CN117172916B - Side flexibility resource end-to-end decentralized transaction method - Google Patents
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Abstract
The invention relates to the technical field of intelligent power distribution network optimization operation and transaction, in particular to an end-to-end decentralized transaction method for side flexible resources, which comprises the following steps: 1. the method comprises the steps of building a Stackelberg game framework, and constructing a master multi-slave game model which takes an active power distribution network ADN as a main body and a multi-micro power grid and a shared energy storage system SESS as slave bodies; 2. optimizing an upper layer; 3. optimizing a lower layer; 4. decomposing a lower model; 5. solving algorithm: and solving the asymmetric Nash bargaining problem by adopting a full-distributed ADMM method. The method provides a double-layer optimization operation strategy of the P2P transaction and lease shared energy storage system for the multi-microgrid, and realizes the full call of flexible resources; an asymmetric Nash negotiation model is provided, and fair profit distribution among multiple micro networks is realized; a master-multiple-slave game model taking the participation of multiple micro-networks into account is provided, and the maximum operation benefit of each main body is realized.
Description
Technical Field
The invention relates to an end-to-end decentralized transaction method for side flexible resources, and belongs to the technical field of intelligent power distribution network optimized operation and transaction.
Background
In order to cope with a series of problems such as energy crisis and greenhouse effect caused by massive consumption of fossil energy, the development of clean renewable energy sources is greatly promoted, and the sustainable development road is already a common knowledge of people. In recent years, renewable energy power generation technologies represented by photovoltaic and wind power have been rapidly developed at an unprecedented speed. The high-proportion new energy grid connection consumption is a typical form of a novel power system in the future.
However, the high permeability of distributed power sources (Distributed generation, DG) also presents new challenges for the proper operation and control of the distribution network. On one hand, the bidirectional flowing active network replaces the traditional passive management unidirectional transmission power distribution network, and the low communication level and the underdeveloped automatic control mode of the traditional power distribution network severely restrict the digestion capacity of the whole power system on renewable clean energy sources; on the other hand, renewable energy sources such as solar energy, wind energy and the like have intermittence and unpredictability, and have strong uncertainty on real-time operation control of a power distribution network, so that a larger operation risk is caused. In order to eliminate or attenuate the influence of renewable energy volatility and uncertainty on the power flow and real-time power balance of the power system, new power systems in the future need to have sufficient regulation capabilities, i.e. sufficient flexibility. In an active distribution network (Active distribution network, ADN), distributed generator sets, energy storage devices and distributed power sources including flexible loads together form a source network load storage multi-element flexible resource library.
In order for ADNs to fully exploit flexibility to achieve more flexible operation, decentralized flexible resources must be quantified and aggregated. The micro-grid is one of important forms of large-scale grid connection of the distributed power supply, and has the advantages of autonomous operation, optimal management, coordinated control and the like. The multiple micro-grids with different scales are integrated into the regional distribution network to form the multi-micro-grid distribution system. The micro networks with different source-load characteristics can be mutually used by information exchange and Peer-to-Peer (P2P) electric energy, so that the on-site consumption of distributed energy sources can be promoted, and the win-win of a multiparty benefit body can be realized. As a decentralised trading mode of the power distribution network side market, a P2P trading mechanism stimulates each main body to release power flexibility, power producers and consumers can directly conduct energy trading and sharing, and trading prices are formulated by a P2P trading platform or negotiated and determined by both trading parties.
Existing P2P trade studies can be divided into three aspects according to the degree of convergence of the trade mechanism: centralized markets, decentralized markets, and distributed markets. The centralized marketplace has a marketplace coordinator that can communicate with all participants of the P2P transaction. The market coordinator formulates trade prices or distributes benefits of the P2P trade according to rules formulated in advance. The uncertainty of the power generation mode of the main body in the market is low under the centralized management. However, the expansion of the scale of distributed energy sources brings exponential computing and communication burden to the centralized management system, and there is a risk of revealing the privacy of the user. There is no centralized market coordinator in the decentralized P2P energy trading market, and users directly contact and trade with other users. Thus, the privacy and autonomy of the market subject are well protected. Compared with a centralized market, the form of the distributed P2P energy trading market is more diversified, including a bilateral contract network, a consensus method, a blockchain mechanism, a multi-agent method and the like. The distributed market is a market mechanism between a centralized market and a decentralized market, which means that market subjects adopt a decentralised trading mode, and meanwhile, a market coordinator similar to the centralized market is provided for completing information communication among the subjects. The coordinator of the distributed market often affects the user's behavior indirectly through price signals. Network information security, however, is a potential problem in the distributed P2P transaction process. Therefore, the decentralized P2P energy trading mechanism with decentralization is finally adopted to realize P2P trading of side flexible resources.
In view of the correlation of interests and information privacy among multiple principals, gambling theory provides a good choice for this de-centralized multi-principal decision problem, including collaborative and non-collaborative gambling. In the non-cooperative game, the energy surplus or shortage of the consumer is generally classified into two opposite buyers and sellers, and the minimum energy cost based on Nash equilibrium is realized through the Stackelberg game. Another method of auctioning games based on bidding has the advantage of facilitating the writing of intelligent contracts. Although the non-cooperative game method can obtain Nash equilibrium solutions among the main bodies, benefit improvement caused by potential cooperation among operators is ignored, and non-pareto optimal and non-social optimal are usually caused, and cooperative game can encourage participants to jointly act to improve the pareto optimal and the social optimal. In collaborative gaming, P2P transactions are typically modeled as either a coalition gaming model or a nash negotiation model. The alliance game model has high computational complexity, and can only ensure that the benefit inside the stable alliance is maximized, but not the global benefit is maximized. The Nash negotiation model can realize fair distribution of social benefits while ensuring the maximization of global benefits.
The existing side flexible resource transaction mechanism has the following defects: under the existing transaction framework, the flexibility potential of each link of the system is not fully excavated. 1) Considering the problem of power fluctuation caused by high-proportion grid connection of renewable energy sources, the regional micro-networks lack coordination and mutual aid, and flexible resources are difficult to realize mutual aid in a larger range; 2) The existing transaction frameworks lack reasonable quantification mechanisms and income distribution mechanisms for the flexibility value of market members, and have difficulty in playing a sustainable incentive role in providing flexibility; 3) The privacy protection and information security of the participants are not fully guaranteed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an end-to-end decentralized transaction method for side flexible resources, and provides a double-layer optimized operation strategy of a P2P transaction and lease shared energy storage system for multiple micro-networks, so as to realize the full call of the flexible resources; an asymmetric Nash negotiation model is provided, and fair profit distribution among multiple micro networks is realized; a master-multiple-slave game model taking the participation of multiple micro-networks into account is provided, and the maximum operation benefit of each main body is realized.
The technical scheme for solving the technical problems is as follows: an edge-side flexible resource end-to-end decentralized transaction method, said method comprising:
S1, stackelberg game framework
Constructing a master multi-slave game model which takes an active power distribution network ADN as a main body and a multi-micro power grid and a shared energy storage system SESS as slave bodies; the power distribution system operator DSO is taken as a leader main body, self-contained new energy is preferentially consumed, a time-of-use electricity price strategy is formulated according to an ADN payload curve, and the micro-grid operator MGO and the shared energy storage operator SESO are stimulated to participate in peak clipping and valley filling scheduling of the ADN; MGO and SESO are taken as follower slaves, time-of-use electricity prices are responded, and electricity purchasing and selling plans are formulated; obtaining an ADN optimized pricing strategy and a follower energy strategy by solving a game equilibrium solution;
s2, upper layer optimization:
at the upper layer, the micro-grid establishes a SESS charging and discharging strategy to stabilize self-payload power fluctuation, calculates the capacity requirement and the power requirement of leased shared energy storage, and uploads demand information to lease energy storage as required; the SESO gathers information from the micro-grid, and establishes an SESS charging and discharging strategy according to the total net energy storage charging and discharging requirement of the multi-micro-grid;
s3, lower layer optimization: after the micro-grid stabilizes partial net load power fluctuation, adopting a P2P electric energy sharing transaction strategy based on asymmetric Nash bargaining under a cooperative game; firstly, performing energy transaction amount negotiation, wherein each micro-grid main body takes social cost minimization as a target to form a benefit community, and transmits the expected energy transaction amount to other main bodies to finally obtain multi-micro-grid electric energy transaction amount capable of realizing social cost minimization; and then, carrying out energy transaction price negotiation, and transmitting the expected energy transaction price to other subjects by each subject, wherein the energy transaction price acceptable by each subject is finally obtained through the negotiation.
S4, decomposing a lower model: the nash negotiation model is a multiple variable coupled non-convex nonlinear problem, so the decomposition is converted into two sub-problems: social cost minimization sub-problem P1 and revenue distribution sub-problem P2;
s5, solving algorithm: and solving asymmetric Nash bargaining problems P1 and P2 by adopting a full-distributed ADMM method.
Further, in step S1, a master-slave model of a tuckelberg:
ADN's own new energy output has a volatility, expressed by the payload as the following expression (1):
wherein,new energy output for ADN, +.>For ADN load, ++>Is the payload of ADN;
the decision variable of ADN is time-of-use electricity price, the maximum operation benefit is used as an optimization target, and the objective function is shown as the following expression (2)
maxU ADN ＝I MMG +I SESS -C grid (2)
Wherein I is MMG The power interactive income of the ADN and the multi-micro-grid is obtained; i SESS Power interactive benefits for ADN and siso; c (C) grid The electricity purchasing cost of the ADN from the upper-level power grid is set; maxU ADN The maximum operation benefit of ADN;
I MMG 、I SESS and C grid The following formulas (3) to (5) are respectively adopted:
t represents the number of scheduled time periods; t represents a period;is ADNPrice of electricity, ->Electricity purchase price for ADN; />Is the total power of the multiple micro-grids and ADN,>is the total power of the multi-micro grid and ADN, MMG represents the short name of multi-micro grid (MMG) >Is the electricity selling power of the ith micro-grid and the ADN; />The power purchasing power of the ith micro-grid and ADN is that M is the number of the micro-grids;
is the selling electric power of SESO and ADN, < ->The power of electricity purchase of SESO and ADN;
selling electricity price for the power grid; />The power purchasing power of the ADN from the upper-level power grid is realized;
the constraint conditions of ADN are: and
is the upper limit of the average electricity selling price.
Further, in step S2, the upper layer optimization model:
constructing a multi-objective optimization model with minimum net load mean square error and minimum SESS lease cost of the micro-grid;
the minimum objective function of the net load mean square error of the micro-grid is:
wherein f 1 The mean square error of the payload after the ses is leased for the microgrid,load power for the ith micro-grid in period t,/->For the new energy power of the ith micro-grid in the period t, P MGi，ave For the equivalent load average value of the ith micro-grid in period t,/>Charging power for the SESS of the ith microgrid in period t, < >>SESS discharge power of the ith micro-grid in a t period;
minimal objective function of SESS lease cost:
the SESS charges according to the leasing capacity and the charging and discharging power, and the micro-grid energy storage using cost model is shown in the following formula (9):
wherein f 2 The energy storage and use cost of the micro-grid is u is the unit power charge and discharge cost of the SESO, v is the unit energy capacity lease cost of the SESO, and w is the unit power capacity lease cost of the SESO; For the power capacity required by the micro-grid,the energy capacity requirement required for the microgrid; wherein->Maximum value of charge and discharge power for leasing energy storage for micro-grid Expressed as:
wherein alpha is Lea Representing the capacity margin coefficient,respectively obtaining the maximum value and the minimum value of the electric quantity of the leased SESS of the micro-grid in the dispatching period;
the micro-grid leases share the conservation of total charge and discharge of stored energy within one scheduling period, and can not be charged and discharged at the same time:
wherein eta is i，c Charging efficiency for shared energy storage; η (eta) i.d Discharge efficiency for shared energy storage;
sharing an energy storage optimization model:
SESO (semiconductor integrated circuit) summarizing all micro-grid charging and discharging power requirements The multi-microgrid lease shared energy storage and the charging power requirement thereof are shown as follows:
wherein M is the number of micro-nets;
the objective function of SESO is expressed as:
U SESS ＝I ADN -C SESS (14)
in U SESS To share the operational benefits of energy storage, I ADN C for power interactive benefits of SESS and ADN SESS The cost of charge-discharge operation and maintenance of energy storage,charging power for SESS, +.>Zeta is the operation maintenance cost coefficient of charging and discharging of the energy storage unit power for SESS discharging power;
in the SESS charge and discharge constraint, the charge and discharge power does not exceed the SESS limit power and can not be charged and discharged at the same time;
in the constraint of upper and lower bounds of the shared energy storage electric quantity:
In the method, in the process of the invention,is the maximum value of the charge and discharge power of SESS; />Is the maximum energy capacity of the SESS; η (eta) c Charge efficiency η of SESS d Discharge efficiency for SESS; mu (mu) s The standby capacity percentage of the energy storage system; />The electricity storage capacity of SESS at the time t; />The electricity storage capacity of SESS at the time t-1;
in the shared energy storage system power balance constraint:
in the method, in the process of the invention,and->Taking 1 time as a binary variable to represent electricity purchasing operation of SESO and ADN in t period.
Further, the lower optimization model:
after the charge and discharge of each micro-grid stabilize part of net load fluctuation, regenerating an equivalent load curve:
wherein P is MGi，eq Is a new load after the micro-grid stabilizes the net load fluctuation;
the total cost of the ith micro grid operator consists essentially of the power interactive revenue I with ADN i，ADN Operation cost C of energy storage system in micro-grid i，BESS Translatable load scheduling compensation cost C i，L Power generation and operation cost C of controllable unit MT i，MT ；
The total cost of the ith micro-grid, MGi is the ith micro-grid; />Is the power of the electricity sold by MGi to ADN, < >>Is the electricity purchasing power from micro-grid MGi to ADN, ζ b Representing the degradation cost coefficient of the energy storage battery; />Is the charging power of the BESS inside the MGi, < >>Is the discharge power of the BESS inside the micro-grid MGi, +. >The compensation unit price given to the user by transferring the load; />The electric load can be transferred for the MGi user; a, a Mi 、b Mi 、c Mi Generating cost coefficients for micro-grid MGi; />Is the output power of MT (controllable unit) in micro-grid MGi.
1) Power balance constraint of micro grid system:
in the method, in the process of the invention,the total output power of the renewable energy source in the MGi;
2) Energy transaction amount constraint:
in the method, in the process of the invention,representing the upper limit of the interaction quantity of the micro-grid operators and the upper-level network energy sources;
3) MT operating power constraint:
in the method, in the process of the invention,is the lower output power limit of the MT; />An upper output power limit for the MT;
4) Load constraints can be transferred:
the transferable electric load should ensure that the total load before and after the demand response is unchanged within the comfort range of the user
Wherein: alpha i，tran Representing a transferable electrical load coefficient;a transferable load represented by a time t is transferred to another time;
5) Energy storage device constraints:
in the method, in the process of the invention,representing the energy storage energy of energy storage equipment in the micro-grid at the time t; />Representing the energy storage energy of the energy storage equipment in the micro-grid at the time t-1; Δt represents the time interval between two moments; />Representing the energy storage capacity of the energy storage device at the moment 0; />Representing the energy storage energy of the energy storage device at the moment T; θ i A self-loss coefficient representing the stored energy; η (eta) i，c And eta i，d Respectively representing the energy charging efficiency of the energy storage device and the energy discharging efficiency of the energy storage device; />And->Respectively representing the maximum value of the charging power of the energy storage device and the maximum value of the discharging power of the energy storage device; />Representing a maximum value of the energy storage capacity of the energy storage device;
the transmission cost of each micro-grid is as follows:
wherein,the cost is converted into a coefficient; />Transmission cost for MGi;
trade fees between the microgrid and other microgrid MGs:
wherein,trade electricity prices representing MGi and MGj power flexibility trade; />Represents the electric power supplied to MGj by MGi at time t,/-)>Positive value indicates sales->Negative values indicate purchase, and-> Transaction fees for MGi and other micro-networks;
the objective function is:
in the method, in the process of the invention,to consider the objective function of MGi after electric energy transaction between micro-networks; />To disregard the total cost of MGi in the electric energy transaction between micro-networks; the micro-grid is considered in the formula (35), so that the micro-grid users can obtain higher benefits, and more benefits are effectively improved.
The power balance constraint is:
and is also provided with
In step S4, the asymmetric nash bargaining model:
the Nash negotiation model is a cooperative game, and a plurality of participants perform cooperative profit distribution through mutual negotiation while minimizing social cost of the participants. Through nash bargaining, the value of flexibility provided by each microgrid operator is quantified and corresponding revenue allocated thereto. Each MGO is stimulated to consume flexible resources, activating the system flexibility potential to the maximum extent.
The Nash negotiating standard model is shown in formula (37):
in the method, in the process of the invention,representing cost of MGi prior to participation in P2P power sharing, i.e. talkingJudging a breaking point; />Representing benefits of MGi promotion through cooperative operation;
the nash negotiation model is a multiple variable coupled non-convex nonlinear problem, thus transforming the model decomposition into two sub-problems: the social cost minimization sub-problem P1 and the income distribution sub-problem P2 are solved in sequence; p1 determines the optimal energy trading and scheduling for each microgrid, and P2 studies the problem of revenue distribution after the multi-microgrid power sharing collaboration, the social cost being defined as the sum of all the total MGO costs in the multi-microgrid system.
By using the Nash negotiation standard model, the method for allocating the benefits of the micro-networks can be improved, and the benefits are different when the contributions of each micro-network to the system are different, so that the benefit allocation is fairer.
Further, the solving method of P1 is as follows:
and (3) making:micro-grid alliance cost minimization sub-problem P1:
bargained capability size d of each micro-net i The method comprises the following steps:
in the method, in the process of the invention,to supply the maximum value of the electric energy to each micro-grid; />Maximum value of the received electric energy in each micro-network; />Is->Is the maximum value of (2); />Is->Is a maximum value of (a).
The solving method of P2 is as follows:
substituting the optimal interaction electric quantity obtained in the P1 into the P2, wherein the profit distribution model of the asymmetric bargaining is as follows:
Wherein, the variables marked with "+" are the optimal solutions obtained by P1;
revenue distributor problem P2:
the beneficial effects of the invention are as follows:
(1) The invention provides a double-layer optimization operation strategy of a P2P transaction and lease shared energy storage system for a plurality of micro-networks, and the full calling of flexible resources is realized;
by introducing a P2P transaction mechanism of a plurality of micro-networks and a double-layer optimization operation strategy of sharing the leasing of an energy storage system (Shared energy storage system, SESS), the high-efficiency utilization of energy storage and new energy resources can be realized, and the problem of power fluctuation caused by high-proportion grid connection of renewable energy sources can be solved. Meanwhile, the electric energy sharing and the reciprocal sharing among the multiple micro-networks with different source-load characteristics can realize the cooperation of flexible resources in a larger range, thereby improving the operation flexibility and the economy of the whole system.
(2) The invention provides an asymmetric Nash negotiation model, which realizes fair profit distribution among multiple micro networks;
reasonable profit distribution mechanism is the key to encourage market members to participate in P2P transactions and promote system flexibility. By improving the Nash negotiation model, on the premise of maximizing the overall benefit of the micro-networks, the contribution of each micro-network in the electric energy sharing process is quantized by adopting a nonlinear energy mapping function, so that fair distribution benefit is realized.
(3) The invention provides a master multi-slave game model taking into account participation of a plurality of micro-networks, and realizes the maximum operation benefit of each main body;
an ADN-based multi-slave game model with multi-micro-grid alliance and a shared energy storage operator as slaves is constructed, and an ADN optimized pricing strategy and a follower energy utilization strategy are obtained by solving a game equilibrium solution, so that the maximum operation benefit of each main body is realized.
Drawings
FIG. 1 is an active distribution network architecture with multiple micro-grids;
FIG. 2 is a graph of renewable energy output and load power of an ADN;
FIG. 3 is a renewable energy output and load power of an MG1 micro-grid;
FIG. 4 is a renewable energy output and load power of an MG2 micro-grid;
FIG. 5 is a renewable energy output and load power of an MG3 microgrid;
FIG. 6 is an iterative diagram of distribution network operator optimization;
FIG. 7 is a graph of shared energy storage charge-discharge strategy and SOC variation;
FIG. 8 is SESS lease requirements of MG 1;
FIG. 9 is SESS lease requirements of MG 2;
FIG. 10 is SESS lease requirements of MG 3;
FIG. 11 is a graph showing the results of an inter-microgrid power trade;
fig. 12 is a graph showing the results of the power price of the power transaction between micro-grids.
Detailed Description
The following describes the present invention in detail. The present invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, so that the invention is not limited to the specific embodiments disclosed.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
An edge-side flexible resource end-to-end decentralized transaction method, said method comprising:
s1, stackelberg game framework
Constructing a master multi-slave game model which takes an active power distribution network ADN as a main body and a multi-micro power grid and a shared energy storage system SESS as slave bodies; the power distribution system operator DSO is taken as a leader main body, self-contained new energy is preferentially consumed, a time-of-use electricity price strategy is formulated according to an ADN payload curve, and the micro-grid operator MGO and the shared energy storage operator SESO are stimulated to participate in peak clipping and valley filling scheduling of the ADN; MGO and SESO are taken as follower slaves, time-of-use electricity prices are responded, and electricity purchasing and selling plans are formulated; obtaining an ADN optimized pricing strategy and a follower energy strategy by solving a game equilibrium solution;
s2, upper layer optimization:
at the upper layer, the micro-grid establishes a SESS charging and discharging strategy to stabilize self-payload power fluctuation, calculates the capacity requirement and the power requirement of leased shared energy storage, and uploads demand information to lease energy storage as required; the SESO gathers information from the micro-grid, and establishes a SESS flexible charging and discharging strategy according to the total net energy storage charging and discharging requirement of the multi-micro-grid; the SESS residual capacity responds to the time-sharing electricity price of the distribution network to realize low-storage high-discharge arbitrage; the SESO provides energy storage leasing services for each MGO according to a specified charging mode and price;
S3, lower layer optimization: after stabilizing partial net load power fluctuation of the micro-grids, fully considering the energy complementary relation between the micro-grids, and adopting a P2P electric energy sharing transaction strategy based on asymmetric Nash bargaining under a cooperative game; firstly, performing energy trading volume negotiation, wherein each micro-grid main body takes social cost minimization as a target to form a benefit community, and only the expected energy trading volume is transmitted to other main bodies, so that the optimal electric energy trading volume among multiple micro-grids capable of realizing the social cost minimization is finally obtained; and then, carrying out energy transaction price negotiation, wherein each main body only needs to transmit the expected energy transaction price to other main bodies, and finally obtaining the energy transaction price acceptable by each main body through the negotiation so as to ensure the respective benefit requirements.
S4, decomposing a lower model: the nash negotiation model is a multiple variable coupled non-convex nonlinear problem, so the decomposition is converted into two sub-problems: social cost minimization sub-problem P1 and revenue distribution sub-problem P2;
s5, solving algorithm: and solving asymmetric Nash bargaining problems P1 and P2 by adopting a full-distributed ADMM method.
Stackelberg master-slave model:
ADN's own new energy output has a volatility, expressed by the payload as the following expression (1):
Wherein,new energy output for ADN，/>For ADN load, ++>Is the payload of ADN;
the decision variable of ADN is time-of-use electricity price, the maximum operation benefit is used as an optimization target, and the objective function is shown as the following expression (2)
max U ADN ＝I MMG +I SESS -C grid (2)
Wherein I is MMG The power interactive income of the ADN and the multi-micro-grid is obtained; i SESS Power interactive benefits for ADN and siso; c (C) grid The electricity purchasing cost of the ADN from the upper-level power grid is set; maxU ADN The maximum operation benefit of ADN;
I MMG 、I SESS and C grid The following formulas (3) to (5) are respectively adopted:
t represents the number of scheduled time periods, and the value is 24 in the embodiment of the invention; t represents a period, and all the following formulas are the same;for the selling price of ADN, +.>Electricity purchase price for ADN; />Is the total power of the multiple micro-grids and ADN,>the total power purchase of the multi-micro grid and the ADN is that MMG represents short for multi-micro grid (MMG); />Is the electricity selling power of the ith micro-grid and the ADN; />Is the electricity purchasing power of the ith micro-grid and the ADN;
is the selling electric power of SESO and ADN, < ->The power of electricity purchase of SESO and ADN;
selling electricity price for the power grid; />The power purchasing power of the ADN from the upper-level power grid is realized;
the constraint condition of the ADN comprises that the time-sharing electricity selling price is higher than the electricity purchasing price, and in addition, the constraint limit is applied to the time-sharing electricity selling price because the ADN has the independent pricing right;
For average electricity selling priceUpper limit of (2).
Upper layer optimization model:
the micro-grid leases a multi-objective optimization model for sharing energy storage, and a multi-objective optimization model with minimum net load mean square error and minimum SESS leasing cost of the micro-grid is established for considering the power fluctuation stabilizing effect and the energy storage leasing cost of the multi-micro-grid;
the minimum objective function of the net load mean square error of the micro-grid is:
wherein f 1 The mean square error of the payload after the ses is leased for the microgrid,load power for the ith micro-grid in period t,/->For the new energy power of the ith micro-grid in the period t, P MGi，ave For the equivalent load average value of the ith micro-grid in period t,/>Charging power for the SESS of the ith microgrid in period t, < >>SESS discharge power of the ith micro-grid in a t period;
minimal objective function of SESS lease cost:
the SESS charges according to the leasing capacity and the charging and discharging power, and the micro-grid energy storage using cost model is shown in the following formula (9):
wherein f 2 The energy storage and use cost of the micro-grid is u is the unit power charge and discharge cost of the SESO, v is the unit energy capacity lease cost of the SESO, and w is the unit power capacity lease cost of the SESO;for the power capacity required by the micro-grid,the energy capacity requirement required for the microgrid; wherein- >Maximum value of charge and discharge power for leasing energy storage for micro-grid Expressed as: />
Wherein alpha is Lea Representing the capacity margin coefficient,respectively obtaining the maximum value and the minimum value of the electric quantity of the leased SESS of the micro-grid in the dispatching period;
the total charge and discharge conservation of the shared energy storage of the micro-grid leases in one scheduling period can not be charged and discharged simultaneously, and is specifically expressed as:
wherein eta is i，c Charging efficiency for shared energy storage; η (eta) i.d Discharge efficiency for shared energy storage;
sharing an energy storage optimization model:
SESO (semiconductor integrated circuit) summarizing all micro-grid charging and discharging power requirements The multi-microgrid lease shared energy storage and the charging power requirement thereof are shown as follows:
wherein M is the number of micro-nets;
the objective function of SESO is expressed as:
U SESS ＝I ADN -C SESS (14)
in U SESS To share the operational benefits of energy storage, I ADN C for power interactive benefits of SESS and ADN SESS The cost of charge-discharge operation and maintenance of energy storage,charging power for SESS, +.>Zeta is the operation maintenance cost coefficient of charging and discharging of the energy storage unit power for SESS discharging power;
in the SESS charge and discharge constraint, the charge and discharge power does not exceed the SESS limit power and can not be charged and discharged at the same time;
in the constraint of upper and lower bounds of the shared energy storage electric quantity:
/>
in the method, in the process of the invention,is the maximum value of the charge and discharge power of SESS; / >Is the maximum energy capacity of the SESS; η (eta) c Charge efficiency η of SESS d Discharge efficiency for SESS; mu (mu) s The standby capacity percentage of the energy storage system; />The electricity storage capacity of SESS at the time t; />The electricity storage capacity of SESS at the time t-1;
in the shared energy storage system power balance constraint:
in the method, in the process of the invention,and->Taking 1 time as a binary variable to represent electricity purchasing operation of SESO and ADN in t period.
And (3) a lower layer optimization model:
after the charge and discharge of each micro-grid stabilize part of net load fluctuation, regenerating an equivalent load curve:
wherein P is MGi，eq Is a new load after the micro-grid stabilizes the net load fluctuation;
from an economic optimization perspective, the total cost of the ith microgrid operator consists essentially of the interactive revenue I with ADN power, without regard to the energy bargaining trade between the microgrid operators i，ADN Operation cost C of energy storage system in micro-grid i，BESS Translatable load scheduling compensation cost C i，L Power generation and operation cost C of controllable unit MT i，MT ；
The total cost of the ith micro-grid, MGi is the ith micro-grid; />Is the power of the electricity sold by MGi to ADN, < >>Is the electricity purchasing power from micro-grid MGi to ADN, ζ b Representing the degradation cost coefficient of the energy storage battery; />Charging power of BESS inside micro grid MGi, +. >Is the discharge power of the BESS inside the micro-grid MGi, +.>The compensation unit price given to the user by transferring the load; />The electric load can be transferred for the MGi user; a, a Mi 、b Mi 、c Mi The power generation cost coefficient in MGi; />Is the output power of the MT in the micro grid MGi.
1) Power balance constraint of micro grid system:
in the method, in the process of the invention,the total output power of the renewable energy source in the MGi;
2) Energy transaction amount constraint:
to prevent the upper network blocking caused by multi-micro network system energy transaction, adding constraint on the upper limit of electric energy transaction amount:
in the method, in the process of the invention,representing the upper limit of the interaction quantity of the micro-grid operators and the upper-level network energy sources;
3) MT operating power constraint:
in the method, in the process of the invention,is the lower output power limit of the MT; />An upper output power limit for the MT;
4) Load constraints can be transferred:
the transferable electric load should ensure that the total load before and after the demand response is unchanged within the comfort range of the user
Wherein: alpha i，tran Representing a transferable electrical load coefficient;a transferable load represented by a time t is transferred to another time;
5) Energy storage device constraints:
since the battery should reserve the adjustment margin to satisfy the task of the next scheduling period after the scheduling period is finished, the capacity of the battery should be made to be the initial electric quantity after the scheduling period is finished;
/>
In the method, in the process of the invention,representing the energy storage energy of energy storage equipment in the micro-grid at the time t; />Representing the energy storage energy of the energy storage equipment in the micro-grid at the time t-1; Δt represents the time interval between two moments; />Representing the energy storage capacity of the energy storage device at the moment 0; />Representing the energy storage energy of the energy storage device at the moment T; θ i A self-loss coefficient representing the stored energy; η (eta) i，c And eta i，d Respectively representing the energy charging efficiency of the energy storage device and the energy discharging efficiency of the energy storage device; />And->Respectively represent the maximum value of the charging power of the energy storage deviceAnd energy storage device energy release power maximum; />Representing a maximum value of the energy storage capacity of the energy storage device;
when the energy bargaining transaction between the MGOs is considered, each micro-grid needs to supplement corresponding transmission cost according to corresponding transmission capacity:
wherein,the cost is converted into a coefficient; />Transmission cost for MGi;
in addition, the cost of the micro-grid operators also needs to consider the transaction cost with other micro-grid MG:
wherein,trade electricity prices representing flexible trade of electric energy of the micro-grid MGi and the micro-grid MGj; />Represents the electric power supplied by the micro-grid MGi to the micro-grid MGj at time t, +.>Positive value indicates sales->Negative values indicate purchase, and transaction fees for MGi and other micro-networks;
therefore, after considering the energy trade between regions, the objective function will become:
In the method, in the process of the invention,to consider the objective function of MGi after electric energy transaction between micro-networks; />To disregard the total cost of MGi in the electric energy transaction between micro-networks;
the power balance constraint may be rewritten as:
in order to promote the smooth achievement of the multi-main-body cooperation alliance in the multi-micro-grid system, each main body preferably considers the transaction in the multi-micro-grid system, and the flexible electric energy transaction price between MGOs is ensured to be between the upper-level network selling and purchasing electric energy price, specifically:
in step S4, the asymmetric nash bargaining model:
the Nash negotiation model is a cooperative game, and a plurality of participants perform cooperative profit distribution through mutual negotiation while minimizing social cost of the participants. Through nash bargaining, the value of flexibility provided by each microgrid operator is quantified and corresponding revenue allocated thereto. Each MGO is stimulated to consume flexible resources, activating the system flexibility potential to the maximum extent.
The Nash negotiating standard model is shown in formula (37):
in the method, in the process of the invention,representing the cost of MGi before participating in P2P power sharing, namely negotiating a breaking point; />Representing benefits of MGi promotion through cooperative operation;
the Nash negotiation model is a multiple variable coupled non-convex nonlinear problem, thus decomposing and converting the Nash negotiation standard model into two sub-problems: the social cost minimization sub-problem P1 and the income distribution sub-problem P2 are solved in sequence; p1 determines the optimal energy trading and scheduling for each microgrid, and P2 studies the problem of revenue distribution after the multi-microgrid power sharing collaboration, the social cost being defined as the sum of all the total MGO costs in the multi-microgrid system.
The solving method of P1 is as follows:
and (3) making:micro-grid alliance cost minimization sub-problem P1:
selecting an exponential function to form a nonlinear energy mapping function to quantify the bargained capability of each micro-grid according to the contribution of the participation in the electric energy sharingd i ：
In the method, in the process of the invention,to supply the maximum value of the electric energy to each micro-grid; />Maximum value of the received electric energy in each micro-network; />Is->Is the maximum value of (2); />Is->Is a maximum value of (a).
The solving method of P2 is as follows:
substituting the optimal interaction electric quantity obtained in the P1 into the P2, wherein the profit distribution model of the asymmetric bargaining is as follows:
/>
wherein, the upper scale is the optimal solution obtained by P1, the formula (45) ensures that each micro-grid with energy sharing contribution can obtain benefits, and the objective function expression takes logarithm to be converted into a minimum problem formula (46) for solving; revenue distributor problem (P2):
an active distribution network architecture with multiple micro-grids is shown in fig. 1. The new energy form of the MG1 is mainly photovoltaic, and the MG2 and the MG3 are mainly wind power resources; while MG2 is photovoltaic mounted. The micro-grid comprises a fan, a photovoltaic device, a micro-combustion engine, an electric energy storage device, a controllable load and other devices. Assume that the parameters of each piconet operator device are the same. In fig. 1, the 6, 11 and 13 nodes are respectively connected to 3 micro-grids, and the shared energy storage is connected to 12 nodes. Node 1 is the public connection point of ADN and the upper power grid, and nodes 4, 5 and 6 are respectively provided with two photovoltaic units and a fan of the distribution network. Under a typical day, the distribution network and the new energy output and load curves of all micro-grids are shown in fig. 2-5. The ADMM algorithm parameter settings are as follows: the maximum iteration number is 300; problem 1 Convergence threshold of 10 –4 Penalty factor of 10 –2 The method comprises the steps of carrying out a first treatment on the surface of the Problem 2 Convergence threshold of 10 –4 The penalty factor is 1. The electricity selling price of the high-voltage main network is 0.65 yuan/kWh,is 0.8 yuan/kWh. Suppose that the time-of-use internet power price established by ADN remains unchanged at 0.3 yuan/kWh. The parameters of each micro-grid are shown in table 1, and the operation parameters of SESS are shown in table 2. The scheduling period of the energy market is set to 1 hour for a total of 24 time periods.
Table 1 microgrid operating parameters
TABLE 2SESS operating parameters
(5-1) Master-Slave gaming optimization results
The optimization iterative process of DSO is shown in fig. 6. A stabelberg equilibrium was reached at the 38 th iteration.
(5-2) shared energy storage charging and discharging strategy
Fig. 7 is a diagram of the charge-discharge strategy and SOC variation of the SESO during a complete energy storage cycle. It can be seen that 0:00-8:00 is a distribution network valley period, and the SES is charged from distribution network power purchase after meeting all micro-grid charging and discharging requirements; during peak and flat periods of the distribution network, the SESS is discharged as much as possible to be sold to the distribution network for profit after meeting the charging and discharging requirements of all micro-grids. The SESS reaches a peak value of 0.698 at 10:00, and the energy storage SOC value returns to 0.309 near the initial value at 24:00, so that a complete energy storage charging and discharging period is formed in one day.
(5-3) Multi-microgrid lease strategy
Under the multi-objective optimization scene, the weight given to the two objective functions is 0.5. Fig. 8 to 10 are graphs comparing the charge and discharge power requirements of the micro-grids MG1, MG2, MG3 for stabilizing the net load fluctuation rental SESS with the net load curves before and after stabilizing. Table 3 shows the net load mean square error comparison and the energy storage lease costs for the micro-grid. It can be seen that each micro-grid stabilizes a degree of net load fluctuation by storing energy for charging and discharging.
TABLE 3 Multi-objective optimization results for each micro grid
(5-4) Multi-microgrid P2P electric energy transaction results
As can be seen from fig. 11, during the early days, the electric energy transaction amount between the 3 micro-grids is in a real-time balance state. In the period of 8:00-12:00, the MG1 photovoltaic power generation energy is excessive, and the electric energy is selected to be sold; local renewable energy sources of the MG2 and the MG3 are insufficient in power generation, and electric energy is selected to be purchased. During periods 18:00-21:00, the MG1 load demand increases resulting in insufficient self-power, and the purchase of electrical energy from MG2 and MG3 is selected. In the period of 00:00-7:00, the energy source of the wind power generation of the MG3 is surplus, and the electric energy is sold to the MG1. The trade electricity price formulated by bargained trade between micro-networks is shown in fig. 12, and the time-sharing electricity selling price is lower than the power distribution network and higher than the internet electricity price in each period. Therefore, the electricity selling micro-grid can acquire more electric energy sales profits through bargained transactions, and the electricity purchasing micro-grid can reduce the purchase cost of electric energy through bargained transactions.
Electric energy flexibility output conditions of each MG operator under cooperative operation: taking MG1 as an example: MG1 is mainly photovoltaic power supply. The photovoltaic unit has no output at night 0:00-6:00 and in the evening 19:00-24:00; firstly, MG1 leases the SESS at 0:00-6:00 MG1 to support part load requirements through SESS charging in order to smooth out fluctuations in the payload; leasing the SESS at 8:00-18:00 MG1 to consume a portion of the excess photovoltaic through SESS discharge; the period of 0:00-7:00 is a distribution network valley period, and after the MG1 rents the SESS, power shortage is made up by purchasing power from the distribution network, and the gas turbine is not put into. During the period 8:00-18:00, there is still excess REs (renewable energy source (renewable energys)) after the MG1 leases the SESS, at the same time, the MG2 and MG3 are in electric energy deficiency, the MG1 mutually supplements the excess electric energy to the MG2 and MG3, during the period 11:00-18:00, the MG1 utilizes the gas turbine output to increase the electric energy output to assist the micro-grids MG2 and MG3 to support the load demand thereof, the period 19:00-21:00 is the distribution network peak period, the MG1 load demand is higher and the local REs are insufficient in power generation, the MG1 firstly purchases electricity from the MG2 and MG3 through BESS discharge, the MT output is used for supporting the local load demand, and the residual power is compensated by purchasing electricity from the distribution network.
The BESS inside the MG1 mainly at the flat valley time of electricity price period such as 23:00-24:00 and 7:00-9:00 charging and discharging in the high electricity price time periods of 11:00-13:00 and 19:00-21:00, so that the electricity purchasing quantity to the distribution network is reduced, and the running cost is reduced; the electric load after the demand response achieves the effect of peak clipping and valley filling, so that the load in the period with higher electricity price is transferred to other periods with lower electricity price, and the energy supply pressure of the micro-grid operators at the peak time is relieved to a certain extent.
The technical features of the above-described embodiments may be arbitrarily combined, and in order to simplify the description, all possible combinations of the technical features in the above-described embodiments are not exhaustive, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the appended claims.
Claims (8)
1. An end-to-end decentralized transaction method for side flexible resources, comprising:
S1, stackelberg game framework
Constructing a multi-slave Stackelberg game model which takes an active power distribution network ADN as a main body and a multi-micro power grid and a shared energy storage system SESS as a slave body; the power distribution system operator DSO is taken as a leader main body, self-contained new energy is preferentially consumed, a time-of-use electricity price strategy is formulated according to an ADN payload curve, and the micro-grid operator MGO and the shared energy storage operator SESO are stimulated to participate in peak clipping and valley filling scheduling of the ADN; MGO and SESO are taken as follower slaves, time-of-use electricity prices are responded, and electricity purchasing and selling plans are formulated; obtaining an ADN optimized pricing strategy and a follower energy strategy by solving a game equilibrium solution;
the Stackelberg game model is:
ADN's own new energy output has a volatility, expressed by the payload as the following expression (1):
wherein,new energy output for ADN, +.>For ADN load, ++>Is the payload of ADN;
the decision variable of ADN is time-of-use electricity price, the maximum operation benefit is used as an optimization target, and the objective function is shown as the following expression (2)
max U ADN ＝I MMG +I SESS -C grid (2)
Wherein I is MMG The power interactive income of the ADN and the multi-micro-grid is obtained; i SESS Power interactive benefits for ADN and siso; c (C) grid The electricity purchasing cost of the ADN from the upper-level power grid is set; max U ADN The maximum operation benefit of ADN;
I MMG 、I SESS And C grid The following formulas (3) to (5) are respectively adopted:
t represents the number of scheduled time periods; t represents a period;for the selling price of ADN, +.>Electricity purchase price for ADN; />Is a plurality of micro-sizedTotal power sold by the power grid and ADN, < >>The total power purchasing power of the multiple micro-grids and the ADN is achieved; />Is the electricity selling power of the ith micro-grid and the ADN; />The power purchasing power of the ith micro-grid and ADN is that M is the number of the micro-grids;
is the selling electric power of SESO and ADN, < ->The power of electricity purchase of SESO and ADN;
selling electricity price for the power grid; />The power purchasing power of the ADN from the upper-level power grid is realized;
the constraint conditions of ADN are:
the upper limit of the average electricity selling price;
s2, upper layer optimization:
at the upper layer, the micro-grid establishes a SESS charging and discharging strategy to stabilize self-payload power fluctuation, calculates the capacity requirement and the power requirement of leased shared energy storage, and uploads demand information to lease energy storage as required; the SESO gathers information from the micro-grid, and establishes an SESS charging and discharging strategy according to the total net energy storage charging and discharging requirement of the multi-micro-grid;
s3, lower layer optimization: after the micro-grid stabilizes partial net load power fluctuation, adopting a P2P electric energy sharing transaction strategy based on asymmetric Nash bargaining under a cooperative game; firstly, energy transaction amount negotiations are conducted, and then energy transaction price negotiations are conducted;
S4, decomposing a lower model: using a nash negotiation model and decomposing and converting the nash negotiation model into two sub-questions: social cost minimization sub-problem P1 and revenue distribution sub-problem P2;
s5, solving algorithm: and solving asymmetric Nash bargaining problems P1 and P2 by adopting a full-distributed ADMM method.
2. The method for end-to-end distributed transaction of side-to-side flexible resources according to claim 1, wherein in step S2, the upper layer optimization model:
constructing a multi-objective optimization model with minimum net load mean square error and minimum SESS lease cost of the micro-grid;
the minimum objective function of the net load mean square error of the micro-grid is:
wherein f 1 The mean square error of the payload after the ses is leased for the microgrid,load power for the ith micro-grid in period t,/->For the new energy power of the ith micro-grid in the period t, P MGi，ave For the equivalent load average value of the ith micro-grid in period t,/>Charging power for the SESS of the ith microgrid in period t, < >>SESS discharge power of the ith micro-grid in a t period;
minimal objective function of SESS lease cost:
the SESS charges according to the leasing capacity and the charging and discharging power, and the micro-grid energy storage using cost model is shown in the following formula (9):
Wherein f 2 The energy storage and use cost of the micro-grid is u is the unit power charge and discharge cost of the SESO, v is the unit energy capacity lease cost of the SESO, and w is the unit power capacity lease cost of the SESO;power capacity required for a microgrid, +.>The energy capacity requirement required for the microgrid; wherein->Maximum value of charge and discharge power for leasing energy storage for micro-grid Expressed as:
wherein alpha is Lea Representing the capacity margin coefficient,for a maximum value of microgrid leased SESS power during a dispatch period,the minimum value of the SESS electric quantity is leased for the micro-grid in the dispatching period;
the micro-grid leases share the conservation of total charge and discharge of energy storage in one scheduling period, and can not be charged and discharged at the same time;
wherein eta is i，c Charging efficiency for shared energy storage; η (eta) i，d Discharge efficiency for shared energy storage;
sharing an energy storage optimization model:
SESO (semiconductor integrated circuit) summarizing all micro-grid charging and discharging power requirementsThe multi-microgrid lease shared energy storage and the charging power requirement thereof are shown as follows:
wherein M is the number of micro-nets;
the objective function of SESO is expressed as:
U SESS ＝I ADN -G SESS (14)
in U SESS To share the operational benefits of energy storage, I ADN C for power interactive benefits of SESS and ADN SESS The cost of charge-discharge operation and maintenance of energy storage,charging power for SESS, +. >Zeta is the operation maintenance cost coefficient of charging and discharging of the energy storage unit power for SESS discharging power;
in the SESS charge and discharge constraint, the charge and discharge power does not exceed the SESS limit power and can not be charged and discharged at the same time;
in the constraint of upper and lower bounds of the shared energy storage electric quantity:
in the method, in the process of the invention,is the maximum value of the charge and discharge power of SESS; />Is the maximum energy capacity of the SESS; η (eta) c Charge efficiency η of SESS d Discharge efficiency for SESS; mu (mu) s The standby capacity percentage of the energy storage system; />The electricity storage capacity of SESS at the time t; />The electricity storage capacity of SESS at the time t-1;
in the shared energy storage system power balance constraint:
in the method, in the process of the invention,and->Taking 1 time as a binary variable to represent electricity purchasing operation of SESO and ADN in t period.
3. The end-to-end decentralized transaction method according to claim 2, wherein in step S3, firstly, energy transaction amount negotiation is performed, each micro-grid main body forms a benefit community with the aim of minimizing social cost, and the expected energy transaction amount is transmitted to other main bodies, so as to obtain multi-micro-grid electric energy transaction amount capable of realizing the minimization of social cost; and then carrying out energy transaction price negotiation, and transmitting the expected energy transaction price to other subjects by each subject, and finally obtaining the energy transaction price acceptable by each subject through the negotiation.
4. A side-to-side flexible resource end-to-end decentralized transaction method according to claim 3, wherein the underlying optimization model:
after the charge and discharge of each micro-grid stabilize part of net load fluctuation, regenerating an equivalent load curve:
wherein P is MGi，eq Is a new load after the micro-grid stabilizes the net load fluctuation;
the total cost of the ith micro grid operator consists essentially of the power interactive revenue I with ADN i，ADN Operation cost C of energy storage system in micro-grid i，BESS Translatable load scheduling compensation cost C i，L Power generation and operation cost C of controllable unit MT i，MT ；
The total cost of the ith micro-grid, MGi is the ith micro-grid; />Is the electricity selling power of MGi to ADN,is the electricity purchasing power from MGi to ADN, ζ b Representing the degradation cost coefficient of the energy storage battery; />Is the charging power of the BESS inside the MGi, < >>Is the discharge power of the BESS inside MGi, < >>The compensation unit price given to the user by transferring the load; />The electric load can be transferred for the MGi user; a, a Mi 、b Mi 、c Mi The power generation cost coefficient in MGi; />Is the output power of the MT in MGi.
5. The method for peer-to-peer distributed transaction of side-flexibility resources of claim 4,
1) Power balance constraint of micro grid system:
in the method, in the process of the invention, The total output power of the renewable energy source in the MGi;
2) Energy transaction amount constraint:
in the method, in the process of the invention,representing the upper limit of the interaction quantity of the micro-grid operators and the upper-level network energy sources;
3) MT operating power constraint:
in the method, in the process of the invention,is the lower output power limit of the MT; />An upper output power limit for the MT;
4) Load constraints can be transferred:
wherein: alpha i，tran Representing a transferable electrical load coefficient;a transferable load represented by a time t is transferred to another time;
5) Energy storage device constraints:
in the method, in the process of the invention,representing the energy storage energy of energy storage equipment in the micro-grid at the time t; />Representing the energy storage energy of the energy storage equipment in the micro-grid at the time t-1; Δt represents the time interval between two moments; />Representing the energy storage capacity of the energy storage device at the moment 0; />Representing the energy storage energy of the energy storage device at the moment T; θ i A self-loss coefficient representing the stored energy; η (eta) i，c And eta i，d Respectively representing the energy charging efficiency of the energy storage device and the energy discharging efficiency of the energy storage device; />And->Respectively representing the maximum value of the charging power of the energy storage device and the maximum value of the discharging power of the energy storage device; />Representing a maximum value of the energy storage capacity of the energy storage device;
the transmission cost of each micro-grid is as follows:
wherein,the cost is converted into a coefficient; />Transmission cost for MGi;
trade fees between the microgrid and other microgrid MGs:
Wherein,trade electricity prices representing MGi and MGj power flexibility trade; />Represents the electric power supplied to MGj by MGi at time t,/-)>Positive value indicates sales->Negative values indicate purchase, and->Transaction fees for MGi and other micro-networks;
the objective function is:
in the method, in the process of the invention,to consider the objective function of MGi after electric energy transaction between micro-networks; />To disregard the total cost of MGi in the electric energy transaction between micro-networks;
the power balance constraint is:
and is also provided with
6. The end-to-end decentralized transaction method according to claim 5, wherein in step S4, the nash negotiation standard model is represented by formula (37):
in the method, in the process of the invention,representing the cost of MGi prior to participating in P2P power sharing; />Representing benefits of MGi promotion through cooperative operation;
the above model decomposition is converted into two sub-problems: the social cost minimization sub-problem P1 and the income distribution sub-problem P2 are solved in sequence; p1 determines the optimal energy transaction and scheduling of each micro-grid, P2 researches the income distribution problem after the multi-micro-grid electric energy sharing cooperation, and the social cost is the sum of all the total MGO cost in the multi-micro-grid system.
7. The method for end-to-end decentralized transaction of side flexible resources according to claim 6, wherein the solving method of P1 is as follows:
And (3) making:
micro-grid alliance cost minimization sub-problem P1:
bargained capability size d of each micro-net i The method comprises the following steps:
in the method, in the process of the invention,supplying the maximum value of the electric energy to each micro-grid; />Maximum value of the received electric energy in each micro-network;is->Is the maximum value of (2); />Is->Is a maximum value of (a).
8. The method for end-to-end decentralized transaction of side flexible resources according to claim 7, wherein the solving method of P2 is as follows:
substituting the optimal interaction electric quantity obtained in the P1 into the P2, wherein the profit distribution model of the asymmetric bargaining is as follows:
wherein, the variables marked with "+" are the optimal solutions obtained by P1;
the revenue distributor problem P2 is:
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