Figure BDA00027372719500001211
the upper limit of the current-carrying capacity of the VSC.
The ampacity constraints of the VSC are similar to the ampacity constraints of the ac branch.
(4) Restriction of electricity purchase/sale
The DNO exchange power with the main network and the microgrid is constrained as follows:
Figure BDA0002737271950000131
in the formula (I), the compound is shown in the specification,
Figure BDA0002737271950000132
and
Figure BDA0002737271950000133
respectively representing the maximum value of the DNO for purchasing and selling electricity from the main network;
Figure BDA0002737271950000134
and
Figure BDA0002737271950000135
the maximum values of the purchase and sale of electricity from DNO to MG are respectively shown.
Revenue function and operational constraints of LA
The area of a shadow part enclosed by the actual power consumption LDR and the marginal utility is the actual power utilization benefit of the LA
Figure BDA0002737271950000136
The expenditure of LA is the user paying the electricity fee, the revenue function B of LALAGiving it a net profit. Preferably, the game profit function for the LA is expressed as follows:
Figure BDA0002737271950000137
in the formula (I), the compound is shown in the specification,
Figure BDA0002737271950000138
for LA, the flexible load in the t period is
Figure BDA0002737271950000139
The power utilization benefit function value is the shadow area enclosed by the actual power consumption LDR and the marginal utility.
And the beneficial agent LA aggregates a large amount of adjustable flexible load resources, interacts with DNO on behalf of the beneficial agent LA, participates in electric power market bidding competition, and the schedulable resources of the LA mainly comprise various types of flexible loads such as aggregated industry and commerce. And describing the profit of the LA by adopting a utility function model which can represent the influence condition of the electricity price change on the electric energy demand and can calculate the user electricity profit. And further selecting a trapezoidal marginal utility function model capable of better determining the optimal power consumption as the LA model in the utility function. The trapezoidal marginal utility function model is shown in fig. 3.
The models of the gain function, the constraint and the like of the benefit agents show that: the interests are not uniform among the subjects. The invention adopts the game theory to solve the benefit distribution problem. The game theory is a new branch of modern mathematics, and is mainly used for researching how each decision principal makes a strategy beneficial to the decision principal or a group according to the information mastered by the decision principal and the situation of the decision principal when benefit association or conflict exists between different decision principals in the same system.
And S2, taking each benefit subject as a game participant, wherein the game participant comprises a bargaining participant and a plurality of decision participants, the bargaining participant has the right of making electricity purchasing/selling prices related to the bargaining participant, the decision participants have the right of negotiating the electricity purchasing/selling prices with the bargaining participant, electricity purchasing/selling price constraint in the DNO and MG interaction process and electricity price constraint in the DNO and LA interaction process are taken as game constraint, each benefit subject maximizes the income function thereof as a game target, an AC/DC distribution network complete information dynamic game model is constructed, the particle position represents the game electricity price, the fitness of the particles represents the income function value of each benefit subject, and the game model is solved by adopting a dynamic game particle swarm algorithm to obtain the electricity price obtained by the game.
According to the method, on the basis of analyzing rights and interests, responsibilities and schedulable resources of all benefit subjects in the AC/DC power distribution network, a profit function of multi-benefit subjects in the AC/DC power distribution network is obtained, and an operation constraint model of the multi-benefit subjects of the AC/DC power distribution network is established, wherein the operation constraint model takes the demand side response, the controllable distributed power supply, the voltage source converter and other scheduling resources into consideration.
Generally, a game includes three elements: decision-making participants, policies and revenue functions. According to the understanding degree of the participants to other participants, the full information game and the incomplete information game can be divided. The complete information game means that each participant completely masters the characteristics, strategies and gain function information of all other participants; the incomplete information game refers to a situation in which the participant does not completely grasp the above information. From the time sequence of the game participant decision, the game can be divided into static game and dynamic game. Wherein, the static game refers to that game participants make decisions at the same time, or the later decision-maker does not master the strategy of the former decision-maker although not at the same time; in contrast, dynamic game refers to the fact that the decision behaviors of participants have a sequence, and a later decision maker grasps the historical information of game and can optimize own strategy according to the information.
In the game model of the AC/DC power distribution network, people including DNO, MG and LA participate. Assuming that the participators have rationality, the participators all master all strategies and income information of all other participators with the maximum goal of self income.
For ease of description, a participant who proposes a new electricity rate is defined as an artificial bargaining participant, and the other participants decide whether to accept the new electricity rate proposal or not, which is called a decision participant. Since the DNO is responsible for scheduling operation of the entire distribution network, the DNO is first used as a bargaining participant to establish electricity prices for buying/selling electricity with all participants. The other participants become bargained participants in sequence, and negotiate their own price for electricity purchased/sold with the DNO.
In each round of game, the DNO makes and releases the operation mode of the power distribution network according to the electricity price made by the current bargained participants on the premise of ensuring the power supply and safe operation of all participants. Other participators can calculate own income function according to the issued operation mode of the power distribution network and decide whether to accept the electricity price and the operation mode. In conclusion, the invention establishes a complete information dynamic game model of the multi-benefit subject of the AC/DC power distribution network.
Preferably, the gaming process is as follows:
firstly, DNO is used as a bargaining participant, and other participants become the bargaining participants in sequence, the bargaining participant has the right of making the electricity price for buying/selling electricity with all participants, and the bargaining participant has the right of bargaining own electricity price for buying/selling electricity with the DNO;
in each game, the DNO makes and releases the operation mode of the power distribution network according to the electricity price made by the current bargained participants on the premise of ensuring the power supply and safe operation of all participants; and other participants calculate own income functions according to the issued operation mode of the power distribution network and decide whether to accept the electricity price and the operation mode.
Preferably, the method for restricting the price of electricity purchased/sold in the interaction process of the DNO and the MG so as to protect the fairness of the benefit of both parties and the electricity market comprises the following steps:
1) the electricity price of the DNO for purchasing electricity to each MG is larger than that of the MG for purchasing electricity to the DRES (distributed renewable energy source) thereof
Figure BDA0002737271950000151
To protect the benefit of MG and is less than the price of DNO purchasing electricity from main network
Figure BDA0002737271950000152
To protect the benefits of DNO, the following constraints apply:
Figure BDA0002737271950000153
2) selling electricity from DNO to MGThe electricity price of the main network is larger than that of electricity sold to the main network
Figure BDA0002737271950000154
To protect the income of DNO and at the same time, the price of electricity is less than that for selling electricity to the conventional load
Figure BDA0002737271950000155
To protect the gain of the MG, there are the following constraints:
Figure BDA0002737271950000156
preferably, in order to protect the benefit of both parties and the fairness of the power market, the electricity price in the DNO and LA interaction process is constrained:
the electricity price for selling the electricity from the DNO to the LA is larger than that for selling the electricity to the main network
Figure BDA0002737271950000157
At the same time, the price of electricity is less than that of electricity sold to a conventional load
Figure BDA0002737271950000161
To protect the revenue of both parties, there are the following constraints:
Figure BDA0002737271950000162
in an objective function of the optimization model, energy consumption cost and income of each subject are considered; in the operation constraint, the power consumption requirements of each main body, safety requirements, the operation range of the schedulable resource and other limitations are considered. In order to protect the reasonable benefits of all benefit subjects from being damaged in the interaction process of the alternating current-direct current power distribution network with the micro-grid and the load aggregator, the range of game variables, namely electricity prices, is reasonably limited, and the electricity prices proposed by a certain benefit subject can not cause other benefit subjects to fall into a loss state or give up purchasing electricity when the benefit subjects play electricity price games. On one hand, the method is used for ensuring that the reasonable benefits of all benefit subjects are not damaged in the electricity price game process, and on the other hand, the method prevents the electricity price provided by one party from excessively influencing the benefits of other subjects to cause high-probability rejection of other subjects, thereby influencing the convergence of the game process.
The method comprises the steps that the Particle position and the objective function of a Dynamic Game Particle Swarm Optimization (DGPSO) are adopted to reflect the strategy and the income function of each participant, and the process of 'bargaining and counter-pricing' of Game participants in the actual process is simulated through the change of the Particle position and the objective function; the problem of optimal operation of the power distribution network after the game variable, namely the electricity price is determined, is processed by a linear programming method, so that a method of combining the DGPSO and the linear programming method is adopted.
In the game problem researched by the invention, DNO is used as a proactive party in a dynamic game and has proactive advantages. Meanwhile, the DNO is also responsible for formulating the running mode of the alternating current-direct current distribution network, so that the income functions of other game participants are further influenced, and the DNO has strong advantages in the game. Faced with this situation, the other participants might make the following 2 choices: firstly, accepting a DNO dominance decision, so that the game result is biased to DNO, and the game fairness can not be ensured; and secondly, a DNO (digital network operator) domination decision is rejected, the game is possibly in a rigid state at the moment, an alternating current-direct current distribution network operation scheme with uniform opinions is difficult to form, information blockage and scheduling command delay of the distribution network are caused, and the safe operation of the power grid is damaged.
In order to guarantee the fairness of the game and promote the game to be smoothly carried out, each game participant needs to make certain compromise and concession without seriously damaging the interests of the game participant. The method adopts a sequential bargaining function based on the psychophysics Weber-Fisher law to describe the process of judging whether the decision-making participants give way or not and receiving the strategy proposed by the bargaining participants.
The probability that a decision-making participant rejects the proposal offered by the bargaining participant is related to the amount of revenue reduction of the decision-making participant. And when the yield reduction amount of the decision-making participants is less than the minimal perceivable difference, the decision-making participants accept a new strategy proposed by the bargaining participants. As the amount of revenue reduction increases, the probability of a decision-making participant rejecting a proposed strategy by an bargaining participant increases. The decision participant refuses the proposed policy probability of the bargaining participant as a function of:
Figure BDA0002737271950000171
wherein, P (I)j) Probability of rejecting the proposed policy of the current bargained participant for decision participant j; k is the Weber coefficient; s0Is the stimulation constant; i isjRepresenting the amount of objective stimulation, referred to in the model herein as decision participant revenue reduction, IMTo minimize the perceived difference, the two are calculated as follows:
Figure BDA0002737271950000172
in the formula II0An original revenue function value for decision participant j; i'jAnd (4) deciding the profit function value of the participator under a new strategy provided for the bargaining participator.
The particle position and the objective function of the particle swarm optimization can reflect the strategy and the gain function of each participant, and the change of the particle position and the objective function can simulate the process of bargaining and counter-charging of game participants in the actual process.
It should be noted that, by adopting the manner that each participant judges whether to reject each particle, the situation that the current generation of particles is rejected as a whole can be reduced, and the game efficiency can be improved. However, when the number of particles is too large, the probability that the scheme with a high rejection probability is accepted increases, and the game convergence speed may be slow. Therefore, the particle number needs to be set reasonably according to the game situation.
And S3, taking the electricity price obtained by the game as the input of a scheduling model, taking the profit function maximization of DNO as a scheduling target, taking the operation constraint of MG and DNO as a scheduling constraint, establishing the scheduling model, and solving the scheduling model by adopting linear programming to obtain a scheduling scheme and the profit function values of all benefit subjects.
In the dynamic game problem, after the game variable, namely the electricity price, is determined, the optimized operation problem of the power distribution network becomes a linearization problem, so that the game problem can be solved by adopting a method of combining a dynamic game particle swarm optimization algorithm and linear programming. The specific solving process is as follows:
1) inputting system parameters, including: and (4) predicting the conventional load of each node, the load in MG and the DRES output in the AC/DC distribution network. DNO is set as the initial bargaining participant, MG 1-n and LA are set as the decision participants.
2) And initializing a particle position and a particle speed, wherein the particle position corresponds to the electricity price, and the particle speed corresponds to the variation of the electricity price.
3) And (4) processing constraint conditions of particle position and velocity.
4) And solving a dispatching operation mode of the AC/DC distribution network, and determining the income function of each participant (including bargaining participants and decision participants) according to a dispatching result. In addition, the optimal value in each participant profit function obtained from the initialized particle positions is recorded as the initial global optimal value BG of each participant profit functionx,0X is 1,2, 3.., n +2(x is the number of game participants).
5) And calculating the rejection probability of each decision participant to the current operation scheme through the sequential bargaining function. Wherein the original income function value adopts the global optimum value BG of the income function of each participant recorded in the previous generation cycle (y-1)x,y-1When y is 1, the original gain function value adopts BG of step 4)x,0。
6) And determining whether the decision participant refuses the proposal proposed by the bargaining participant according to the refusing probability of the step 5) through sampling. If all particle recorded solutions are rejected, return to step 3). Otherwise, the rejected solution is discarded and the accepted solution is recorded.
7) And updating the global and individual optimal solutions (including the optimal positions and the corresponding optimal objective function values) according to the scheme recorded in the step 6).
8) It is determined whether all participants have proposed their own policy. If not, the bargaining participator is replaced according to the sequence of DNO → MG 1-n → LA, the particle position and speed are updated, and then the step 3) is returned to.
9) And comparing the optimization result with the previous time, and if the deviation ratio of the gain function of each participant is smaller than that of the previous time, ending the game. If not, replacing the bargaining participator, updating the position and the speed of the particles, and returning to the step 3).
When the deviation ratios of the revenue functions of all participants are smaller than each other, the current scheme can be considered as the optimal response of each participant to other participant strategies, namely achieving nash equilibrium. The algorithm flow is shown in fig. 4.
And S4, sequentially changing bargaining participants, repeating the steps S2-S3 until all game participants participate in bargaining, all decision participants agree with the current power price and scheduling mode, and the income function deviation ratio of each participant between the last two games is smaller than a set threshold value, so that the final power price and scheduling result is obtained.
In the dynamic game process designed by the invention, after game variables are determined, the optimal scheduling problem of the alternating current-direct current power distribution network is a mixed integer linear programming problem, and the optimal scheduling strategy is formulated by a power distribution network operator (in order to protect the scheduling weight of the power distribution network operator and the safe operation of the power distribution network), so that the optimal scheduling strategy can be solved by adopting a linear programming method. The optimized scheduling strategy obtained by solving can directly reflect the income functions of all the subjects, so that the method can be directly applied to the game process. In conclusion, the dynamic game process and the mixed integer linear programming problem solving process realize respective solving and can interact smoothly.
In the following, a 49-node system as shown in fig. 5 is obtained by modification. An alternating current MG is connected at nodes 9 and 26, a LA is connected at node 28, and a direct current MG is connected at a direct current node 47.
Before formally describing the embodiments, some parameters that can be directly obtained and specific values in the present invention are described in the embodiments. The selection and setting of parameters for the 49-node system are described as follows:
1) wind power and photovoltaic are respectively selected as DRES of the alternating current micro-grid and the direct current micro-grid. Because the area of the distribution network is limited, the same wind power and photovoltaic output curves are adopted, and meanwhile, in order to highlight the main problem, the per unit value curves of the conventional load and the microgrid load in the system are assumed to be the same. The curves of wind power, photovoltaic output and load are shown in fig. 6.
2) The ac line parameters and ac load peak values are shown in tables 2 and 3, respectively; the dc line parameters and the dc load peak values are shown in tables 4 and 5, respectively; DRES output peak values of MG are respectively 4MW, 4MW and 1 MW; the peak load values in MG were 0.8MW, 0.8MW, 0.4MW, respectively.
3) The installed 4 VSC parameters were identical: rated capacity 2MVA, VSC impedance Z ═ 0.5+ j1.5) omega, reactive compensation interval [ -300,300] kvar.
4) The voltage grade of the alternating current distribution network is 12.66kV, and the voltage grade of the direct current distribution network is +/-10 kV.
5) The node voltage safety range of the AC/DC power distribution network is as follows: [0.95,1.05] (per unit value); the maximum current-carrying capacity of the alternating current branch is 6 MVA; the maximum current-carrying capacity of the direct current branch is 1 MW.
6) The DNO uses node 0 as an interface to perform power exchange with the main network, and the power exchange range is as follows: [ -6,6] MW, positive with main network flow to distribution network.
7) The upper and lower output limits of DDG are respectively 2.5MW and 0.5MW, and the maximum value of the output complex power is 4 MVA.
7) The values of the fixed electricity prices and the ranges of the variable electricity prices are shown in table 6.
8) The trapezoidal demand response utility function parameters are shown in table 7.
TABLE 2 AC Branch parameters
Figure BDA0002737271950000201
TABLE 3 peak load of AC distribution network
Figure BDA0002737271950000202
Figure BDA0002737271950000211
TABLE 4 DC Branch parameters
Figure BDA0002737271950000212
TABLE 5 peak load of DC distribution network
Figure BDA0002737271950000213
TABLE 6 Electricity price parameters
Figure BDA0002737271950000214
Figure BDA0002737271950000221
TABLE 7 trapezoidal demand response utility function parameters
Figure BDA0002737271950000222
This example sets up 3 scenarios for comparison to verify the validity of the invention:
in the scene 1, all electricity purchasing/selling prices in the power grid are fixed prices, demand response load scheduling is not performed on LA, and games are not performed among benefit agents. In this case, the price of electricity sold by DNO to LA is 55$/(MW · h), and the prices of electricity sold and purchased by DNO to all MGs are 55$/(MW · h) and 30$/(MW · h), respectively.
And in a scene 2, changing electricity prices are adopted, DNO sets out all electricity prices and operation modes of an AC/DC power distribution network, MG and LA have no bargaining right, LA can change own electricity consumption according to the electricity prices, and MG can only accept DNO strategies.
And in the scene 3, by adopting the complete information dynamic game model, all benefit agents such as DNO, MG and LA are used as game participants to jointly determine the electricity price.
Wherein, scene 1 is solved by calling CPLEX12.8.0 solver directly through YALMIP (R20180612) toolbox of MATLAB, scene 2 is solved by traditional particle swarm optimization, and scene 3 is solved by the method of combining DGPSO established by the invention and linear programming. The scheduling results are shown in fig. 7(a) -7(c) and table 8.
TABLE 8 revenue scenarios for each scenario
Figure BDA0002737271950000223
Figure BDA0002737271950000231
Analysis of fig. 8 and table 8 reveals that:
since all stakeholders in scenario 3 have bargaining rights, LA and MG are most involved in scheduling. Therefore, in all scenes, the wind and light curtailment phenomenon of scene 3 is the least, and the consumption rate of DRES is the highest in all scenes, and is 98.32%. And because LA and MG have the bargaining right, their reasonable rights and interests are protected, for example, the photovoltaic consumption rate of MG3 in scene 3 is significantly improved compared with scene 1 and scene 2, and its profit is also significantly increased. Although the dynamic gaming mechanism is introduced, the benefit of DNO is reduced to some extent, which is 13.40% lower than that of scenario 2, but the benefits of other benefit agents are obviously improved compared with scenario 2, so that the total social benefit of scenario 3 is the highest in all scenarios. From the perspective of local consumption of DRES, the time period for DNO to transmit power to the main network in scenario 3 is the minimum, and the segment transmission power is the minimum, which shows that scenario 3 has the best local consumption of DRES and the least influence on the main network.
In conclusion, compared with the traditional scheduling method, the multi-benefit main body coordinated scheduling method for the alternating current and direct current power distribution network based on the complete information dynamic game, provided by the invention, can not only improve the social overall benefit of the alternating current and direct current power distribution network on the premise of ensuring the safety of the alternating current and direct current power distribution network, but also improve the consumption rate of DRES (dry raw materials recovery) and reduce the backward power of the power distribution network to a main network, and simultaneously protects the reasonable rights and benefits of each main body, so that the benefits of each main body are more matched with the grasped resource quantity (such as renewable energy of MG and flexible load of LA). Therefore, the economy, the environmental protection and the market fairness of the power distribution network are improved.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for cooperatively dispatching an AC/DC power distribution network jointly participated in by MG, LA and DNO is characterized by comprising the following steps:
s1, constructing a profit function and operation constraint of each benefit subject in an AC/DC power distribution network, wherein the types of the benefit subjects comprise: each benefit subject has bargaining right and only DNO has scheduling right;
s2, taking each benefit subject as a game participant, wherein the game participant comprises a bargaining participant and a plurality of decision participants, the bargaining participant has the right of making electricity purchasing/selling prices related to the bargaining participant, the decision participants have the right of negotiating the electricity purchasing/selling prices with the bargaining participant, electricity purchasing/selling price constraint in the DNO and MG interaction process and electricity price constraint in the DNO and LA interaction process are taken as game constraint, each benefit subject maximizes a game target by virtue of a self income function, an AC/DC distribution network complete information dynamic game model is constructed, the game electricity price is represented by the particle position, the income function value of each benefit subject is represented by the fitness of the particles, the game model is solved by adopting a dynamic game particle swarm algorithm, and the electricity price obtained by the game is output;
s3, taking the electricity price obtained by the game as the input of a scheduling model, taking the profit function maximization of DNO as a scheduling target, taking the operation constraint of MG and DNO as a scheduling constraint, establishing the scheduling model, and solving the scheduling model by adopting linear programming to obtain a scheduling scheme and the profit function values of all benefit subjects;
and S4, sequentially changing the bargaining participants, repeating the steps S2-S3 until all game participants participate in bargaining, all decision participants agree to the current power price and scheduling mode, and the income function deviation ratio of each participant between the last two games is smaller than a set threshold value, so that the final power price and scheduling result is obtained.
2. The method of claim 1, wherein the gaming process is as follows:
firstly, DNO is used as a bargaining participant, and other participants become the bargaining participants in sequence;
in each game, the DNO makes and releases the operation mode of the power distribution network according to the electricity price made by the current bargained participants on the premise of ensuring the power supply and safe operation of all participants; and other participants calculate own income functions according to the issued operation mode of the power distribution network and decide whether to accept the electricity price and the operation mode.
3. The method of claim 2, wherein the limiting of the price of electricity purchased/sold during interaction of the DNO with the MG comprises:
1) the electricity price of the DNO for purchasing electricity to each MG is larger than that of the MG for purchasing electricity to the DRES (distributed renewable energy source) thereof
Figure FDA0002737271940000021
To protect the benefit of MG and is less than the price of DNO purchasing electricity from main network
Figure FDA0002737271940000022
To protect the benefits of DNO, the following constraints apply:
Figure FDA0002737271940000023
2) the electricity price for selling electricity from DNO to MG is greater than that for selling electricity to main network
Figure FDA0002737271940000024
To protect the income of DNO and at the same time, the price of electricity is less than that for selling electricity to the conventional load
Figure FDA0002737271940000025
To protect the gain of the MG, there are the following constraints:
Figure FDA0002737271940000026
4. the method of claim 2 or 3, wherein the electricity prices during interaction of DNO with LA are constrained:
the electricity price for selling the electricity from the DNO to the LA is larger than that for selling the electricity to the main network
Figure FDA0002737271940000027
At the same time, the price of electricity is less than that of electricity sold to a conventional load
Figure FDA0002737271940000028
To protect the revenue of both parties, there are the following constraints:
Figure FDA0002737271940000029
5. the method of any one of claims 1 to 4, wherein the benefit agent MG is divided into an alternating current MG and a direct current MG, which are respectively connected to an alternating current region and a direct current region of an alternating current/direct current distribution network, purchase power for distributed renewable energy sources in the MG, supply power for loads in the MG, and interact with DNO;
the game profit function for the MG is expressed as follows:
Figure FDA00027372719400000210
in the formula, rMG,mAnd cMG,mRespectively obtaining the income of the mth MG and the equivalent operation energy consumption, wherein the income of the mth MG comprises the income of the MG for selling electricity to the DNO and the load in the microgrid, and the equivalent social energy consumption of the MG for purchasing electricity from the DNO and the DRES in the microgrid;
Figure FDA00027372719400000211
and
Figure FDA00027372719400000212
the load size and the electricity price in the mth MG respectively;
Figure FDA0002737271940000031
and
Figure FDA0002737271940000032
and respectively purchasing power and electricity prices from the mth MG to the DRES in the microgrid.
6. The method of any one of claims 1 to 5, wherein the operational constraints of the benefit agent MG comprise: 1) a power balance constraint; 2) and (5) purchasing power to DRES.
7. The method of any of claims 1 to 6, wherein the bet revenue function of the DNO is expressed as follows:
BDNO＝rDNO-cDNO
in the formula, rDNOAnd cDNORespectively the income and the operation energy consumption of DNO;
rDNOthe calculation formula of (a) is as follows:
Figure FDA00027372719400000314
wherein t is a time period; NT and delta T are the number of the scheduling time segments and the scheduling time length respectively;
Figure FDA0002737271940000033
and
Figure FDA0002737271940000034
respectively carrying out conventional load size and electricity price;
Figure FDA0002737271940000035
and
Figure FDA0002737271940000036
respectively the load size and the electricity selling price of the load aggregator; NMG is the number of piconets;
Figure FDA0002737271940000037
and
Figure FDA0002737271940000038
the DNO respectively sells electricity quantity and price to the mth MG; pt GsAnd
Figure FDA0002737271940000039
the electric quantity and the price of the sold electricity are respectively sold to the main network by the DNO;
cDNOthe calculation formula of (a) is as follows:
Figure FDA00027372719400000310
in the formula (I), the compound is shown in the specification,
Figure FDA00027372719400000311
and Pt GbRespectively purchasing electric quantity and electricity purchasing price from the main network for DNO;
Figure FDA00027372719400000312
and
Figure FDA00027372719400000313
respectively purchasing electricity quantity and electricity price from the DNO to the mth MG; c (P)t DDG) The power generation energy consumption of the DDG and the output P of the DDGt DDGIt is related.
8. The method of any of claims 1 to 7, wherein the operational constraints of the benefit agent DNO comprise: 1) the branch load flow and the node voltage of the alternating-current power distribution network are constrained; 2) the branch power flow and the node voltage of the direct-current power distribution network are constrained; 3) VSC operation constraints; 4) the DNOs exchange power constraints with the primary and micro networks.
9. The method of any of claims 1 to 8, wherein the game profit function for LA is expressed as follows:
Figure FDA0002737271940000041
in the formula (I), the compound is shown in the specification,
Figure FDA0002737271940000042
for LA, the flexible load in the t period is
Figure FDA0002737271940000043
The function value of the electricity utilization benefit is the actual electricity consumption LDRThe area of the shaded portion enclosed by the marginal utility.
10. A system for cooperatively dispatching an AC/DC power distribution network with joint participation of MG, LA and DNO is characterized by comprising: a computer-readable storage medium and a processor;
the computer-readable storage medium is used for storing executable instructions;
the processor is configured to read executable instructions stored in the computer-readable storage medium, and execute the method for cooperatively dispatching the ac/dc distribution network in which MG, LA and DNO participate together according to any one of claims 1 to 9.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112801349A (en) * 2021-01-13 2021-05-14 南京航空航天大学 Data center resource optimization scheduling method powered by renewable energy
CN113162066A (en) * 2021-04-02 2021-07-23 云南电网有限责任公司 Game behavior analysis method considering participation of electrolytic aluminum industrial users in frequency modulation market
CN113852073A (en) * 2021-09-29 2021-12-28 福州大学 Day-ahead optimization scheduling method based on excitation-response charging decision estimation
CN115271438A (en) * 2022-07-27 2022-11-01 河海大学 Multi-subject game cooperative scheduling method capable of considering carbon emission and electronic equipment

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109919472A (en) * 2019-02-27 2019-06-21 华南理工大学 A kind of GENERATION MARKET iteration price competing method considering more Interest Main Body games
CN111626569A (en) * 2020-05-06 2020-09-04 云南电网有限责任公司怒江供电局 Micro-grid group electric power energy trading method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109919472A (en) * 2019-02-27 2019-06-21 华南理工大学 A kind of GENERATION MARKET iteration price competing method considering more Interest Main Body games
CN111626569A (en) * 2020-05-06 2020-09-04 云南电网有限责任公司怒江供电局 Micro-grid group electric power energy trading method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
SHUNBO LEI等: ""Identification of Critical Switches for Integrating Renewable Distributed Generation by Dynamic Network Reconfiguration"", 《 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY》 *
李力行等: "多利益主体参与下主动配电网完全信息动态博弈行为", 《电工技术学报》 *
李力行等: "源荷双侧不确定因素影响下基于Rubinstein博弈的电网双层定价模型", 《电工技术学报》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112801349A (en) * 2021-01-13 2021-05-14 南京航空航天大学 Data center resource optimization scheduling method powered by renewable energy
CN113162066A (en) * 2021-04-02 2021-07-23 云南电网有限责任公司 Game behavior analysis method considering participation of electrolytic aluminum industrial users in frequency modulation market
CN113162066B (en) * 2021-04-02 2023-03-28 云南电网有限责任公司 Game behavior analysis method considering participation of electrolytic aluminum industrial users in frequency modulation market
CN113852073A (en) * 2021-09-29 2021-12-28 福州大学 Day-ahead optimization scheduling method based on excitation-response charging decision estimation
CN113852073B (en) * 2021-09-29 2023-12-15 福州大学 Day-ahead optimal scheduling method based on excitation-response charging decision estimation
CN115271438A (en) * 2022-07-27 2022-11-01 河海大学 Multi-subject game cooperative scheduling method capable of considering carbon emission and electronic equipment
CN115271438B (en) * 2022-07-27 2023-07-25 河海大学 Multi-main-body game collaborative scheduling method capable of considering carbon emission and electronic equipment

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