CN112054508A - Wind-solar hybrid storage and photovoltaic hybrid storage system optimization scheduling method based on non-cooperative game - Google Patents

Wind-solar hybrid storage and photovoltaic hybrid storage system optimization scheduling method based on non-cooperative game Download PDF

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CN112054508A
CN112054508A CN202010794297.9A CN202010794297A CN112054508A CN 112054508 A CN112054508 A CN 112054508A CN 202010794297 A CN202010794297 A CN 202010794297A CN 112054508 A CN112054508 A CN 112054508A
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赵龙
王定美
周强
沈渭程
吕清泉
刘丽娟
张金平
李津
陟晶
丁坤
董海鹰
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Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
Lanzhou Jiaotong University
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Lanzhou Jiaotong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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 discloses a wind-solar pumped storage combined system optimization scheduling method based on a non-cooperative game, belongs to the technical field of power transmission, and aims to solve the problem that stable operation of a regional power grid is challenged greatly by large-scale wind and light grid connection. The method comprises the following steps of establishing a non-cooperative game model: the model consists of four elements, namely a game participant, a participant strategy set, participant income and a game balancing strategy; solving the model: and solving the Nash equilibrium solution by adopting an iterative search method. The output characteristics of various power plants are comprehensively considered, the respective profit maximization of three participants is taken as an optimization target, a unit output strategy is taken as a decision space, and a Nash equilibrium solving algorithm corresponding to the model is provided; the method is more suitable for the current situation that the openness degree of the power market is greatly increased, and is beneficial to the comprehensive dispatching of wind, light and storage power stations of power plants participating in the dispatching, the quantity of abandoned wind and abandoned light is reduced, the minimum output fluctuation of a combined system is guaranteed, and the maximum economic benefit is achieved in operation.

Description

Wind-solar hybrid storage and photovoltaic hybrid storage system optimization scheduling method based on non-cooperative game
Technical Field
The invention belongs to the technical field of power transmission, and particularly relates to a wind-solar hybrid storage and photovoltaic hybrid storage system optimization scheduling method based on a non-cooperative game.
Background
The output of new energy such as wind and light has great uncertainty and intermittency, and large-scale wind and light grid connection brings huge challenges to the stable, reliable and economic operation of a regional power grid. The regional power grid must be equipped with sufficient matching power supply to meet the peak regulation and standby requirements of the regional power grid and ensure safe, stable and economic operation of the system. The installed capacity of new energy is rapidly developed, but the absorption capacity of the new energy is relatively poor, the power grid abandons wind and light in order to ensure the stable operation of the power grid, and the dual unfavorable situation of income reduction and clean energy waste of wind and light power generation enterprises occurs. The pumped storage power plant has excellent regulation characteristic and can weaken the fluctuation amplitude of a power system caused by wind and light power generation to a certain extent.
At present, a combined system optimization scheduling model considering both power grid economy and wind-light utilization rate is constructed in the aspects of power grid optimization scheduling containing renewable energy sources, mainly aiming at the aspects of minimum power generation cost and environmental cost, minimum wind and light abandonment quantity, minimum output fluctuation of a combined system, maximum operation economic benefit and the like. However, the open degree and daily increase of the current power market cannot be fully considered in the modeling process, and the power plants participating in scheduling output the situation of independently deciding for pursuing benefit maximization, so that the established model has certain irrationality in the current power market environment. Therefore, a new theoretical method is actively tried under the condition of considering the requirement of the wind power utilization rate, a scheduling model of the power plant main body for pursuing self benefit maximization under the condition of matching the current market openness is discussed, and the situation that the balance state of the power generation amount and the income of the power plant is obtained is extremely necessary.
Disclosure of Invention
The invention aims to provide a wind-solar pumped storage combined system optimization scheduling method based on a non-cooperative game, and the method is used for solving the problem that the stable operation of a regional power grid is challenged greatly by large-scale wind and light grid connection.
In order to solve the problems, the technical scheme of the invention is as follows:
a wind-solar hybrid storage and photovoltaic hybrid storage system optimization scheduling method based on a non-cooperative game comprises the following steps.
Step A, establishing a non-cooperative game model;
the model consists of four elements of game participants, a participant strategy set, participant income and a game balancing strategy.
1. Determining a game participant:
the game participants are: the method comprises the steps of wind power generation, photovoltaic generation and a storage power plant, wherein W, P, CX is selected to represent the wind power generation, the photovoltaic generation and the storage power plant respectively.
2. Determining participant policy:
at time t W, P, CX, three parties make independent decision and playThe respective generated output is taken as a strategy and is respectively marked as PW,t、PP,t、PCX,t；
The actual decision is constrained by the unit characteristics and the environment, and the decision variable is continuously valued therein, that is, the three-party policy set is always valued in the continuous policy space, which is specifically represented as:
PW,t∈ΩW (1)；
PP,t∈ΩP (2)；
PCX,t∈ΩCX (3)；
in the formula:
ΩWan output strategy space determined for wind power constraint;
ΩPa force strategy space determined for photovoltaic constraints;
ΩCXand (4) determining an output strategy space for the constraint of the pumped storage power station.
3. Determining participant revenue:
w, P, CX the income of each three-party independent participant is composed of profit income and expenditure, and the income function of the three-party participants is marked as M1、M2、M3。
4. Determining a game balancing strategy:
the game balancing strategy is a constraint condition of the three participants.
B, solving the model;
solving the Nash equilibrium solution by adopting an iterative search method, wherein the flow is as follows;
1. inputting original load data and parameters:
and giving load, electricity price and parameters required for calculating wind power, photovoltaic and pumped storage income.
2. Establishing a non-cooperative game model:
and constructing a non-cooperative game scheduling model of the wind-light-pumped storage power system according to the modeling process.
3. Giving an initial value of a Nash equilibrium solution of the model;
following in the respective corresponding policy sets of the three-party participantsSelecting an initial value (P)W,t,0，PP,t,0， PCX,t,0)。
4. Three-party participant independent decision:
the ith round of three-party participants solve the output scheme of the round of three-party according to the interest function of the participants to be (P)W,t,i，PP,t,i，PCX,t,i) And when the ith round of optimization decision is carried out, the result of the previous round needs to be considered, namely:
PW,t,i＝arg max M1(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (24)；
PP,t,i＝arg max M2(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (25)；
PCX,t,i＝arg max M3(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (26)。
5. judging whether a Nash equilibrium solution of the model is found:
if the optimization results of the ith round are the same as the optimization results of the (i-1) th round, the non-cooperative game model achieves Nash balance according to the definition of Nash balance, and the step 6 is carried out;
if the optimization results of the ith round and the (i-1) th round are different, turning to the step 4;
and repeating the steps 4 and 5 until a Nash equilibrium solution is found.
6. Outputting the Nash equilibrium solution and the equilibrium degree of the model:
if the algorithm can not reach the convergence state, an initial value needs to be given again in the step 3, and the judgment on whether convergence is needed needs to be combined with the I, i-1 and i-2 rounds of three-party participants to solve the output scheme (P) of the round of the three-party participants according to the interest function thereofW,t,i,PP,t,i,PCX,t,i)、(PW,t,i-1,PP,t,i-1,PCX,t,i-1)、 (PW,t,i-2,PP,t,i-2,PCX,t,i-2)；
Comprehensive consideration:
the condition that the difference value of the optimized results of the output schemes of two adjacent power plants is converged along with time is met, namely:
|PW,t,i-PW,t,i-1|≤|PW,t,i-1-PW,t,i-2| (27)；
|PP,t,i-PP,t,i-1|≤|PP,t,i-1-PP,t,i-2| (28)；
|PCX,t,i-PCX,t,i-1|≤|PCX,t,i-1-PCX,t,i-2| (29)；
considering the algorithm to reach a convergence state according to the definition of convergence;
the solving process is carried out on the premise that the Nash equilibrium solution exists in the non-cooperative game model.
Meanwhile, the information of the current electric power market is not strictly symmetrical, but with the continuous development of the electric power market, the information of all parties in the game is continuously transparent; in order to avoid the solution being difficult to realize, when the method is modeled, the information is considered to be transparent among the interest gambling parties.
Further, said M in step A31The income function calculation mode of the wind turbine generator is as follows:
for a wind generating set, the profit M1Including electricity selling income, scrapping income, operation and maintenance expenditure, investment expenditure and wind power income M1The calculation formula is as follows:
Figure RE-GDA0002713938840000041
Figure RE-GDA0002713938840000042
in the formula: t is the duration of the whole scheduling period;
NWthe number of the wind driven generators participating in scheduling is determined;
λtthe price of the power on the internet at the time t;
PW,i,tsupplying active power to a power grid for a wind turbine generator i at the moment t;
SW,i,tconvert to every small for the whole investment life cycleThe discard income;
YW,i,tconverting the whole investment life cycle into operation and maintenance expenditure of each hour;
Dwtotal scrap income;
r is the discount rate; l iswIs the average life of the fan.
Further, said M in step A32The revenue function calculation mode of the photovoltaic unit is as follows:
for a photovoltaic unit, the yield M2Including electricity selling income, scrapping income, operation and maintenance expenditure, investment expenditure and photovoltaic income M2The calculation formula is as follows:
Figure RE-GDA0002713938840000051
Figure RE-GDA0002713938840000052
in the formula: t is the duration of the whole scheduling period;
NPthe number of photovoltaic units participating in scheduling is determined;
γtthe price of the power on the internet at the time t;
PP,i,tsupplying active power to a power grid for the photovoltaic unit i at the moment t;
SP,i,tconverting the whole investment life cycle into the scrapping income of each hour;
YP,i,tconverting the whole investment life cycle into operation and maintenance expenditure of each hour;
DPtotal scrap income; e is the discount rate; l isPThe average life of the photovoltaic unit.
Further, said M in step A33The calculation method of the profit function of the pumping and storage unit is as follows:
the pumping and storing unit has two operating conditions of power generation and water pumping and energy storage, and the benefit of the pumping and storing unit is researched from the operating condition of the pumping and storing unit to be the difference between the income of power generation and the expenditure of water pumping;
the pumping storage unit is different from other units such as thermal power units, wind power units and the like, and has the greatest effects of peak clipping, valley filling and stable electric energy output in the system in the power system, so the income of the auxiliary power generation part needs to be considered in the process of calculating the income, and the income M of the auxiliary power generation part is obtained3The expression of (a) is:
Figure RE-GDA0002713938840000053
in the formula: n is a radical ofcxThe number of pumping units participating in scheduling;
Figure RE-GDA0002713938840000061
the state variables of the pumping and storage unit i in the pumping and power generation states at the moment t are respectively in the power generation and pumping states when the value is 1 and in the non-power generation and non-pumping states when the value is 0;
Figure RE-GDA0002713938840000062
and (4) pumping water and generating power of the pumping and storage unit i at the moment t.
Further, in step a 4, the constraints of the stroke and the optical engine group output of the three participants are as follows:
the generated energy of the wind and light unit depends heavily on weather conditions, the active power supplied to the system by the unit is less than the maximum output determined by the weather conditions, and the constraint is as follows:
Figure RE-GDA0002713938840000063
Figure RE-GDA0002713938840000064
in the formula:
Figure RE-GDA0002713938840000065
are respectively asAnd (3) determining the maximum output of the wind and light mechanical set i at the time t according to the meteorological conditions.
Further, in step a 4, the contribution constraint of the pump storage unit in the three participants is as follows:
the operation constraints of the pumping unit comprise operation conditions, output, on-value daily available capacity of an upper reservoir and on-value daily available capacity constraints of a lower reservoir, and meanwhile, the pumping unit needs a time period for switching between two operation states, so that start-stop frequency constraints of the two states are introduced;
Figure RE-GDA0002713938840000066
Figure RE-GDA0002713938840000067
Figure RE-GDA0002713938840000068
Figure RE-GDA0002713938840000069
Figure RE-GDA00027139388400000610
Figure RE-GDA0002713938840000071
Figure RE-GDA0002713938840000072
in the formula:
Figure RE-GDA0002713938840000073
the maximum power generation and water pumping power of the unit i are respectively;
Figure RE-GDA0002713938840000074
respectively minimum and maximum values of daily reservoir capacity change equivalent electric quantity of an upper reservoir and a lower reservoir of the pumped storage power station;
Figure RE-GDA0002713938840000075
the maximum times of power generation and pumping state change of the pumping unit i in one day are shown.
Further, the three-party participants in step A4 introduce system active power balance and wind and light abandoning amount constraints:
active power of the whole power system is always in dynamic balance at any time, and simultaneously, as primary energy is increasingly tense in recent years, the nation vigorously plants new energy power generation forms such as wind and light and the like on the energy policy, and tries to reduce system abandoned wind and abandoned light, so that the constraint of abandoned light quantity is introduced.
The active power balance and the wind and light abandoning amount constraints of the system are respectively as follows:
Figure RE-GDA0002713938840000076
Figure RE-GDA0002713938840000077
Figure RE-GDA0002713938840000078
in the formula: pt dThe load of the system at the moment t;
q and j are respectively the wind and light abandoning rate of the system in a scheduling period;
the scheduling strategy is a Nash equilibrium solution of the established non-cooperative game model;
according to the definition of Nash balance, the non-cooperative game model has Nash balance
Figure RE-GDA0002713938840000079
It should satisfy:
Figure RE-GDA00027139388400000710
Figure RE-GDA0002713938840000081
Figure RE-GDA0002713938840000082
in the formula:
Figure RE-GDA0002713938840000083
the best output of the other two parties is obtained under the best output, namely wind power, photovoltaic and pumping storage under the output strategy set reach the maximum yield under Nash balance.
Further, the number of game participants is 2, and the game participants are respectively wind power and photovoltaic power.
Further, summer load and photovoltaic data of corresponding seasons are selected from participant strategies.
Further, the participant strategy selects the winter load and wind power data of the corresponding season.
The invention has the following beneficial effects:
the wind power, light power and storage power stations are taken as three independent main bodies participating in the game, the output characteristics of various power plants are comprehensively considered, the respective maximum income of the three participants is taken as an optimization target, the unit output strategy is taken as a decision space, and a Nash equilibrium solving algorithm corresponding to the model is provided. The method is more suitable for the current situation that the openness degree of the power market is greatly increased, and is beneficial to the comprehensive dispatching of wind, light and storage power stations of power plants participating in the dispatching, the quantity of abandoned wind and abandoned light is reduced, the minimum output fluctuation of a combined system is guaranteed, and the maximum economic benefit is achieved in operation.
Drawings
Fig. 1 is a flow chart of Nash equilibrium solving in a wind-solar-pumped-storage combined system optimization scheduling method based on a non-cooperative game.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The following embodiment depends on a drinking fountain Changma pumped storage power station, the total installed capacity is planned to be 1200 megawatts (4 multiplied by 300 megawatts), and the station is close to a drinking fountain new energy base and is an important matched peak shaving power supply of the drinking fountain new energy base. According to the layout plan of the pumped storage power station of the Gansu power system compiled by the water regulation institute and the development and improvement committee, seven pumped storage power stations are built in the riverside corridor in the future. The subject of the invention is implemented by depending on the research subject and achievement of the project, namely how to adopt wind-light pumped storage combined optimization operation to make the comprehensive output meet the system load requirement on the premise of minimizing the wind-light electric output, and how to maximize the benefits of the wind-light electric and pumped storage power station, which is specifically shown in the following embodiment.
Examples
As shown in fig. 1, a wind-solar-pumped storage combined system optimization scheduling method based on a non-cooperative game includes the following steps:
step A, establishing a non-cooperative game model;
the model consists of four elements of game participants, a participant strategy set, participant income and a game balancing strategy.
A.1, determining game participants:
the game participants are: the method comprises the steps of wind power generation, photovoltaic generation and a storage power plant, wherein W, P, CX is selected to represent the wind power generation, the photovoltaic generation and the storage power plant respectively.
A.2, determining participant strategy:
when three parties carry out independent decision making and game playing at t moment W, P, CX, the generated output of each party is taken as a strategy and is respectively marked as PW,t、PP,t、PCX,t。
The actual decision is constrained by the unit characteristics and the environment, and the decision variable is continuously valued therein, that is, the three-party policy set is always valued in the continuous policy space, which is specifically represented as:
PW,t∈ΩW (1)；
PP,t∈ΩP (2)；
PCX,t∈ΩCX (3)；
in the formula:
ΩWan output strategy space determined for wind power constraint;
ΩPa force strategy space determined for photovoltaic constraints;
ΩCXand (4) determining an output strategy space for the constraint of the pumped storage power station.
And A.3, determining participant income:
w, P, CX the income of each three-party independent participant is composed of profit income and expenditure, and the income function of the three-party participants is marked as M1、M2、M3。
A.3.1 wherein M1The income function calculation mode of the wind turbine generator is as follows:
for a wind generating set, the profit M1Including electricity selling income, scrapping income, operation and maintenance expenditure, investment expenditure and wind power income M1The calculation formula is as follows:
Figure RE-GDA0002713938840000101
Figure RE-GDA0002713938840000102
in the formula: t is the duration of the whole scheduling period;
NWthe number of the wind driven generators participating in scheduling is determined;
λtthe price of the power on the internet at the time t;
PW,i,tsupplying active power to a power grid for a wind turbine generator i at the moment t;
SW,i,tconverting the whole investment life cycle into the scrapping income of each hour;
YW,i,tconverting the whole investment life cycle into operation and maintenance expenditure of each hour;
Dwtotal scrap income;
r is the discount rate; l iswIs the average life of the fan.
A.3.2 wherein: m2The revenue function calculation mode of the photovoltaic unit is as follows:
for a photovoltaic unit, the yield M2Including electricity selling income, scrapping income, operation and maintenance expenditure, investment expenditure and photovoltaic income M2The calculation formula is as follows:
Figure RE-GDA0002713938840000103
Figure RE-GDA0002713938840000111
in the formula: t is the duration of the whole scheduling period;
NPthe number of photovoltaic units participating in scheduling is determined;
γtthe price of the power on the internet at the time t;
PP,i,tsupplying active power to a power grid for the photovoltaic unit i at the moment t;
SP,i,tconverting the whole investment life cycle into the scrapping income of each hour;
YP,i,tconverting the whole investment life cycle into operation and maintenance expenditure of each hour;
DPtotal scrap income; e is the discount rate; l isPThe average life of the photovoltaic unit.
A.3.3 wherein: m3The calculation method of the profit function of the pumping and storage unit is as follows:
the pumping and storing unit has two operating conditions of power generation and water pumping and energy storage, and the benefit of the pumping and storing unit is researched from the operating condition of the pumping and storing unit to be the difference between the income of power generation and the expenditure of water pumping;
the pumping storage unit is different from other units such as thermal power units, wind power units and the like, and has the greatest effects of peak clipping, valley filling and stable electric energy output in the system in the power system, so the income of the auxiliary power generation part needs to be considered in the process of calculating the income, and the income M of the auxiliary power generation part is obtained3The expression of (a) is:
Figure RE-GDA0002713938840000112
in the formula: n is a radical ofcxThe number of pumping units participating in scheduling;
Figure RE-GDA0002713938840000113
the state variables of the pumping and storage unit i in the pumping and power generation states at the moment t are respectively in the power generation and pumping states when the value is 1 and in the non-power generation and non-pumping states when the value is 0;
Figure RE-GDA0002713938840000114
and (4) pumping water and generating power of the pumping and storage unit i at the moment t.
A.4, determining a game balancing strategy:
the game balancing strategy is a constraint condition of the three participants.
A.4.1 first: the output constraints of the three participants on stroke and optical units are as follows:
the generated energy of the wind and light unit depends heavily on weather conditions, the active power supplied to the system by the unit is less than the maximum output determined by the weather conditions, and the constraint is as follows:
Figure RE-GDA0002713938840000121
Figure RE-GDA0002713938840000122
in the formula:
Figure RE-GDA0002713938840000123
and the maximum output of the wind and light machine set i at the time t is determined for the meteorological conditions respectively.
Secondly, the output constraint of the pumping and storage unit among the three participants is as follows:
the operation constraints of the pumping unit comprise operation conditions, output, on-value daily available capacity of an upper reservoir and on-value daily available capacity constraints of a lower reservoir, and meanwhile, the pumping unit needs a time period for switching between two operation states, so that start-stop frequency constraints of the two states are introduced;
Figure RE-GDA0002713938840000124
Figure RE-GDA0002713938840000125
Figure RE-GDA0002713938840000126
Figure RE-GDA0002713938840000127
Figure RE-GDA0002713938840000128
Figure RE-GDA0002713938840000129
Figure RE-GDA00027139388400001210
in the formula:
Figure RE-GDA00027139388400001211
the maximum power generation and water pumping power of the unit i are respectively;
Figure RE-GDA00027139388400001212
respectively minimum and maximum values of daily reservoir capacity change equivalent electric quantity of an upper reservoir and a lower reservoir of the pumped storage power station;
Figure RE-GDA0002713938840000131
the maximum times of power generation and pumping state change of the pumping unit i in one day are shown.
4.3 again, the above three participants introduce the system active balance and the wind and light abandoning amount constraints:
for the whole power system, the active power is always in dynamic balance at any moment. Meanwhile, as primary energy is increasingly tense in recent years, the nation vigorously plants new energy power generation forms such as wind and light and the like on the energy policy, and tries to reduce the wind and light abandoning of the system, so that the constraint of the wind and light abandoning amount is introduced.
The active power balance and the wind and light abandoning amount constraints of the system are respectively as follows:
Figure RE-GDA0002713938840000132
Figure RE-GDA0002713938840000133
Figure RE-GDA0002713938840000134
in the formula: pt dThe load of the system at the moment t;
q and j are respectively the wind and light abandoning rate of the system in a scheduling period;
the scheduling strategy is a Nash equilibrium solution of the established non-cooperative game model;
according to the definition of Nash balance, the non-cooperative game model has Nash balance
Figure RE-GDA0002713938840000135
It should satisfy:
Figure RE-GDA0002713938840000136
Figure RE-GDA0002713938840000137
Figure RE-GDA0002713938840000138
in the formula:
Figure RE-GDA0002713938840000139
the best output of the other two parties is obtained under the best output, namely wind power, photovoltaic and pumping storage under the output strategy set reach the maximum yield under Nash balance.
B, solving the model;
solving the Nash equilibrium solution by adopting an iterative search method, wherein the flow is as follows;
b.1, inputting original load data and parameters:
and giving load, electricity price and parameters required for calculating wind power, photovoltaic and pumped storage income.
B.2, establishing a non-cooperative game model:
and constructing a non-cooperative game scheduling model of the wind-light-pumped storage power system according to the modeling process.
B.3, giving an initial value of a Nash equilibrium solution of the model;
randomly selecting an initial value (P) in a policy set corresponding to each of three participantsW,t,0，PP,t,0， PCX,t,0)。
And B.4, independently deciding by three participants:
the ith round of three-party participants solve the output scheme of the round of three-party according to the interest function of the participants to be (P)W,t,i，PP,t,i，PCX,t,i) And when the ith round of optimization decision is carried out, the result of the previous round needs to be considered, namely:
PW,t,i＝arg max M1(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (24)；
PP,t,i＝arg max M2(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (25)；
PCX,t,i＝arg max M3(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (26)。
b.5, judging whether a Nash equilibrium solution of the model is found:
if the optimization results of the ith round are the same as the optimization results of the (i-1) th round, the non-cooperative game model achieves Nash balance according to the definition of Nash balance, and the step 6 is carried out;
if the optimization results of the ith round and the (i-1) th round are different, turning to the step 4;
and repeating the steps 4 and 5 until a Nash equilibrium solution is found.
B.6, outputting the Nash equilibrium solution and the equilibrium degree of the model:
if the algorithm can not reach the convergence state, an initial value needs to be given again in the step 3, and the judgment on whether convergence is needed needs to be combined with the I, i-1 and i-2 rounds of three-party participants to solve the output scheme (P) of the round of the three-party participants according to the interest function thereofW,t,i,PP,t,i,PCX,t,i)、(PW,t,i-1,PP,t,i-1,PCX,t,i-1)、 (PW,t,i-2,PP,t,i-2,PCX,t,i-2)；
Comprehensive consideration:
the condition that the difference value of the optimized results of the output schemes of two adjacent power plants is converged along with time is met, namely:
|PW,t,i-PW,t,i-1|≤|PW,t,i-1-PW,t,i-2| (27)；
|PP,t,i-PP,t,i-1|≤|PP,t,i-1-PP,t,i-2| (28)；
|PCX,t,i-PCX,t,i-1|≤|PCX,t,i-1-PCX,t,i-2| (29)；
considering the algorithm to reach a convergence state according to the definition of convergence;
the solving process is carried out on the premise that the Nash equilibrium solution exists in the non-cooperative game model.
Meanwhile, the information of the current electric power market is not strictly symmetrical, but with the continuous development of the electric power market, the information of all parties in the game is continuously transparent. In order to avoid the solution being difficult to realize, when the method is modeled, the information is considered to be transparent among the interest gambling parties.
In specific implementation, 2 game participants can be respectively wind power and photovoltaic, and the situation can be simulated in a system without a pumped storage power station.
In a specific implementation, the participant strategy can select summer load and photovoltaic data of a corresponding season, and the situation can be set up to simulate the situation of maximum photovoltaic output in one year.
In specific implementation, the participant strategy can select winter loads and wind power data of corresponding seasons, and the situation can simulate the situation of maximum wind power output in one year.

Claims (10)

1. A wind-solar hybrid system optimization scheduling method based on a non-cooperative game is characterized by comprising the following steps: the method comprises the following steps:
step A, establishing a non-cooperative game model;
the model consists of four elements, namely a game participant, a participant strategy set, participant income and a game balancing strategy;
1. determining a game participant:
the game participants are: wind power, photovoltaic and a storage power plant are respectively selected from W, P, CX to represent the wind power, photovoltaic and storage power plant;
2. determining participant policy:
when three parties carry out independent decision making and game playing at t moment W, P, CX, the generated output of each party is taken as a strategy and is respectively marked as PW,t、PP,t、PCX,t；
The actual decision is constrained by the unit characteristics and the environment, and the decision variable is continuously valued therein, that is, the three-party policy set is always valued in the continuous policy space, which is specifically represented as:
PW,t∈ΩW (1)；
PP,t∈ΩP (2)；
PCX,t∈ΩCX (3)；
in the formula:
ΩWan output strategy space determined for wind power constraint;
ΩPa force strategy space determined for photovoltaic constraints;
ΩCXan output strategy space determined for the pumped storage power station constraint;
3. determining participant revenue:
w, P, CX the income of each three-party independent participant is composed of profit income and expenditure, and the income function of the three-party participants is marked as M1、M2、M3；
4. Determining a game balancing strategy:
the game balance strategy is a constraint condition of the three participants;
b, solving the model;
solving the Nash equilibrium solution by adopting an iterative search method, wherein the flow is as follows;
1. inputting original load data and parameters:
giving load, electricity price and parameters required for calculating wind power, photovoltaic and pumped storage income;
2. establishing a non-cooperative game model:
according to the modeling process, a non-cooperative game scheduling model of the wind-light-pumped storage power system is constructed;
3. giving an initial value of a Nash equilibrium solution of the model;
randomly selecting an initial value (P) in a policy set corresponding to each of three participantsW,t,0，PP,t,0，PCX,t,0)；
4. Three-party participant independent decision:
the ith round of three-party participants solve the output scheme of the round of three-party according to the interest function of the participants to be (P)W,t,i，PP,t,i，PCX,t,i) And when the ith round of optimization decision is carried out, the result of the previous round needs to be considered, namely:
PW,t,i＝arg max M1(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (24)；
PP,t,i＝arg max M2(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (25)；
PCX,t,i＝arg max M3(PW,t,i-1,PP,t,i-1,PCX,t,i-1) (26)；
5. judging whether a Nash equilibrium solution of the model is found:
if the optimization results of the ith round are the same as the optimization results of the (i-1) th round, the non-cooperative game model achieves Nash balance according to the definition of Nash balance, and the step 6 is carried out;
if the optimization results of the ith round and the (i-1) th round are different, turning to the step 4;
repeating the steps 4 and 5 until a Nash equilibrium solution is found;
6. outputting the Nash equilibrium solution and the equilibrium degree of the model:
if the algorithm can not reach the convergence state, an initial value needs to be given again in the step 3, and the judgment of convergence needs to be combined with the i, i-1 and i-2 rounds of three participants according to the algorithmThe benefit function solves the output scheme (P) of the wheel ownerW,t,i,PP,t,i,PCX,t,i)、(PW,t,i-1,PP,t,i-1,PCX,t,i-1)、(PW,t,i-2,PP,t,i-2,PCX,t,i-2)；
Comprehensive consideration:
the condition that the difference value of the optimized results of the output schemes of two adjacent power plants is converged along with time is met, namely:
|PW,t,i-PW,t,i-1|≤|PW,t,i-1-PW,t,i-2| (27)；
|PP,t,i-PP,t,i-1|≤|PP,t,i-1-PP,t,i-2| (28)；
|PCX,t,i-PCX,t,i-1|≤|PCX,t,i-1-PCX,t,i-2| (29)；
considering the algorithm to reach a convergence state according to the definition of convergence;
the solving process is carried out on the premise that the Nash equilibrium solution exists in the non-cooperative game model.
2. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in claim 1, wherein: said M in step A31The income function calculation mode of the wind turbine generator is as follows:
for a wind generating set, the profit M1Including electricity selling income, scrapping income, operation and maintenance expenditure, investment expenditure and wind power income M1The calculation formula is as follows:
Figure FDA0002624951930000031
Figure FDA0002624951930000032
in the formula: t is the duration of the whole scheduling period;
NWto take part in the tuneThe number of wind driven generators;
λtthe price of the power on the internet at the time t;
PW,i,tsupplying active power to a power grid for a wind turbine generator i at the moment t;
SW,i,tconverting the whole investment life cycle into the scrapping income of each hour;
YW,i,tconverting the whole investment life cycle into operation and maintenance expenditure of each hour;
Dwtotal scrap income;
r is the discount rate; l iswIs the average life of the fan.
3. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in claim 1, wherein: said M in step A32The revenue function calculation mode of the photovoltaic unit is as follows:
for a photovoltaic unit, the yield M2Including electricity selling income, scrapping income, operation and maintenance expenditure, investment expenditure and photovoltaic income M2The calculation formula is as follows:
Figure FDA0002624951930000041
Figure FDA0002624951930000042
in the formula: t is the duration of the whole scheduling period;
NPthe number of photovoltaic units participating in scheduling is determined;
γtthe price of the power on the internet at the time t;
PP,i,tsupplying active power to a power grid for the photovoltaic unit i at the moment t;
SP,i,tconverting the whole investment life cycle into the scrapping income of each hour;
YP,i,tfor conversion of the whole investment life cycleOperational expenditure to each hour;
DPtotal scrap income; e is the discount rate; l isPThe average life of the photovoltaic unit.
4. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in claim 1, wherein: said M in step A33The calculation method of the profit function of the pumping and storage unit is as follows:
the pumping and storing unit has two operating conditions of power generation and water pumping and energy storage, and the benefit of the pumping and storing unit is researched from the operating condition of the pumping and storing unit to be the difference between the income of power generation and the expenditure of water pumping;
the income of the auxiliary power generation part is considered when the income is calculated, so the income M is obtained3The expression of (a) is:
Figure FDA0002624951930000051
in the formula: n is a radical ofcxThe number of pumping units participating in scheduling;
Figure FDA0002624951930000052
the state variables of the pumping and storage unit i in the pumping and power generation states at the moment t are respectively in the power generation and pumping states when the value is 1 and in the non-power generation and non-pumping states when the value is 0;
Figure FDA0002624951930000053
and (4) pumping water and generating power of the pumping and storage unit i at the moment t.
5. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in claim 1, wherein: in step A4, the constraint of the forces of the stroke and the optical-mechanical group of the three participants is as follows:
the generated energy of the wind and light unit depends heavily on weather conditions, the active power supplied to the system by the unit is less than the maximum output determined by the weather conditions, and the constraint is as follows:
Figure FDA0002624951930000054
Figure FDA0002624951930000055
in the formula:
Figure FDA0002624951930000056
and the maximum output of the wind and light machine set i at the time t is determined for the meteorological conditions respectively.
6. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in claim 1, wherein: in step A4, the output constraint of the pump storage unit in the three participants is as follows:
the operation constraints of the pumping unit comprise operation conditions, output, on-value daily available capacity of an upper reservoir and on-value daily available capacity constraints of a lower reservoir, and meanwhile, the pumping unit needs a time period for switching between two operation states, so that start-stop frequency constraints of the two states are introduced;
Figure FDA0002624951930000057
Figure FDA0002624951930000058
Figure FDA0002624951930000059
Figure FDA0002624951930000061
Figure FDA0002624951930000062
Figure FDA0002624951930000063
Figure FDA0002624951930000064
in the formula:
Figure FDA0002624951930000065
the maximum power generation and water pumping power of the unit i are respectively;
Figure FDA0002624951930000066
respectively minimum and maximum values of daily reservoir capacity change equivalent electric quantity of an upper reservoir and a lower reservoir of the pumped storage power station;
θi f、θi cthe maximum times of power generation and pumping state change of the pumping unit i in one day are shown.
7. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in any one of claims 1 to 6, wherein: and B, introducing system active power balance and wind and light abandoning amount constraint into the three participants in the step A4:
the active power balance and the wind and light abandoning amount constraints of the system are respectively as follows:
Figure FDA0002624951930000067
Figure FDA0002624951930000068
Figure FDA0002624951930000069
in the formula: pt dThe load of the system at the moment t;
q and j are respectively the wind and light abandoning rate of the system in a scheduling period;
the scheduling strategy is a Nash equilibrium solution of the established non-cooperative game model;
according to the definition of Nash balance, the non-cooperative game model has Nash balance
Figure FDA0002624951930000071
It should satisfy:
Figure FDA0002624951930000072
Figure FDA0002624951930000073
Figure FDA0002624951930000074
in the formula:
Figure FDA0002624951930000075
the best output of the other two parties is obtained under the best output, namely wind power, photovoltaic and pumping storage under the output strategy set reach the maximum yield under Nash balance.
8. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in any one of claims 7, wherein: the number of game participants is 2, and the game participants are respectively wind power and photovoltaic.
9. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in claim 7, wherein: and selecting summer load and photovoltaic data of corresponding seasons from the participant strategy.
10. The wind-solar-pumped-storage combined system optimization scheduling method based on the non-cooperative game as claimed in claim 7, wherein: and selecting the winter load and wind power data of a corresponding season from the participant strategy.
CN202010794297.9A 2020-08-10 2020-08-10 Wind-solar hybrid storage and photovoltaic hybrid storage system optimization scheduling method based on non-cooperative game Pending CN112054508A (en)

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