CN115115096A - Active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing - Google Patents

Active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing Download PDF

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CN115115096A
CN115115096A CN202210647014.7A CN202210647014A CN115115096A CN 115115096 A CN115115096 A CN 115115096A CN 202210647014 A CN202210647014 A CN 202210647014A CN 115115096 A CN115115096 A CN 115115096A
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李飞
李咸善
方子健
李振兴
张彬桥
鲁明芳
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China Three Gorges University CTGU
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Abstract

An active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing is used for establishing an active power distribution network-multi-microgrid alliance-microgrid collaborative optimization scheduling model based on a double game; obtaining model parameters, and solving an initial value by a given algorithm; nesting the solving process of the lower-layer optimization model into the optimization problem of the upper-layer model based on a double-layer optimization theory and Nash equilibrium definition, solving the upper-layer optimization model by using a PSO particle swarm algorithm, and solving the lower-layer optimization model by using a Yalmip/Cplex tool box; the Nash equilibrium state is achieved through multiple rounds of iteration, and a final Nash equilibrium solution set is output; and further based on a cooperative game theory, distributing the cooperative residue of the multi-micro-grid alliance by using a xiapril value method. According to the invention, the time-of-use electricity price is optimized on the side of the power distribution network, and the benefit balance between the power distribution network and a plurality of micro-grids is considered; the mutual energy compensation of the interior of the multiple micro-grids is realized at the multi-micro-grid alliance side, and the new energy consumption capability on the spot is improved.

Description

Active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing
Technical Field
The invention relates to the technical field of power distribution network optimization scheduling, in particular to an active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing.
Background
In order to promote local consumption of new energy, wind and light new energy is generally connected to an Active Distribution Network (ADN) in the form of a Microgrid (MG) to form an Active distribution network system including multiple microgrids, and the multiple microgrids participate in optimal operation scheduling of the ADN together, so that the overall reliability and economy of the system can be improved, as in document [1 ]: consider active power distribution network double-layer energy management [ J ] of microgrid access, southern grid technology, 2020, 14 (7): 30-38.
When multiple micro-grids are connected to the same power distribution network and independently operate, the stored energy of each micro-grid is only used for balancing the power difference between the new energy and the load of each micro-grid, and the surplus power or the surplus power is continuously balanced through interaction with the upper ADN. Because the energy storage charging and discharging and energy mass production consumption characteristics of each microgrid are different, the energy of a certain microgrid may be excessive in certain scheduling periods, and the power shortage phenomenon exists in adjacent microgrids, and the microgrid energy storage in an independent operation mode only serves the self load and cannot coordinate the energy storage behaviors of the adjacent microgrids, so that the disorder characteristics exist in the energy storage charging and discharging behaviors of a plurality of microgrids in the whole view angle, and the energy storage resource waste is caused.
While the multi-microgrid grid-connected system has two complementarity: 1) the charging and discharging behaviors of the multi-microgrid energy storage application are different and have complementarity; 2) the energy production of multiple micro-grids has different energy consumption characteristics and complementarity. By utilizing the two characteristics, a plurality of micro-grids are united to form a Multi-micro-grid alliance (MMG), which is beneficial to mutual assistance of interconnection among micro-grids, plays a complementary effect of each micro-grid resource and promotes the operation benefit of the active power distribution network to be improved. As in document [2 ]: X.Feng, J.Gu and X.Guan.optimal allocation of hybrid Energy storage for microorganisms based on multi-attribute reliability the term [ J ]. Journal of model Power Systems and Clean Energy,2018,6(1):107-117.
Document [3 ]: the method comprises the following steps of (1) obtaining a multi-micro-grid double-layer optimized dispatching [ J ] considering interactive power control and double-side bidding transaction, 2020, 48 (11): 10-17, an optimized scheduling method considering interaction power control of the multi-microgrid and the distribution network is provided, interaction power fluctuation between the multi-microgrid and the distribution network is inhibited, and comprehensive cost of the multi-microgrid system can be reduced.
Document [4 ]: huangzhanhao, Zhang yao super, Zhengfeng, etc. based on the active power distribution network day-real time energy management method of different benefit subject coordination optimization [ J ] power grid technology, 2021, 45 (6): 2299-.
Document [5 ]: old wave, grand 26107, formerly, great, day-ahead optimized scheduling of active distribution networks considering micro-energy network access [ J ]. power systems and their automated papers, 2020, 32 (11): 102-108, a scheduling strategy of multiple micro-grids accessing an active power distribution network at the later date is researched, and the comprehensive loss of the system is effectively reduced.
Document [6 ]: Y.Fu, Z.Zhang, Z.Li, et al.energy management for hybrid AC/DC distribution system with micro computer using master-slave Grid and robust optimization [ J ]. IEEE Transactions on Smart Grid,2020,11(2):1510-1525. an energy management framework of AC/DC hybrid power distribution system containing micro power Grid group is established, and benefit balance of each main body is realized through a master-slave game model.
Document [7 ]: marzband M, Javadi M, Pourmous S A, et al, an advanced detail market for active distribution Systems and home micro-networked based on the business [ J ]. Electric Power Systems Research,2018,157:187-199. A model for home microgrid optimization operations and retail market transactions is proposed to increase the profits of all participants.
Document [8 ]: the method comprises the steps of Y, Zhang, X, Ai, J, Wen, J, Fang and H, He, data-adaptive robust optimization method for the electronic dispatch of active distribution networks [ J ], IEEE Transactions on Smart Grid,2019,10(4): 3791-.
Document [9 ]: H.Sheng, C.Wang, B.Li, J.Liang, et al.Multi-time active distribution network scheduling, scheduling and user comprehensive utilization [ J ]. IEEE Transactions on Industrial Applications,2021,57(3):1995-2005. A scheduling method considering user demand response and electricity utilization satisfaction is proposed to level down load fluctuations and promote new energy consumption.
Document [10 ]: wei C, Fadlullah Z M, Kato N, et al GT-CFS, A gate the electronic correlation for the conversion of power loss in micro grids [ J ]. IEEE Transactions on Parallel and Distributed Systems,2014,25(9) 2307 + 2317, a multi-microgrid alliance scheduling strategy based on game theory is provided, and power loss is effectively reduced.
At present, a large number of research achievements exist for optimizing and scheduling an active power distribution network comprising multiple micro-grids, but the multiple micro-grids are rarely involved in sharing energy storage.
Disclosure of Invention
Aiming at the problems of charging and discharging disorder, resource waste and the like generated after a plurality of micro-grids are connected into an active power distribution network, the invention provides an active power distribution network game optimization scheduling method considering multi-micro-grid energy storage sharing, and an active power distribution network (ADN) -multi-micro-grid alliance (MMG) -micro-grid (MG) cooperative optimization scheduling model based on a double game is established; the active distribution network ADN and the multi-micro-grid-alliance MMG form a master-slave game relation, and the active distribution network ADN stimulates the multi-micro-grid-alliance MMG to buy electricity by changing the price of the purchased electricity, so that the profit maximization of electricity selling is realized; the MMG of the multi-microgrid union responds to the ADN quotation of the active power distribution network to formulate a power purchasing strategy so as to realize the minimum total running cost; further based on the cooperative game theory, a Sharey Value Method (SVM) is adopted to distribute the cooperative residue in the MMG. The dual game model is solved by nesting a Yalmip/Cplex solver in a particle swarm algorithm, the upper ADN layer is solved by the particle swarm algorithm, and the optimization problem of the lower MMG layer is solved by the Yalmip/Cplex solver. The method optimizes the time-of-use electricity price of the ADN, considers the benefit balance between the power distribution network and the multiple micro-grids, realizes the power mutual aid in the multiple micro-grids, and further improves the local consumption capability of new energy.
The technical scheme adopted by the invention is as follows:
an active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing comprises the following steps:
step 1: establishing a master-slave game optimization model according to the game relation of the ADN and the MMG of the multi-micro power grid alliance;
step 2: constructing constraint conditions of a master-slave game optimization model;
and step 3: acquiring parameters of an active power distribution network (ADN) and a multi-micro power grid alliance (MMG) and particle swarm algorithm parameters for solving a master-slave game optimization model;
and 4, step 4: setting starkeberg-Nash equilibrium solution initial value of master-slave game optimization model
Figure BDA0003686361570000031
And calculates the response set of the lower MMG
Figure BDA0003686361570000032
Substituting the obtained product into upper ADN to perform a new round of optimization;
and 5: during the k cycle, the upper layer ADN is given by k-1 round of MMG equalization solution
Figure BDA0003686361570000033
As an input strategy, the optimal strategy set of ADN in the current k-th round is obtained
Figure BDA0003686361570000034
And corresponding response set of lower MMG
Figure BDA0003686361570000035
Step 6: judging whether the solving result of the step five is a Stackelberg-Nash equilibrium solution or not; if the conditions are met, outputting a game equilibrium solution, and entering the step 7; otherwise, executing k to k +1, and returning to the step 5;
and 7: and distributing the cooperation surplus of the MMG of the multi-microgrid alliance according to the SVM method, and calculating the income allocated by each microgrid member.
In the step 1, the master-slave game optimization model specifically comprises the following steps:
the ADN is used as an upper leader, and the electricity selling price and the electricity purchasing price are made according to the total system operation cost; the MMG is used as a lower-layer follower, and the electricity purchasing and selling amount of the transaction is adjusted according to the electricity purchasing and selling price set by the ADN, so that the lowest operation cost is realized.
The ADN utilizes the set electricity purchasing and selling price to guide the MMG to dynamically adjust the electricity purchasing and selling strategy, so that the balance of the internal power of the ADN and the optimal economic benefit of the ADN are guaranteed, the MMG responds to the electricity purchasing and selling price to adjust the electricity strategy, the ADN also continuously and dynamically corrects the electricity price according to the updated alliance strategy, and when the electricity trading strategy and the ADN electricity price strategy of the MMG are stable and unchanged, the optimization model reaches a game Nash equilibrium state.
The mathematical expression for the master-slave gaming model G can be described as:
G＝{{ADN,MMG}；{ρ s ,ρ b }；{P PCC,MMG,b ,P PCC,MMG,s }；{J ADN }；{J MMG }} (1)；
in the formula: { ADN, MMG } represents game parties and is respectively an active power distribution network and a multi-micro power grid alliance; { ρ } b ,ρ s Representing an ADN electricity purchasing and selling price strategy set; rho b Representing the ADN buyback price; rho s Representing the ADN electricity selling price; { P PCC,MMG,b ,P PCC,MMG,s Represents the MMG electricity purchasing and selling strategy set, P PCC,MMG,b 、P PCC,MMG,s Respectively representing the electricity purchasing quantity and the electricity selling quantity of the MMG.
When running cost J of ADN ADN And running cost J of MMG MMG When the optimal conditions are jointly reached, the game model G has a unique Nash equilibrium solution and meets the following conditions:
Figure BDA0003686361570000041
in the formula, equilibrium solution of game model
Figure BDA0003686361570000042
Respectively represent: ADN electricity selling price, ADN electricity purchasing price, MMG electricity purchasing quantity and MMG electricity selling quantity.
The upper-layer ADN objective function is the total operation cost of the system, including the power generation cost and the operation and maintenance cost of the unit, the power interaction cost between the ADN and the large power grid, the power interaction cost between the ADN and the MMG, and the like, and the objective function is shown in the formula (3).
Figure BDA0003686361570000043
In the formula, C ADN Representing the total ADN operating cost; c PCC,MMG Representing ADN and MMG power interaction costs; c g Representing the power interaction cost of the ADN and the superior large power grid;
Figure BDA0003686361570000044
the generating cost of the controllable unit in the ADN is obtained;
Figure BDA0003686361570000045
the operation and maintenance cost of the generator set in the ADN is reduced.
The objective function of the lower MMG is shown in equation (4).
J MMG ＝minC MMG ＝min(C MTΣ +C PCC,MMG +C OMΣ ) (4)；
In the formula: c MMG Represents the total cost of MMG operation; c MTΣ Represents the total cost of gas turbine power generation in the MMG; c OMΣ And the total operation and maintenance cost of the generator set in the MMG is represented.
In the step 2, the constraint conditions of the master-slave game optimization model include:
constraint conditions of the upper layer ADN and constraint conditions of the lower layer MMG, wherein the constraint conditions of energy mutual compensation of the multiple micro-grids are contained in the lower layer MMG model.
The constraints for the upper ADN are as follows:
1) power flow constraint of the power distribution network:
Figure BDA0003686361570000051
in the formula (5), n 1 The number of ADN nodes; p is i 、Q i Respectively injecting active power and reactive power into the node i; u shape i Is the voltage amplitude of node i; g ij 、B ij 、δ ij Respectively, the conductance value, the susceptance value and the voltage phase angle difference between the node i and the node j.
2) ADN line power constraint:
Figure BDA0003686361570000052
in the formula (6), the reaction mixture is,
Figure BDA0003686361570000053
for the active power on the ith branch of the distribution network during the period t,
Figure BDA0003686361570000054
the maximum transmission power of the l branch.
3) ADN node voltage constraint:
Figure BDA0003686361570000055
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000056
respectively, the lower and upper voltage limits of node i.
4) ADN unit output constraint:
Figure BDA0003686361570000057
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000058
respectively representing the lower limit and the upper limit of the output of the unit j;
Figure BDA0003686361570000059
representing the output of the unit j at the moment t.
5) Time-of-use electricity price constraint:
the basic model of the time-of-use electricity price is as follows:
Figure BDA00036863615700000510
Figure BDA0003686361570000061
formulas (9) and (10) respectively represent time-of-use electricity selling price and electricity purchasing price constraints set by the ADN; t is g ,T p ,T f Respectively representing the valley, flat and peak time periods; rho sg ,ρ sp ,ρ sf Respectively representing the electricity selling price in valley, flat and peak time periods; rho bg ,ρ bp ,ρ bf The buyback prices of the valley, the plateau and the peak time periods are represented respectively; the time-of-use electricity price needs to satisfy the inequality relation rho sf ≥ρ sp ≥ρ sg 、ρ bf ≥ρ bp ≥ρ bg And the electricity selling price is larger than the electricity purchasing price in the same time period
Figure BDA0003686361570000062
The constraints for the lower MMG are as follows:
1) each microgrid power balance constraint:
Figure BDA0003686361570000063
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000064
the load capacity of the microgrid i in the time period t is shown;
Figure BDA0003686361570000065
the new energy output is provided for the micro-grid i in the time period t,
Figure BDA0003686361570000066
the power purchased from the distribution grid for the microgrid i,
Figure BDA0003686361570000067
power purchased back from the microgrid i for the distribution grid,
Figure BDA0003686361570000068
the output of a gas turbine in the microgrid i is obtained;
Figure BDA0003686361570000069
representing the mutual aid power of the microgrid i in the multi-microgrid alliance, wherein n represents the number of alliance members; d ji And D ij All are binary state variables, represent the state of mutual power compensation between the micro-grids, represent no power transmission when taking 0, D ji 1 denotes the transmission of power from the microgrid j to the microgrid i, D ij 1 represents that the micro-grid i transfers power to the micro-grid j;
Figure BDA00036863615700000610
representing the power value transmitted from the microgrid j to the microgrid i at the moment t;
Figure BDA00036863615700000619
representing the power value transmitted from the microgrid i to the microgrid j at the moment t;
Figure BDA00036863615700000611
and
Figure BDA00036863615700000612
and respectively representing the self energy storage charging and discharging power of the microgrid i at the moment t.
2) Constraint condition of MMG internal energy mutual aid:
Figure BDA00036863615700000613
in the formula (I), the compound is shown in the specification,
Figure BDA00036863615700000614
representing the residual electric quantity of the microgrid i in the period t,
Figure BDA00036863615700000615
and (4) representing the residual load of the microgrid i in the t period.
3) Tie line constraints between each microgrid and the ADN:
Figure BDA00036863615700000616
in the formula (I), the compound is shown in the specification,
Figure BDA00036863615700000617
and
Figure BDA00036863615700000618
the lower and upper limits of the microgrid i and ADN tie line power are indicated, respectively.
4) And (3) constraining the upper and lower output limits of each microgrid gas turbine:
Figure BDA0003686361570000071
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000072
and
Figure BDA0003686361570000073
respectively representing the lower limit and the upper limit of the gas turbine power of the micro-grid i;
Figure BDA0003686361570000074
representing the output of the gas turbine of the microgrid i at time t.
5) Energy storage and charge-discharge constraints of each micro-grid:
Figure BDA0003686361570000075
in the formula, P i Cap The energy storage power capacity of the microgrid i.
6) And (3) upper and lower bound constraint of energy storage electric quantity of each micro-grid:
Figure BDA0003686361570000076
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000077
a minimum state of charge for storing energy for the microgrid i;
Figure BDA0003686361570000078
the energy storage residual capacity of the micro-grid i at the moment t is obtained;
Figure BDA0003686361570000079
is the energy storage capacity of the microgrid i.
7) The electric quantity relation of each microgrid energy storage two adjacent time periods is as follows:
Figure BDA00036863615700000710
in the formula (I), the compound is shown in the specification,
Figure BDA00036863615700000711
the energy storage residual capacity of the micro-grid i at the time t-1 is obtained;
Figure BDA00036863615700000712
charging efficiency and discharging efficiency of the energy storage of the micro-grid i are respectively obtained; Δ t denotes a scheduling time interval.
In the step 3, initial parameters of the active power distribution network ADN are obtained, including a power distribution network frame structure, node data, branch data, new energy unit and controllable unit capacity and operation parameters, a tie line power limit value, an initial electricity selling price, an initial buyback price and the like.
And acquiring the operation parameters of the MMG of the multi-microgrid alliance, including the capacity, the operation parameters and the like of each microgrid gas turbine, the new energy source unit, the energy storage system.
The method for obtaining the particle swarm algorithm parameters solved by the master-slave game optimization model comprises the following steps: population quantity, maximum iteration times, particle flight speed range, learning factors, initial inertia weight and the like;
in the step 4: the follower MMG responds to the initial electricity purchase and sale price of the leader ADN, the minimum total running cost of the MMG is taken as an optimization target, and as shown in a formula (4), a constraint condition is met: equation (11) to equation (17) to obtain the response set of MMG under the first-wheel ADN electrovalence excitation
Figure BDA00036863615700000713
Continuously substituting the response set of the MMG into an upper-layer ADN model, taking the formula (3) as an objective function, considering constraint conditions from the formula (5) to the formula (10), and solving a new electricity price strategy set of the next round of ADN
Figure BDA00036863615700000714
The MMG continuously responds to the ADN electricity price strategy and gives a response set
Figure BDA00036863615700000715
In this manner, alternating iterations are repeatedAnd solving to obtain the optimal strategy set of the game participants in each cycle.
Wherein:
Figure BDA0003686361570000081
respectively representing the ADN purchase and sale electricity price strategy initial values;
Figure BDA0003686361570000082
respectively representing MMG purchase and sale electric quantity strategy initial values;
Figure BDA0003686361570000083
respectively representing the electricity purchasing and selling strategy of the ADN in the first round circulation;
Figure BDA0003686361570000084
respectively represent the power purchasing and selling strategies of the MMG in the first round circulation.
In the step 5, the strategies of the game participants obtained in the k-th cycle are as follows:
Figure BDA0003686361570000085
in the formula: gamma ray 1,k A set of policies in the kth round for leader ADN; gamma ray 2,k Response set of MMG at k-th wheel for follower;
Figure BDA0003686361570000086
representing the purchase and sale price strategy of the ADN in the k round;
Figure BDA0003686361570000087
representing the power purchasing and selling strategy of the MMG in the k-th round;
Figure BDA0003686361570000088
strategy for indicating k-1 round with MMG
Figure BDA0003686361570000089
As input, the optimal strategy of the current k-th round leader ADN is obtained;
Figure BDA00036863615700000810
strategy for representing the k-th round with leader ADN
Figure BDA00036863615700000811
As an excitation signal, solving the optimal response strategy of the current kth wheel follower MMG; arg denotes the argument, argminJ ADN Representing the objective function J ADN Optimum strategy of ADN corresponding to minimum value
Figure BDA00036863615700000812
In the step 6, the method for judging whether the Stackelberg-Nash equilibrium solution is found or not is as follows:
if the optimization results of all game participants in the kth round are consistent with the optimization results of all game participants in the kth round-1, namely:
Figure BDA00036863615700000813
the game model finds the Stackelberg-Nash equilibrium solution
Figure BDA00036863615700000814
Otherwise, updating the iteration cycle k to k +1, and returning to the step five to continue solving until a Stackelberg-Nash equilibrium solution is found.
In formula (19):
Figure BDA00036863615700000815
a Nash equilibrium strategy set of ADN in k rounds of circulation;
Figure BDA00036863615700000816
a Nash equilibrium strategy set of the MMG in k rounds of circulation;
Figure BDA00036863615700000817
representing ADN electricity purchasing and selling price and MMG electricity purchasing and selling strategy corresponding to Nash equilibrium solution;
Figure BDA00036863615700000818
represents the optimal electricity selling price of the k-1 th round ADN;
Figure BDA00036863615700000819
represents the optimal buyback price of the k-1 th round of ADN;
Figure BDA00036863615700000820
the optimal electricity purchasing quantity of the k-1 th round of MMG is represented;
Figure BDA00036863615700000821
represents the optimal electricity selling amount of the k-1 th round MMG.
In step 7, the cooperation residual allocation model is specifically as follows:
and (4) obtaining the profit distributed by each member by utilizing an SVM method through the cooperation residue generated by the members of the alliance, and calculating the profit as shown in a formula (20).
Figure BDA0003686361570000091
In the formula (20), s is a union set formed by all micro-grids, v (i) is the income distributed by the micro-grid i, | s | is the number of subsets, ω (| s |) is a weight factor, v(s) is the cooperation residue of the sub-union s, v (s/i) is the cooperation residue of the sub-union except the member i, and n is the number of micro-grids participating in the union; (n- | s |)! Representing factorial of MMG member number n minus subset s member number; (| s | -1) |! Expressing factorial of the number of members of the subset s minus 1; n! Indicating a factorial in the MMG membership n.
The invention relates to an active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing, which has the following technical effects:
1) the invention fully utilizes the game relation of competition and cooperation of all benefit agents participating in scheduling, establishes an active power distribution network-multi-micro power grid alliance-micro power grid cooperative optimization scheduling model based on multiple games, and solves the model by combining Nash equilibrium definition with a particle swarm optimization algorithm to obtain an optimized scheduling scheme of all systems in the active power distribution network.
2) The invention optimizes the electricity price and the energy using strategy of the MMG when the ADN scores by utilizing the master-slave game competition between the ADN and the MMG, and gives consideration to the benefit balance between the ADN and the multiple micro-grids.
3) The complementary characteristics of the charging and discharging behaviors and the energy generation and consumption behaviors of each micro-grid energy storage system are fully utilized, the multi-micro-grid is united, the cooperative residue after the union is distributed according to the cooperative game theory, the power mutual aid in the multi-micro-grid is realized to the maximum extent, and the local consumption capability of new energy is further improved.
4) According to the invention, the time-of-use electricity price is optimized on the ADN side, and the benefit balance between the ADN and the MMG is considered; energy mutual aid in multiple micro-grids is achieved on the MMG side, and the on-site consumption capacity of new energy is improved.
Drawings
Fig. 1 is a diagram of an Active Distribution Network (ADN) dual-layer gaming framework including a multi-micro-grid alliance (MMG).
FIG. 2 is a flow chart of the ADN-MMG master-slave gambling model solving process.
FIG. 3 is a diagram of an exemplary system for ADN testing.
FIG. 4(a) is a graph of load and new energy output of MG 1;
FIG. 4(b) is a graph of load and new energy output of MG 2;
FIG. 4(c) is a graph of load and new energy output of MG 3;
fig. 4(d) is a graph of ADN load and new energy output.
FIG. 5(a) is a schematic diagram of the output of each device in MG 1;
FIG. 5(b) is a schematic diagram of the output of each device in MG 2;
fig. 5(c) is a schematic diagram of the output of each device in MG 3.
FIG. 6 is a graph of the energy mutual-aid power in the MMG.
Detailed Description
An active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing is used for establishing an active power distribution network-microgrid alliance-microgrid cooperative optimization scheduling model based on a double game and is implemented according to the following steps:
the method comprises the following steps:
s1.1, constructing an Active Distribution Network (ADN) -multi-micro power grid alliance (MMG) master-slave game relationship:
the ADN and the MMG belong to different benefit subjects respectively, have respective operation targets, and when the combined scheduling is carried out, the master-slave game scheduling between the ADN and the MMG is preferably adopted based on the game theory; and each member in the MMG has the same operation target, and cooperative game scheduling among the MMG members can be adopted. Thereby forming a double-layer game scheduling framework between an active power distribution network-a multi-micro power grid alliance-a micro power grid, as shown in figure 1.
The double-layer game process comprises the following steps:
1) and the ADN is used as a leader to make an initial purchase and sale electricity price according to the peak-valley period of the net load curve.
2) According to the electricity price established by the ADN, multiple micro-grids cooperate to form the MMG, the electricity price strategy is responded in an integrated alliance mode, a corresponding alliance electricity purchasing and selling quantity (power) strategy is established, and the strategy is reported to the ADN.
3) And the ADN updates the electricity price according to the electricity purchasing and selling strategy reported by the MMG, guides the MMG to dynamically adjust the electricity purchasing and selling strategy and ensures the balance of the internal power of the ADN and the optimal economic benefit of the ADN.
4) And the MMG adjusts the electricity price strategy again according to the updated electricity price, and the ADN continuously and dynamically corrects the electricity price according to the updated alliance strategy until the MMG electricity price strategy and the ADN electricity price strategy are stable and unchanged, so that the Nash equilibrium solution of the game is achieved.
5) And the MMG members distribute the residual profits of the cooperative game according to the contribution degree of the MMG members, so that the improvement of the energy utilization rate and the optimized distribution of the alliance power among the MG units are ensured.
The mathematical expression for the master-slave gaming model G can be described as:
G＝{{ADN,MMG}；{ρ s ,ρ b }；{P PCC,MMG,b ,P PCC,MMG,s }；{J ADN }；{J MMG }} (1)
wherein: { ADN, MMG } represents game parties and is respectively an active power distribution network and a multi-micro power grid alliance; { rho b ,ρ s Representing an ADN electricity purchasing and selling price strategy set; { P PCC,MMG,b ,P PCC,MMG,s And represents an MMG electricity purchasing and selling strategy set.
When running cost J of ADN ADN And running cost J of MMG MMG When the optimal conditions are achieved together, the master-slave game optimization model G has a unique Nash equilibrium solution, which is as follows:
Figure BDA0003686361570000111
equilibrium solution of master-slave game optimization model in formula
Figure BDA0003686361570000112
Respectively represent: ADN electricity selling price, ADN electricity purchasing price, MMG electricity purchasing quantity and electricity selling quantity.
S1.2, constructing an objective function of an upper leader (ADN);
the target function of the upper-layer ADN is the total operation cost of the system, the operation cost comprises the power generation cost and the operation maintenance cost of the unit, the power interaction cost with a large power grid and the power interaction cost with a multi-microgrid alliance system, and the target function is shown as a formula (3).
Figure BDA0003686361570000113
In the formula, C ADN Representing the total ADN operating cost; c PCC,MMG Representing ADN and MMG power interaction costs; c g Representing the power interaction cost of the ADN and the superior large power grid;
Figure BDA0003686361570000114
the generating cost of the controllable unit in the ADN is obtained;
Figure BDA0003686361570000115
the operation and maintenance cost of the generator set in the ADN is reduced.
1) Interaction cost of ADN with large grids:
Figure BDA0003686361570000116
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000117
the price of electricity sold and the price of electricity purchased by the large power grid in the period t are respectively,
Figure BDA0003686361570000118
the ADN respectively represents the electricity purchasing power and the electricity selling power of the large power grid in the time period t.
2) Interaction cost of ADN with MMG:
Figure BDA0003686361570000119
in the formula (I), the compound is shown in the specification,
Figure BDA00036863615700001110
respectively the total power sold and the total power purchased by the MMG in the period t.
3) ADN unit power generation cost:
Figure BDA00036863615700001111
in the formula, a mj 、b mj 、c mj Is the generating cost coefficient of the unit j, k is the number of the units,
Figure BDA00036863615700001112
and the output of the jth unit in the t period is obtained.
4) ADN unit operation and maintenance cost:
Figure BDA00036863615700001113
in the formula, k mj And the unit power operation and maintenance cost coefficient of the unit j is obtained.
S1.3, constructing an objective function of a lower-layer follower (MMG):
the objective function of the lower MMG includes the total cost C of power generation of the gas turbine in the microgrid MTΣ MMG and ADN junctor total interaction cost C PCC,MMG MMG Total cost of operation and maintenance C OMΣ The objective function is shown in formula (4).
J MMG ＝minC MMG ＝min(C MTΣ +C PCC,MMG +C OMΣ ) (4)；
1) Total cost of power generation of MMG internal combustion turbine:
C MTΣ ＝C MT,Σfuel +C MT,Σen ；
Figure BDA0003686361570000121
in the formula, C MT,Σfuel The total power generation cost of the MMG internal combustion turbine is obtained; c MT,Σen The pollution control cost of the MMG internal combustion gas turbine is reduced. In the formula, a M,i 、b M,i 、c M,i The gas turbine power generation cost coefficients of the microgrid i,
Figure BDA0003686361570000122
the output power of the gas turbine in the period t for the microgrid i; m is the number of types of pollutants; p is a different contaminant species; alpha is alpha ep Is the emission cost of pollutants; beta is a ep Is the discharge amount.
2) MMG and ADN junctor total interaction cost:
Figure BDA0003686361570000123
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000124
and
Figure BDA0003686361570000125
and respectively representing the purchased electricity quantity and the sold electricity quantity of the microgrid i in the period t.
3) MMG Total cost of operation and maintenance
Figure BDA0003686361570000126
In the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000127
the maintenance cost coefficient of the j-th micro source in the micro grid i is obtained;
Figure BDA0003686361570000128
and generating power for the j-th micro source in the micro grid i in the t period.
Step two: constructing constraint conditions of a master-slave game optimization model:
s2.1, the upper leader ADN constraint conditions are as follows:
1) power flow constraint of a power distribution network:
Figure BDA0003686361570000129
in the formula (5), n 1 The number of ADN nodes; p i 、Q i Respectively injecting active power and reactive power into the node i; u shape i Is the voltage amplitude of node i; g ij 、B ij 、δ ij Respectively, the conductance value, the susceptance value and the voltage phase angle difference between the node i and the node j.
2) ADN line power constraint:
0≤P l t ≤P l max (6)；
in the formula (6), P l t For active power, P, on the first branch of the distribution network during t periods l max The maximum transmission power of the l branch.
3) ADN node voltage constraint:
Figure BDA0003686361570000132
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000133
respectively, the lower and upper voltage limits of node i.
4) ADN unit output restraint:
Figure BDA0003686361570000134
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000135
respectively representing the lower limit and the upper limit of the output of the unit j;
Figure BDA0003686361570000136
representing the output of the unit j at the moment t.
5) And (3) time-of-use electricity price constraint:
the basic model of the time-of-use electricity price is as follows:
Figure BDA0003686361570000137
Figure BDA0003686361570000138
formulas (9) and (10) respectively represent time-of-use electricity selling price and electricity purchasing price constraints set by the ADN; t is g ,T p ,T f Respectively representing the valley, flat and peak time periods; ρ is a unit of a gradient sg ,ρ sp ,ρ sf Respectively representing the electricity selling price in valley, flat and peak time periods; ρ is a unit of a gradient bg ,ρ bp ,ρ bf Respectively representing the buyback price in valley, flat and peak periods; the time-of-use electricity price needs to meet the inequality relation rho sf ≥ρ sp ≥ρ sg 、ρ bf ≥ρ bp ≥ρ bg And the electricity selling price is larger than the electricity purchasing price in the same time period
Figure BDA0003686361570000139
S2.2, constraint conditions of lower-layer MMG:
1) each microgrid power balance constraint:
Figure BDA00036863615700001310
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000141
the load capacity of the microgrid i in the time period t is shown;
Figure BDA0003686361570000142
the new energy output for the micro-grid i in the time period t,
Figure BDA0003686361570000143
power purchased from the ADN for the microgrid i,
Figure BDA0003686361570000144
for ADN buys back power from microgrid i,
Figure BDA0003686361570000145
the output of a gas turbine in the microgrid i is obtained;
Figure BDA0003686361570000146
representing the mutual aid power of the microgrid i in the MMG, wherein n represents the number of the members in the alliance; d ji And D ij All binary state variables represent the state of mutual power coordination between the micro-grids, when 0 is taken, no power is transmitted, D ji 1 denotes that the microgrid j delivers power to the microgrid i, D ij 1 denotes that the microgrid i delivers power to the microgrid j.
Figure BDA0003686361570000147
Representing the power value transmitted from the microgrid j to the microgrid i at the moment t;
Figure BDA0003686361570000148
and representing the power value transmitted from the microgrid i to the microgrid j at the moment t.
Figure BDA0003686361570000149
And
Figure BDA00036863615700001410
and respectively representing the self energy storage charging and discharging power of the microgrid i at the moment t.
2) Constraint condition of MMG internal energy mutual aid:
Figure BDA00036863615700001411
in the formula (I), the compound is shown in the specification,
Figure BDA00036863615700001412
representing the residual electric quantity of the microgrid i in the period t,
Figure BDA00036863615700001413
and (4) representing the residual load of the microgrid i in the t period.
3) Each microgrid and ADN tie line are constrained:
Figure BDA00036863615700001414
in the formula (I), the compound is shown in the specification,
Figure BDA00036863615700001415
and
Figure BDA00036863615700001416
the lower and upper limits of the microgrid i and ADN tie line power are indicated, respectively.
4) And (3) constraining the upper and lower output limits of each microgrid gas turbine:
Figure BDA00036863615700001417
in the formula (I), the compound is shown in the specification,
Figure BDA00036863615700001418
and
Figure BDA00036863615700001419
respectively representing the lower limit and the upper limit of the gas turbine power of the micro-grid i;
Figure BDA00036863615700001420
representing the gas turbine output of the microgrid i at time t.
5) Energy storage and charge-discharge constraints of each micro-grid:
Figure BDA00036863615700001421
in the formula, P i Cap The energy storage power capacity of the microgrid i.
6) And (3) upper and lower bound constraint of energy storage electric quantity of each micro-grid:
Figure BDA00036863615700001422
in the formula (I), the compound is shown in the specification,
Figure BDA00036863615700001423
a minimum state of charge for storing energy for the microgrid i;
Figure BDA00036863615700001424
the residual capacity of the energy storage of the micro-grid i at the moment t is obtained;
Figure BDA00036863615700001425
is the energy storage capacity of the microgrid i.
7) The electric quantity relation of each micro-grid energy storage two adjacent time intervals is as follows:
Figure BDA0003686361570000151
in the formula (I), the compound is shown in the specification,
Figure BDA0003686361570000152
energy storage surplus for the micro-grid i at the moment t-1Capacity;
Figure BDA0003686361570000153
respectively obtaining charging efficiency and discharging efficiency of the energy storage of the micro-grid i; Δ t denotes a scheduling time interval.
Step three: acquiring parameters of ADN and MMG and parameters of a particle swarm algorithm for model solution;
the active power distribution network and the parameters of the multi-microgrid system connected with the active power distribution network comprise: a system grid structure, line parameters, load distribution conditions and prediction data thereof, a distributed power supply output limit value and the like; the particle swarm algorithm parameters used for model solution comprise: population quantity, maximum iteration times, particle flight speed range, learning factor, initial inertia weight and the like. In order to enhance the local convergence capability of the particle swarm algorithm, the inertia weight is reduced along with the increase of the iteration times, and the adjustment formula of the inertia weight is as follows:
w＝w max -(w max -w min )·Iter/D
wherein w is the inertial weight, w max ,w min Maximum and minimum inertial weights respectively, Iter is the current iteration round, and D is the maximum iteration number.
The double-layer optimization model adopts a particle swarm optimization nesting mode to optimize a Nash equilibrium solution of a master-slave game, optimizes a corresponding leader game model at the outer layer and adopts a particle swarm optimization to solve; the inner-layer optimization corresponds to the follower game model, the Yalmip/Cplex toolbox is adopted for solving, and the solving process is shown in figure 2. The main solving link is shown in the step four to the step six.
Step four: setting the initial value of Stackerberg-Nash (Stackelberg-Nash) equilibrium solution of master-slave model
Figure BDA0003686361570000154
Computing MMG response sets
Figure BDA0003686361570000155
And substituting the model into an upper ADN model to carry out a new round of optimization.
The optimization process is as follows: follower MMG responds to initial purchase and sale of leader ADNThe electricity price is optimized by taking the minimum MMG operation total cost as an optimization target (formula (4)), constraint condition formulas (11) to (17) are met, and a response set under the first-wheel ADN electricity price excitation is obtained
Figure BDA0003686361570000156
Continuously substituting the MMG response set into the ADN optimization model, taking the formula (3) as an objective function, considering the constraint conditions from the formula (5) to the formula (10), and solving a new electricity price strategy set of the next round of ADN
Figure BDA0003686361570000157
The MMG continuously responds to the ADN electricity price strategy and gives a response set
Figure BDA0003686361570000158
And repeatedly and alternately solving the strategies in the mode to obtain the optimal strategy set of the game participants in each cycle.
Wherein:
Figure BDA0003686361570000159
respectively representing the ADN purchase and sale electricity price strategy initial values;
Figure BDA00036863615700001510
respectively representing MMG purchase and sale electric quantity strategy initial values;
Figure BDA00036863615700001511
respectively representing the electricity purchasing and selling price strategy of the ADN in the first round of circulation;
Figure BDA00036863615700001512
respectively represent the power purchasing and selling strategies of the MMG in the first round circulation.
Step five: during the k-th cycle, the upper layer ADN is solved by k-1 MMG equalization
Figure BDA0003686361570000161
As input, an optimal strategy set of ADN and a corresponding lower-layer response set are found.
The strategy of each game participant obtained in the k-th cycle is as follows:
Figure BDA0003686361570000162
in the formula: gamma ray 1,k A set of policies in the kth round for leader ADN; gamma ray 2,k Response set of MMG at k-th wheel for follower;
Figure BDA0003686361570000163
representing the purchase and sale price strategy of the ADN in the k-th round;
Figure BDA0003686361570000164
representing the power purchase and sale strategy of the MMG in the k-th round.
Step six: the method for judging whether the Stackelberg-Nash equilibrium solution of the model is found or not comprises the following steps:
if the optimization results of all game participants in the kth round are consistent with the optimization results of all game participants in the kth round-1, namely:
Figure BDA0003686361570000165
the game model finds the Stackelberg-Nash equilibrium solution
Figure BDA0003686361570000166
Otherwise, updating the iteration cycle k to k +1, and returning to the step five to continue solving until a Stackelberg-Nash equilibrium solution is found.
In formula (19):
Figure BDA0003686361570000167
a Nash equilibrium strategy set of ADN in k rounds of circulation;
Figure BDA0003686361570000168
a Nash equilibrium strategy set of the MMG in k rounds of circulation;
Figure BDA0003686361570000169
ADN electricity purchasing and selling price and MMG electricity purchasing and selling strategy corresponding to Nash equilibrium solution。
Step seven: and distributing the cooperation surplus of the MMG by adopting an SVM method, and calculating the cost saved by each microgrid member.
The SVM value can be interpreted as the average of the marginal contributions of all possible federation scenarios for participant i, contributing more revenue for the more members. The computational expression for distributing the remainder of the cooperation using the SVM method is shown in equation (20).
Figure BDA00036863615700001610
In the formula, s is a union set formed by all micro grids, v (i) is the income distributed by the micro grid i, | s | is the number of subsets, ω (| s |) is a weight factor, v(s) is the cooperation residue of the sub-union s, v (s/i) is the cooperation residue of the sub-union except the member i, and n is the number of micro grids participating in the union. MC (monomer casting) i Representing the marginal contribution of participant i.
The cooperation surplus is defined as the difference between the total profit generated by all the members in the league in cooperation and the sum of the profit of each member, in combination with the master-slave game model described above. The difference in income between multi-microgrid alliance and non-alliance mainly comes from energy mutual aid inside alliances, in other words, if power mutual aid is considered among members, the members are alliance, and otherwise, the members are not alliance. The difference in revenue generated before and after power trade-off can then be understood as the remaining of the cooperation.
Specifically, it is assumed that MMG contains 3 members, MG1, MG2, and MG3, respectively. Note N ═ MG1, MG2, MG 3. The three participants form a coalition of 7 forms, namely phi { { MG1}, { MG2}, { MG3}, { MG1, MG2}, { MG1, MG3}, { MG2, MG3}, { MG1, MG2, MG3} }.
Taking the micro-grid MG1 as an example, the profit of each member obtained by distributing the remaining cooperation through the SVM method is:
Figure BDA0003686361570000171
in the formula: x (MG1) represents the profit gained by the microgrid MG1 when distributing the cooperation surplus using the SVM method。MC 1 ({ MG1}) represents the marginal contribution of MG1 in the form of { MG1} of the federation. Accordingly, MC 1 ({ MG1, MG2}) represents the marginal contribution of MG1 in the federation form { MG1, MG2 }; MC (monomer casting) 1 ({ MG1, MG2, MG3}) represents the marginal contribution of MG1 in the form of a federation { MG1, MG2, MG3 }.
Similarly, the gains distributed to the micro-grid MG2 and the MG3 can be calculated and obtained by adopting an SVM method.
The SVM method is described in document [11 ]: liu monile, zhao jing, royal, brave warrior. photovoltaic microgrid group transaction model based on cooperative game theory [ J ] proceedings of electrotechnics, 2018, 33 (8): 1903 and 1910. above.
Verification of the examples:
the example is based on an IEEE14 node power distribution network system, simulation calculation of day-ahead scheduling is carried out, and the network topology structure is shown in FIG. 3. In the system, three micro-grids are arranged at nodes 6,11 and 13, and the three micro-grids form a multi-micro-grid alliance. Node 1 is the public connection point of ADN and higher level high voltage electric network, and node 2,3,8 are equipped with the photovoltaic unit, the wind turbine generator system and the controllable unit of distribution network respectively. The new energy output and load curves of the distribution network and each microgrid in a typical day are shown in fig. 4(a) to 4 (d).
The maximum transmission power of the connecting lines of the micro-grids 1-3 and the active power distribution network is 750 kW. The operation and maintenance coefficients of photovoltaic power generation and wind power generation are respectively 0.0096 yuan/kW and 0.0296 yuan/kW. The price of electricity sold by the high-voltage main grid is 0.55 yuan/kW, the parameters of the gas turbine inside each microgrid are shown in Table 1, the pollution emission cost of the gas turbine is shown in Table 2, and the energy storage parameters of each microgrid are shown in Table 3. The charge-discharge efficiency of each micro-grid energy storage system is 0.95.
The particle swarm algorithm parameters for model solution include: the population number is set to 500, the maximum number of iterations is set to 2000, the particle flight velocity range [ -1,1], the learning factors are all 1.5, and the initial inertial weight is set to 0.9. The testing calculation adopts MatlabR2018b software combined with a Yalmip plug-in to call a Cplex solver to solve, and the computer is configured with an Intel core i7 processor, the main frequency is 1.8GHz and the memory is 16 GB.
TABLE 1 gas turbine related parameters within each microgrid
Figure BDA0003686361570000181
TABLE 2 gas turbine pollutant emission cost
Figure BDA0003686361570000182
TABLE 3 microgrid energy storage system parameters
Figure BDA0003686361570000183
Analysis of test results of examples:
(1) analyzing the master-slave game optimization results of the ADN and the MMG:
the optimized time-of-use electricity price is shown in table 4, and the output scheduling results of each device of the micro-grid MG 1-MG 3 after optimization are shown in fig. 5.
TABLE 4 ADN time of use electricity price (yuan/kWh) in game equilibrium
Figure BDA0003686361570000184
With reference to fig. 5(a) to 5(c), the scheduling strategy of the present invention is analyzed by taking the microgrid 1 as an example:
1) ADN valley price period:
at 0:00-6:00, in the ADN valley time electricity price period, the photovoltaic output in the micro-grid 1 is 0, which is not enough to meet the electricity demand of the load, because the electricity price is lower at the moment, a gas turbine is not used for supplying power, most of power is obtained by transmitting from other micro-grids, at 6:00-8:00, the photovoltaic output gradually rises to meet the power demand of the load, and the energy storage battery of the micro-grid is charged by adopting a valley charging strategy, so that the electricity selling to the ADN is reduced.
2) ADN flat rate period:
at 8:00-10:00 and 14:00-18:00, the photovoltaic output is large, the output of new energy is generally larger than that of a load, the power is stabilized by charging the energy storage battery, meanwhile, the redundant power of the new energy is transmitted to other power-deficient micro-grids, and at 23:00-24:00, the output of the new energy is insufficient, the self energy storage battery is used for discharging, and meanwhile, the electric energy transmitted by other micro-grids is received to match the output of the gas turbine to make up for the power shortage.
3) ADN peak electricity rate period:
at 10:00-14:00, the photovoltaic output is large, the self energy storage battery is not considered to be charged at the time, redundant new energy is transmitted to other micro-grids, the power price is high at the time, the gas turbine is used for outputting more power, the electricity selling quantity in the time period is increased, at 20:00-23:00, the new energy output is insufficient, the energy storage battery is preferentially used for discharging, and meanwhile, the gas turbine is used, so that the electricity purchasing from the ADN can be reduced.
The economic benefit and peak-valley difference of both sides of the game are shown in table 5:
TABLE 5 economic benefits comparison Table of ADN and MMG before and after game
Figure BDA0003686361570000191
As can be seen from table 5, the ADN adopts a time-of-use electricity price, adopts a rational charge-discharge strategy by exciting the MMG, and excites the MMG to control the unit to exert more output in the peak period, so as to meet the demand of ADN peak clipping and valley filling scheduling. Under the condition that the electricity selling income is not changed, the load curve of the ADN can be improved by the incentive effect of the time-of-use electricity price, the peak-valley difference in a typical day is reduced, and the load standard deviation is also reduced, namely the whole trend of the load curve becomes smoother. Meanwhile, the microgrid alliance adopts a rational charging and discharging strategy under the time-of-use electricity price, so that the operation cost can be reduced.
(2) Mutual-aid power and income analysis of each member of multiple micro-grids
Table 6 is a cost saving case in the form of various cooperative associations. Table 7 is a comparison table of the operating costs of each microgrid.
TABLE 6 cost savings in various forms of collaboration
Figure BDA0003686361570000192
TABLE 7 cost comparison before and after alliance of each microgrid (Yuan)
Figure BDA0003686361570000201
As can be seen from tables 6 and 7, the total operating cost after the multi-microgrid alliance is 4802.21 yuan, which is less than the sum of the costs of independent operation of the three MGs 5161.54 yuan, and the costs allocated after the three MGs form the alliance are less than the costs of independent operation of each MG, which indicates that the cooperation condition can be satisfied. Combining the power mutual-aid relationship between the micro-grids in fig. 6, it can be found that the new energy sources of MG1 and MG3 are photovoltaic, the output of the new energy sources in the early morning and at night is zero, the new energy source of MG2 is wind power, the output of the new energy sources in the evening is large, and the output of the new energy sources in the daytime is low, so that the MG2 and the MG1 and the MG3 respectively perform power mutual-aid, and the MG1 and the MG3 both have the same quality of the photovoltaic micro-grids, and only have power interaction at 18: 00. Therefore, when allocating benefits according to the federated contribution degree, the cost savings of MG1 and MG3 allocation are relatively small, and the cost savings of MG2 allocation are large, as shown in table 7.

Claims (8)

1. An active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing is characterized by comprising the following steps:
step 1: establishing a master-slave game optimization model according to the game relation of the ADN and the MMG of the multi-microgrid alliance;
and 2, step: constructing constraint conditions of a master-slave game optimization model;
and step 3: acquiring parameters of an active power distribution network (ADN) and a multi-micro power grid alliance (MMG) and particle swarm algorithm parameters for solving a master-slave game optimization model;
and 4, step 4: setting a Stackelberg-Nash equilibrium solution initial value of a master-slave game optimization model
Figure FDA0003686361560000011
And calculates the returns of the lower MMGCollection of essences
Figure FDA0003686361560000012
Substituting into upper ADN to perform a new round of optimization;
and 5: during the k-th cycle, the upper layer ADN is solved by k-1 MMG equalization
Figure FDA0003686361560000013
As an input strategy, the optimal strategy set of the ADN in the current k-th round is obtained
Figure FDA0003686361560000014
And corresponding response set of lower MMG
Figure FDA0003686361560000015
Step 6: judging whether the solving result of the step five is a Stackelberg-Nash equilibrium solution or not; if the conditions are met, outputting a game equilibrium solution, and entering the step 7; otherwise, executing k to k +1, and returning to the step 5;
and 7: and distributing the cooperation surplus of the MMG of the multi-microgrid alliance according to the SVM method, and calculating the income allocated by each microgrid member.
2. The active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing according to claim 1, characterized in that: in the step 1, the master-slave game optimization model specifically comprises the following steps:
the mathematical expression for the master-slave gaming model G can be described as:
G＝{{ADN,MMG}；{ρ s ,ρ b }；{P PCC,MMG,b ,P PCC,MMG,s }；{J ADN }；{J MMG }} (1)；
wherein: { ADN, MMG } represents game parties and is respectively an active power distribution network and a multi-micro power grid alliance; { rho b ,ρ s Representing an ADN electricity purchasing and selling price strategy set; rho b Representing the ADN buyback price; rho s Representing the ADN electricity selling price; { P PCC,MMG,b ,P PCC,MMG,s Represents MMG purchasing and selling electricity quantitySet of policies, P PCC,MMG,b 、P PCC,MMG,s Respectively representing the electricity purchasing quantity and the electricity selling quantity of the MMG;
when running cost J of ADN ADN And running cost J of MMG MMG When the optimal conditions are jointly reached, the game model G has a unique Nash equilibrium solution and meets the following conditions:
Figure FDA0003686361560000021
in the formula, equilibrium solution of game model
Figure FDA0003686361560000022
Respectively represent: ADN electricity selling price, ADN electricity purchasing price, MMG electricity purchasing quantity and MMG electricity selling quantity;
the upper-layer ADN objective function is the total operation cost of the system, including the power generation cost and the operation maintenance cost of the unit, the power interaction cost between the ADN and the large power grid, the power interaction cost between the ADN and the MMG, and the like, and the objective function is shown in formula (3):
Figure FDA0003686361560000023
in the formula, C ADN Representing the total ADN operating cost; c PCC,MMG Representing ADN and MMG power interaction costs; c g Representing the power interaction cost of the ADN and the superior large power grid;
Figure FDA0003686361560000024
the generating cost of the controllable unit in the ADN is obtained;
Figure FDA0003686361560000025
the operation and maintenance cost of the generator set in the ADN is obtained;
the objective function of the lower MMG is shown in equation (4):
J MMG ＝minC MMG ＝min(C MTΣ +C PCC,MMG +C OMΣ ) (4)；
in the formula: c MMG Represents the total cost of MMG operation; c MTΣ Represents the total cost of gas turbine power generation in the MMG; c OMΣ And the operation and maintenance total cost of the generator set in the MMG is represented.
3. The active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing according to claim 1, characterized in that: in the step 2, the constraint conditions of the master-slave game optimization model include: the constraints for the upper ADN are as follows:
1) power flow constraint of the power distribution network:
Figure FDA0003686361560000026
in the formula (5), n 1 The number of ADN nodes; p i 、Q i Respectively injecting active power and reactive power into the node i; u shape i Is the voltage amplitude of node i; g ij 、B ij 、δ ij Respectively is a conductance value, a susceptance value and a voltage phase angle difference between a node i and a node j;
2) ADN line power constraints:
0≤P l t ≤P l max (6)；
in the formula (6), P l t For active power, P, on the first branch of the distribution network during t periods l max The maximum transmission power of the l branch;
3) ADN node voltage constraint:
Figure FDA0003686361560000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003686361560000032
the lower voltage limit and the upper voltage limit of the node i are respectively;
4) ADN unit output restraint:
Figure FDA0003686361560000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003686361560000034
respectively representing the lower limit and the upper limit of the output of the unit j;
Figure FDA0003686361560000035
representing the output of the unit j at the time t;
5) and (3) time-of-use electricity price constraint:
the basic model of the time-of-use electricity price is as follows:
Figure FDA0003686361560000036
Figure FDA0003686361560000037
formulas (9) and (10) respectively represent time-of-use electricity selling price and electricity purchasing price constraints set by the ADN; t is g ,T p ,T f Respectively representing the valley, flat and peak time periods; rho sg ,ρ sp ,ρ sf Respectively representing the electricity selling price in valley, flat and peak time periods; rho bg ,ρ bp ,ρ bf Respectively representing the buyback price in valley, flat and peak periods; the time-of-use electricity price needs to satisfy the inequality relation rho sf ≥ρ sp ≥ρ sg 、ρ bf ≥ρ bp ≥ρ bg And the electricity selling price is larger than the electricity purchasing price in the same time period
Figure FDA0003686361560000038
The constraints for the lower MMG are as follows:
1) each microgrid power balance constraint:
Figure FDA0003686361560000039
in formula (11):
Figure FDA00036863615600000310
the load capacity of the microgrid i in the time period t is shown;
Figure FDA00036863615600000311
the new energy output for the micro-grid i in the time period t,
Figure FDA00036863615600000312
the power purchased from the distribution network for the microgrid i,
Figure FDA00036863615600000313
power purchased back from the microgrid i for the distribution grid,
Figure FDA00036863615600000314
the output of a gas turbine in the microgrid i is obtained;
in formula (11):
Figure FDA00036863615600000315
representing the mutual aid power of the microgrid i in the multi-microgrid alliance, wherein n represents the number of alliance members; d ji And D ij All binary state variables represent the state of mutual power coordination between the micro-grids, when 0 is taken, no power is transmitted, D ji 1 denotes that the microgrid j delivers power to the microgrid i, D ij 1 represents that the micro-grid i transfers power to the micro-grid j;
in formula (11):
Figure FDA0003686361560000041
representing the power value transmitted from the microgrid j to the microgrid i at the moment t;
Figure FDA0003686361560000042
representing the i-direction micro-grid of the micro-grid at the t momentj the power value delivered;
Figure FDA0003686361560000043
and
Figure FDA0003686361560000044
respectively representing self energy storage charging and discharging power of the microgrid i at the moment t;
2) constraint condition of mutual energy coordination in MMG:
Figure FDA0003686361560000045
in the formula (I), the compound is shown in the specification,
Figure FDA0003686361560000046
representing the residual electric quantity of the microgrid i in the period t,
Figure FDA0003686361560000047
representing the residual load of the microgrid i in the t period;
3) tie line constraints between each microgrid and the ADN:
Figure FDA0003686361560000048
in the formula (I), the compound is shown in the specification,
Figure FDA0003686361560000049
and
Figure FDA00036863615600000410
respectively representing the lower limit and the upper limit of the power of the microgrid i and the ADN tie line;
4) and (3) constraining the upper and lower output limits of each microgrid gas turbine:
Figure FDA00036863615600000411
in the formula (I), the compound is shown in the specification,
Figure FDA00036863615600000412
and
Figure FDA00036863615600000413
respectively representing the lower limit and the upper limit of the gas turbine power of the micro-grid i;
Figure FDA00036863615600000414
representing the output of the gas turbine of the microgrid i at the moment t;
5) energy storage and charge-discharge constraint of each microgrid:
Figure FDA00036863615600000415
in the formula, P i Cap The energy storage power capacity of the micro-grid i is obtained;
6) and (3) upper and lower bound constraint of energy storage electric quantity of each micro-grid:
Figure FDA00036863615600000416
in the formula (I), the compound is shown in the specification,
Figure FDA00036863615600000417
a minimum state of charge for storing energy for the microgrid i;
Figure FDA00036863615600000418
the energy storage residual capacity of the micro-grid i at the moment t is obtained;
Figure FDA00036863615600000419
the energy storage capacity of the micro-grid i;
7) the electric quantity relation of each micro-grid energy storage two adjacent time intervals is as follows:
Figure FDA00036863615600000420
in the formula (I), the compound is shown in the specification,
Figure FDA00036863615600000421
the energy storage residual capacity of the micro-grid i at the time t-1 is obtained;
Figure FDA00036863615600000422
respectively obtaining charging efficiency and discharging efficiency of the energy storage of the micro-grid i; Δ t denotes a scheduling time interval.
4. The active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing according to claim 1, characterized in that: in the step 3, acquiring initial parameters of the active power distribution network ADN, wherein the initial parameters comprise a power distribution network frame structure, node data, branch data, the capacity and the operation parameters of a new energy machine set and a controllable machine set, a tie line power limit value, an initial electricity selling price and an initial buyback price;
acquiring operation parameters of a multi-microgrid alliance MMG, wherein the operation parameters comprise the capacity and the operation parameters of each microgrid gas turbine, a new energy source unit, an energy storage system;
the method comprises the following steps of obtaining particle swarm algorithm parameters solved by a master-slave game optimization model, wherein the particle swarm algorithm parameters comprise: population quantity, maximum iteration times, particle flight speed range, learning factor and initial inertia weight.
5. The active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing according to claim 3, characterized in that: in the step 4:
the follower MMG responds to the initial electricity purchase and sale price of the leader ADN, the minimum total running cost of the MMG is taken as an optimization target, and as shown in a formula (4), a constraint condition is met: equation (11) to equation (17) to obtain the response set of MMG under the first-wheel ADN electrovalence excitation
Figure FDA0003686361560000051
Continue substituting the response set of MMG into the upper layer ADN model, taking formula (3) as an objective function, considering constraint conditions from formula (5) to formula (10), and solving the new electricity price strategy set of the next round of ADN
Figure FDA0003686361560000052
The MMG continuously responds to the ADN electricity price strategy and gives a response set
Figure FDA0003686361560000053
According to the method, repeatedly and alternately solving the strategies to obtain the optimal strategy set of the game participants in each cycle;
wherein:
Figure FDA0003686361560000054
respectively representing the ADN purchase and sale electricity price strategy initial values;
Figure FDA0003686361560000055
respectively representing MMG purchase and sale electric quantity strategy initial values;
Figure FDA0003686361560000056
respectively representing the electricity purchasing and selling price strategy of the ADN in the first round of circulation;
Figure FDA0003686361560000057
respectively represent the power purchasing and selling strategies of the MMG in the first round circulation.
6. The active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing according to claim 1, characterized in that: in the step 5, the strategies of all game participants obtained in the kth cycle are as follows:
Figure FDA0003686361560000058
in the formula: gamma ray 1,k A set of policies in the kth round for leader ADN; gamma ray 2,k A response set of MMG at the kth round for the follower;
Figure FDA0003686361560000059
representing the purchase and sale price strategy of the ADN in the k-th round;
Figure FDA00036863615600000510
representing the power purchasing and selling strategy of the MMG in the k-th round;
Figure FDA00036863615600000511
representing strategy in MMG round k-1
Figure FDA00036863615600000512
As input, the optimal strategy of the current k-th round leader ADN is obtained; the character arg denotes an argument, argminJ ADN Represents J ADN ADN optimal strategy corresponding to minimum value
Figure FDA0003686361560000061
Figure FDA0003686361560000062
Strategy for representing k-th round with leader ADN
Figure FDA0003686361560000063
And as an excitation signal, solving the optimal response strategy of the current kth follower MMG.
7. The active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing according to claim 1, characterized in that: in the step 6, the method for judging whether a Stackelberg-Nash equilibrium solution is found is as follows:
if the optimization results of all game participants in the kth round are consistent with those of the kth-1 round, namely:
Figure FDA0003686361560000064
the game model finds the Stackelberg-Nash equilibrium solution
Figure FDA0003686361560000065
Otherwise, updating the iteration cycle k to k +1, and returning to the step 5 to continue solving until a Stackelberg-Nash equilibrium solution is found;
in formula (19):
Figure FDA0003686361560000066
a Nash equilibrium strategy set of ADN in k rounds of circulation;
Figure FDA0003686361560000067
a Nash equilibrium strategy set for MMG in k rounds of circulation;
Figure FDA0003686361560000068
representing ADN electricity purchasing and selling price and MMG electricity purchasing and selling strategy corresponding to Nash equilibrium solution;
Figure FDA0003686361560000069
the optimal electricity selling price of the k-1 th ADN is represented;
Figure FDA00036863615600000610
represents the optimal buyback price of the k-1 th round of ADN;
Figure FDA00036863615600000611
the optimal electricity purchasing quantity of the k-1 th round of MMG is represented;
Figure FDA00036863615600000612
the optimal electricity selling amount of the k-1 th round MMG is shown.
8. The active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing according to claim 1, characterized in that: in step 7, the cooperation residual allocation model is specifically as follows:
the rest of cooperation generated by all the members of the alliance through cooperation obtains the income distributed by each member by utilizing an SVM method, and the calculation is shown as a formula (20);
Figure FDA00036863615600000613
in the formula (20), s is a union set formed by all micro-grids, v (i) is the income distributed by the micro-grid i, | s | is the number of subsets, ω (| s |) is a weight factor, v(s) is the cooperation residue of the sub-union s, v (s/i) is the cooperation residue of the sub-union except the member i, and n is the number of micro-grids participating in the union; (n- | s |)! Representing factorial of MMG member number n minus subset s member number; (| s | -1) |! Expressing factorial of the number of members of the subset s minus 1; n! Indicating a factorial in the MMG membership n.
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