CN110443398B - Optimal operation method of regional comprehensive energy system based on repeated game model - Google Patents

Optimal operation method of regional comprehensive energy system based on repeated game model Download PDF

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CN110443398B
CN110443398B CN201910392141.5A CN201910392141A CN110443398B CN 110443398 B CN110443398 B CN 110443398B CN 201910392141 A CN201910392141 A CN 201910392141A CN 110443398 B CN110443398 B CN 110443398B
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李鹏
王子轩
侯磊
张雪
马显
刘洋
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Xiongan New Area Power Supply Company State Grid Hebei Electric Power Co
Xuji Group Co Ltd
North China Electric Power University
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Abstract

A regional comprehensive energy system optimization operation method based on a repeated game model comprises the steps of firstly, carrying out steady-state modeling and trend analysis on a power distribution network, a gas distribution network and a micro-energy network in the regional comprehensive energy system; then, considering the interaction influence between an electric link and an energy coupling link in the regional comprehensive energy system, taking a micro energy network and a power distribution network as game participants, respectively taking the daily operating cost of the micro energy network and the comprehensive satisfaction degree of the power distribution network as respective utility functions, constructing a repeated game optimization model of the regional comprehensive energy system, and solving the repeated game optimization model by adopting a self-adaptive variation particle swarm algorithm to obtain a game equilibrium optimization result of the regional comprehensive energy system; and finally verifying the correctness and the effectiveness of the repeated game model-based regional comprehensive energy system optimization operation method. The invention can fully play the active regulation and control role of the power distribution network, simultaneously considers the benefits of the micro energy network and the power distribution network, and realizes the cooperative economic optimization operation of the regional comprehensive energy system.

Description

Optimal operation method of regional comprehensive energy system based on repeated game model
Technical Field
The invention relates to an optimized operation method of a regional comprehensive energy system. In particular to a regional comprehensive energy system optimization operation method based on a repeated game model.
Background
With the deep adjustment of the world energy pattern and the increasing global environmental pollution problem, integrated Energy Systems (IES) are developed to meet a series of challenges in the field of energy technology. The IES can be regarded as a natural extension of the microgrid, which is mainly composed of an energy supply network, an energy conversion link, an energy storage link, an integrated energy supply and utilization unit and an end user. On one hand, the advantages of distributed energy source multi-energy complementation are fully exerted through overall scheduling and collaborative optimization of multiple energy sources, and the utilization rate of renewable energy sources is improved; on the other hand, the cascade utilization of energy can be realized, and the comprehensive utilization level of the energy is improved.
In recent years, the construction and related research of the IES have been rapidly developed in China, and the 973 plan and the 863 plan of the national ministry of science and technology have placed research and development projects related to the IES into a key subsidization scope. In the national "Innovation action plan of energy technology revolution" (2016-2030), it is pointed out that technologies such as multi-energy complementary integrated energy networks are mainly studied. Therefore, IES is in an extremely important position in the field of energy transformation in China, and will certainly become a main form of a future energy system.
At present, most researches mainly summarize the problems to be solved in the aspects of modeling and energy flow analysis of the IES, collaborative planning and optimized operation, energy supply reliability assessment, demand response mechanism and the like from the aspects of models, algorithms, index systems and the like. The optimal operation of an Integrated Communication Energy System (ICES) is an important research subject, and the key for realizing the ICES coordinated economic optimal operation is to consider the coupling interaction influence of different links in the ICES and fully play the active regulation and control function of a power distribution network.
The power link and the energy coupling link in the ICES belong to different benefit subjects, targets, requirements and adjustment strategies are different, energy coupling is achieved between the targets, the requirements and the adjustment strategies, and the strategies and target benefits are influenced mutually, so that the ICES is a typical game problem. Repeat gaming is a special case of dynamic gaming, where participants face the same game pattern in each stage, but can modify their own strategy based on the opponent's behavior in the preceding stages, thereby affecting the overall gaming process. In ICES optimization operation based on a repeated game model, the micro energy network and the power distribution network are in turn decided to change the energy interaction boundaries of the two parties, so that the strategy sets of the two parties are dynamically changed, a multi-loop game is circularly carried out until Nash equilibrium solution of the game is obtained, and the benefits of the micro energy network and the power distribution network are considered.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a repeated game model-based regional comprehensive energy system optimization operation method which takes coupling interaction influences of different links in ICES into consideration and fully exerts the active regulation and control effect of a power distribution network.
The technical scheme adopted by the invention is as follows: a regional comprehensive energy system optimization operation method based on a repeated game model comprises the following steps:
1) According to typical daily loads and wind power and photovoltaic output data of a regional power distribution network, a regional gas distribution network and a micro energy network in the regional integrated energy system, performing steady-state modeling and load flow analysis on the regional power distribution network, the regional gas distribution network and the micro energy network in the regional integrated energy system;
2) Considering the interaction influence between a regional power distribution network and a micro energy network in the regional integrated energy system, and constructing a repeated game model of the regional integrated energy system, wherein the micro energy network and the regional power distribution network are used as game participants, daily operating cost of the micro energy network and comprehensive satisfaction of the regional power distribution network are respectively used as respective utility functions, and the constraint conditions of the regional power distribution network, the regional gas distribution network and the micro energy network are comprehensively considered;
3) And solving the repeated game model by adopting a self-adaptive variation particle swarm algorithm, and verifying the correctness and the effectiveness of the optimized operation method of the regional comprehensive energy system based on the repeated game model.
The steady-state modeling and load flow analysis of the regional power distribution network, the regional gas distribution network and the micro-energy network in the regional integrated energy system in the step 1) comprises the following steps:
(1.1) modeling of regional distribution networks
As an electric link and a supporting platform of a regional comprehensive energy system, a power distribution network is not only an output object of other energy links, but also an energy supplier of other energy system coupling links, and a regional power distribution network load flow calculation model is as follows:
Figure BDA0002056885080000021
wherein, P i 、Q j Respectively injecting active power and reactive power into the node i; n is the number of nodes; u shape i 、U j The voltage amplitudes of the nodes i and j are respectively; g ij 、B ij Conductance and susceptance of branch ij, respectively; delta ij Is the voltage phase angle difference between nodes i, j;
(1.2) regional gas distribution network modeling
The natural gas system comprises a gas source, a pipeline, a compressor, a gas storage point and a load, the regional gas distribution network model construction comprises a natural gas node and a gas transmission pipeline, the node type comprises a gas source point and a load node, wherein the pressure of the gas source point is known, and the flow of the load node is known; the regional distribution network node equation is as follows:
M＝A 1 F
wherein A is 1 Is a reduced branch-node incidence matrix; f is the natural gas flow vector in the branch; m is a gas load vector in a natural gas pipe network; for the gas pipeline ij, calculating the flow F of the gas pipeline under the pressure of 0-75 mbar by adopting a Lacey formula ij ：
Figure BDA0002056885080000022
Figure BDA0002056885080000023
Wherein i and j are natural gas pipeline start respectivelyA last node; f ij Is the flow rate of the pipeline; p is a radical of i 、p j Pressure on the sections i and j; d ij Is the diameter of the pipe; l. the ij Is the length of the pipeline; f. of ij A non-directional coefficient of friction; g is the specific gravity of natural gas; all the above values are measured under the standard conditions of 288K temperature and 0.1MPa of atmospheric pressure;
(1.3) modeling of micro energy grid
The micro energy network modeling is based on an energy concentrator model, an electric-gas-thermal coupling micro energy network model is built, and the coupling relation between the energy input P and the energy output L is described through a coupling matrix C: l = CP, the energy conversion relationships of the two typical energy hubs EH1, EH2 employed are as follows:
Figure BDA0002056885080000031
Figure BDA0002056885080000032
wherein L is e1 、L h1 、P e1 、P g1 、P DG1 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH1 model; l is e2 、L h2 、P e2 、P g2 、P DG2 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH2 model; lambda [ alpha ] 1 、λ 2 Distributing coefficients for electric power and natural gas; eta T 、η AC And η GF Respectively representing the energy conversion efficiency of a transformer T, an air conditioning system AC and a gas boiler GF link;
Figure BDA0002056885080000033
respectively representing the electric efficiency and the thermal efficiency of the MT link of the micro-combustion engine;
carrying out load flow calculation solving on a power distribution network in the regional integrated energy system by adopting a forward-backward substitution method, carrying out load flow calculation solving on a gas distribution network in the regional integrated energy system by adopting a Newton node method, and obtaining a regional integrated energy system load flow analysis mathematical model as follows:
Figure BDA0002056885080000034
wherein, f e 、f g 、f eh Respectively representing the flow equations of a regional power distribution network, a regional gas distribution network and a micro energy network; x is the number of e State variables of the regional power distribution network comprise node voltage and branch power; x is a radical of a fluorine atom g State variables of the regional gas distribution network comprise node pressure and pipeline flow; x is the number of eh The state variables of the micro energy network comprise electricity, heat load, electricity purchasing quantity, gas purchasing quantity and distributed power supply input power;
the repeated game model E for constructing the regional integrated energy system in the step 2) is as follows:
E＝<N,H,S,U>
wherein N is a participant set, H is an action history set, S is a strategy set, and U is a profit/payment function; wherein, the participants comprise a micro energy network and a regional power distribution network, and the micro energy network adjusts the energy distribution coefficient lambda 1 、λ 2 And purchase electricity/gas quantity P e 、P g Changing self income/payment, optimizing distribution network topology structure X by regional distribution network e And capacitor switching combination X c Change self income/payment as a policy;
dividing 1 day into 24 time periods, and assuming that strategies of all participants in each game in each time period are mutually informed; in the repeated game process, in order to describe the time cost, a discount factor alpha is introduced, and the profit/payment of both game parties is multiplied by alpha in each increasing stage, wherein for game participants s, each game is expressed as:
Figure BDA0002056885080000035
wherein, A s A strategy space of a participant s in each stage game; of participants s at stage kThe payment is as follows:
Figure BDA0002056885080000036
wherein the content of the first and second substances,
Figure BDA0002056885080000041
a strategy combination of game participants s in the stage k;
the specific payment functions of the two parties in the game in the repeated game model are respectively as follows:
(2.1) Payment function of micro energy network
Daily operating cost of micro energy network C 1 As a function of payment, by a purchase of electricity fee C grid And gas purchase fee C gas DG operation and maintenance fee C om And environmental protection fee C ce The structure is specifically as follows:
min C 1 ＝min(C grid +C gas +C om +C ce )
C grid 、C gas 、C om and C ce Respectively expressed as:
Figure BDA0002056885080000042
wherein the content of the first and second substances,
Figure BDA0002056885080000043
P e (t)、P g (t)、P pv (t) and P wt (t) the power of electricity purchased/sold, the power of gas purchased, the photovoltaic output and the fan output in the period of t are respectively; p is a radical of be (t) and p se (t) electricity purchase price and electricity sale price in a time period t, respectively; p is a radical of formula g Fixing the price for natural gas; p is a radical of pv And p wt Respectively representing the operation and maintenance costs of unit photovoltaic and fan output; beta is a e 、β g Respectively representing the equivalent carbon emission coefficients of electricity and gas purchase; beta represents a unit of CO 2 The cost of treatment of;
(2.2) Payment function of regional distribution network
Network reconstruction is combined with capacitor switching to serve as an optimization regulation and control means, the power distribution network loss and load balancing rate is selected as a dual-objective to carry out regional power distribution network optimization, a multi-objective interactive decision mathematical model is introduced to form comprehensive satisfaction f of regional power distribution network optimization, and the comprehensive satisfaction f is used as a comprehensive payment function of a regional power distribution network side, and the method specifically comprises the following steps:
Figure BDA0002056885080000044
Figure BDA0002056885080000045
wherein f is 1 (X e ,X c )、f 2 (X e ,X c ) Two targets for power distribution network optimization respectively; x e For power distribution network topologies, X c Switching combination for the capacitor; nl is the total number of the branch circuits of the power distribution network; k is a radical of formula b The switch b is in an open-close state; r is b Represents the impedance of branch b; p b And Q b Respectively the active power and the reactive power flowing through the branch b; u shape b The voltage at the end node of branch b; s b Is the power flowing through branch b; s bmax Rated power for branch b;
to f 1 (X e ,X c )、f 2 (X e ,X c ) Respectively carrying out normalization processing to obtain satisfaction functions:
Figure BDA0002056885080000051
Figure BDA0002056885080000052
/>
wherein f is 1 min (X e ,X c ) And
Figure BDA0002056885080000053
are respectively given by f 1 (X e ,X c ) And f 2 (X e ,X c ) Respectively optimizing target values when the power distribution network is optimized for a single target; f. of 1 max (X e ,X c ) And &>
Figure BDA0002056885080000054
Is f in the original network state 1 (X e ,X c ) And f 2 (X e ,X c ) A value of (d);
the reconstructed comprehensive satisfaction degree f, namely the payment function f of the regional power distribution network side is obtained by using the Euclidean distance, and the function is as follows:
Figure BDA0002056885080000055
wherein the content of the first and second substances,
Figure BDA0002056885080000056
and &>
Figure BDA0002056885080000057
Are respectively given by 1 (X e ,X c ) And f 2 (X e ,X c ) And setting the optimal satisfaction degree to be 1 when the power distribution network is optimized for a single target.
The constraint conditions of the regional power distribution network, the regional gas distribution network and the micro energy network in the step 2) are as follows:
(2.3) regional distribution network constraints
The equality constraint satisfied by the regional power distribution network is a power balance constraint, and the inequality constraint comprises a node voltage constraint, a branch circuit capacity constraint, a reactive compensation capacity constraint, a power distribution network topology constraint, a maximum switching time constraint and a purchase/sale electric quantity constraint, and is as follows:
Figure BDA0002056885080000058
Figure BDA0002056885080000059
Figure BDA00020568850800000510
0≤Q ci ≤Q ci,max
Figure BDA00020568850800000511
Figure BDA00020568850800000512
Figure BDA00020568850800000513
wherein, in the time period t, Ω i A set of associated nodes that are nodes i;
Figure BDA00020568850800000514
is the switch state of branch ij; />
Figure BDA00020568850800000515
The switching power of the node i and the micro energy network is obtained; />
Figure BDA00020568850800000516
And &>
Figure BDA00020568850800000517
Respectively injecting active power and reactive power of a node i into a source node; />
Figure BDA00020568850800000518
And
Figure BDA00020568850800000519
with injection nodes i for distributed power supplies respectivelyPower and reactive power; />
Figure BDA00020568850800000520
And &>
Figure BDA00020568850800000521
Respectively the active load and the reactive load of the node i; />
Figure BDA00020568850800000522
And &>
Figure BDA00020568850800000523
Respectively the active power and the reactive power of the head end of the branch ij; u shape i Is the node i voltage, U i,min And U i,max The minimum value and the maximum value of the voltage of the node i are respectively; />
Figure BDA00020568850800000524
Flowing power, S, for branch ij ij,max Maximum power allowed for branch ij; q ci,max To compensate for the upper capacity limit, Q, of node i ci Is the reactive compensation quantity at the node i; />
Figure BDA00020568850800000525
The method comprises the following steps that a power distribution network topology is defined, and K is a set of all feasible radial topologies of the power distribution network; t is 24 time periods in one day, NL is a power distribution network branch set and total delta Z ij,t Switching change times for adjacent time periods; SW max The total upper limit value of the switch action times in one day; />
Figure BDA0002056885080000061
To purchase/sell electricity, P e,min And P e,max The minimum value and the maximum value of the power for buying/selling electricity are respectively; />
(2.4) regional gas distribution network constraints
The equality constraint satisfied by the regional gas distribution network is a power flow balance constraint, and the inequality constraint comprises a node air pressure constraint, a pipeline flow constraint and a gas purchase constraint, and is as follows:
Figure BDA0002056885080000062
Figure BDA0002056885080000063
Figure BDA0002056885080000064
Figure BDA0002056885080000065
wherein, during the time period t,
Figure BDA0002056885080000066
injecting natural gas flow into the node i for a gas source; />
Figure BDA0002056885080000067
And &>
Figure BDA0002056885080000068
Respectively the gas sales volume and the natural gas load of the node i; />
Figure BDA0002056885080000069
The natural gas flow rate for conduit ij; omega i A set of associated nodes that are nodes i; />
Figure BDA00020568850800000610
Is node i air pressure, p i,min And p i,max Respectively is the minimum value and the maximum value of the air pressure of the node i; />
Figure BDA00020568850800000611
For flow in pipe ij, F ij,min And F ij,max Respectively the minimum value and the maximum value of the pipeline flow; />
Figure BDA00020568850800000612
To purchase/sell gas, F g,min And F g,max Respectively the minimum value and the maximum value of the gas purchasing quantity;
(2.5) micro energy grid constraints
The equality constraint of the micro energy network is an energy conversion relation of the energy hub, and the inequality constraint comprises the operation constraint of AC, GF and MT equipment and the upper and lower limit constraint of electric power and natural gas exchange power, and is as follows:
Figure BDA00020568850800000613
Figure BDA00020568850800000614
Figure BDA00020568850800000615
Figure BDA00020568850800000616
the upper and lower limits of the electric and gas exchange power in the two typical energy hub EH1 and EH2 models are as follows:
Figure BDA00020568850800000617
/>
Figure BDA0002056885080000071
Figure BDA0002056885080000072
Figure BDA0002056885080000073
wherein, in the time period t, L e1 、L h1 、P e1 、P g1 、P DG1 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH1 model; l is e2 、L h2 、P e2 、P g2 、P DG2 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH2 model; lambda 1 、λ 2 Distributing coefficients for electric power and natural gas; eta T 、η AC And η GF Respectively representing the energy conversion efficiency of a transformer T, an air conditioning system AC and a gas boiler GF link;
Figure BDA0002056885080000074
respectively representing the electric efficiency and the thermal efficiency of the MT link of the micro-combustion engine; />
Figure BDA0002056885080000075
For AC output of air-conditioning system, P AC,min And P AC,max Minimum and maximum values of the AC output, respectively; />
Figure BDA0002056885080000076
For MT output of gas boiler, P MT,min And P MT,max The minimum value and the maximum value of MT output are respectively; />
Figure BDA0002056885080000077
For GF output of gas boiler, P GF,min And P GF,max The minimum value and the maximum value of GF output are respectively; />
Figure BDA0002056885080000078
Purchase/sell electricity for EH1, P e1,min And P e1,max The minimum value and the maximum value of the electric quantity purchased/sold by the EH1 are respectively; />
Figure BDA0002056885080000079
For EH1 gas purchase, P g1,min And P g1,max Respectively the minimum value and the maximum value of the EH1 gas purchase amount; />
Figure BDA00020568850800000710
Purchase/sell electricity for EH2, P e2,min And P e2,max The minimum value and the maximum value of the EH2 electric quantity purchased/sold are respectively; />
Figure BDA00020568850800000711
For EH2 gas purchase, P g2,min And P g2,max Respectively the minimum value and the maximum value of the EH2 gas purchase amount;
the step 3) comprises the following steps:
in a repeated game in each time period t, the micro energy network and the regional power distribution network make a decision in turn to change the energy interaction boundaries of the two parties, so that the strategy sets of the two parties are dynamically changed, which is specifically represented as follows: in each time period t, firstly, the micro energy network makes a decision under the condition of giving the combination of the initial network topology and the capacitor switching of the regional power distribution network, even if the minimum distribution coefficient and the electricity/gas purchasing quantity are paid by self, the regional power distribution network adjusts the own strategy according to the micro energy network strategy, the topological structure and the capacitor switching combination are fed back to the micro energy network, the micro energy network updates the strategy and feeds back the strategy to one side of the regional power distribution network for decision making, the established repeated game model is solved by adopting the self-adaptive variation particle swarm algorithm, the multi-loop game is carried out in a circulating mode until the Nash equilibrium solution of the game is obtained, and the equilibrium solution of the time period is used as a reference scheme for the game solving of the next adjacent time period.
According to the optimal operation method of the regional comprehensive energy system based on the repeated game model, the coupling interaction influence of the power link and the energy coupling link in the ICES is fully considered, the active regulation and control function of the power distribution network is fully exerted (power distribution network reconstruction and capacitor switching are used as one of the optimal regulation and control means), and the coordinated economic optimal operation of the ICES is realized. The method has the following specific advantages:
1. according to the repeated game optimization model established by the invention, the micro energy network and the power distribution network are used as game participants, the daily operating cost of the micro energy network and the comprehensive satisfaction degree of the power distribution network are respectively used as respective utility functions, the objective function obeys the relevant constraints of an electric link, a natural gas link and an energy coupling link, the energy interaction boundary and the strategy set of the two parties are dynamically changed through the interactive game between the two parties, the Nash equilibrium solution of the game is finally obtained, the benefits of the micro energy network and the power distribution network are taken into consideration, and the method is favorably applied to practical engineering.
2. Aiming at the established repeated game optimization model, the self-adaptive variation particle swarm algorithm is adopted to effectively solve the nonlinear optimization problem containing the multi-energy flow constraint, the algorithm is simple in structure and high in convergence speed, and early-maturing convergence can be effectively avoided by introducing variation operation.
Drawings
FIG. 1 is a flow chart of a method for optimizing operation of a regional integrated energy system based on a repetitive game model according to the present invention;
FIG. 2 is a schematic diagram of a regional energy integration system according to the present invention;
FIG. 3 is a typical daily heat, electrical load, photovoltaic, wind power curve of an embodiment of the present invention;
FIG. 4a is a diagram of the optimization results of the micro-energy grid power and natural gas input;
fig. 4b is a graph of distribution coefficient optimization results of two typical energy hubs EH1, EH2 in the micro energy network of the present invention;
FIG. 5 is a diagram of the convergence of the game at various time intervals;
fig. 6a is a diagram illustrating the result of optimizing the energy flow of each device in a typical energy hub EH1 in the micro energy grid according to the present invention;
fig. 6b is a diagram showing the result of optimizing the energy flow of each device in the typical energy hub EH2 in the micro energy grid according to the present invention;
fig. 7 is a comparison graph of optimization results considering the network loss and the load balancing rate of the power distribution network in each time period before and after optimization of the power distribution network.
Detailed Description
The method for optimizing the operation of the regional integrated energy system based on the repeated game model is described in detail below with reference to the embodiments and the accompanying drawings.
As shown in fig. 1, the optimal operation method of the regional comprehensive energy system based on the repeated game model fully considers the coupling interaction effect of the power link and the energy coupling link in the ICES and fully exerts the active regulation and control function of the power distribution network (power distribution network reconstruction and capacitor switching are used as one of the optimal regulation and control means), so as to realize the collaborative economic optimal operation of the ICES. The method specifically comprises the following steps:
1) According to typical daily loads of a power distribution network, a gas distribution network and a micro energy network in the regional integrated energy system and wind power and photovoltaic output data, performing steady-state modeling and load flow analysis on the regional power distribution network, the regional gas distribution network and the micro energy network in the regional integrated energy system;
the steady-state modeling and power flow analysis of the regional power distribution network, the regional gas distribution network and the micro-energy network in the regional comprehensive energy system comprises the following steps:
(1.1) regional distribution network modeling (electric power link)
As an electric link and a supporting platform of a regional comprehensive energy system, a power distribution network is an output object of other energy links and an energy supplier of other energy system coupling links, and a regional power distribution network load flow calculation model is as follows:
Figure BDA0002056885080000091
wherein, P i 、Q j Respectively injecting active power and reactive power into the node i; n is the number of nodes; u shape i 、U j The voltage amplitudes of the nodes i and j are respectively; g ij 、B ij Conductance and susceptance of branch ij, respectively; delta. For the preparation of a coating ij Is the voltage phase angle difference between nodes i, j;
(1.2) regional distribution network modeling (Natural gas Link)
The natural gas system comprises a gas source, a pipeline, a compressor, a gas storage point and a load, a regional gas distribution network model is constructed to comprise a natural gas node and a gas transmission pipeline, the node type comprises a gas source point and a load node, wherein the pressure of the gas source point is known, and the flow of the load node is known; the node pressure and flow in the natural gas pipeline network are similar to the node voltage and power in the power system, and the regional distribution network node equation is as follows:
M＝A 1 F
wherein A is 1 Is a reduced branch-node incidence matrix; f is the natural gas flow vector in the branch; m is a gas load vector in a natural gas pipe network; for the gas pipeline ij, calculating the flow F of the gas pipeline under the pressure of 0-75 mbar by adopting a Lacey formula ij ：
Figure BDA0002056885080000092
Figure BDA0002056885080000093
Wherein i and j are respectively the starting and ending nodes of the natural gas pipeline; f ij Is the flow rate of the pipeline; p is a radical of formula i 、p j Pressure on sections i and j; d ij Is the diameter of the pipe; l ij Is the length of the pipeline; f. of ij A non-directional coefficient of friction; g is the specific gravity of natural gas; all the above values are measured under the standard conditions of 288K temperature and 0.1MPa atmospheric pressure;
(1.3) modeling of micro energy network (micro energy network)
The micro energy network is used as an important micro energy network of a regional comprehensive energy system, optimizes and coordinates operation through energy interaction among a plurality of energy links to realize multi-energy complementation and improve the energy utilization efficiency; the micro energy network modeling of the invention builds an electric-gas-thermal coupling micro energy network model based on an energy concentrator model, and describes the coupling relation between an energy input P and an energy output L through a coupling matrix C: l = CP, the energy conversion relationships of the two typical energy hubs EH1, EH2 employed are as follows:
Figure BDA0002056885080000094
Figure BDA0002056885080000095
wherein L is e1 、L h1 、P e1 、P g1 、P DG1 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH1 model; l is e2 、L h2 、P e2 、P g2 、P DG2 Respectively representing the electric load, the heat load, the electricity purchasing quantity, the gas purchasing quantity and the distributed power supply input power in the typical energy hub EH2 model; lambda [ alpha ] 1 、λ 2 Distributing coefficients for electric power and natural gas; eta T 、η AC And η GF Respectively representing the energy conversion efficiency of a transformer T, an air conditioning system AC and a gas boiler GF link;
Figure BDA0002056885080000101
respectively representing the electrical efficiency and the thermal efficiency of the MT link of the micro-combustion engine; (ii) a
The invention mainly researches a regional comprehensive energy system comprising a power distribution network and a gas distribution network, and only the heat load power is considered in the thermal link; the invention relates to an electric-gas-heat multi-energy flow calculation which is a foundation and a tool for optimizing operation of a regional integrated energy system, and solves the steady-state power flow of the regional integrated energy system by adopting a discrete model, namely, a forward-backward substitution method is adopted for a power distribution network in the regional integrated energy system to carry out power flow calculation, a Newton node method is adopted for a gas distribution network in the regional integrated energy system to carry out power flow calculation, and the obtained regional integrated energy system power flow analysis mathematical model is as follows:
Figure BDA0002056885080000102
wherein f is e 、f g 、f eh Respectively representing the flow equations of a regional power distribution network, a regional gas distribution network and a micro-energy network; x is a radical of a fluorine atom e State variables of the regional power distribution network comprise node voltage and branch power; x is a radical of a fluorine atom g State variables of the regional gas distribution network comprise node pressure and pipeline flow; x is the number of eh For micro-energy network state variables, bagsElectricity, heat load, electricity purchasing quantity, gas purchasing quantity and distributed power supply input power;
from the viewpoint of trend analysis, energy flows among the energy systems are mutually influenced and coupled, so that a game pattern is formed between the power distribution network and the micro energy network in the operation optimization of the regional comprehensive energy system.
2) Considering the interaction influence between a regional power distribution network and a micro energy network in the regional comprehensive energy system, and constructing a repeated game model of the regional comprehensive energy system, wherein the micro energy network and the regional power distribution network are used as game participants, daily operating cost of the micro energy network and comprehensive satisfaction of the regional power distribution network are respectively used as respective utility functions, and the constraint conditions of the regional power distribution network, the regional gas distribution network and the micro energy network are comprehensively considered; wherein the content of the first and second substances,
the repeated game model E for constructing the regional integrated energy system is as follows:
E＝<N,H,S,U>
wherein N is a participant set, H is an action history set, S is a strategy set, and U is a profit/payment function; wherein, the participants comprise a micro energy network and a regional power distribution network, and the micro energy network adjusts the energy distribution coefficient lambda 1 、λ 2 And purchase electricity/gas volume P e 、P g Changing self income/payment, optimizing distribution network topology structure X by regional distribution network e And capacitor switching combination X c Change self-income/payment as a policy;
dividing 1 day into 24 time periods, and assuming that strategies of all participants in each round of game in each time period are mutually informed; in the repeated game process, in order to describe the time cost, a discount factor alpha is introduced, and the profit/payment of both game parties is multiplied by alpha in each increasing stage, wherein for game participants s, each game is expressed as:
Figure BDA0002056885080000103
wherein A is s A strategy space for the participant s in each stage of the game; the payment of participant s at stage k is:
Figure BDA0002056885080000104
wherein the content of the first and second substances,
Figure BDA0002056885080000111
combining the strategies of game participants s in the stage k;
the specific payment functions of the two game parties in the repeated game model are respectively as follows:
(2.1) Payment function of micro energy network
Daily operating cost of micro energy network C 1 As a function of payment, by a purchase of electricity fee C grid And gas purchase fee C gas DG operation and maintenance fee Co m And environmental protection fee C ce The structure is specifically as follows:
min C 1 ＝min(C grid +C gas +C om +C ce )
C grid 、C gas 、C om and C ce Respectively expressed as:
Figure BDA0002056885080000112
wherein the content of the first and second substances,
Figure BDA0002056885080000113
P e (t)、P g (t)、P pv (t) and P wt (t) the power of electricity purchased/sold, the power of gas purchased, the photovoltaic output and the fan output in the period of t are respectively; p is a radical of be (t) and p se (t) electricity purchase price and electricity sale price in a time period t, respectively; p is a radical of g Fixing the price for natural gas; p is a radical of pv And p wt Respectively representing the operation and maintenance costs of unit photovoltaic and fan output; beta is a e 、β g Respectively representing equivalent carbon emission coefficients of electricity purchase and gas purchase; beta represents a unit of CO 2 The cost of treatment of;
(2.2) Payment function of regional distribution network
Network reconstruction is combined with capacitor switching to serve as an optimization regulation and control means, the power distribution network loss and load balancing rate is selected as a dual-objective to carry out regional power distribution network optimization, a multi-objective interactive decision mathematical model is introduced to form comprehensive satisfaction f of regional power distribution network optimization, and the comprehensive satisfaction f is used as a comprehensive payment function of a regional power distribution network side, and the method specifically comprises the following steps:
Figure BDA0002056885080000114
Figure BDA0002056885080000115
wherein f is 1 (X e ,X c )、f 2 (X e ,X c ) Respectively optimizing two targets of the power distribution network; x e For power distribution network topologies, X c Switching combination is carried out on the capacitor; nl is the total number of the branch circuits of the power distribution network; k is a radical of formula b The switch b is in an open-close state; r is b Represents the impedance of branch b; p is b And Q b Respectively the active power and the reactive power flowing through the branch b; u shape b The voltage at the end node of branch b; s. the b Is the power flowing through branch b; s. the bmax Is the rated power of branch b;
to f is paired 1 (X e ,X c )、f 2 (X e ,X c ) Respectively carrying out normalization processing to obtain satisfaction functions:
Figure BDA0002056885080000121
Figure BDA0002056885080000122
wherein, f 1 min (X e ,X c ) And
Figure BDA0002056885080000123
are respectively given by f 1 (X e ,X c ) And f 2 (X e ,X c ) Respectively optimizing target values when the power distribution network is optimized for a single target; f. of 1 max (X e ,X c ) And &>
Figure BDA0002056885080000124
Is f in the original network state 1 (X e ,X c ) And f 2 (X e ,X c ) A value of (d);
the reconstructed comprehensive satisfaction degree f, namely the payment function f of the regional power distribution network side is obtained by using the Euclidean distance, and the function is as follows:
Figure BDA0002056885080000125
wherein the content of the first and second substances,
Figure BDA0002056885080000126
and &>
Figure BDA0002056885080000127
Are respectively represented by f 1 (X e ,X c ) And f 2 (X e ,X c ) And setting the optimal satisfaction degree to be 1 when the power distribution network is optimized for a single target.
The constraint conditions of the regional power distribution network, the regional gas distribution network and the micro energy network are as follows:
(2.3) regional distribution network constraints
The equality constraint satisfied by the regional power distribution network is a power balance constraint, and the inequality constraint comprises a node voltage constraint, a branch circuit capacity constraint, a reactive compensation capacity constraint, a power distribution network topology constraint, a maximum switching time constraint and a purchase/sale electric quantity constraint, and is as follows:
Figure BDA0002056885080000128
Figure BDA0002056885080000129
Figure BDA00020568850800001210
0≤Q ci ≤Q ci,max
Figure BDA00020568850800001211
Figure BDA00020568850800001212
Figure BDA00020568850800001213
wherein, in the time period t, Ω i A set of associated nodes that are nodes i;
Figure BDA00020568850800001214
is the switch state of branch ij; />
Figure BDA00020568850800001215
The switching power of the node i and the micro energy network is obtained; />
Figure BDA00020568850800001216
And &>
Figure BDA00020568850800001217
Respectively injecting active power and reactive power of a node i into a source node; />
Figure BDA00020568850800001218
And
Figure BDA00020568850800001219
injecting active power and reactive power of a node i into the distributed power supply respectively; />
Figure BDA00020568850800001220
And &>
Figure BDA00020568850800001221
Respectively the active load and the reactive load of the node i; />
Figure BDA00020568850800001222
And &>
Figure BDA00020568850800001223
Respectively the active power and the reactive power of the head end of the branch ij; u shape i Is the voltage of node i, U i,min And U i,max The minimum value and the maximum value of the voltage of the node i are respectively; />
Figure BDA00020568850800001224
Flowing power, S, for branch ij ij,max Maximum power allowed to pass for branch ij; q ci,max To compensate for the upper capacity limit, Q, of node i ci Is the reactive compensation quantity at the node i; />
Figure BDA00020568850800001225
The method comprises the following steps that a power distribution network topology is defined, and K is a set of all feasible radial topologies of the power distribution network; t is 24 time periods in one day, NL is a power distribution network branch set and total delta Z ij,t Switching change times for adjacent time periods; SW max The total upper limit value of the switch action times in one day; />
Figure BDA0002056885080000131
To buy/sell electricity, P e,min And P e,max The minimum value and the maximum value of the power for buying/selling electricity are respectively;
(2.4) regional gas distribution network constraints
The equality constraint satisfied by the regional gas distribution network is a power flow balance constraint, and the inequality constraint comprises a node air pressure constraint, a pipeline flow constraint and a gas purchase constraint, and is as follows:
Figure BDA0002056885080000132
Figure BDA0002056885080000133
Figure BDA0002056885080000134
Figure BDA0002056885080000135
wherein, during the time period t,
Figure BDA0002056885080000136
injecting natural gas flow into the node i for a gas source; />
Figure BDA0002056885080000137
And &>
Figure BDA0002056885080000138
Respectively the gas sales volume and the natural gas load of the node i; />
Figure BDA0002056885080000139
The natural gas flow rate for conduit ij; omega i A set of associated nodes that are nodes i; />
Figure BDA00020568850800001310
Is node i air pressure, p i,min And p i,max Respectively the minimum value and the maximum value of the air pressure of the node i; />
Figure BDA00020568850800001311
For flow in pipe ij, F ij,min And F ij,max Respectively the minimum value and the maximum value of the pipeline flow; />
Figure BDA00020568850800001312
To purchase/sell gas, F g,min And F g,max Respectively the minimum value and the maximum value of the gas purchasing quantity;
(2.5) micro energy grid constraints
The equality constraint of the micro energy network is an energy conversion relation of the energy hub, and the inequality constraint comprises the operation constraint of AC, MT and GF equipment and the upper and lower limit constraint of electric power and natural gas exchange power, and is as follows:
Figure BDA00020568850800001313
Figure BDA00020568850800001314
Figure BDA00020568850800001315
Figure BDA00020568850800001316
the upper and lower limits of the electric and gas exchange power in the two typical energy hub EH1 and EH2 models are as follows:
Figure BDA00020568850800001317
/>
Figure BDA0002056885080000141
Figure BDA0002056885080000142
Figure BDA0002056885080000143
wherein, in the time period t, L e1 、L h1 、P e1 、P g1 、P DG1 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH1 model; l is a radical of an alcohol e2 、L h2 、P e2 、P g2 、P DG2 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH2 model; lambda [ alpha ] 1 、λ 2 Distributing coefficients for electric power and natural gas; eta T 、η AC And η GF Respectively representing the energy conversion efficiency of a transformer T, an air conditioning system AC and a gas boiler GF link;
Figure BDA0002056885080000144
respectively representing the electric efficiency and the thermal efficiency of the MT link of the micro-combustion engine; />
Figure BDA0002056885080000145
For AC output of air-conditioning system, P AC,min And P AC,max The minimum value and the maximum value of the AC output are respectively; />
Figure BDA0002056885080000146
For MT output of gas boiler, P MT,min And P MT,max The minimum value and the maximum value of MT output are respectively; />
Figure BDA0002056885080000147
Output of GF, P GF,min And P GF,max The minimum value and the maximum value of GF output are respectively; />
Figure BDA0002056885080000148
Purchase/sell electricity for EH1, P e1,min And P e1,max The minimum value and the maximum value of the EH1 purchasing/selling electric quantity are respectively; />
Figure BDA0002056885080000149
For EH1 gas purchase, P g1,min And P g1,max Are respectively provided withMinimum and maximum values for the amount of gas purchased by EH 1; />
Figure BDA00020568850800001410
Purchase/sell electricity for EH2, P e2,min And P e2,max The minimum value and the maximum value of the EH2 electric quantity purchased/sold are respectively; />
Figure BDA00020568850800001411
For gas purchase of EH2, P g2,min And P g2,max The minimum value and the maximum value of the EH2 gas purchase amount are respectively;
3) And solving the repeated game model by adopting a self-adaptive variation particle swarm algorithm, and verifying the correctness and the effectiveness of the optimized operation method of the regional comprehensive energy system based on the repeated game model. The method comprises the following steps:
in a repeated game in each time period t, the micro energy network and the regional power distribution network make a decision in turn to change the energy interaction boundaries of the two parties, so that the strategy sets of the two parties are dynamically changed, which is specifically represented as follows: in each time period t, firstly, the micro energy network makes a decision under the condition of giving the combination of the initial network topology and the capacitor switching of the regional power distribution network, even if the minimum distribution coefficient and the electricity/gas purchasing quantity are paid by self, the regional power distribution network adjusts the own strategy according to the micro energy network strategy, the topological structure and the capacitor switching combination are fed back to the micro energy network, the micro energy network updates the strategy and feeds back the strategy to one side of the regional power distribution network for decision making, the established repeated game model is solved by adopting the self-adaptive variation particle swarm algorithm, the multi-loop game is carried out in a circulating mode until the Nash equilibrium solution of the game is obtained, and the equilibrium solution of the time period is used as a reference scheme for the game solving of the next adjacent time period. The algorithm has simple structure and high convergence speed, and can effectively avoid premature convergence by introducing variation operation.
The correctness and the effectiveness of the method for optimizing the operation of the regional comprehensive energy system based on the repeated game model are verified below.
The following example is given, and the present invention performs simulation analysis on the regional integrated energy system shown in fig. 2 based on MATLAB platform, and the ICES mainly consists of the following three parts: (1) In an IEEE33 node power distribution system, load classification and time distribution data of all nodes of the system are known, node voltage meets 0.95-U-straw (Tsu) 1.05pu, and the installation position and the capacity of a capacitor are shown in table 1; (2) In the 11-node low-pressure natural gas system, the node load and the pipe network data are shown in tables 2 and 3, the node pressure is not lower than 20mbar, and the gas source point pressure is a fixed value of 75mbar; (3) The micro energy network is represented by an EH1 model and an EH2 model respectively, the access positions of a power distribution system and a natural gas system are shown in fig. 2, the predicted values of power, thermal demand load and DG output are shown in fig. 3, and the related equipment parameters and the energy conversion efficiency of the micro energy network are shown in table 4. The price of the natural gas is 0.35 yuan/(kW.h), and the electricity price adopts the time-of-use electricity price, which is specifically shown in Table 5.
TABLE 1 capacitor mounting location and Capacity
Figure BDA0002056885080000151
TABLE 2 Natural gas System nodal parameters
Figure BDA0002056885080000152
TABLE 3 Natural gas System pipe network parameters
Figure BDA0002056885080000153
/>
Figure BDA0002056885080000161
TABLE 4 micro energy grid parameters
Figure BDA0002056885080000162
TABLE 5 time of use of electricity prices at different time periods
Figure BDA0002056885080000163
Firstly, typical daily load and wind power and photovoltaic output data of a power distribution network, a gas distribution network and a micro-energy network in a regional comprehensive energy system are collected, as shown in fig. 3.
Then, based on an ICES repeated game optimization model constructed by the invention, a given example is simulated, and a micro-energy network power and natural gas input optimization result is shown in fig. 4a, a corresponding distribution coefficient optimization result of EH1 and EH2 is shown in fig. 4b, a game convergence condition in each period is shown in fig. 5, and an energy flow optimization result of each device in EH1 and EH2 is shown in fig. 6a and fig. 6 b.
The optimization results of fig. 4a, 4b, 6a and 6b are analyzed with reference to fig. 3, taking 8 th to 15 th as an example, where L is e1 、L h1 Rise, L e2 、L h2 When the energy (electricity or gas) input corresponding to EH1 or EH2 decreases, the energy (electricity or gas) input also tends to increase or decrease, but the trend curves also change under the influence of DG output. In addition, when the electricity price is higher, the micro energy network selects and increases the gas purchasing quantity for generating electricity so as to change the energy distribution coefficient. From the above analysis, it can be known that the energy optimization result of the ICES is closely related to the grid power of micro energy, the heat load demand, the DG output and the energy price, and the diversity of energy coupling can improve the flexibility of energy supply (by supplementing heat with electricity, by supplementing electricity with gas).
As can be seen from a further analysis of fig. 6a and 6b, the micro-energy grid electrical load supply comes from the grid, DG and MT. In a time period with higher electricity price, the micro energy grid preferentially selects the MT to supply power so as to reduce the operation cost, and the surplus power shortage is met by the DG and the power grid; in the low-price valley period, the micro energy grid preferentially selects a power grid to purchase power, and the surplus power shortage is met by the DG and the MT; when DG can not be completely consumed, surplus power can be sold to the power grid, and the time period is shown in the figure 6b 11-18. The heat load supply of the micro energy grid comes mainly from AC, GF and MT. Because the AC electric heat conversion efficiency is high, the heat load in the EH1 is preferentially provided by the AC, and the heat shortage is provided by the MT; for EH2, during periods of higher electricity prices, the heat load is supplied primarily by MT; during the low price valley period, the heat load is preferentially supplied by the GF with higher conversion efficiency and limited by GF maximum power, and the thermal shortage is provided by the MT.
Fig. 7 shows the network loss and load balancing rate of the power distribution network in each time period before and after optimization of the power distribution network is considered, and the topological structure of the power distribution network in each time period is detailed in table 6. For the case of not considering distribution network optimization, the distribution network topology is not variable and capacitor switching is not considered. As can be seen from fig. 7, the reconstruction of the power distribution network and the capacitor switching are used as a regulation and control means for participating in the game on the power distribution network side, the power distribution of the power distribution network is changed by changing the combination of the topology structure of the network and the capacitor switching, the network loss of the power distribution network is obviously reduced, the load balance degree is enhanced, and therefore the comprehensive payment of the power distribution network is reduced, which is specifically shown in table 6.
The comparison results of ICES daily operating costs before and after optimization of the distribution network are shown in Table 7. It can be seen that the ICES daily integrated operating cost is reduced by 206.7 yuan after the optimization of the power distribution network is considered. The power distribution network topology structure and capacitor switching combination can be dynamically adjusted according to the energy utilization condition and the energy price of the micro energy network through establishing a repeated game optimization model by taking power distribution network reconstruction and capacitor switching as one of regulation and control means in ICES optimization operation, the power supply capacity of the power distribution network topology structure and capacitor switching combination to the micro energy network is enhanced through improving the power distribution network trend distribution, and therefore the energy interaction boundary and the game strategy set are changed. The micro energy source network can select a more appropriate scheduling strategy when the electricity price is low, so that the operation cost is reduced.
Table 6 power distribution network reconstruction and capacitor switching optimization results
Figure BDA0002056885080000171
/>
Figure BDA0002056885080000181
TABLE 7 daily operating cost optimization results for micro-energy grids
Figure BDA0002056885080000182
In conclusion, the optimization results of the embodiment of the invention show that the optimal operation method of the regional integrated energy system based on the repeated game model can effectively solve the nonlinear optimization problem containing multi-energy flow constraint, takes the coupling interaction influence of different links in the ICES into consideration, fully exerts the active regulation and control function of the power distribution network, gives consideration to the benefits of the micro-energy network and the power distribution network, and realizes the cooperative economic optimal operation of the regional integrated energy system. The correctness and the effectiveness of the regional comprehensive energy system optimization operation method based on the repeated game model are verified.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (4)

1. A regional comprehensive energy system optimization operation method based on a repeated game model is characterized by comprising the following steps:
1) According to typical daily loads and wind power and photovoltaic output data of a regional power distribution network, a regional gas distribution network and a micro energy network in the regional integrated energy system, performing steady-state modeling and load flow analysis on the regional power distribution network, the regional gas distribution network and the micro energy network in the regional integrated energy system;
the steady-state modeling and load flow analysis of the regional power distribution network, the regional gas distribution network and the micro-energy network in the regional integrated energy system comprises the following steps:
(1.1) modeling of regional distribution networks
As an electric link and a supporting platform of a regional comprehensive energy system, a power distribution network is an output object of other energy links and an energy supplier of other energy system coupling links, and a regional power distribution network load flow calculation model is as follows:
Figure QLYQS_1
wherein, P i 、Q j Respectively injecting active power and reactive power into the node i; n is the number of nodes; u shape i 、U j The voltage amplitudes of the nodes i and j are respectively; g ij 、B ij Conductance and susceptance of branch ij, respectively; delta ij Is the voltage phase angle difference between nodes i, j;
(1.2) regional gas distribution network modeling
The natural gas system comprises a gas source, a pipeline, a compressor, a gas storage point and a load, a regional gas distribution network model is constructed to comprise a natural gas node and a gas transmission pipeline, the node type comprises a gas source point and a load node, wherein the pressure of the gas source point is known, and the flow of the load node is known; the regional gas distribution network node equation is as follows:
M＝A 1 F；
wherein A is 1 Is a reduced branch-node incidence matrix; f is the natural gas flow vector in the branch; m is a gas load vector in a natural gas pipe network; for the gas pipeline ij, the flow F of the natural gas pipeline under the pressure of 0-75 mbar is calculated by adopting a Lacey formula ij ：
Figure QLYQS_2
Figure QLYQS_3
Wherein i and j are respectively the starting and ending nodes of the natural gas pipeline; f ij Is the pipeline flow; p is a radical of i 、p j Pressure on sections i and j; d ij Is the diameter of the pipe; l ij Is the length of the pipeline; f. of ij A non-directional coefficient of friction; g is the specific gravity of natural gas; all the above values are measured under the standard conditions of 288K temperature and 0.1MPa atmospheric pressure;
(1.3) modeling of micro energy grid
The micro energy network modeling is based on an energy concentrator model, an electric-gas-thermal coupling micro energy network model is built, and the coupling relation between the energy input P and the energy output L is described through a coupling matrix C: l = CP, the energy conversion relationships of the two typical energy hubs EH1, EH2 employed are as follows:
Figure QLYQS_4
Figure QLYQS_5
wherein L is e1 、L h1 、P e1 、P g1 、P DG1 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH1 model; l is e2 、L h2 、P e2 、P g2 、P DG2 Respectively representing electric load, heat load, electric quantity purchasing, air quantity purchasing and distributed power supply input power in a typical energy hub EH2 model; lambda [ alpha ] 1 、λ 2 Distributing coefficients for electric power and natural gas; eta T 、η AC And η GF Respectively representing the energy conversion efficiency of a transformer T, an air conditioning system AC and a gas boiler GF link;
Figure QLYQS_6
respectively representing the electrical efficiency and the thermal efficiency of the MT link of the micro-combustion engine;
carrying out load flow calculation solution on a power distribution network in the regional comprehensive energy system by adopting a forward-backward substitution method, carrying out load flow calculation solution on a gas distribution network in the regional comprehensive energy system by adopting a Newton node method, and obtaining a mathematical model for load flow analysis of the regional comprehensive energy system as follows:
Figure QLYQS_7
wherein f is e 、f g 、f eh Respectively representing the flow equations of a regional power distribution network, a regional gas distribution network and a micro energy network; x is the number of e State variables of the regional power distribution network comprise node voltage and branch power; x is the number of g State variables of the regional gas distribution network comprise node pressure and pipeline flow; x is the number of eh The state variables of the micro energy network comprise electricity, heat load, electricity purchasing quantity, gas purchasing quantity and distributed power supply input power;
2) Considering the interaction influence between a regional power distribution network and a micro energy network in the regional integrated energy system, and constructing a repeated game model of the regional integrated energy system, wherein the micro energy network and the regional power distribution network are used as game participants, daily operating cost of the micro energy network and comprehensive satisfaction of the regional power distribution network are respectively used as respective utility functions, and the constraint conditions of the regional power distribution network, the regional gas distribution network and the micro energy network are comprehensively considered;
3) And solving the repeated game model by adopting a self-adaptive variation particle swarm algorithm, and verifying the correctness and the effectiveness of the optimized operation method of the regional comprehensive energy system based on the repeated game model.
2. The method for optimizing the operation of the regional integrated energy system based on the repetitive game model as claimed in claim 1, wherein the repetitive game model E for constructing the regional integrated energy system in step 2) is as follows:
E＝<N,H,S,U>；
wherein N is a participant set, H is an action history set, S is a strategy set, and U is a profit/payment function; wherein, the participants comprise a micro energy network and a regional power distribution network, and the micro energy network adjusts the energy distribution coefficient lambda 1 、λ 2 And purchase electricity/gas quantity P e 、P g Changing self income/payment, optimizing distribution network topology structure X by regional distribution network e And capacitor switching combination X c Change self-income/payment as a policy;
dividing 1 day into 24 time periods, and assuming that strategies of all participants in each game in each time period are mutually informed; in the repeated game process, in order to describe the time cost, a discount factor alpha is introduced, and the profit/payment of both game parties is multiplied by alpha in each increasing stage, wherein for game participants s, each game is expressed as:
Figure QLYQS_8
wherein A is s A strategy space for the participant s in each stage of the game; the payment of participant s at stage k is:
Figure QLYQS_9
wherein the content of the first and second substances,
Figure QLYQS_10
a strategy combination of game participants s in the stage k;
the specific payment functions of the two parties in the game in the repeated game model are respectively as follows:
(2.1) Payment function of micro energy network
Daily operating cost of micro energy network C 1 As a function of payment, by a purchase of electricity fee C grid And gas purchase fee C gas DG operation and maintenance fee C om And environmental protection fee C ce The structure is specifically as follows:
minC 1 ＝min(C grid +C gas +Co m +C ce )；
C grid 、C gas 、Co m and C ce Respectively expressed as:
Figure QLYQS_11
wherein the content of the first and second substances,
Figure QLYQS_12
P e (t)、P g (t)、P pv (t) and P wt (t) the power of electricity purchased/sold, the power of gas purchased, the photovoltaic output and the fan output in the period of t are respectively; p is a radical of be (t) and p se (t) electricity purchase price and electricity sale price in the time period t respectively; p is a radical of formula g Fixing the price for natural gas; p is a radical of formula pv And p wt Respectively representing the operation and maintenance costs of unit photovoltaic and fan output; beta is a e 、β g Respectively representing the equivalent carbon emission coefficients of electricity and gas purchase; beta represents the unit CO 2 The treatment cost of (2);
(2.2) Payment function of regional distribution network
Network reconstruction is combined with capacitor switching to serve as an optimization regulation and control means, the power distribution network loss and load balancing rate is selected as a dual-objective to carry out regional power distribution network optimization, a multi-objective interactive decision mathematical model is introduced to form comprehensive satisfaction f of regional power distribution network optimization, and the comprehensive satisfaction f is used as a comprehensive payment function of a regional power distribution network side, and the method specifically comprises the following steps:
Figure QLYQS_13
Figure QLYQS_14
wherein f is 1 (X e ,X c )、f 2 (X e ,X c ) Respectively optimizing two targets of the power distribution network; x e For power distribution network topologies, X c Switching combination for the capacitor; nl is the total number of the branch circuits of the power distribution network; k is a radical of formula b The switch b is in an open-close state; r is b Represents the impedance of branch b; p b And Q b Respectively the active power and the reactive power flowing through the branch b; u shape b The voltage at the end node of branch b; s b Is the power flowing through branch b; s. the bmax Is the rated power of branch b;
to f 1 (X e ,X c )、f 2 (X e ,X c ) Respectively carrying out normalization processing to obtain satisfaction functions:
Figure QLYQS_15
Figure QLYQS_16
wherein f is 1 min (X e ,X c ) And
Figure QLYQS_17
are respectively given by f 1 (X e ,X c ) And f 2 (X e ,X c ) Respectively optimizing target values when the power distribution network is optimized for a single target; f. of 1 max (X e ,X c ) And f 2 max (X e ,X c ) Is f in the original network state 1 (X e ,X c ) And f 2 (X e ,X c ) A value of (d);
the reconstructed comprehensive satisfaction degree f, namely the payment function f of the regional power distribution network side is obtained by using the Euclidean distance, and the function is as follows:
Figure QLYQS_18
wherein the content of the first and second substances,
Figure QLYQS_19
and &>
Figure QLYQS_20
Are respectively given by 1 (X e ,X c ) And f 2 (X e ,X c ) And setting the optimal satisfaction degree to be 1 when the power distribution network is optimized for a single target.
3. The method for optimizing the operation of the regional integrated energy system based on the repeated game model according to claim 1, wherein the constraints of the regional power distribution network, the regional gas distribution network and the micro energy network in the step 2) are as follows:
(2.3) regional distribution network constraints
The equality constraint satisfied by the regional power distribution network is a power balance constraint, and the inequality constraint comprises a node voltage constraint, a branch circuit capacity constraint, a reactive compensation capacity constraint, a power distribution network topology constraint, a maximum switching time constraint and a purchase/sale electric quantity constraint, and is as follows:
Figure QLYQS_21
Figure QLYQS_22
Figure QLYQS_23
Figure QLYQS_24
Figure QLYQS_25
Figure QLYQS_26
Figure QLYQS_27
wherein, in the time period t, Ω i A set of associated nodes that are nodes i;
Figure QLYQS_30
is the switch state of branch ij; />
Figure QLYQS_32
For the exchange of node i with the micro energy networkPower; />
Figure QLYQS_36
And &>
Figure QLYQS_31
Respectively injecting active power and reactive power of a node i into a source node; />
Figure QLYQS_35
And &>
Figure QLYQS_37
Injecting active power and reactive power of a node i into the distributed power supply respectively; />
Figure QLYQS_39
And &>
Figure QLYQS_28
Respectively the active load and the reactive load of the node i; />
Figure QLYQS_33
And &>
Figure QLYQS_38
Respectively the active power and the reactive power of the head end of the branch ij; u shape i Is the node i voltage, U i,min And U i,max Respectively the minimum value and the maximum value of the voltage of the node i; />
Figure QLYQS_40
Flowing power, S, for branch ij ij,max Maximum power allowed for branch ij; q ci,max Upper limit of compensation capacity, Q, for node i ci The reactive compensation quantity at the node i is obtained; />
Figure QLYQS_29
The method comprises the following steps that a power distribution network topology is defined, and K is a set of all feasible radial topologies of the power distribution network; t is 24 time periods in one day, NL is a power distribution network branch set and total delta Z ij,t For adjacent time periodsThe number of off-changes; SW max The total upper limit value of the switch action times in one day; />
Figure QLYQS_34
To purchase/sell electricity, P e,min And P e,max The minimum value and the maximum value of the power for buying/selling electricity are respectively;
(2.4) regional gas distribution network constraints
The equality constraint satisfied by the regional gas distribution network is a power flow balance constraint, and the inequality constraint comprises a node air pressure constraint, a pipeline flow constraint and a gas purchase constraint, and is as follows:
Figure QLYQS_41
Figure QLYQS_42
Figure QLYQS_43
Figure QLYQS_44
wherein, during the time period t,
Figure QLYQS_45
injecting natural gas flow into the node i for a gas source; />
Figure QLYQS_46
And &>
Figure QLYQS_47
Respectively the gas sales volume and the natural gas load of the node i; />
Figure QLYQS_48
Natural gas stream for pipeline ijAn amount; omega i A set of associated nodes that are nodes i; />
Figure QLYQS_49
Is node i air pressure, p i,min And p i,max Respectively is the minimum value and the maximum value of the air pressure of the node i; />
Figure QLYQS_50
For flow in pipe ij, F ij,min And F ij,max Respectively the minimum value and the maximum value of the pipeline flow; />
Figure QLYQS_51
To purchase/sell gas, F g,min And F g,max Respectively the minimum value and the maximum value of the gas purchasing quantity;
(2.5) micro energy grid constraints
The equality constraint of the micro energy network is an energy conversion relation of the energy hub, and the inequality constraint comprises the operation constraint of AC, GF and MT equipment and the upper and lower limit constraint of electric power and natural gas exchange power, and is as follows:
Figure QLYQS_52
Figure QLYQS_53
Figure QLYQS_54
Figure QLYQS_55
/>
the upper and lower limits of the electric and gas exchange power in the two typical energy hub EH1 and EH2 models are as follows:
Figure QLYQS_56
Figure QLYQS_57
Figure QLYQS_58
Figure QLYQS_59
wherein, in the time period t, L e1 、L h1 、P e1 、P g1 、P DG1 Respectively representing the electric load, the heat load, the electricity purchasing quantity, the gas purchasing quantity and the distributed power supply input power in the typical energy hub EH1 model; l is e2 、L h2 、P e2 、P g2 、P DG2 Respectively representing the electric load, the heat load, the electricity purchasing quantity, the gas purchasing quantity and the distributed power supply input power in the typical energy hub EH2 model; lambda [ alpha ] 1 、λ 2 Distributing coefficients for electric power and natural gas; eta T 、η AC And η GF Respectively representing the energy conversion efficiency of a transformer T, an air conditioning system AC and a gas boiler GF link;
Figure QLYQS_62
respectively representing the electric efficiency and the thermal efficiency of the MT link of the micro-combustion engine; />
Figure QLYQS_63
For AC output of air-conditioning system, P AC,min And P AC,max The minimum value and the maximum value of the AC output are respectively; />
Figure QLYQS_65
For MT output of gas boiler, P MT,min And P MT,max The minimum value and the maximum value of MT output are respectively; />
Figure QLYQS_61
For GF output of gas boiler, P GF,min And P GF,max The minimum value and the maximum value of GF output are respectively; />
Figure QLYQS_64
Purchase/sell electricity for EH1, P e1,min And P e1,max The minimum value and the maximum value of the EH1 purchasing/selling electric quantity are respectively; />
Figure QLYQS_66
For gas purchase of EH1, P g1,min And P g1,max Respectively the minimum value and the maximum value of the EH1 gas purchase amount; />
Figure QLYQS_67
Purchase/sell electricity for EH2, P e2,min And P e2,max The minimum value and the maximum value of the EH2 electric quantity purchased/sold are respectively; />
Figure QLYQS_60
For EH2 gas purchase, P g2,min And P g2,max The minimum value and the maximum value of the EH2 gas purchase amount are respectively.
4. The method for optimizing the operation of the regional integrated energy system based on the repeated game model as claimed in claim 1, wherein the step 3) comprises:
in the repeated game in each time period t, the micro energy network and the regional power distribution network make decisions in turn to change the energy interaction boundaries of the two parties, so that the strategy sets of the two parties are dynamically changed, which is specifically represented as follows: in each time period t, firstly, the micro energy network makes a decision under the condition of giving the combination of the initial network topology and the capacitor switching of the regional power distribution network, even if the minimum distribution coefficient and the electricity/gas purchasing amount are paid by self, the regional power distribution network adjusts a strategy according to the micro energy network strategy, the strategy comprises a topological structure and the capacitor switching combination and feeds back the strategy to the micro energy network, the micro energy network updates the strategy and feeds back the strategy to the regional power distribution network for decision making, the established repeated game model is solved by adopting the self-adaptive variation particle swarm algorithm, the multi-loop game is carried out in a circulating mode until the Nash equilibrium solution of the game is obtained, and the equilibrium solution of each time period is used as a reference scheme for the game solving of the next adjacent time period.
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