CN115528670A - Distributed power supply and matching power grid investment decision modeling method based on multi-main game - Google Patents
Distributed power supply and matching power grid investment decision modeling method based on multi-main game Download PDFInfo
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Abstract
The invention discloses a distributed power supply and matching power grid investment decision modeling method based on a multi-body game, which comprises the following steps: constructing an investment decision model of a distributed power supply and an investment decision model of a matched power grid, and acquiring original data; respectively generating corresponding strategy sets according to a power generation equipment candidate set and a distributed power supply investment plan candidate set in the original data; performing iterative game according to elements in the strategy set and the trend information of each game, and obtaining the investment income of the game according to a distributed power supply investment decision model and an investment decision model of a matched power grid; and if the game reaches the equilibrium state, outputting the equilibrium solution and the final investment income. In the combined game process of the distributed power supply and the matched power grid, the optimal result is achieved by measuring and calculating the mixed power flow and continuously optimizing the investment decisions of the two main bodies based on the power flow.
Description
Technical Field
The invention relates to the field of power grid investment decision modeling, in particular to a distributed power supply based on multi-agent game and a matching power grid investment decision modeling method.
Background
With the advancement of energy reform and marketization, the energy pattern of China gradually changes significantly, and distributed power supplies become more and more important parts of energy systems. The physical structures of the distributed power supply and the matched power grid are shown in fig. 1, the distributed power supply (distributed wind power and photovoltaic power) and a traditional thermal power plant are boosted by a boosting transformer substation and then are connected to a transformer substation (node) of the power grid, and electric energy is transmitted to a load node through the power grid. The distributed power supply, the boosting transformer substation and lines connecting the boosting transformer substation with the power grid nodes are invested by a distributed operation company; the transformer substation and the transmission line network between the transformer substation and the user are invested by a power grid company. The power grid company purchases electric energy from the distributed power supply and resells the electric energy to load users through the power grid. In the model, if the distributed power supply is connected to a power grid of 10kV or more, the situation of spontaneous self-use and surplus power on-line is not considered; if the distributed power supply is connected to a 380V/220V power grid, the load also comprises the conditions of self-use and residual power on-line.
In recent years, with respect to investment decision-making research of distributed power sources and power distribution networks, an incidence relation analysis method of power distribution network investment benefits is often introduced, variables such as power distribution network construction and transformation measures, affected operation parameters and investment benefit indexes are introduced, and then a power distribution network planning investment decision-making method is provided with the aim of optimizing comprehensive benefits of the power distribution networks. Or a multi-objective investment optimization model is established by taking functions such as investment and operation cost, network loss and transaction cost of the distributed power supply as targets, and an investment optimization scheme suitable for the distributed power supply item is obtained through an optimization algorithm and case demonstration. However, in the prior art, the research on the investment of the distributed power supply considers the transaction cost, the time-of-use electricity price, the operation benefit, the wind-light time sequence characteristic, the electric energy quality, the environmental protection property and the like of the power distribution network, but rarely considers the power distribution network trend. All of the results result in that the existing constructed model is not in accordance with the actual situation and cannot obtain an accurate result.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides a distributed power supply based on a multi-subject game and a matching power grid investment decision modeling method.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a distributed power supply and matching power grid investment decision modeling method based on a multi-agent game comprises the following steps:
constructing an investment decision model of a distributed power supply and an investment decision model of a matched power grid, and acquiring original data;
respectively generating a first strategy set and a second strategy set according to a power generation equipment candidate set and a distributed power supply investment plan candidate set in original data;
performing game iteration according to elements in the first strategy set and the second strategy set, wherein each game round selects target elements from the first strategy set according to elements selected from the second strategy set in the previous game round, selects target elements from the second strategy set according to elements selected from the first strategy set in the game round, changes the network structure of the power system according to the target elements, calculates and obtains the power flow information of the game round according to the network structure and checks the power flow information, and if the check is passed, the distributed power supply investment decision model and the investment decision model of a matched power grid are solved according to the network structure and the original data of the game round to obtain the investment income of the game round;
if the game reaches an equilibrium state, outputting an equilibrium solution and final investment income, if the elements in the first strategy set and the second strategy set are selected completely and the game does not reach the equilibrium state, changing the network structure of the power system according to the to-be-selected power generation equipment and the to-be-selected power supply investment plan in a preset scheme, calculating to obtain the trend information of the game of the current round according to the network structure and the original data, if the verification is passed, solving the distributed power supply investment decision model and the investment decision model of a matched power grid according to the network structure and the original data of the game of the current round to obtain the investment income of the game of the current round, and iterating the game according to the elements in the first strategy set and the second strategy set again until the game reaches the equilibrium state.
Further, the target income function of the investment decision model of the distributed power supply is the electricity selling income I GSE Renewable energy quota revenue I GRE And government subsidy C B The sum minus the investment cost C of the distributed power supply unit IG And the running cost C of the distributed power supply unit GO 。
Further, electricity sales income I GSE The expression of (a) is as follows:
wherein N is the horizontal year of life cycle, N is the total number of life cycles, E Bd,n For the nth horizontal year of the distributed power supply, rho S,n The average internet electricity price of n horizontal years;
renewable energy quota revenue I GRE The expression of (c) is as follows:
wherein, gamma is gre For the green certificate trade price, k is the quantization factor that quantifies the renewable energy quota into the green certificate, θ s is the number of days of typical day s, P s,t Is the power generation amount of the distributed power supply at the time t of the S typical day, S GGU A candidate set of power generation equipment is set;
government subsidy C B The expression of (c) is as follows:
wherein, P G,i Active power for distributed power plant i, num is the number of distributed generator sets, C i,t Subsidizing the unit price for the nth level year government;
investment cost C of distributed power supply unit IG The expression of (a) is as follows:
wherein x is i Investment variable, α, for distributed power plants i i For the investment cost of the distributed power plant i, T G Omega is the capital discount rate for the service life of the distributed power generation equipment;
operating cost C of distributed power supply unit GO The expression of (a) is as follows:
wherein i is the number of the distributed power generation equipment, g i,n For distributed generation of i operating time in n horizontal years, C GC,i,n For the operating cost of the n horizontal year power plant i at unit power, P G,i Is the active power of the distributed generation equipment i.
Further, the constraints of the distributed power supply investment decision model include:
the power constraint, the expression is as follows:
wherein x is the confidence capacity coefficient of the distributed power unit, P d,max For annual maximum load, R d As capacity reserve factor, P G,i Active power, x, for distributed power plants i i Investment variable for distributed power plants i, S GGU A candidate set of power generation equipment;
and (3) the installed scale constraint is expressed as follows:
wherein, P G,i Active power, x, for distributed power plants i i Investment variable for distributed power plants i, S GGU For a candidate set of power generation equipment, C max The method is the largest installation scale of the distributed power supply unit.
Further, the target income of the investment decision model of the matching power grid is sales power income I ESE Deducting electricity sales income I GSE Net frame investment cost C IT Loss cost C TL And a cost penalty of electricity abandonment C for renewable energy REN 。
Further, sales power revenue I ESE The expression of (a) is as follows:
wherein, E Ldt Year load of horizontal year t, p E,t Selling the electricity price for the grid company of the level t;
net frame investment cost C IT The expression of (a) is as follows:
wherein S is TL The method comprises the following steps of (1) obtaining a candidate set of power grid investment projects; y is j Is the investment variable for project j; beta is a j The investment cost for project j; t is a unit of TL Is the asset life;
loss on grid cost C TL The expression of (a) is as follows:
wherein l is the item number;
the network loss of a distributed power supply matching line l in n horizontal years; u. of l,n The nth horizontal year of the unit line is the network loss cost;
penalty cost C of electricity abandonment of renewable energy source REN The expression of (c) is as follows:
wherein,
representing the predicted output of the distributed power supply unit in the time period t, S GGU Rho is a candidate set of power generation equipment t Representing the actual force, p pun A penalty factor is indicated.
Further, the constraint conditions of the investment decision model of the complete power grid include:
power network constraints, the expression is as follows:
h, J and K respectively represent incidence matrixes of the power transmission line, the generator, the load and the power network node; f. of Ll,t Representing the flow of the current on the horizontal annual line l; p is gm,t Representing the output of the generator m in the horizontal year t; e Ldk,t Represents the load of the node k of the horizontal year t; s1, S2, S3 and S4 respectively represent a power transmission line set, a generator set, a power load set and a power network node set;
and (3) power flow constraint, wherein the expression is as follows:
wherein, P q 、Q q Respectively injecting active power and reactive power at a node q; u shape q 、U r The voltage amplitudes of the nodes q and r respectively; g qr 、B qr Respectively the conductance and susceptance of the branch qr; theta qr Is the voltage phase angle difference between nodes q and r;
a line transport capacity constraint, expressed as follows:
wherein, f Lqr Is the flow of line qr between nodes q and r;
the maximum capacity allowed for transmission for the line qr between nodes q and r.
Further, the step of obtaining and checking the power flow information of the game in the current round according to the network structure calculation comprises the following steps:
calculating the power flow of the power system under the network structure, and transmitting the power flow to the coupling node after the power flow passes the first check;
and calculating the energy flow of the coupling node, substituting the energy flow into the load flow model for calculation, and performing secondary check on the calculation result.
Further, calculating the power system load flow under the network structure comprises the following steps:
calculating the unbalance amount of the node voltage square of the injected power according to the voltage initial value of each node;
calculating the change value of each node voltage according to the unbalance amount;
and according to the change value of each node voltage, executing the step of calculating the unbalance amount of the square of the node voltage of the injected power until the corrected voltage value of each node meets the precision requirement.
Further, the expression of calculating the square unbalance of the node voltage of the injected power is as follows:
wherein e is i 、f i For real and imaginary part, P, of node voltage obtained in iterative process i Active power injection for PQ and PV nodes, G ij As a network conductance matrix, B ij A network susceptance matrix;
wherein e is i 、f i For real and imaginary part, Q, of node voltage found in iterative process i Reactive power injected for PQ node, G ij As a network conductance matrix, B ij Is a network susceptance matrix.
Compared with the prior art, the invention has the advantages that:
the invention respectively constructs investment decision models aiming at a distributed power supply and a matched power grid, establishes a combined game mechanism of two main bodies, ensures an optimal result through continuous iteration, and ensures that the result accords with the actual condition of a power system by selecting a game scheme of each round and depending on whether the trend information of the round passes the check, and the constructed investment decision model relates to multiple income and cost, and also sets corresponding constraint conditions based on a network structure of the power system, thereby ensuring that the result considers both economy and accuracy.
Drawings
Fig. 1 is a physical block diagram of a typical distributed power system.
Fig. 2 is a diagram of a distributed power source and a matching grid gaming relationship according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating gaming behavior according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of steps according to an embodiment of the present invention.
Fig. 5 is a flow diagram of gaming activities according to an embodiment of the present invention.
Fig. 6 is an initial scenario designed in an embodiment of the present invention.
Fig. 7 is a detailed flowchart of a scenario corresponding to the method according to the embodiment of the present invention.
FIG. 8 is a graph comparing the results of two other scenarios with the results of the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and the specific preferred embodiments, without thereby limiting the scope of protection of the invention.
Before describing particular embodiments of the present invention in detail, it is necessary to state in advance the following for the related concepts and preconditions involved in the particular embodiments:
for distributed power investors, it is desirable to reduce waste of investment through joint planning of games. The grid structure and the load can be clarified through modeling analysis, so that whether a network line needs to be newly built or not is determined, the power consumption cost of the distributed power supply can be reduced, and the line investment can be fundamentally saved.
For a power grid company, whether the investment of a distributed power supply can be adapted to a grid frame of a power grid is determined through model modeling planning, the optimal power flow of the power grid is further determined, and the line loss is minimized, so that the power transmission and distribution cost of the power grid is reduced, unnecessary investment of the power grid is reduced, the price of electricity transmitted to users is reduced, and the social electricity utilization cost is reduced.
Investment game relation transmission of a distributed power supply and a matched power grid:
the decision of the investment main body (or called as a distributed power supply operator) of the distributed power supply is a new distributed power supply establishment scheme, and the investment decision of a power grid company is a matched grid construction scheme of the distributed power supply. The distributed power supply and the power grid company affect each other through a mixed power flow model, and the influence is realized in the safety check process. In the decision process, a distributed power supply operator can decide the investment, the power utilization plan, the internet surfing scheme and the trading mode of the power generation equipment, so that the investment decision and the power selling income of a power grid company are influenced; the power grid company can make a decision on the investment scheme for the construction of the grid structure and pursue the optimized investment, so that the investment decision and the transaction mode of the distributed power supply operator are influenced.
The distributed power supply investment main body firstly makes a power supply capacity investment strategy, on the basis, a power grid company makes a grid frame construction scheme, the distributed power supply and the power grid company respectively aim at maximizing respective income, a game strategy is optimally adjusted to be optimal, when an optimal strategy is selected by an opposite side, the own side is also the optimal strategy, namely under the strategy, the whole system achieves the highest income under a Nash equilibrium state. The gaming relationship transfer diagram is shown in fig. 2. The distributed power supply investment candidate set comprises distributed power supply generator set candidate decisions in the whole life cycle, the internet surfing scheme and the power utilization plan refer to the scheme and plan of each time interval, and the network frame construction investment comprises the main distribution network construction scheme in the whole life cycle.
The distributed power supply-matching power grid joint game investment decision model comprises the following steps:
in the new building process of the distributed power supply, the direct connection between grid connection and a load node of a power distribution network needs to be comprehensively considered, so that the original network tide is changed correspondingly. When a user for production and consumption and the distributed energy storage are connected into a power grid, the tide flows from the bus to the load end in a single direction, and the size and the direction of the tide are changed, so that the tide is not fixed any more. Based on the method, the hybrid power flow method is adopted for calculation and analysis, and the energy consumption condition which is beneficial to research results close to reality is researched from the power flow angle. The game process is carried out based on a sequential idea, an initial investment scheme is given by the distributed power supply, and an initial decision scheme is given by a power grid company, so that the investment strategy of the distributed power supply is influenced. As shown in fig. 6, the real part is a generator set and a power transmission line, the dotted part is a generator set to be selected and a power transmission line to be selected, when the distributed power source selects the generator set to be selected, the power grid company selects the corresponding power transmission line to be selected, the subsequent distributed power sources continue to select from the remaining generator sets to be selected according to actual requirements and the original line, the above processes are repeated, a game pattern is formed between the distributed power source and the power grid company, and specific game behaviors are shown in fig. 3. It should be noted that, a specific process of selecting a newly-built generator set from generator sets to be selected according to a line in an electric power system is well known to those skilled in the art, and the present solution does not relate to an improvement of the process, and is not described herein again.
Firstly, calculating the power network tide, deciding an initial investment scheme by a distributed power supply according to the self condition, determining a net rack construction initial investment scheme by a power grid company according to the actual condition, then transmitting information to a coupling node, feeding the tide condition back to the distributed power supply and the power grid company by the coupling node, changing the network structure by each main body, continuing the game until the strategies of the distributed power supply and the power grid company are optimal, and when no progressive space exists, the game behavior of each main body reaches Nash balance:
in the formula,
all the strategies are own optimal strategies under the optimal strategy selected by the opposite side, and the distributed power supply and the power grid company can achieve the maximum benefit under the balanced meaning under the combination of the strategies. argmax () is a set of variables that maximizes the value of the objective function.
As shown in fig. 4, the embodiment provides a distributed power supply and matching grid investment decision modeling method based on multi-agent game, which includes the following steps:
s1) constructing a distributed power supply investment decision model and an investment decision model of a matched power grid to obtain original data;
s2) respectively generating a first strategy set and a second strategy set according to a power generation equipment candidate set and a distributed power supply investment plan candidate set in the original data;
s3) carrying out game iteration according to elements in the first strategy set and the second strategy set, wherein each round of game selects target elements from the first strategy set according to elements selected from the second strategy set in the previous round of game, selects target elements from the second strategy set according to elements selected from the first strategy set in the current round of game, changes the network structure of the power system according to the target elements selected from the first strategy set and the second strategy set, calculates and checks the trend information of the current round of game according to the network structure, and solves the distributed power supply investment decision model and the investment decision model of a matched power grid according to the network structure and original data of the current round of game if the check is passed, so as to obtain the investment income of the current round of game;
and S4) if the game reaches the equilibrium state, outputting an equilibrium solution and final investment income, if the elements in the first strategy set and the second strategy set are completely selected and the game does not reach the equilibrium state, changing the network structure of the power system according to the to-be-selected power generation equipment and the to-be-selected power supply investment plan in a preset scheme, calculating and obtaining trend information of the game of the round according to the network structure and the original data of the game of the round, checking, if the checking passes, solving the investment decision model of the distributed power supply and the investment decision model of the matched power grid according to the network structure and the original data of the game of the round, obtaining the investment income of the game of the round, and iterating the game according to the elements in the first strategy set and the second strategy set until the game reaches the equilibrium state.
In step S1) of this embodiment, the original data includes the original data created in this embodimentThe model of the distributed power supply investment decision model of the embodiment has the required parameters such as user load information, parameters of power generation equipment to be selected, electricity price, cost of distributed power generation equipment, investment cost of grid structure, original network topology parameters and the like, and based on the parameters, the target income function is electricity selling income I GSE Renewable energy quota revenue I GRE And government subsidy C B The sum of the distributed power supply units is subtracted by the investment cost C of the distributed power supply units IG And the running cost C of the distributed power supply unit GO The expression is as follows:
Max F G ＝I GSE +I GRE +C B -C IG -C GO (2)
wherein:
income from selling electricity I GSE The expression of (a) is as follows:
wherein N is the horizontal year of life cycle, N is the total number of life cycles, E Bd,n For the nth horizontal year of the distributed power supply, rho S,n The average online electricity price of n horizontal years;
renewable energy quota revenue I GRE The expression of (a) is as follows:
wherein, γ gre For the green certificate trade price, k is the quantization factor that quantifies the renewable energy quota into the green certificate, θ s is the number of days of typical days, P s,t Is the electric energy generated by the distributed power supply at the time t of the S typical day, S GGU A candidate set of power generation equipment is set;
government subsidy C B The expression of (a) is as follows:
wherein, P G,i Active power for distributed power plant i, num is the number of distributed generator sets, C i,t Subsidizing the unit price for the nth level year government;
investment cost C of distributed power supply unit IG The expression of (a) is as follows:
wherein x is i Investment variable, α, for distributed power plants i i Investment cost, T, for distributed power plants i G Omega is the capital discount rate for the service life of the distributed power generation equipment;
distributed power supply unit operating cost C GO The expression of (c) is as follows:
where i is the number of the distributed power generation equipment, g i,n For the running time of the distributed power generation plant i in n horizontal years, C GC,i,n For the operating cost of the n horizontal year power plant i at unit power, P G,i Is the active power of the distributed power generation equipment i.
In addition, the constraints of the distributed power supply investment decision model include:
the power constraint, the expression is as follows:
wherein x is the confidence capacity coefficient of the distributed power supply unit, P d,max For annual maximum load, R d As capacity reserve factor, P G,i Active power, x, for distributed power plants i i Investment variable for distributed power plants i, S GGU A candidate set of power generation equipment is set;
and (3) the installed scale constraint is expressed as follows:
wherein, P G,i Active power, x, for distributed power plants i i For investment variables of distributed power plants i, S GGU For the candidate set of power plants, C max The maximum installed scale of the distributed power generating unit is obtained.
Based on the parameters of the original data, in this embodiment, the target income of the investment decision model of the supporting power grid is sales power income I ESE Deducting electricity sales income I GSE Net frame investment cost C IT Loss cost C TL And a cost penalty of electricity abandonment C for renewable energy REN The expression is as follows:
Max F E ＝I ESE -I GSE -C IT -C TL -C REN (10)
wherein:
sales power revenue I ESE The expression of (c) is as follows:
wherein, E Ldt Year load of horizontal year t, p E,t Selling the electricity price for the grid company of the horizontal year t;
net frame investment cost C IT The expression of (c) is as follows:
wherein S is TL The method comprises the following steps of (1) obtaining a candidate set of power grid investment projects; y is j Is the investment variable for project j; beta is a beta j Investment cost for project j; t is TL Is the asset life;
loss on grid cost C TL The expression of (a) is as follows:
wherein, l is the item number;
the network loss of a distributed power supply matching line l in n horizontal years; u. of l,n The net loss cost of the nth horizontal year of the unit line is taken as the net loss cost;
penalty cost C for electricity abandonment of renewable energy source REN The expression of (c) is as follows:
wherein,
representing the predicted output of the distributed power supply unit in the time period t, S GGU Rho is a candidate set of power generation equipment t Representing the actual contribution, p pun A penalty factor is indicated.
In addition, the constraint conditions of the investment decision model of the supporting power grid include:
power network constraints, the expression is as follows:
h, J and K respectively represent incidence matrixes of the power transmission line, the generator, the load and the power network node; f. of Ll,t Representing the current flowing on the horizontal annual line l; p is gm,t Representing the output of the generator m in the horizontal year t; e Ldk,t Represents the load of the node k of the horizontal year t; s1, S2, S3 and S4 respectively represent a power transmission line set, a generator set, a power load set and a power network node set;
and (3) power flow constraint, wherein the expression is as follows:
wherein, P q 、Q q Respectively injecting active power and reactive power at a node q; u shape q 、U r The voltage amplitudes of the nodes q and r respectively; g qr 、B qr Respectively the conductance and susceptance of the branch qr; theta.theta. qr Is the voltage phase angle difference between nodes q and r;
a line transport capacity constraint, expressed as follows:
wherein f is Lqr Is the flow of line qr between nodes q and r;
the maximum capacity allowed for transmission for the line qr between nodes q and r.
For step S2 in this embodiment, the distributed power source investment entity generates an investment plan set (first policy set for short) f (i) = { MF1, MF2, \8230;, MFnF }, according to the candidate set of power generation devices in the raw data, and the power grid company generates a power grid policy set (second policy set for short) y (i) = { ME1, ME2, \8230;, MEnE }, according to the candidate set of distributed power source investment plan in the raw data. nF and nE are, in turn, the total number of elements in the first and second policy sets.
Step S3) of the present embodiment is as shown in fig. 5, and includes:
arbitrarily extracting a group of schemes f0 and y0 from the first strategy set and the second strategy set as iteration initial values;
setting an iteration initial value delta =2;
changing the network structure of the power system according to the power generation equipment to be selected corresponding to f0 and the investment plan to be selected corresponding to y0, calculating to obtain the trend information of the current network structure of the game in the initial round, checking, and inputting the current network structure and the original data into the investment decision model of the distributed power supply and the investment decision model of the matched power grid to solve corresponding benefits under the condition that the check is passed;
and during each subsequent game round, selecting a corresponding scheme fn from the first strategy set according to the selected scheme yn-1 in the second strategy set of the previous game round, further selecting a corresponding scheme yn from the second strategy set according to the scheme fn, performing mixed trend measurement and calculation to obtain corresponding trend information, obtaining corresponding income according to the steps after the trend information is checked, comparing the income corresponding to the game round with the income corresponding to the game round, and if the income of the game round is the same, satisfying (fn, yn) = (fn-1, yn-1) = (MF, ME), considering that an equilibrium state is reached, and entering step S4), otherwise, making delta = delta +1 and executing the step.
In the embodiment, when the hybrid power flow is calculated, the distributed power supply and power grid company makes a decision, checks and calculates the scheme of the distributed power supply and power grid company again according to the information of the previous round, and the final income under the game round is obtained after the hybrid power flow calculation. Specifically, the distributed power supply is generally connected to the tail end of the distribution line, and the active and reactive flowing directions of the line are changed in a complex mode due to the fact that the fluctuation range of illumination and wind power is large. The interaction behavior of each main body is realized through safety check, and the mixed power flow needs to be calculated in the process, so that the stable operation of the distributed power supply system is guaranteed. In the game process, the mutual influence among the decision schemes is indirectly realized through the transmission and conversion of the power flow parameters in the hybrid power flow calculation process. The specific process of the mixed tide safety check comprises the following steps:
(1) After a decision scheme is given to a distributed power supply and a power grid company, updating a network structure of a power system according to selected elements in a first strategy set and a second strategy set, calculating power flow of the power system under the grid structure, checking the scheme, namely comparing whether the power flow of the power system at the moment exceeds a preset first threshold value, and transmitting power flow information to a coupling node after the checking is met;
(2) Calculating to obtain the energy flow of the coupling node by utilizing the power balance relation of the coupling node, substituting the energy flow into the load flow model for calculation, and comparing the calculation result with a preset second threshold value so as to realize the safety check of the decision scheme;
(3) And determining a final decision scheme under the game round according to the checking result, namely when the checking is passed, determining that the final decision scheme under the game round is a decision scheme given by a distributed power supply and a power grid company.
In this embodiment, the process of calculating the power flow of the power system is as follows:
according to the initial voltage value of each node, calculating the unbalance amount of the node voltage square of the injected power, wherein the unbalance amount comprises the following steps:
I B ＝Y B ×U B (18)
in the formula I B And U B Respectively, the current, voltage, Y of each node B Is a node admittance matrix.
Y ij ＝G ij +jB ij (19)
Wherein j is a Jacobian matrix, G ij As a network conductance matrix, B ij Is a network susceptance matrix.
In the formula,
is the initial value of each node voltage.
Equations (21) and (22) are amounts of unbalance of the square of the node voltage for calculating the injection power.
In the formula, e i 、f i The real part and the imaginary part of the node voltage obtained in the iteration process. P i Active power is injected for the PQ node and the PV node.
In the formula, Q i Reactive power is injected for the PQ node. U shape i Is the voltage magnitude of the PV node. The initial voltage value of each node is substituted to find the unbalance amount in the correction equation
And the like as shown in formulas (23) to (25).
And after the convergence condition is met, continuously solving and calculating the value of each node after the voltage change, namely the corrected value, as shown in formulas (26) and (27).
In the formula,
the corrected value is the voltage of each node.
And (4) continuing to perform the next iteration from the equation (21) by using the obtained corrected value until the precision requirement is met, and exiting the loop. And finally, calculating the power of the balance node and the power of the line.
Therefore, in step S3) of this embodiment, the step of calculating and obtaining the load flow information of the game according to the network structure and checking includes the following steps:
calculating the power flow of the power system under the network structure, and transmitting the power flow to the coupling node after the power flow passes the first check;
and calculating the energy flow of the coupling node, substituting the energy flow into the load flow model for calculation, and performing secondary check on the calculation result.
The step of calculating the power system load flow under the network structure comprises the following steps:
calculating an unbalance amount of a node voltage square of the injection power according to equations (21) and (22) from the initial voltage value of each node;
calculating the change value of each node voltage according to the unbalance amount;
and according to the change value of each node voltage, executing the step of calculating the unbalance amount of the node voltage square of the injected power until the corrected voltage value of each node meets the precision requirement.
In conclusion, the method provided by the embodiment establishes the investment decision model of the distributed power supply, the matching power grid and the two-main-body combined game in consideration of the investment and operation stages, so that the economy of the decision scheme is ensured, the investment accuracy is also considered, the optimal allocation of resources is realized, the requirements of distributed power supply development and regional economic development are met, and the social and economic benefits of investment are improved. In the combined game process of the distributed power supply and the matched power grid, the mutual influence between decision schemes of the distributed power supply and the matched power grid can be realized by measuring and calculating the mixed power flow and transmitting and converting power flow parameters, and then the investment decisions of the two main bodies are continuously optimized to achieve the optimal decision
The following describes the experimental procedure to validate the method of this example:
firstly, determining the investment game behaviors of all subjects: distributed power operators want to increase the investment benefits of their own power generation equipment and reduce the operating costs. And the distributed power supply mainly uses renewable clean energy, and the consumption of the clean energy can bring environmental benefits. The multi-energy complementary system can effectively utilize the complementarity of distributed wind power and photovoltaic power to realize the integration of power generation and power selling. Grid companies wish to reduce line investment and increase investment efficiency. When a power grid company and a distributed power supply generate power, the configured distributed energy storage can stabilize the fluctuation of grid-connected power by operating and controlling absorption or output power, provide peak clipping and valley filling, participate in primary frequency modulation and the like. The consumer uses electricity from the above three parties.
Then, designing an initial scene of an IEEE24 node model, wherein solid line parts are a generator set and a power transmission line as shown in FIG. 6; the parameters of 13 generator sets are detailed in a table 1, the parameters of 41 power transmission lines are detailed in a table 2.
TABLE 1 existing genset capacity
TABLE 2 existing Transmission line parameters
As shown in fig. 6, the dotted line part is a power generation device to be selected and a power transmission line to be selected in the distributed power system; 6 power generation equipment to be selected and 9 power transmission lines to be selected, wherein the maximum extension quantity of each line is 1, and the parameters are detailed in tables 3 and 4.
TABLE 3 distributed Power plant parameters
Table 4 parameters of power transmission line to be built
By researching the manufacturing cost of the transmission line of a certain province for nearly two years, the investment cost of the 500kV transmission line and the investment cost of the 220kV transmission line are respectively set to be 250 ten thousand yuan/km and 100 ten thousand yuan/km. The operating cost of the distributed generator set is 2% of the investment cost. The load parameters of the power network are shown in table 5.
TABLE 5 maximum load distribution at each node
Because investment decision is mainly used for researching the problems possibly faced by the recent distributed power supply investment, the average internet-surfing electricity price of distributed users is 0.5 yuan/(kWh.h) and the sale electricity price of a power grid company is 0.58 yuan/(kWh.h) by referring to the standard of the internet-surfing electricity price of 2022 years in Beijing City and the sale electricity price of residents in scene design. The renewable energy quota coefficient is 20%, the price of a green certificate is purchased for 100 yuan/piece, and the unit wind and light abandoning punishment cost is 400 yuan/MWh. And taking data of a certain provincial power grid from the typical daily load and wind-solar output data.
Simulation is performed on investment decisions of a distributed power supply and a matched power grid under three different scenes, wherein the scene 1 is as follows: independent decisions of a distributed power supply and a power grid of a game are not considered; scenario 2: a distributed power supply-power grid combined decision of a game is not considered; scenario 3: and (4) considering the distributed power supply-power grid combined decision of the game, namely the situation corresponding to the method of the embodiment.
As shown in fig. 7, the scenario 3 generates a tide flow graph according to an initial condition, derives tide information, performs game calculation by using Matlab software, generates a result if Nash balance is achieved, forms the tide flow graph, and obtains a profit result; if the Nash balance is not achieved, forming a new tide flow graph by using a Power System Analysis (PSASP) Program according to an investment scheme, deriving tide information, and performing game calculation by using Matlab software again.
The distributed user load requirements under the 3 scenarios are the same, and the power network decision scenario pair is shown in fig. 8. In investment decision of the distributed power supply: scenario 1 selects the set of nodes 1, 2, 7 and 22, scenario 2 selects the set of nodes 2, 7 and 22, and scenario 3 selects the set of nodes 2, 7, 15 and 16; in the decision of the power transmission line, the difference of the 3 scenarios lies in that: scenario 1 is that a power transmission line is newly built in all branches to be selected, scenario 2 is that no power transmission line is newly built in 1-5 branches, and scenario 3 is that no power transmission line is newly built in 1-5, 17-22 and 16-17 branches. And the new construction conditions of the other transmission lines are consistent.
The income contrast of the scene 1 and the scene 2 is detailed in the following table, and from the overall income, the scene 2 is improved by 108729 ten thousand yuan compared with the scene 1. The situation 1 is that independent decision is made between a distributed power supply and a power grid company, and a DG company creates power supply equipment at nodes 1 and 2, the total installed capacity reaches 320.4MW, the power grid company creates a new power transmission line in a matched manner, and according to the power grid load, only 171.2MW power generation equipment needs to be created at node 2 to meet the power grid load, so that the capacity is excessive, the utilization rate of the power generation equipment is reduced, and investment waste exists. Therefore, the investment can be optimized on the whole after the joint decision is considered, and further the total income of the system is increased.
TABLE 6 Multi-subject revenue comparison under Scenario 1 and Scenario 2
Unit: ten thousand yuan
The income comparison of the scenario 1 and the scenario 2 is detailed in the following table, and compared with the scenario 2, the investment cost of the power transmission line of the power grid company in the scenario 3 is reduced by 45500 ten thousand yuan, and the income of the power grid company is increased by 45500 ten thousand yuan. The reason is that in scenario 2, a generation facility with a capacity of 415MW is newly built in node No. 22 by a DG operation company, and the node is not consumed by a load of a down-grid, and needs to flow into node 17 through a 22-17 line to transmit to other load nodes of a power grid, so that a power source and the load are transmitted in a long distance, which not only increases line loss, but also increases a transmission line required to be built by the power grid company, and obviously increases investment and network loss cost of the power grid company. After the multi-body game is introduced, in order to achieve a balanced state of a power grid company and a DG operating company, in a scene 3, the DG operating company respectively newly builds 258MW and 157MW power generation equipment at nodes 15 and 16 which are close to load requirements, so that power grid flow is optimized, the power grid company only needs to build 3 lines 16-19, 16-15 and 15-24 to meet power grid load requirements and flow constraints, and although the investment of the DG operating company is increased, the investment and network loss cost of the power grid company are greatly reduced, so that the investment scheme of the scene 3 is better than that of a scene 2.
Table 7 comparison of total profit for distributed power supplies in scenario 2 and scenario 3
Unit: ten thousand yuan
The foregoing is illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (10)
1. A distributed power supply and matched power grid investment decision modeling method based on a multi-agent game is characterized by comprising the following steps:
constructing an investment decision model of a distributed power supply and an investment decision model of a matched power grid, and acquiring original data;
respectively generating a first strategy set and a second strategy set according to a power generation equipment candidate set and a distributed power supply investment plan candidate set in original data;
performing games according to element iteration in a first strategy set and a second strategy set, wherein each round of games selects target elements from the first strategy set according to elements selected from the second strategy set in the previous round of games, selects the target elements from the second strategy set according to the elements selected from the first strategy set in the current round of games, changes a network structure of a power system according to the target elements, calculates and obtains trend information of the current round of games according to the network structure and checks the trend information, and if the check is passed, a distributed power supply investment decision model and an investment decision model of a matched power grid are solved according to the network structure and original data of the current round of games to obtain investment income of the current round of games;
if the game reaches the equilibrium state, outputting an equilibrium solution and final investment income, if the elements in the first strategy set and the second strategy set are completely selected and the game does not reach the equilibrium state, changing the network structure of the power system according to the to-be-selected power generation equipment and the to-be-selected power supply investment plan in a preset scheme, calculating to obtain the trend information of the game of the round according to the network structure and the original data of the game of the round, checking, if the check is passed, solving the investment decision model of the distributed power supply and the investment decision model of the matched power grid according to the network structure and the original data of the game of the round to obtain the investment income of the game of the round, and iterating the game according to the elements in the first strategy set and the second strategy set again until the game reaches the equilibrium state.
2. The multi-agent game-based distributed power supply and supporting grid investment decision modeling method according to claim 1, wherein the target revenue function of the distributed power supply investment decision model is electricity selling income I GSE Renewable energy quota revenue I GRE And government subsidies C B The sum minus the investment cost C of the distributed power supply unit IG And the running cost C of the distributed power supply unit GO 。
3. The multi-agent game-based distributed power supply and supporting grid investment decision modeling method of claim 2, wherein the electricity selling income I is GSE The expression of (a) is as follows:
wherein n is the horizontal year of life cycleN is the total number of life cycles, E Bd,n For the nth horizontal year of the distributed power supply, rho S,n The average online electricity price of n horizontal years;
renewable energy quota revenue I GRE The expression of (a) is as follows:
wherein, γ gre For the green certificate trade price, k is the quantization factor that quantifies the renewable energy quota into the green certificate, θ s is the number of days of typical day s, P s,t Is the electric energy generated by the distributed power supply at the time t of the S typical day, S GGU A candidate set of power generation equipment;
government subsidy C B The expression of (a) is as follows:
wherein, P G,i Active power for distributed power plant i, num is the number of distributed generator sets, C i,t Subsidizing the unit price for the nth level year government;
investment cost C of distributed power supply unit IG The expression of (a) is as follows:
wherein x is i Investment variable, α, for distributed power plants i i Investment cost, T, for distributed power plants i G Omega is the capital discount rate for the service life of the distributed power generation equipment;
operating cost C of distributed power supply unit GO The expression of (a) is as follows:
where i is the number of the distributed power generation equipment, g i,n For the running time of the distributed power generation plant i in n horizontal years, C GC,i,n For the operating cost of the n horizontal year power plant i at unit power, P G,i Is the active power of the distributed generation equipment i.
4. The distributed power supply and supporting grid investment decision modeling method based on multi-agent game according to claim 2, wherein the constraint conditions of the distributed power supply investment decision model include:
the power constraint, the expression is as follows:
wherein x is the confidence capacity coefficient of the distributed power unit, P d,max For annual maximum load, R d For capacity reserve factor, P G,i Active power, x, for distributed power plants i i For investment variables of distributed power plants i, S GGU A candidate set of power generation equipment;
and (3) the installed scale constraint is expressed as follows:
wherein, P G,i Active power, x, for distributed power plants i i Investment variable for distributed power plants i, S GGU For a candidate set of power generation equipment, C max The maximum installed scale of the distributed power generating unit is obtained.
5. The multi-subject game-based distributed power supply and supporting grid investment decision modeling method of claim 1, wherein the target benefit of the supporting grid investment decision model is sales power income I ESE Deducting electricity sales income I GSE Net frame investment cost C IT Loss cost C TL And the electricity abandonment penalty cost C of the renewable energy source REN 。
6. The multi-player game-based distributed power supply and supporting grid investment decision modeling method of claim 5, wherein sales power income I ESE The expression of (c) is as follows:
wherein E is Ldt Year load of horizontal year t, p E,t Selling the electricity price for the grid company of the horizontal year t;
net frame investment cost C IT The expression of (c) is as follows:
wherein S is TL The method comprises the following steps of (1) obtaining a candidate set of power grid investment projects; y is j Is the investment variable for project j; beta is a beta j The investment cost for project j; t is TL Asset life;
loss of network cost C TL The expression of (a) is as follows:
wherein l is the item number;
the network loss of a circuit l matched with the distributed power supply in n horizontal years; u. of l,n The net loss cost of the nth horizontal year of the unit line is taken as the net loss cost;
penalty cost C for electricity abandonment of renewable energy source REN The expression of (a) is as follows:
wherein,
representing the predicted output of the distributed power supply unit in the time period t, S GGU Rho is a candidate set of power generation equipment t Representing the actual force, p pun A penalty factor is indicated.
7. The multi-agent game-based distributed power supply and supporting grid investment decision modeling method according to claim 5, wherein the constraints of the supporting grid investment decision model include:
power network constraints, the expression is as follows:
h, J and K respectively represent incidence matrixes of a power transmission line, a generator, a load and a power network node; f. of Ll,t Representing the current flowing on the horizontal annual line l; p gm,t Representing the output of the generator m in the horizontal year t; e Ldk,t Representing the load of the t horizontal year node k; s1, S2, S3 and S4 respectively represent a power transmission line set, a generator set, a power load set and a power network node set;
and (3) power flow constraint, wherein the expression is as follows:
wherein, P q 、Q q Respectively injecting active power and reactive power at a node q; u shape q 、U r The voltage amplitudes at nodes q and r, respectively; g qr 、B qr Respectively the conductance and susceptance of the branch qr; theta.theta. qr Is the voltage phase angle difference between nodes q and r;
a line transport capacity constraint, expressed as follows:
wherein, f Lqr Is the flow of line qr between nodes q and r;
the maximum capacity allowed for transmission for the line qr between nodes q and r.
8. The distributed power supply and matching power grid investment decision modeling method based on the multi-subject game as claimed in claim 1, wherein the step of calculating and checking the trend information of the game of the current round according to the network structure comprises the following steps:
calculating the power flow of the power system under the network structure, and transmitting the power flow to the coupling node after the power flow passes the first check;
and calculating the energy flow of the coupling node, substituting the energy flow into the load flow model for calculation, and performing secondary check on the calculation result.
9. The multi-agent game-based distributed power supply and supporting power grid investment decision modeling method of claim 8, wherein calculating the power system flow under the network structure comprises the following steps:
calculating the unbalance amount of the node voltage square of the injected power according to the voltage initial value of each node;
calculating the change value of each node voltage according to the unbalance amount;
and according to the change value of each node voltage, executing the step of calculating the unbalance amount of the node voltage square of the injected power until the corrected voltage value of each node meets the precision requirement.
10. The multi-agent game-based distributed power supply and matching grid investment decision modeling method according to claim 9, wherein the expression of the unbalance of the node voltage square for calculating the injected power is as follows:
wherein e is i 、f i For real and imaginary part, P, of node voltage found in iterative process i Active power injection for PQ and PV nodes, G ij As a network conductance matrix, B ij A network susceptance matrix;
wherein e is i 、f i For real and imaginary part, Q, of node voltage obtained in iterative process i Reactive power injected for PQ node, G ij As a network conductance matrix, B ij Is a network susceptance matrix.
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