CN113313305A - Non-cooperative game-based comprehensive energy system optimization scheduling method - Google Patents

Non-cooperative game-based comprehensive energy system optimization scheduling method Download PDF

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CN113313305A
CN113313305A CN202110593743.4A CN202110593743A CN113313305A CN 113313305 A CN113313305 A CN 113313305A CN 202110593743 A CN202110593743 A CN 202110593743A CN 113313305 A CN113313305 A CN 113313305A
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energy system
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natural gas
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程玉林
王振福
常英
王�锋
赵霁辉
李溪龙
段建东
孙茜
脱利浩
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Huaneng Dingbian New Energy Electricity Generating Co ltd
Xian University of Technology
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Xian University of Technology
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/14—Pipes
    • G—PHYSICS
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    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention aims to provide a comprehensive energy system optimal scheduling method based on a non-cooperative game, which comprises the following steps: applying a non-cooperative game theory during scheduling, and establishing a comprehensive energy system scheduling model based on the non-cooperative game; acquiring parameters; giving a particle initial value, and solving a comprehensive energy system scheduling model based on a non-cooperative game by combining a Nash equilibrium definition and a particle swarm optimization algorithm; making output scheme decision in the ith cycle according to the utility function; judging whether the solving result of the step 3 is a Nash equilibrium solution or not; and outputting the finally calculated Nash equilibrium solution, namely the optimal scheduling scheme of each energy system. The invention overcomes the defect that the existing optimization method does not fully consider the competitive relationship of each energy system, and realizes the complete consumption of renewable energy.

Description

Non-cooperative game-based comprehensive energy system optimization scheduling method
Technical Field
The invention belongs to the technical field of comprehensive energy system optimization scheduling, and particularly relates to a comprehensive energy system optimization scheduling method based on a non-cooperative game.
Background
The comprehensive energy system utilizes various energy sources with different operating characteristics, effectively promotes the mutual complementation and coordinated utilization of advantages among the energy sources, improves the energy utilization efficiency, reduces the pollution harm of the traditional energy system to the environment, and achieves the aim of reducing the cost. The national energy supply agency clearly proposes that the multi-energy complementation and cooperative supply is realized in the implementation opinions about promoting the multi-energy complementation integration optimization exemplary engineering construction in the modes of natural gas and combined supply of heat, electricity and cold, distributed renewable energy and energy intelligent micro-grid and the like. Therefore, in the current stage of rapid development of society, the establishment of a comprehensive energy system with the characteristics of coordination and complementation of various energy sources and economy and high efficiency has great significance.
The operation scheduling of the comprehensive energy system is greatly different from the traditional power grid. The coupling between its energy is inseparabler along with the continuous development of energy conversion technique, and energy supply side and user side all have the uncertainty to the kind of energy supply mode and user load is also the form various, contains energy such as electric energy, natural gas, heat energy simultaneously to and user equipment such as energy storage equipment, and coupling structure is more complicated. In order to improve the utilization rate of various energy sources among the comprehensive energy source systems and further promote the efficient consumption of renewable clean energy sources, the method has important theoretical value for the optimal scheduling of the comprehensive energy source system containing various energy sources.
In the scheduling of the comprehensive energy system, each energy system is taken as a participant, the overall stable and efficient operation of the comprehensive energy system needs to be ensured, at the moment, the systems need to be coordinated and matched, but from the individual perspective, each system is independent, the operation of the system needs to be ensured, at the moment, a competitive relationship exists, and the two characteristics are against the idea of the game theory. At present, scholars obtain certain achievements, but most of the research still aims at the integral research of the comprehensive energy system at present, and the competitive relationship among the energy systems participating in the dispatching is ignored. Because the competition and cooperation relationship among the participants is also one of the necessary contents in the coordinated integrated energy system scheduling.
Disclosure of Invention
The invention aims to provide a comprehensive energy system optimization scheduling method based on a non-cooperative game, overcomes the defect that the competition relationship of each energy system is not fully considered in the conventional optimization method, and realizes complete consumption of renewable energy.
The technical scheme adopted by the invention is that the comprehensive energy system optimization scheduling method based on the non-cooperative game is implemented according to the following steps:
step 1, competition and cooperation relations exist among electric energy systems, gas energy systems and heat energy systems participating in the dispatching of the comprehensive energy system, a non-cooperative game theory is applied to the comprehensive energy system during dispatching, and a non-cooperative game-based comprehensive energy system dispatching model is established;
step 2, acquiring parameters of the comprehensive energy system and an access coupling unit thereof, and parameters of a particle swarm algorithm for solving a model;
step 3, giving a particle initial value, and solving a comprehensive energy system scheduling model based on a non-cooperative game by combining Nash equilibrium definition with a particle swarm optimization algorithm;
step 4, solving the numerical value of each particle by combining utility function values in a comprehensive energy system scheduling model based on a non-cooperative game, and in the process of the K-th cycle, referring to the output strategies of other two energy systems in the K-1-th cycle by each energy system, and making an output scheme decision in the ith cycle according to the utility function, wherein K is a natural number which is not 0;
step 5, judging whether the solving result of the step 3 is a Nash equilibrium solution or not;
and 6, outputting the finally calculated Nash equilibrium solution, namely the optimal scheduling scheme of each energy system.
The present invention is also characterized in that,
in the step 1, according to the basic theory of the non-cooperative game theory, a complete non-cooperative game model G comprises a game participant N, a game strategy S, a utility function u and game balance, and the form is G ═ N, S, u }, so that the comprehensive energy system scheduling model based on the non-cooperative game is established as G ═ N, S, u };
a game participant N in a non-cooperative game-based comprehensive energy system scheduling model refers to three energy systems of electric power E, natural gas G and heating power H which participate in the optimized scheduling of the comprehensive energy system; game strategy S refers to participant Ni(i is E, G, H), and the export plan is limited by the constraint condition of each participant, so the game strategy S of the comprehensive energy system scheduling model based on the non-cooperative game is the constraint condition in the operation of each energy system; the utility function u refers to the expectation level of each participant under a specific strategy combination, and the goal of the game participants is to select the mostThe optimal output strategy maximizes the expected utility function, so the utility function u of the comprehensive energy system scheduling model based on the non-cooperative game is the operation cost of each participant; the game balance of the non-cooperative game is also called Nash balance, which means that under the condition that the output strategies of other energy systems are known, the best output strategy combination of the nth energy system is obtained, n is a natural number which is not 0, and the combination shows that when each device is in the output combination, each system participating in the game can obtain the best profit in a balance sense, namely the game model is a solution of the comprehensive energy system scheduling model based on the non-cooperative game;
in summary, the established comprehensive energy system optimization scheduling model based on the non-cooperative game theory is as shown in equation (27):
G＝{N,S,u}＝{1,2,3；s1,s2,s3；u1,u2,u3}＝{E,G,H；sE,sG,sH；uE,uG,uH} (27)。
the constraint conditions of the power system participating in the optimization scheduling of the comprehensive energy system comprise power balance constraint, line capacity constraint, generator set related constraint, unit climbing constraint, minimum start-stop time constraint, start-stop cost constraint, Combined Heat and Power (CHP) unit related constraint and power storage unit constraint, and each constraint is as follows:
the power system power balance constraint is as follows:
Figure BDA0003090227360000041
in the formula (1), i represents an element in the set, t represents a unit runtime, ΩE、ΩGT、ΩCHP、 ΩBE、ΩW、ΩPtG、ΩLRespectively represent: a generator set, a gas turbine set, a combined heat and power CHP set, an electric power energy storage set wind power field set, an PtG equipment set and a load set, PE,i,tRepresenting the power, P, of the generator set i in the set at time tGT,i,tGas engine setThe power of the gas turbine set i at the moment t is converged,
Figure BDA0003090227360000042
representing the power, P, of a cogeneration unit i of a set of cogeneration units at a moment in timeBEd,i,tRepresenting the power, P, of the energy storage device i in the set of electrical energy storage devices at time twind,i,tIs a predicted value of wind power output power, delta PW,i,tRepresenting the wind curtailment quantity P of the wind turbine i at the moment tPtG,i,tRepresenting the electric power consumed by the electric transfer apparatus i of the set at time t, Δ PL,i,tRepresenting the load shedding amount of the node i at the time t;
the line capacity constraints are:
Pline≤Pline.max (2)
in the formula (2), PlineFor the actual transmission power of the power system line, Pline.maxIs the maximum transmission power capacity of the system line;
the output constraint of the generator set is as follows:
Figure BDA0003090227360000043
in the formula (3), ui,tExpressed as a state variable of the thermal power generating unit i at time t,
Figure BDA0003090227360000044
represents the minimum power lower limit of the thermal power generating unit i at the moment t,
Figure BDA0003090227360000045
representing the maximum power upper limit of the thermal power generating unit i at the moment t;
the climbing restriction of the generator set is as follows:
Figure BDA0003090227360000051
in the formula (4), the reaction mixture is,
Figure BDA0003090227360000052
representing the climbing rate of the thermal power generating unit i in the delta t time period,
Figure BDA0003090227360000053
representing the downward climbing rate of the thermal power generating unit i in the delta t time period;
the minimum start-stop time constraint of the generator set is as follows:
Figure BDA0003090227360000054
in the formula (5), the reaction mixture is,
Figure BDA0003090227360000055
and Ti onRespectively the accumulated starting time of the unit i at the last moment and the minimum starting time constraint of the unit,
Figure BDA0003090227360000058
and Ti offRespectively representing the accumulated downtime of the unit i at the last moment and the minimum downtime constraint of the unit;
the constraint of the start-stop cost of the generator is as follows:
Figure BDA0003090227360000056
in formula (6), SUi,tAnd SDi,tRespectively, cost constraints on the start-up and shut-down of the unit, suiAnd sdiRespectively the cost of starting and stopping the machine set each time;
the CHP unit is converted into electric energy and heat energy in the following ratio:
Figure BDA0003090227360000057
in the formula (7), PCHP,i,tFor the total power output of the CHP unit during the t period,
Figure BDA0003090227360000059
and
Figure BDA00030902273600000510
electrical, thermal and natural gas input power, mu, of the CHP, respectivelyCHPAnd muhteThe operating conversion efficiency and thermoelectric conversion ratio of CHP, respectively;
the ramp constraints of the CHP unit are:
Figure BDA0003090227360000061
in the formula (8), the reaction mixture is,
Figure BDA0003090227360000063
ramp rate for CHP unit electrical power;
when there is no heating demand by the user in the integrated energy system, the power constraint of the CHP unit is:
Figure BDA0003090227360000064
in the formula (9), the reaction mixture is,
Figure BDA0003090227360000065
and
Figure BDA0003090227360000066
the power minimum and maximum of the CHP power output respectively;
the power constraint of the CHP unit when there is a heating demand by the user in the system is:
Figure BDA0003090227360000067
in the formula (10), kvThe absolute value of the slope of the natural gas inlet curve is shown;
the power storage unit is constrained as follows:
Figure BDA0003090227360000062
in the formula (11), PBEc,i,tAnd PBEd,i,tPower, P, of charging and discharging the electricity storage unit i at time tBEcmin,i,tAnd PBEcmax,i,tMinimum and maximum power, P, respectively, for charging the storage unitBEdmin,i,tAnd PBEdmax,i,tRespectively minimum and maximum power, delta, of discharge of the storage unitBEc,i,tAnd deltaBEd,i,tThe state of charge and the state of discharge of the storage unit, respectively, are both 0/1 variables.
The natural gas system constraint conditions participating in the optimization scheduling of the comprehensive energy system comprise natural gas system node flow balance constraint, gas source flow and node pressure constraint, pipe storage constraint, pressurization station constraint, PtG constraint and gas storage unit constraint, and the constraints are as follows:
the natural gas system node flow balance constraint is as follows:
Figure BDA0003090227360000071
in the formula (12), the reaction mixture is,
Figure BDA0003090227360000073
representing the gas output of natural gas i at time t,
Figure BDA0003090227360000074
represents the air input quantity, omega, of the natural gas node i at the time tWERepresenting a collection of natural gas sources, PG.E,i,tRepresenting the amount of gas purchased from natural gas node i,
Figure BDA0003090227360000075
to be converted to values of natural gas by PtG equipment, ΩBoloierA collection of gas boilers is shown,
Figure BDA0003090227360000076
and
Figure BDA0003090227360000077
the amount of natural gas, P, consumed by the CHP plant, the gas turbine and the thermal boiler, respectivelyG,i,tIs the load value, Δ P, of the natural gas node iG,i,tExpressed as the gas load reduction of the natural gas node i;
the flow constraint of the gas source of the natural gas system is as follows:
PWE,min≤PWE,i,t≤PWE,max (13)
in formula (13), PWE,minAnd PWE,maxThe lower limit value and the upper limit value of the flow which can be provided by the gas source respectively;
the natural gas system node pressure constraint is as follows:
pi,min≤pi,t≤pi,max (14)
in the formula (14), pi,minAnd pi,maxThe lower pressure limit and the upper pressure limit of the node i are respectively;
the natural gas pipeline flow equation is as follows,
Figure BDA0003090227360000072
in the formula (15), the reaction mixture is,
Figure BDA0003090227360000078
sgn (p) for average flow of pipeline when natural gas flows from node m to nm,t,pn,t) Equal to 1, otherwise equal to-1,
Figure BDA0003090227360000079
are parameters associated with the pipe, including temperature, length, diameter, etc.,
Figure BDA00030902273600000710
is a constant number of times that the number of the first,
Figure BDA00030902273600000711
representing slave nodesThe air inflow of natural gas at the time t from the node m to the node n,
Figure BDA00030902273600000712
representing the gas output of natural gas from the node m to the node n at the time t;
the pipeline constraints of the natural gas pipeline are:
Figure BDA0003090227360000081
in the formula (16), Lmn,tThe natural gas content in the pipeline is the natural gas content,
Figure BDA0003090227360000085
is a constant associated with the pipeline;
the natural gas system pressurization station constraints are:
Figure BDA0003090227360000082
in the formula (17), pc,m,tAnd pc,n,tRespectively, the pressure at both ends of the pressure station, Rc,maxAnd Rc,minRespectively is the lower limit and the upper limit of the pressurizing ratio of the pressurizing station;
natural gas system PtG equipment constraints are:
Figure BDA0003090227360000083
in the formula (18), fPtG,i,tFor conversion to natural gas flow at time t PtG, element iPtG,i,tFor the electric power consumed by unit i at time t PtG, μPtGFor the conversion efficiency of the equipment, GHV is the natural gas heat value; pPtGmin,i,tAnd PPtGmax,i,tPtG cell minimum and maximum force values; delta PPtG,i,tA ramp rate of PtG cells;
the natural gas system gas storage unit is restrained as follows:
Figure BDA0003090227360000084
in the formula (19), PBGc,i,tAnd PBGd,i,tRespectively the power P for charging and discharging gas of the gas storage unit i at the moment tBGcmin,i,tAnd PBGcmax,i,tMinimum and maximum power, P, for inflating gas storage units respectivelyBGdmin,i,tAnd PBGdmax,i,tThe minimum and maximum air release power of the air storage unit are respectively set; deltaBGc,i,tAnd deltaBGd,i,tThe inflation state and the deflation state of the gas storage unit are 0/1 variables respectively.
The constraint conditions of the thermodynamic system participating in the optimization scheduling of the comprehensive energy system comprise thermodynamic node balance constraint, thermodynamic network constraint, heat exchange station constraint, thermodynamic boiler constraint and heat storage unit constraint, and the constraints are as follows:
thermodynamic system thermodynamic node balance constraints are:
Figure BDA0003090227360000091
in the formula (20), PBHd,i,tIndicating the heat release power of the gas storage device i at time t,
Figure BDA0003090227360000093
representing the heat release power P of the cogeneration unit i at the moment tH,i,tRepresents the load value, omega, of the thermodynamic system node iBHDenotes the set of heat storage devices, ΩGHRepresenting a collection of thermal load nodes, ΩLGRepresenting a set of thermal load reductions, Δ PH,i,tRepresenting the reduction amount of the thermal load node i at the time t;
the thermal network constraints are:
Figure BDA0003090227360000092
in the formula (21), omegapipe+、Ωpipe-Respectively taking m nodes as the beginning and the endThe pipe (a) of (b) is,
Figure BDA0003090227360000094
respectively the temperature of the water supply node and the water return node,
Figure BDA0003090227360000095
indicating the temperature of the supplied water at time t in the pipe b,
Figure BDA0003090227360000096
the return water temperature at the t moment in the pipeline b is shown;
the heat exchange station is constrained as follows:
Figure BDA0003090227360000101
in the formula (22), the reaction mixture is,
Figure BDA0003090227360000104
the power required by the heat exchange station of the thermodynamic system, c represents the heat power conversion coefficient of the hot water, c is a constant,
Figure BDA0003090227360000105
is the hot water flow of the initial node of the heat exchange station,
Figure BDA0003090227360000106
respectively the water supply temperature and the water return temperature;
return water temperature of starting node of heat exchange station
Figure BDA0003090227360000107
Satisfies the following conditions:
Figure BDA0003090227360000108
the constraint of the thermal boiler is as follows:
Figure BDA0003090227360000102
in the formula (24), PBoiler,i,tFor the thermal output power of the thermal boiler i at time t,
Figure BDA0003090227360000109
natural gas input power, mu, for this momentBoilerFor the operating efficiency of the boiler, Δ PBoiler,i,tIs the rate of ascent of the boiler, PBoilermax,i,tThe maximum output power of the thermal boiler;
the thermal system heat storage unit is restricted as follows:
Figure BDA0003090227360000103
in formula (25), PBHc,i,tAnd PBHd,i,tPower for charging and discharging heat of the heat storage unit i at time t, PBHcmin,i,tAnd PBHcmax,i,tMinimum and maximum power, P, for charging the heat storage unit respectivelyBHdmin,i,tAnd PBHdmax,i,tThe minimum and maximum heat release power of the heat storage unit are respectively; deltaBHc,i,tAnd deltaBHd,i,tThe charging state and the discharging state of the heat storage unit are 0/1 variables.
When the output strategy of each energy system is game balance
Figure BDA00030902273600001010
When the optimization scheduling model is established, the Nash equilibrium point is represented as follows:
Figure BDA0003090227360000111
in the formula (26), the reaction mixture is,
Figure BDA0003090227360000113
respectively represents Nash equilibrium solution, P, of electric, gas and thermal systemsE,t、 PG,t、PH,tSolutions, u, representing the effective functions of the electrical, gas and thermal systems, respectivelyE＝ME(PE,t,PG,t,PH,t)， uG＝MG(PE,t,PG,t,PH,t)，uH＝MH(PE,t,PG,t,PH,t)。
In step 2, the parameters of the integrated energy system and the access coupling unit thereof include: system grid structure and line parameters, load distribution condition and prediction data thereof, coupling unit type, access node, output limit value and output prediction data;
the particle swarm algorithm parameters for model solution include: maximum number of iterations, particle velocity range, learning factor.
Step 3, giving initial values of the particles, namely S in the game strategy set S ═ S1,s2,s3}＝{sE,sG,sHIn (c) } the reaction solution is,
Figure BDA0003090227360000114
randomly selecting an initial value
Figure BDA0003090227360000115
The control variables of the comprehensive energy system scheduling model based on the non-cooperative game are respectively the equipment in each energy system participating in scheduling,
Figure BDA0003090227360000116
respectively expressed as scheduling control vectors in an electric power system, a natural gas system and a thermal system.
The output strategies of the energy systems in the K cycle in the step 4 are as follows:
Figure BDA0003090227360000112
if the output scheme of the optimized calculation does not reach the optimal value under the premise of meeting the safety and stability constraint of the system, or the condition that the calculated output scheme does not converge as shown in the formula (29) appears, the initial value of the selected particles should be reproduced
Figure BDA0003090227360000122
Until the requirements are met
Figure BDA0003090227360000121
Step 5 is specifically that when the obtained optimized scheduling schemes after two adjacent cycles are completely the same, namely the formula (30) is satisfied,
Figure BDA0003090227360000123
the strict Nash equilibrium solution is found and is taken as the finally calculated equilibrium solution
Figure BDA0003090227360000124
If the optimized scheduling schemes of the systems after two adjacent cycles are approximately consistent, the formula (31) is satisfied
(PE,t,i-PE,t,i-1)2+(PG,t,i-PG,t,i-1)2+(PH,t,i-PH,t,i-1)2≤ε,ε≈0 (31)
The approximate Nash equilibrium solution is found and is used as the finally calculated equilibrium solution
Figure BDA0003090227360000125
The beneficial effect of the invention is that,
1) the method is different from a conventional modeling means for analyzing the integrated energy system as a whole, considers the requirements of each energy system participating in scheduling as an independent individual, fully utilizes the game relationship of competition and cooperation among the energy systems participating in scheduling, combines the game relationship with a non-cooperative game theory, establishes an optimized scheduling model of the integrated energy system based on the non-cooperative game by considering the competition and cooperation relationship among three energy systems of a power system, a natural gas system and a thermal system participating in scheduling, and solves the model by combining Nash equilibrium definition with a particle swarm optimization algorithm to obtain an optimized scheduling scheme of the integrated energy system;
2) complementary characteristics among different energy sources in the comprehensive energy system are fully utilized, mutual transfer among the energy sources is carried out through the coupling equipment, the energy flows in the three systems in different forms, waste of wind power resources is avoided to the maximum extent, and the purpose of completely consuming renewable energy sources is achieved;
3) in order to shorten the solving operation time, an iterative search method used for solving the Nash equilibrium commonly is not adopted, the Nash equilibrium definition is combined with a particle swarm optimization algorithm, the optimization scheduling problem is solved by an intelligent algorithm, and the improvement scheme can effectively improve the determination of long time consumption of the iterative search method, so that the optimization scheduling strategy is more efficiently solved.
Drawings
FIG. 1 is a flow chart of a non-cooperative game-based comprehensive energy system optimization scheduling method of the invention;
FIG. 2 is a flow chart of step 3 of the non-cooperative game-based comprehensive energy system optimization scheduling method of the present invention;
FIG. 3 is a flow chart of step 4 of the non-cooperative game-based comprehensive energy system optimization scheduling method of the present invention;
FIG. 4 is a knowledge graph visualization diagram in step 5 of the non-cooperative game-based comprehensive energy system optimization scheduling method of the present invention;
FIG. 5 is a flow chart of step 7 of the non-cooperative game-based comprehensive energy system optimization scheduling method of the present invention;
FIG. 6 is a graph of the scheduling results of a simulation test thermal boiler;
FIG. 7 is a graph of simulation test CHP thermal output and thermal storage device scheduling results;
FIG. 8 is a graph of output value variation for a coupling device in a simulation test power system;
fig. 9 is a diagram of the output situation of the unit in two schemes of simulation test, wherein fig. 9(a) is a diagram of the output situation of each unit in one scheme, and fig. 9(b) is a diagram of the output situation of each unit in the two schemes.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a comprehensive energy system optimization scheduling method based on a non-cooperative game, which is implemented according to the following steps:
step 1, competition and cooperation relations exist among the electricity, gas and heat energy systems participating in the dispatching of the comprehensive energy system, a non-cooperative game theory is applied in the dispatching process, and a non-cooperative game-based comprehensive energy system dispatching model is established:
according to the basic theory of the non-cooperative game theory, a complete non-cooperative game model G comprises a game participant N, a game strategy S, a utility function u and game balance, and the form is G { N, S, u }, so that the comprehensive energy system scheduling model based on the non-cooperative game is established as G { N, S, u };
a game participant N in a non-cooperative game-based comprehensive energy system scheduling model refers to three energy systems of electric power E, natural gas G and heating power H which participate in the optimized scheduling of the comprehensive energy system; game strategy S refers to participant Ni(i is E, G, H), and the export plan is limited by the constraint condition of each participant, so the game strategy S of the comprehensive energy system scheduling model based on the non-cooperative game is the constraint condition in the operation of each energy system;
the constraint conditions of the power system participating in the optimization scheduling of the comprehensive energy system comprise power balance constraint, line capacity constraint, generator set related constraint, unit climbing constraint, minimum start-stop time constraint, start-stop cost constraint, Combined Heat and Power (CHP) unit related constraint and power storage unit constraint, and each constraint is as follows:
the power system power balance constraint is as follows:
Figure BDA0003090227360000141
in the formula (1), i represents an element in the set, t represents a unit runtime, ΩE、ΩGT、ΩCHP、 ΩBE、ΩW、ΩPtG、ΩLRespectively represent: a generator set, a gas turbine set, a combined heat and power CHP set, an electric power energy storage set wind power field set, an PtG equipment set and a load set, PE,i,tRepresenting the power, P, of the generator set i in the set at time tGT,i,tRepresenting the power of the gas unit i in the gas unit set at the time t,
Figure BDA0003090227360000142
representing the power, P, of a cogeneration unit i of a set of cogeneration units at a moment in timeBEd,i,tRepresenting the power, P, of the energy storage device i in the set of electrical energy storage devices at time twind,i,tIs a predicted value of wind power output power, delta PW,i,tRepresenting the wind curtailment quantity P of the wind turbine i at the moment tPtG,i,tRepresenting the electric power consumed by the electric transfer apparatus i of the set at time t, Δ PL,i,tRepresenting the load shedding amount of the node i at the time t;
the line capacity constraints are:
Pline≤Pline.max (2)
in the formula (2), PlineFor the actual transmission power of the power system line, Pline.maxIs the maximum transmission power capacity of the system line;
the output constraint of the generator set is as follows:
Figure BDA0003090227360000153
in the formula (3), ui,tExpressed as a state variable of the thermal power generating unit i at time t,
Figure BDA0003090227360000154
represents the minimum power lower limit of the thermal power generating unit i at the moment t,
Figure BDA0003090227360000155
indicating thermal power at time tThe maximum power upper limit of the unit i;
the climbing restriction of the generator set is as follows:
Figure BDA0003090227360000151
in the formula (4), the reaction mixture is,
Figure BDA0003090227360000156
representing the climbing rate of the thermal power generating unit i in the delta t time period,
Figure BDA0003090227360000157
representing the downward climbing rate of the thermal power generating unit i in the delta t time period;
the minimum start-stop time constraint of the generator set is as follows:
Figure BDA0003090227360000152
in the formula (5), the reaction mixture is,
Figure BDA0003090227360000158
and Ti onRespectively the accumulated starting time of the unit i at the last moment and the minimum starting time constraint of the unit,
Figure BDA0003090227360000159
and Ti offRespectively representing the accumulated downtime of the unit i at the last moment and the minimum downtime constraint of the unit;
the constraint of the start-stop cost of the generator is as follows:
Figure BDA0003090227360000161
in formula (6), SUi,tAnd SDi,tRespectively, cost constraints on the start-up and shut-down of the unit, suiAnd sdiRespectively the cost of starting and stopping the machine set each time;
the CHP unit is converted into electric energy and heat energy in the following ratio:
Figure BDA0003090227360000162
in the formula (7), PCHP,i,tFor the total power output of the CHP unit during the t period,
Figure BDA0003090227360000164
and
Figure BDA0003090227360000165
electrical, thermal and natural gas input power, mu, of the CHP, respectivelyCHPAnd muhteThe operating conversion efficiency and thermoelectric conversion ratio of CHP, respectively;
the ramp constraints of the CHP unit are:
Figure BDA0003090227360000163
in the formula (8), the reaction mixture is,
Figure BDA0003090227360000166
ramp rate for CHP unit electrical power;
when there is no heating demand by the user in the integrated energy system, the power constraint of the CHP unit is:
Figure BDA0003090227360000167
in the formula (9), the reaction mixture is,
Figure BDA0003090227360000168
and
Figure BDA0003090227360000169
the power minimum and maximum of the CHP power output respectively;
the power constraint of the CHP unit when there is a heating demand by the user in the system is:
Figure BDA00030902273600001610
in the formula (10), kvThe absolute value of the slope of the natural gas inlet curve is shown;
the power storage unit is constrained as follows:
Figure BDA0003090227360000171
in the formula (11), PBEc,i,tAnd PBEd,i,tPower, P, of charging and discharging the electricity storage unit i at time tBEcmin,i,tAnd PBEcmax,i,tMinimum and maximum power, P, respectively, for charging the storage unitBEdmin,i,tAnd PBEdmax,i,tRespectively minimum and maximum power, delta, of discharge of the storage unitBEc,i,tAnd deltaBEd,i,tThe charging state and the discharging state of the power storage unit are 0/1 variables respectively;
the natural gas system constraint conditions participating in the optimization scheduling of the comprehensive energy system comprise natural gas system node flow balance constraint, gas source flow and node pressure constraint, pipe storage constraint, pressurization station constraint, PtG constraint and gas storage unit constraint, and the constraints are as follows:
the natural gas system node flow balance constraint is as follows:
Figure BDA0003090227360000172
in the formula (12), the reaction mixture is,
Figure BDA0003090227360000173
representing the gas output of natural gas i at time t,
Figure BDA0003090227360000174
represents the air input quantity, omega, of the natural gas node i at the time tWERepresenting a collection of natural gas sources, PG.E,i,tRepresenting the amount of gas purchased from natural gas node i,
Figure BDA0003090227360000175
to be converted to values of natural gas by PtG equipment, ΩBoloierA collection of gas boilers is shown,
Figure BDA0003090227360000176
and
Figure BDA0003090227360000177
the amount of natural gas, P, consumed by the CHP plant, the gas turbine and the thermal boiler, respectivelyG,i,tIs the load value, Δ P, of the natural gas node iG,i,tExpressed as the gas load reduction of the natural gas node i;
the flow constraint of the gas source of the natural gas system is as follows:
PWE,min≤PWE,i,t≤PWE,max (13)
in formula (13), PWE,minAnd PWE,maxThe lower limit value and the upper limit value of the flow which can be provided by the gas source respectively;
the natural gas system node pressure constraint is as follows:
pi,min≤pi,t≤pi,max (14)
in the formula (14), pi,minAnd pi,maxThe lower pressure limit and the upper pressure limit of the node i are respectively;
the natural gas pipeline flow equation is as follows,
Figure BDA0003090227360000181
in the formula (15), the reaction mixture is,
Figure BDA0003090227360000183
sgn (p) for average flow of pipeline when natural gas flows from node m to nm,t,pn,t) Equal to 1, otherwise equal to-1,
Figure BDA0003090227360000184
are parameters associated with the pipe, including temperature, length, diameter, etc.,
Figure BDA0003090227360000185
is a constant number of times that the number of the first,
Figure BDA0003090227360000186
representing the amount of natural gas intake at time t from node m to node n,
Figure BDA0003090227360000187
representing the gas output of natural gas from the node m to the node n at the time t;
the pipeline constraints of the natural gas pipeline are:
Figure BDA0003090227360000182
in the formula (16), Lmn,tThe natural gas content in the pipeline is the natural gas content,
Figure BDA0003090227360000188
is a constant associated with the pipeline;
the natural gas system pressurization station constraints are:
Figure BDA0003090227360000189
in the formula (17), pc,m,tAnd pc,n,tRespectively, the pressure at both ends of the pressure station, Rc,maxAnd Rc,minRespectively is the lower limit and the upper limit of the pressurizing ratio of the pressurizing station;
natural gas system PtG equipment constraints are:
Figure BDA0003090227360000191
in the formula (18), fPtG,i,tFor conversion to natural gas flow at time t PtG, element iPtG,i,tFor the consumption of unit i at time t PtGPower, muPtGFor the conversion efficiency of the equipment, GHV is the natural gas heat value; pPtGmin,i,tAnd PPtGmax,i,tPtG cell minimum and maximum force values; delta PPtG,i,tA ramp rate of PtG cells;
the natural gas system gas storage unit is restrained as follows:
Figure BDA0003090227360000192
in the formula (19), PBGc,i,tAnd PBGd,i,tRespectively the power P for charging and discharging gas of the gas storage unit i at the moment tBGcmin,i,tAnd PBGcmax,i,tMinimum and maximum power, P, for inflating gas storage units respectivelyBGdmin,i,tAnd PBGdmax,i,tThe minimum and maximum air release power of the air storage unit are respectively set; deltaBGc,i,tAnd deltaBGd,i,tThe inflation state and the deflation state of the gas storage unit are 0/1 variables respectively;
the constraint conditions of the thermodynamic system participating in the optimization scheduling of the comprehensive energy system comprise thermodynamic node balance constraint, thermodynamic network constraint, heat exchange station constraint, thermodynamic boiler constraint and heat storage unit constraint, and the constraints are as follows:
thermodynamic system thermodynamic node balance constraints are:
Figure BDA0003090227360000193
in the formula (20), PBHd,i,tIndicating the heat release power of the gas storage device i at time t,
Figure BDA0003090227360000194
representing the heat release power P of the cogeneration unit i at the moment tH,i,tRepresents the load value, omega, of the thermodynamic system node iBHDenotes the set of heat storage devices, ΩGHRepresenting a collection of thermal load nodes, ΩLGRepresenting a set of thermal load reductions, Δ PH,i,tRepresenting the reduction amount of the thermal load node i at the time t;
the thermal network constraints are:
Figure BDA0003090227360000201
in the formula (21), omegapipe+、Ωpipe-Respectively taking the m nodes as a starting pipeline and a last pipeline,
Figure BDA0003090227360000204
respectively the temperature of the water supply node and the water return node,
Figure BDA0003090227360000205
indicating the temperature of the supplied water at time t in the pipe b,
Figure BDA0003090227360000206
the return water temperature at the t moment in the pipeline b is shown;
the heat exchange station is constrained as follows:
Figure BDA0003090227360000202
in the formula (22), the reaction mixture is,
Figure BDA0003090227360000207
the power required by the heat exchange station of the thermodynamic system, c represents the heat power conversion coefficient of the hot water, c is a constant,
Figure BDA0003090227360000208
is the hot water flow of the initial node of the heat exchange station,
Figure BDA0003090227360000209
respectively the water supply temperature and the water return temperature;
return water temperature of starting node of heat exchange station
Figure BDA00030902273600002010
Satisfies the following conditions:
Figure BDA00030902273600002011
the constraint of the thermal boiler is as follows:
Figure BDA0003090227360000203
in the formula (24), PBoiler,i,tFor the thermal output power of the thermal boiler i at time t,
Figure BDA00030902273600002012
natural gas input power, mu, for this momentBoilerFor the operating efficiency of the boiler, Δ PBoiler,i,tIs the rate of ascent of the boiler, PBoilermax,i,tThe maximum output power of the thermal boiler;
the thermal system heat storage unit is restricted as follows:
Figure BDA0003090227360000211
in formula (25), PBHc,i,tAnd PBHd,i,tPower for charging and discharging heat of the heat storage unit i at time t, PBHcmin,i,tAnd PBHcmax,i,tMinimum and maximum power, P, for charging the heat storage unit respectivelyBHdmin,i,tAnd PBHdmax,i,tThe minimum and maximum heat release power of the heat storage unit are respectively; deltaBHc,i,tAnd deltaBHd,i,tThe charging state and the discharging state of the heat storage unit are 0/1 variables.
The utility function u refers to the expected level of each participant under a specific strategy combination, and the goal of the game participants is to select the optimal output strategy to maximize the expected utility function, so the utility function u of the comprehensive energy system scheduling model based on the non-cooperative game is the operating cost of each participant;
the game balance of the non-cooperative game is also called Nash balance, and refers to the optimal output strategy combination of the nth energy system under the condition that the output strategies of other energy systems are knownN is a natural number different from 0, the combination shows that when each device is in the output combination, each system participating in the game can obtain the optimal benefit under the balanced meaning, namely, the game model is the solution of the comprehensive energy system scheduling model based on the non-cooperative game, and when the output strategy of each energy system is the game balanced strategy
Figure BDA0003090227360000213
When the optimization scheduling model is established, the expression form of the Nash equilibrium point is as follows:
Figure BDA0003090227360000212
in the formula (26), the reaction mixture is,
Figure BDA0003090227360000221
respectively represents Nash equilibrium solution, P, of electric, gas and thermal systemsE,t、 PG,t、PH,tSolutions, u, representing the effective functions of the electrical, gas and thermal systems, respectivelyE＝ME(PE,t,PG,t,PH,t)， uG＝MG(PE,t,PG,t,PH,t)，uH＝MH(PE,t,PG,t,PH,t)。
In summary, the established comprehensive energy system optimization scheduling model based on the non-cooperative game theory is as shown in equation (27):
G＝{N,S,u}＝{1,2,3；s1,s2,s3；u1,u2,u3}＝{E,G,H；sE,sG,sH；uE,uG,uH} (27)。
step 2, acquiring parameters of the comprehensive energy system and an access coupling unit thereof, and parameters of a particle swarm algorithm for solving a model;
the parameters of the integrated energy system and the access coupling unit thereof comprise: system grid structure and line parameters, load distribution condition and prediction data thereof, coupling unit type, access node, output limit value and output prediction data;
the particle swarm algorithm parameters for model solution include: maximum number of iterations, particle velocity range, learning factor.
Step 3, giving initial values of the particles, namely giving the initial values of the particles in a game strategy set S ═ S1,s2,s3}＝{sE,sG,sHIn (c) } the reaction solution is,
Figure BDA0003090227360000222
randomly selecting an initial value
Figure BDA0003090227360000223
The Nash equilibrium definition is combined with the particle swarm optimization algorithm to solve the comprehensive energy system scheduling model based on the non-cooperative game, the control variables of the comprehensive energy system scheduling model based on the non-cooperative game are respectively the devices in each energy system participating in scheduling,
Figure BDA0003090227360000224
respectively expressed as scheduling control vectors in an electric power system, a natural gas system and a thermal system.
Step 4, solving the numerical value of each particle by combining utility function values in a comprehensive energy system scheduling model based on a non-cooperative game, and in the process of the K-th cycle, each energy system refers to the output strategies of other two energy systems in the K-1-th cycle, and decides that K is a natural number which is not 0 according to the output scheme in the ith cycle of the energy system according to the utility functions;
the output strategy obtained by each energy system in the K-th cycle is as follows:
Figure BDA0003090227360000231
if the output scheme of the optimized calculation does not reach the optimal value under the premise of meeting the safety and stability constraint of the system, or the condition that the calculated output scheme does not converge as shown in the formula (29) appears, the initial value of the selected particles should be reproduced
Figure BDA0003090227360000233
Until the requirements are met
Figure BDA0003090227360000232
Step 5, judging whether the solving result of the step 3 is Nash equilibrium solution:
step 5 is specifically that when the obtained optimized scheduling schemes after two adjacent cycles are completely the same, namely the formula (30) is satisfied,
Figure BDA0003090227360000234
the strict Nash equilibrium solution is found and is taken as the finally calculated equilibrium solution
Figure BDA0003090227360000235
If the optimized scheduling schemes of the systems after two adjacent cycles are approximately consistent, the formula (31) is satisfied
(PE,t,i-PE,t,i-1)2+(PG,t,i-PG,t,i-1)2+(PH,t,i-PH,t,i-1)2≤ε,ε≈0 (31)
The approximate Nash equilibrium solution is found and is used as the finally calculated equilibrium solution
Figure BDA0003090227360000236
And 6, outputting the finally calculated Nash equilibrium solution, namely the optimal scheduling scheme of each energy system.
Simulation test
As shown in fig. 2, an extended standard test system IEEE30 bus power system, a 10-node natural gas system, and a 7-node thermal system are selected and combined to form a simulated electricity-gas-heat comprehensive energy system for simulation test.
The wind power fields are respectively connected to bus nodes 15 and 22 of a power system, rated output of the two wind power fields is 150MW, and wind abandon penalty cost is set to be 500$/(MW & h) for maximum wind power consumption; meanwhile, in order to reduce the load reduction of the system as much as possible, the load cutting penalty cost is set to be 1000$/(MW & h). The coupling relationship between the power generation equipment and the coupling equipment in the integrated energy system is detailed in table 1.
TABLE 1 coupling relationship between power generation equipment and coupling equipment in integrated energy system
Figure BDA0003090227360000241
In the calculation example, 24 hours are taken as an optimization period, the scheduling time interval is set to be 1 hour, and the conditions of typical solar wind power plant output in winter and summer and load requirements of each energy system in one day are shown in figure 3.
Establishing a comprehensive energy system scheduling model based on a non-cooperative game;
acquiring parameters of the comprehensive energy system and an access coupling unit thereof, parameters and data required by a built comprehensive energy system scheduling model based on non-cooperative play, and particle swarm algorithm parameters for model solution;
giving a particle initial value, and solving a comprehensive energy system scheduling model based on a non-cooperative game by combining a Nash equilibrium definition and a particle swarm optimization algorithm;
solving the numerical value of each particle by combining utility function values in a comprehensive energy system scheduling model based on a non-cooperative game, and in the process of the K-th cycle, referring to the output strategies of other two energy systems in the K-1-th cycle by each energy system, and making an output scheme decision in the K-th cycle according to the utility function, wherein K is a natural number which is not 0;
and judging whether the solving result of the comprehensive energy system scheduling model based on the non-cooperative game is a Nash equilibrium solution, namely an optimal output strategy, and outputting the obtained optimal output strategy after seeking the Nash equilibrium solution.
Finally, the equipment combination for supplying power to the power load in the comprehensive energy system is obtained as shown in fig. 4, and it can be seen that as the wind power output shows a decreasing trend in the daytime and the power load is larger in the daytime, the thermal power units are all put into use at 11:00, and in order to meet the power demand at 12:00, the gas turbine G5 is started, and at the peak stage of the power load demand, each generator is close to a full power generation state. In the early morning of 0:00-2:00 time interval when the wind power output is large and the power demand is small, the surplus electric energy is stored through the electricity storage equipment, and then in the 13:00-14:00 time interval when the power demand is large and the wind power output is small, the electricity storage equipment provides necessary electric energy supply for the system by releasing the stored electric energy. During the daytime 14:00-15:00, part of the excess natural gas is converted into electric energy by the CHP equipment. During the period of 22:00-24:00 when the electricity demand is less and the gas load is increased at night, PtG equipment is used for converting the surplus electric energy into natural gas. In the whole operation process, wind energy is fully utilized, and load shedding phenomenon does not occur in the system through the operation of each coupling device, so that the power consumption requirement of a user is ensured. The specific output values of each genset are shown in fig. 5. The scheduling result of the thermal boiler is shown in fig. 6. The CHP heat output and thermal storage device scheduling results are shown in fig. 7. Fig. 8 shows a variation in the output value of the coupling device in the power system.
In order to explain the economic distribution condition of the non-cooperative game-based comprehensive energy system optimization scheduling model established by simulation tests, the following two schemes are set: in the first scheme, the comprehensive energy system is regarded as a whole, and the minimum overall operation cost is solved; and in the second scheme, a non-cooperative game idea is adopted, each energy system is independently optimized, the operating cost is determined and calculated respectively, and the unit output conditions of the two schemes are shown in fig. 9. It can be seen that, when the comprehensive energy system is considered as a whole to be optimized, in order to ensure that the system operation cost is minimum, on the premise that the unit meets the power load of the power system as far as possible, in order to reduce the starting and stopping cost of the unit, the thermal power generating units G1-G4 are started to supply power, and the output values of the units in the power consumption peak period are close to the maximum output value. However, at 16:00, the wind power output is small, the power load demand and the natural gas demand are large, the started thermal power generating units G1-G4 are all in a full power generation state, the CHP equipment reaches the maximum conversion power, and at the moment, 12.66MW of electric load shortage exists, so that the load shedding penalty cost 12660 is generated. When the optimized dispatching model is adopted, the load power consumption requirement in the system is ensured by increasing the number of the output units, so that the gas turbine G5 is started at 14:00 to coordinate the output values of the power generation equipment, and the power supply is ensured although the start-stop cost of the units is increased.
The operating costs for both schemes are shown in table 2:
TABLE 2 comparison of the cost of the integrated energy system under two scenarios
Figure BDA0003090227360000261
Figure BDA0003090227360000271
Comparing the first scheme with the second scheme, it can be seen that the total cost of the comprehensive energy system of the second scheme is increased by 5104.67$ and the operation cost of the natural gas system and the thermodynamic system is respectively reduced by 8002.57$ and 3022.86 $. In addition, in order to guarantee the load power demand in the system, the power system does not blindly pursue the minimization of the overall cost, but generates power by increasing the number of the starting generator sets, although the operation cost of the power system is increased by 16130.1$, the load shedding penalty cost is reduced by 12660 $comparedwith the scheme one. The operation cost difference of the two schemes can be understood as the embodiment of benefit redistribution in the comprehensive energy system, namely, the scheme II realizes the further distribution of the benefits of each energy system participating in the comprehensive energy system on the premise of ensuring the safe and stable operation of the system through the non-cooperative game optimization scheduling model.

Claims (10)

1. A comprehensive energy system optimization scheduling method based on a non-cooperative game is characterized by comprising the following steps:
step 1, competition and cooperation relations exist among electric energy systems, gas energy systems and heat energy systems participating in the dispatching of the comprehensive energy system, a non-cooperative game theory is applied to the comprehensive energy system during dispatching, and a comprehensive energy system dispatching model based on the non-cooperative game is established;
step 2, acquiring parameters of the comprehensive energy system and an access coupling unit thereof, and parameters of a particle swarm algorithm for model solution;
step 3, giving a particle initial value, and solving a comprehensive energy system scheduling model based on a non-cooperative game by combining Nash equilibrium definition with a particle swarm optimization algorithm;
step 4, solving the numerical value of each particle by combining utility function values in a comprehensive energy system scheduling model based on a non-cooperative game, and in the process of the K-th cycle, referring to the output strategies of other two energy systems in the K-1-th cycle by each energy system, and making an output scheme decision in the ith cycle according to the utility function, wherein K is a natural number which is not 0;
step 5, judging whether the solving result of the step 3 is a Nash equilibrium solution or not;
and 6, outputting the finally calculated Nash equilibrium solution, namely the optimal scheduling scheme of each energy system.
2. The method for optimizing and scheduling the comprehensive energy system based on the non-cooperative game as claimed in claim 1, wherein in the step 1, as known from the basic theory of the non-cooperative game theory, a complete non-cooperative game model G includes game participants N, a game strategy S, a utility function u and game balance, and has a form of G ═ N, S, u }, so that the comprehensive energy system scheduling model based on the non-cooperative game is established as G ═ N, S, u };
a game participant N in a non-cooperative game-based comprehensive energy system scheduling model refers to three energy systems of electric power E, natural gas G and heating power H which participate in the optimized scheduling of the comprehensive energy system; game strategy S refers to participant Ni(i ═ E, G, H) and the participation plan is limited by the constraints of the participants, so that the energy system is based on the non-cooperative gameThe game strategy S of the overall scheduling model is a constraint condition in the operation of each energy system; the utility function u refers to the expected level of each participant under a specific strategy combination, and the goal of the game participants is to select the optimal output strategy to maximize the expected utility function, so the utility function u of the comprehensive energy system scheduling model based on the non-cooperative game is the operating cost of each participant; the game balance of the non-cooperative game is also called Nash balance, which means that under the condition that the output strategies of other energy systems are known, the best output strategy combination of the nth energy system is obtained, n is a natural number which is not 0, and the combination shows that when each device is in the output combination, each system participating in the game can obtain the best benefit under the balance meaning, namely the solution of the comprehensive energy system scheduling model of the game model based on the non-cooperative game;
in conclusion, the established comprehensive energy system optimization scheduling model based on the non-cooperative game theory is as shown in formula (27):
G＝{N,S,u}＝{1,2,3；s1,s2,s3；u1,u2,u3}＝{E,G,H；sE,sG,sH；uE,uG,uH} (27)。
3. the method for optimizing and scheduling the comprehensive energy system based on the non-cooperative game as claimed in claim 2, wherein the constraints of the power system participating in the optimized scheduling of the comprehensive energy system include power balance constraint, line capacity constraint, generator set-related constraint, unit ramp-up constraint, minimum start-stop time constraint, start-stop cost constraint, cogeneration CHP unit-related constraint, and power storage unit constraint, and each constraint is specifically as follows:
the power system power balance constraint is as follows:
Figure FDA0003090227350000021
in the formula (1), i represents an element in the set, t represents a unit runtime, ΩE、ΩGT、ΩCHP、ΩBE、ΩW、ΩPtG、ΩLRespectively represent: a generator set, a gas turbine set, a combined heat and power CHP set, an electric power energy storage set wind power field set, an PtG equipment set and a load set, PE,i,tRepresenting the power, P, of the generator set i in the set at time tGT,i,tRepresenting the power of the gas unit i in the gas unit set at the time t,
Figure FDA0003090227350000031
representing the power, P, of a cogeneration unit i in a set of cogeneration units at a timeBEd,i,tRepresenting the power, P, of the energy storage device i in the set of electrical energy storage devices at time twind,i,tIs a predicted value of wind power output power, delta PW,i,tRepresenting the wind curtailment quantity P of the wind turbine i at the moment tPtG,i,tRepresenting the electric power consumed by the electric transfer apparatus i of the set at time t, Δ PL,i,tRepresenting the load shedding amount of the node i at the time t;
the line capacity constraints are:
Pline≤Pline.max (2)
in the formula (2), PlineFor the actual transmission power of the power system line, Pline.maxIs the maximum transmission power capacity of the system line;
the output constraint of the generator set is as follows:
Figure FDA0003090227350000032
in the formula (3), ui,tExpressed as a state variable of the thermal power generating unit i at time t,
Figure FDA0003090227350000033
represents the minimum power lower limit of the thermal power generating unit i at the moment t,
Figure FDA0003090227350000034
indicates the time tThe maximum power upper limit of the thermal power generating unit i;
the climbing restriction of the generator set is as follows:
Figure FDA0003090227350000035
in the formula (4), the reaction mixture is,
Figure FDA0003090227350000036
representing the climbing rate of the thermal power generating unit i in the delta t time period,
Figure FDA0003090227350000037
representing the downward climbing rate of the thermal power generating unit i in the delta t time period;
the minimum start-stop time constraint of the generator set is as follows:
Figure FDA0003090227350000041
in the formula (5), the reaction mixture is,
Figure FDA0003090227350000042
and Ti onRespectively the accumulated starting time of the unit i at the last moment and the minimum starting time constraint of the unit,
Figure FDA0003090227350000043
and Ti offRespectively representing the accumulated downtime of the unit i at the last moment and the minimum downtime constraint of the unit;
the constraint of the start-stop cost of the generator is as follows:
Figure FDA0003090227350000044
in formula (6), SUi,tAnd SDi,tRespectively, cost constraints on the start-up and shut-down of the unit, suiAnd sdiRespectively for starting the unit each timeThe cost of shutdown;
the CHP unit is converted into electric energy and heat energy in the following ratio:
Figure FDA0003090227350000045
in the formula (7), PCHP,i,tFor the total power output of the CHP unit during the t period,
Figure FDA0003090227350000046
and
Figure FDA0003090227350000047
electrical, thermal and natural gas input power, mu, of the CHP, respectivelyCHPAnd muhteThe operating conversion efficiency and thermoelectric conversion ratio of CHP, respectively;
the ramp constraints of the CHP unit are:
Figure FDA0003090227350000048
in the formula (8), the reaction mixture is,
Figure FDA0003090227350000049
ramp rate for CHP unit electrical power;
when there is no heating demand by the user in the integrated energy system, the power constraint of the CHP unit is:
Figure FDA00030902273500000410
in the formula (9), the reaction mixture is,
Figure FDA0003090227350000051
and
Figure FDA0003090227350000052
are respectively CHP electricityMinimum and maximum power of force output;
the power constraint of the CHP unit when there is a heating demand by the user in the system is:
Figure FDA0003090227350000053
in the formula (10), kvThe absolute value of the slope of the natural gas inlet curve is shown;
the power storage unit is constrained as follows:
Figure FDA0003090227350000054
in the formula (11), PBEc,i,tAnd PBEd,i,tPower, P, for charging and discharging the electricity storage unit i at time tBEcmin,i,tAnd PBEcmax,i,tMinimum and maximum power, P, respectively, for charging the storage unitBEdmin,i,tAnd PBEdmax,i,tRespectively minimum and maximum power, delta, of discharge of the storage unitBEc,i,tAnd deltaBEd,i,tThe state of charge and the state of discharge of the storage unit, respectively, are both 0/1 variables.
4. The method for optimizing and scheduling the comprehensive energy system based on the non-cooperative game as claimed in claim 3, wherein the natural gas system constraints participating in the optimized scheduling of the comprehensive energy system include natural gas system node flow balance constraints, gas source flow and node pressure constraints, storage constraints, pressurization station constraints, PtG constraints, and gas storage unit constraints, and the constraints are as follows:
the natural gas system node flow balance constraint is as follows:
Figure FDA0003090227350000055
in the formula (12), the reaction mixture is,
Figure FDA0003090227350000056
representing the gas output of natural gas i at time t,
Figure FDA0003090227350000057
represents the air input quantity, omega, of the natural gas node i at the time tWERepresenting a collection of natural gas sources, PG.E,i,tRepresenting the amount of gas purchased from natural gas node i,
Figure FDA0003090227350000061
to be converted to values of natural gas by PtG equipment, ΩBoloierA collection of gas-fired boilers is shown,
Figure FDA0003090227350000062
and
Figure FDA0003090227350000063
the amount of natural gas, P, consumed by the CHP plant, the gas turbine and the thermal boiler, respectivelyG,i,tIs the load value, Δ P, of the natural gas node iG,i,tExpressed as the gas load reduction of the natural gas node i;
the flow constraint of the gas source of the natural gas system is as follows:
PWE,min≤PWE,i,t≤PWE,max (13)
in formula (13), PWE,minAnd PWE,maxThe lower limit value and the upper limit value of the flow which can be provided by the gas source respectively;
the natural gas system node pressure constraint is as follows:
pi,min≤pi,t≤pi,max (14)
in the formula (14), pi,minAnd pi,maxThe lower pressure limit and the upper pressure limit of the node i are respectively;
the natural gas pipeline flow equation is as follows,
Figure FDA0003090227350000064
in the formula (15), the reaction mixture is,
Figure FDA0003090227350000065
sgn (p) for average flow of pipeline when natural gas flows from node m to nm,t,pn,t) Equal to 1, otherwise equal to-1,
Figure FDA0003090227350000066
are parameters associated with the pipe, including temperature, length, diameter, etc.,
Figure FDA0003090227350000067
is a constant number of times that the number of the first,
Figure FDA0003090227350000068
representing the amount of natural gas intake at time t from node m to node n,
Figure FDA0003090227350000069
representing the gas output of natural gas from the node m to the node n at the time t;
the pipeline constraints of the natural gas pipeline are:
Figure FDA00030902273500000610
in the formula (16), Lmn,tThe natural gas content in the pipeline is the natural gas content,
Figure FDA00030902273500000611
is a constant associated with the pipe;
the natural gas system pressurization station constraints are:
Figure FDA0003090227350000071
in the formula (17), pc,m,tAnd pc,n,tRespectively, the pressure at both ends of the pressure station, Rc,maxAnd Rc,minThe lower limit and the upper limit of the pressurizing ratio of the pressurizing station are respectively;
natural gas system PtG equipment constraints are:
Figure FDA0003090227350000072
in the formula (18), fPtG,i,tFor conversion to natural gas flow at time t PtG, element iPtG,i,tFor the electric power consumed by unit i at time t PtG, μPtGFor the conversion efficiency of the equipment, GHV is the heat value of the natural gas; pPtGmin,i,tAnd PPtGmax,i,tPtG cell minimum and maximum force values; delta PPtG,i,tA ramp rate of PtG units;
the natural gas system gas storage unit is restrained as follows:
Figure FDA0003090227350000073
in the formula (19), PBGc,i,tAnd PBGd,i,tRespectively the power P for charging and discharging gas of the gas storage unit i at the moment tBGcmin,i,tAnd PBGcmax,i,tMinimum and maximum power, P, for inflating gas storage units respectivelyBGdmin,i,tAnd PBGdmax,i,tRespectively the minimum and maximum air release power of the air storage unit; deltaBGc,i,tAnd deltaBGd,i,tThe inflation state and the deflation state of the gas storage unit are 0/1 variables respectively.
5. The non-cooperative game-based optimization scheduling method for the integrated energy system according to claim 4, wherein the constraint conditions of the thermodynamic system participating in the optimization scheduling of the integrated energy system include thermodynamic node balance constraint, thermodynamic network constraint, heat exchange station constraint, thermodynamic boiler constraint and heat storage unit constraint, and each constraint is as follows:
thermodynamic system thermodynamic node balance constraints are:
Figure FDA0003090227350000081
in the formula (20), PBHd,i,tIndicating the heat release power of the gas storage device i at time t,
Figure FDA0003090227350000082
representing the heat-release power, P, of the cogeneration unit i at time tH,i,tRepresents the load value, omega, of the thermodynamic system node iBHDenotes the set of heat storage devices, ΩGHRepresenting a collection of thermal load nodes, ΩLGRepresenting the aggregate of the thermal load reduction, Δ PH,i,tRepresenting the reduction amount of the thermal load node i at the time t;
the thermal network constraints are:
Figure FDA0003090227350000083
in the formula (21), omegapipe+、Ωpipe-Respectively taking the m nodes as a starting pipeline and a last pipeline,
Figure FDA0003090227350000084
respectively the temperature of the water supply node and the water return node,
Figure FDA0003090227350000085
indicating the temperature of the supplied water at time t in the pipe b,
Figure FDA0003090227350000086
the return water temperature at the t moment in the pipeline b is shown;
the heat exchange station is constrained as follows:
Figure FDA0003090227350000087
in the formula (22), the reaction mixture is,
Figure FDA0003090227350000088
the power required by the heat exchange station of the thermodynamic system, c represents the heat power conversion coefficient of the hot water, c is a constant,
Figure FDA0003090227350000089
is the hot water flow of the initial node of the heat exchange station,
Figure FDA00030902273500000810
respectively the supply water temperature and the return water temperature;
return water temperature of starting node of heat exchange station
Figure FDA0003090227350000091
Satisfies the following conditions:
Figure FDA0003090227350000092
the constraint of the thermal boiler is as follows:
Figure FDA0003090227350000093
in the formula (24), PBoiler,i,tFor the thermal output power of the thermal boiler i at time t,
Figure FDA0003090227350000094
for the natural gas input power at that moment, muBoilerFor the operating efficiency of the boiler, Δ PBoiler,i,tIs the rate of ascent of the boiler, PBoilermax,i,tThe maximum output power of the thermal boiler;
the thermal system heat storage unit is restricted as follows:
Figure FDA0003090227350000095
in formula (25), PBHc,i,tAnd PBHd,i,tPower for charging and discharging heat of the heat storage unit i at time t, PBHcmin,i,tAnd PBHcmax,i,tMinimum and maximum power, P, for charging the heat storage unit respectivelyBHdmin,i,tAnd PBHdmax,i,tThe minimum power and the maximum power of heat release of the heat storage unit are respectively; deltaBHc,i,tAnd deltaBHd,i,tThe charging state and the discharging state of the heat storage unit are 0/1 variables.
6. The method for optimized dispatching of integrated energy systems based on non-cooperative game as claimed in claim 2, wherein when the strategy of each energy system is game balancing
Figure FDA0003090227350000096
When the optimization scheduling model is established, the Nash equilibrium point is represented as follows:
Figure FDA0003090227350000101
in the formula (26), the reaction mixture is,
Figure FDA0003090227350000102
respectively represents Nash equilibrium solution, P, of electric, gas and thermal systemsE,t、PG,t、PH,tSolutions, u, representing the effective functions of the electrical, gas and thermal systems, respectivelyE＝ME(PE,t,PG,t,PH,t)，uG＝MG(PE,t,PG,t,PH,t)，uH＝MH(PE,t,PG,t,PH,t)。
7. The method for optimized dispatching of the integrated energy system based on the non-cooperative game as claimed in claim 2, wherein in the step 2, the parameters of the integrated energy system and the access coupling unit thereof include: system grid structure and line parameters, load distribution condition and prediction data thereof, coupling unit type, access node, output limit value and output prediction data;
the particle swarm algorithm parameters for model solution include: maximum number of iterations, particle velocity range, learning factor.
8. The method for optimized dispatching of an integrated energy system based on non-cooperative game as claimed in claim 5, wherein the step 3 gives initial values of particles, that is, S ═ S in game strategy set1,s2,s3}＝{sE,sG,sHIn (c) } the reaction solution is,
Figure FDA0003090227350000103
randomly selecting an initial value
Figure FDA0003090227350000104
The control variables of the comprehensive energy system scheduling model based on the non-cooperative game are respectively the equipment in each energy system participating in scheduling,
Figure FDA0003090227350000105
respectively expressed as scheduling control vectors in an electric power system, a natural gas system and a thermal system.
9. The method for optimized dispatching of an integrated energy system based on non-cooperative game as claimed in claim 6, wherein the output strategy obtained by each energy system in the K-th cycle in step 4 is as follows:
Figure FDA0003090227350000111
if the output scheme of the optimized calculation meets the safety and stability constraint of the system, the objective function does not reach the optimal value, or the situation that the calculated result is not converged as shown in the formula (29) appears, the initial value of the selected particles should be reproduced
Figure FDA0003090227350000112
Until the requirements are met
Figure FDA0003090227350000113
10. The method for optimized dispatching of an integrated energy system based on non-cooperative game as claimed in claim 9, wherein the step 5 is to obtain the same optimized dispatching scheme after two adjacent cycles, that is, to satisfy the formula (30),
Figure FDA0003090227350000114
the strict Nash equilibrium solution is considered to be found and is taken as the finally calculated equilibrium solution
Figure FDA0003090227350000115
If the optimized scheduling schemes of the systems after two adjacent cycles are approximately consistent, the formula (31) is satisfied
(PE,t,i-PE,t,i-1)2+(PG,t,i-PG,t,i-1)2+(PH,t,i-PH,t,i-1)2≤ε,ε≈0 (31)
The approximate Nash equilibrium solution is considered to be found and is used as the finally calculated equilibrium solution
Figure FDA0003090227350000116
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* Cited by examiner, † Cited by third party
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