CN114188987A - Shared energy storage optimal configuration method of large-scale renewable energy source sending end system - Google Patents

Shared energy storage optimal configuration method of large-scale renewable energy source sending end system Download PDF

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CN114188987A
CN114188987A CN202111474379.6A CN202111474379A CN114188987A CN 114188987 A CN114188987 A CN 114188987A CN 202111474379 A CN202111474379 A CN 202111474379A CN 114188987 A CN114188987 A CN 114188987A
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end system
formula
energy storage
sending end
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唐君毅
秦艳辉
董雪涛
刘震
朱子民
李德存
南东亮
孙冰
段青熙
段玉
王小云
祁晓笑
张媛
马星
糟伟红
马健
杨琪
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Xinjiang Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Xinjiang Electric Power Co Ltd
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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
    • H02J3/38—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
    • 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
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H02J3/241—The oscillation concerning frequency
    • 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
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The invention relates to the technical field of power system operation planning methods, in particular to a shared energy storage optimal configuration method of a large-scale renewable energy source sending end system, which is used for solving the competition and cooperation relationship among a plurality of main bodies based on a cooperation game theory; and considering the power generation uncertainty and the frequency modulation requirement caused by large-scale new energy grid connection, a cooperation mode that the shared energy storage power station and the live generating sets in the sending end systems jointly participate in frequency modulation is designed. The invention scientifically configures the shared energy storage capacity for providing auxiliary service for a plurality of renewable energy source sending end systems under the condition of considering extreme deviation of extreme generating power, realizes the purpose of making the best use of things, and provides a basis for sustainable development of an energy sharing mechanism while improving the operation flexibility of a sending end system accessed by large-scale renewable energy sources.

Description

Shared energy storage optimal configuration method of large-scale renewable energy source sending end system
Technical Field
The invention relates to the technical field of power system operation planning methods, in particular to a shared energy storage optimal configuration method of a large-scale renewable energy source sending end system.
Background
With the large-scale grid connection of the centralized renewable energy, the flexible operation capacity of the power grid is challenged. The fast peak regulation and frequency modulation of the stored energy is an excellent technical means for improving the flexibility of the power grid. However, users facing a single user are usually fully utilized due to the fluctuation of the usage demand, and under the rapid development of the sharing economy, a business model of "sharing energy storage" is developed. The mode can improve the energy storage utilization rate and make up the defect of overlarge investment cost at the initial stage of energy storage.
At present, research on 'shared energy storage' mainly focuses on aspects such as mode design and technical implementation, and related exploration is also carried out on a pricing mechanism in the shared energy storage transaction process: for example, based on fixed prices, peak-to-valley prices, profit or cost amortization, auction prices, etc. Most of the energy storage configuration strategies focus on optimizing the economy to realize the configuration of the capacity and the model of the equipment on the basis of considering the uncertainty of power generation and utilization of the system. Including consideration of annual energy utilization, carbon dioxide emissions, long-time scale operating costs of the system, etc. Most of energy storage configuration researches related to dynamic frequency constraint also adopt fixed empirical values or approximate functions to describe frequency characteristic indexes, do not consider the difference of power generation disturbance of different renewable energy sources, and have the problems of large deviation from the actual condition, inaccurate result and the like. In addition, the shared energy storage surface is oriented to multiple users, and different users have different benefits and are in a diversified trend. The configuration scheme of the shared energy storage and the influence of each user on the user need to be considered when making a decision. The cooperative game theory can describe competition and cooperative relationship among a plurality of subjects, and is one of the excellent technical means for solving the benefit conflict among different participating subjects.
Disclosure of Invention
The invention provides a sharing energy storage optimal configuration method of a large-scale renewable energy source sending end system, which overcomes the defects of the prior art and predicts the output by renewable energy sources in the market at the day before to optimize the running state of a thermoelectric generator set in each sending end system; the real-time market considers the uncertainty of renewable energy power generation, and a shared energy storage configuration scheme meeting the frequency modulation requirements of each sending end system is obtained under the extreme condition of power generation deviation.
The technical scheme of the invention is realized by the following measures: a shared energy storage optimal configuration method of a large-scale renewable energy source sending end system comprises the following steps:
s1: constructing an energy interaction mode between a sending end system accessed by large-scale renewable energy sources and shared energy storage;
s2: on the basis of an energy interaction mode among multiple participants of S1, analyzing a cooperative game relation between a shared energy storage and a sending-end system accessed by multiple large-scale renewable energy sources, and determining main elements of a cooperative game;
s3: on the basis of the S2 cooperative game elements, a strategy type game model between a transmitting-end system sharing stored energy and a plurality of large-scale renewable energy sources is constructed;
s4: based on a robust optimization theory, constructing an uncertainty set of uncertain variables of the power generation power in the policy type game model S3;
s5: constructing a payment of the sending-end system of the S2 cooperative game element on the basis of the uncertain set of S4;
s6: constructing payouts of the shared stored energy in the S2 cooperative gaming elements;
s7: deducing the adjustment satisfied by the Nash balance results of different modes in the strategic game model of S3;
s8: solving the strategic gambling model of S3 by using an improved whale algorithm;
s9: and analyzing the payment and the strategy of each participant under different strategy type game models based on the solving result of the S8.
In S1, on the basis of forecasting wind speed, irradiation intensity and temperature, the day-ahead market of the sending-end system does not consider the randomness of renewable energy power generation, does not participate in energy storage, and optimizes the running state of the thermal power generating unit by taking the minimum power generation cost in the renewable energy access system as a target. The real-time market considers the power generation randomness of renewable energy source station groups in each sending end system, and under the unit running state optimized in the day-ahead, the frequency modulation requirement and the inertia support requirement caused by large-scale renewable energy source grid connection in each sending end system are met by utilizing the shared energy storage and the thermal power generating unit to run cooperatively. And obtaining a capacity allocation scheme of the shared energy storage system, which meets the balance between the frequency modulation requirement and the power supply and demand under the extreme condition of the new energy power generation deviation of each sending end system, by utilizing robust optimization.
The complex interactivity caused by the shared energy storage due to the frequent change of the allocation task makes the dynamic allocation of the shared energy storage difficult to realize. The invention makes the assumption of the static allocation of the shared energy storage, allocates a fixed energy storage unit to each user in a virtual state, considers that the allocation is fixed in the whole planning and operation stage, and can be more intuitively expressed that a plurality of users access a plurality of energy storage units in the shared energy storage, the shared energy storage allows different users to charge and discharge simultaneously, and the same user cannot charge and discharge simultaneously.
The following is further optimization or/and improvement of the technical scheme of the invention:
in the above S2, the main elements of the cooperative game include participants, payments, and policies, which are specifically as follows:
the method comprises the following steps that S201, the participants comprise a sending end system A, a sending end system B and a shared energy storage, DA, DB and SES are used for representing the three participants respectively, and a participant set N is recorded as { DA, DB and SES };
s202, based on the electric energy transaction of the day-ahead scale and the day-inside scale in the spot market, taking 24h as a total scheduling period, and in order to ensure sustainable development of shared energy storage, requiring the sum of the charging and discharging amounts in the total scheduling period to be 0, designing payment of each participant on the premise that the sum of the charging and discharging amounts is 0, wherein the payment is the difference between the income and the cost of the participant in the total scheduling period and is respectively marked as IDA、IDB、ISES；
S203, the strategies include a strategy of the sending end system A, B and a strategy of sharing energy storage, the strategy of the sending end system A, B is the output of the thermal power generating units in their respective jurisdiction areas and their participating frequency modulation power, and the strategy of sharing energy storage is the configured capacity and the charging and discharging power limits of the sending end system a and the sending end system B (DA and DB), which are expressed as follows:
Figure BDA0003392186540000021
wherein the content of the first and second substances,
Figure BDA0003392186540000022
the output of the fire motor set i in DA and DB at the time t is obtained;
Figure BDA0003392186540000023
representing the number of thermal power generating units;
Figure BDA0003392186540000031
indicating that the thermal power generating unit participates in frequency modulation power;
Figure BDA0003392186540000032
representing a shared energy storage rated capacity;
Figure BDA0003392186540000033
representing the charge-discharge power limit of the shared energy storage pair DA and DB; decision variables are continuous values within a certain range, and all participants have continuous decision spaces.
All information related to the strategy type game model is disclosed, and the strategy type game is complete information. In the shared energy storage configuration decision for the new energy sending end system, the DA, DB and SES have 5 possible strategy modes.
In the above S4, based on the robust optimization theory, the constructed uncertainty set is a box type uncertainty set, the uncertainty set of the wind power output is expressed as follows,
Figure BDA0003392186540000034
in the formula, Pt WFor the actual output of wind power, Pt W_sIs a predicted value of wind power output, delta Pt WIs the maximum deviation of the wind power output,
Figure BDA0003392186540000035
the value of the deviation coefficient of the wind power output is between (0, 1);
in the above S5, the payment of the sending end system is the cost of the sending end system, including the cost of the two stages of the day-ahead market and the real-time market, which is specifically as follows:
s501, the sending end system accessed by the large-scale renewable energy sources participates in optimization of a day-ahead market and a real-time market at the same time, wherein the day-ahead market does not relate to games, expressions of the sending end system A and the sending end system B in the first stage of the day-ahead market are the same, and the cost of the sending end system A in the first stage of the day-ahead market is as follows:
Figure BDA0003392186540000036
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000037
fuel cost and threshold effect coefficient;
Figure BDA0003392186540000038
in order to provide a future market contribution plan,
Figure BDA0003392186540000039
the lower limit of its output;
Figure BDA00033921865400000310
indicating the running state of the generator i (1 is running and 0 is off);
Figure BDA00033921865400000311
a unit start-stop cost coefficient;
Figure BDA00033921865400000312
the Boolean type variable is the starting and stopping state of the thermal power generating unit; the unit is changed from shutdown to startup
Figure BDA00033921865400000313
If the number is 1 or not 0, the unit is changed from starting to stopping
Figure BDA00033921865400000314
1, otherwise 0.
S502, the first-stage optimization of the day-ahead market in which the sending-end system participates also needs to meet the running constraint of a sending-end power grid and the running constraint of a medium-voltage generator set; the cost expression, the transmission end power grid operation constraint expression and the thermal power generating unit operation constraint expression of the transmission end system B in the first stage of the day-ahead market are the same as those of the transmission end system A;
the operation constraint of the power grid at the sending end ensures that the power supply and demand in the power grid at the sending end is balanced, and the following expression is provided:
Figure BDA0003392186540000041
in the formula, Pt L-DARepresenting a load in the sending-end system;
the thermal power unit operation constraint comprises a thermal power output constraint, a climbing constraint, a minimum stop-start time constraint and a logical relation of operation/stop-start state variables;
the output constraint expression of the thermal power generating unit is as follows:
Figure BDA0003392186540000042
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000043
representing the minimum and maximum limits of the output force of the thermal power generating unit i at the moment t;
the thermal power generating unit climbing constraint expression is as follows:
Figure BDA0003392186540000044
Figure BDA0003392186540000045
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000046
the maximum upward and downward climbing power of the thermal power generating unit i is obtained;
wherein the expression of the minimum stop-start time constraint is as follows:
Figure BDA0003392186540000047
Figure BDA0003392186540000048
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000049
the minimum starting time and the minimum stopping time of the unit are set;
wherein, the expression of the logical relation of the run/stop state variables is as follows:
Figure BDA00033921865400000410
Figure BDA00033921865400000411
s503, the sending end system accessed by the large-scale renewable energy sources participates in the second stage of the real-time market, and the sending end system A and the sending end system B are in the second stage of the real-time marketThe expressions are the same, the cost of the sending end system A comprises the power generation cost of the thermal power generating unit under extreme conditions
Figure BDA00033921865400000412
Frequency modulation cost of thermal power generating unit
Figure BDA00033921865400000413
Shared energy storage usage cost
Figure BDA00033921865400000414
The following expression is given:
Figure BDA0003392186540000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000052
representing the output of a thermal power generating unit in a real-time market;
Figure BDA0003392186540000053
representing the frequency modulation cost coefficient and the participating frequency modulation power of the thermal power generating unit;
Figure BDA0003392186540000054
for the state of charging and discharging the shared stored energy to DA, Pt DA_dis、Pt DA_chRepresents the charge and discharge power thereof;
Figure BDA0003392186540000055
representing the time-sharing electricity set by the government, and alpha represents a subsidy coefficient;
s504, performing second-stage optimization of a real-time market in which the sending-end system participates, wherein the second-stage optimization also needs to meet the running safety constraint of a sending-end power grid, and the running safety constraint of the sending-end power grid comprises a power balance constraint, a frequency modulation capacity requirement constraint, a thermal power unit dynamic frequency output constraint, a thermal power unit running constraint and a dynamic frequency constraint; the cost expression and the transmission-end power grid operation safety constraint expression of the transmission-end system B in the second stage of the real-time market are the same as those of the transmission-end system A;
wherein the expression of the power balance constraint of the second stage is as follows:
Figure BDA0003392186540000056
in the formula, Pt WThe output of the wind power under the extreme condition of the prediction deviation,
Figure BDA0003392186540000057
the allowable power of the sending end system is lost at the moment t;
the frequency modulation capacity requirement constraint is that the thermal power generating unit and the shared energy storage participate in frequency modulation and should meet a primary frequency modulation (PFR) capacity requirement, and the expression is as follows:
Figure BDA0003392186540000058
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000059
Δ P for PFR capacity requirementRN-DAThe disturbance quantity of the predicted output of the wind power is obtained;
the thermal power generating unit dynamic frequency output constraint comprises the following expressions:
Figure BDA00033921865400000510
Figure BDA00033921865400000511
in the formula,. DELTA.fmaxThe maximum deviation of the frequency is indicated,
Figure BDA00033921865400000512
is a frequency modulation dead zone of the generator set i,
Figure BDA00033921865400000513
the power frequency static characteristic coefficient is the power frequency static characteristic coefficient of the thermal power generating unit i;
the operation constraint expression of the thermal power generating unit is the same as the expressions (5) to (11) in the S502;
the dynamic frequency constraint satisfies a limit constraint formula of a frequency change rate and a dynamic frequency lowest point constraint formula during the safe operation of the power grid, the limit constraint of the frequency change rate is as shown in a formula (17), and the dynamic frequency lowest point constraint is as shown in a formula (18);
Figure BDA0003392186540000061
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000062
the maximum rate of change of frequency required for the system,
Figure BDA0003392186540000063
is the rate of change of frequency at time t.
Figure BDA0003392186540000064
Is the equivalent inertia constant of the system,
Figure BDA0003392186540000065
is the system capacity, f0In order to be the initial frequency of the frequency,
Figure BDA0003392186540000066
power loss at time t;
Figure BDA0003392186540000067
in the formula (f)UFLSTo trigger the frequency limit for the UFLS action,
Figure BDA0003392186540000068
is the lowest point reached before frequency recovery,
Figure BDA0003392186540000069
the gain coefficient of the thermal power generating unit i at the moment t is obtained;
gain factor
Figure BDA00033921865400000610
Represented by formula (19):
Figure BDA00033921865400000611
in the formula (I), the compound is shown in the specification,
Figure BDA00033921865400000612
is the power frequency static characteristic coefficient, T, of the thermal power generating unit iiIs its inertial time constant;
Figure BDA00033921865400000613
time to lowest point for frequency;
time to lowest point of frequency
Figure BDA00033921865400000614
Represented by formula (20):
Figure BDA00033921865400000615
the payment of the shared energy storage is specifically as follows:
s601, paying the shared energy storage as the cost of the shared energy storage, wherein the cost comprises the initial investment daily chemical cost, the daily maintenance daily chemical cost and the electricity purchasing cost, and the cost is expressed as follows:
Figure BDA0003392186540000071
in the formula, xiSES_P、ξSES_EIs the cost per unit power and per unit capacity; gamma denotes capital discount rate; trt denotes the full life cycle of the shared storage；
Figure BDA0003392186540000072
Maintaining cost factors for the year-averaged shared energy storage;
s602, the payment of the shared energy storage further needs to satisfy the operation constraint of the shared energy storage, which is specifically as follows:
the same user sharing the stored energy can not be charged and discharged at the same time, and the expression is as follows:
Figure BDA0003392186540000073
in order to ensure sustainable development of shared energy storage, the sum of the charging and discharging amounts in the total scheduling period is 0;
Figure BDA0003392186540000074
wherein the charge constraints for sharing stored energy are expressed as follows:
Figure BDA0003392186540000075
where ρ and ηch、ηdisIs the self-discharge/charge/discharge rate;
wherein, the rated power constraint of the shared energy storage is expressed as follows:
Figure BDA0003392186540000076
in the above S8, the problem of mixed integer non-convex and non-linear programming is solved based on the characteristics of the policy type game model in the S6. The improved whale algorithm is based on the evolutionary principle among species in the field of ecology, adopts a multi-population cooperative mechanism, adopts an improved whale algorithm for continuous variables, and adopts an improved differential evolution algorithm for Boolean-type variables. The improved whale algorithm specifically comprises the following steps:
(1) based on a cubic chaotic mapping initialization strategy, the mathematical expression of cubic mapping is as follows:
Figure BDA0003392186540000081
in the formula, n represents the current self-iteration times of the cubic mapping, and t represents the current iteration times of the whole algorithm; setting a D-dimensional decision variable as an optimization problem to be solved, firstly, producing a single D as a vector by utilizing a mode of generating random numbers, wherein each element in the vector is between [ -1,1], and is called as an individual 1, then, completing the rest n-1 iterations by utilizing an equation (26), mapping the normalized chaotic sequence to a feasible domain interval of the decision variable as a chaotic variable, and referring to an equation (27):
Figure BDA0003392186540000082
in the formula, xminDenotes the minimum value, x, of the decision variablemaxRepresents the maximum value of the decision variable, [ x ]min,xmax]Representing a feasible domain range of the decision variable; y istRepresents the normalized chaotic variable, x, produced by equation (26)tExpressing the chaotic variables in the feasible domain range of the decision variables after mapping on the solution interval, namely utilizing a cubic chaotic sequence to generate an initial population and then mapping the initial population to a candidate solution space;
(2) the variable scale chaotic variation strategy is to utilize the characteristic of better randomness of a chaotic sequence to carry out chaotic disturbance on the optimal individual generated in each iteration process in a population, and the Logistic mixed sequence mathematical expression is as follows:
yi(n+1)＝μyi(n)(1-yi(n))，n＝1,2,…,logistic.max (28)
in the formula, the logistic.max is the maximum iteration number of the Logistic perturbation sequence; the value of x gradually enters a chaotic state with the change of the mu value. When 0 is present<When mu is less than or equal to 1, the dynamic form of Logistic chaotic mapping is very simple, and the fixed point y is removed0No other period than 0And (4) point. When 1 is<μ<3, the dynamic form is relatively simple, and the fixed point y0＝0、y01- (1/. mu.) is the only two cycle points. When mu is more than or equal to 3 and less than or equal to 4, the dynamic characteristics of the system are more complex and gradually lead to chaos from a double period. When mu is>4, the dynamics of the system are more complex, and the model is completely open to chaos. Therefore, the optimal candidate solution is disturbed in a complete chaotic state, and mu is set to be 4. The value is generated by the equation (28) at [0, 1] as in the idea at initialization]The chaos sequence between, and x is mapped to the search space of the actual candidate solution by using the formula (30)i；
Figure BDA0003392186540000083
xi(n+1)＝(1-λg)xi(n)+λg·L (30)
In the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000084
the maximum value and the minimum value on the corresponding dimension are obtained; if the fitness value of the individuals after the chaos sequence disturbance is superior to the fitness value of the individuals before the disturbance, the disturbed individuals are used for replacing the original individuals, and the process is repeated for a time of logic.max and lambdagThe scale variation factor is determined by a variable beta for controlling the contraction speed of the variation scale and is also related to the current iteration times;
the expression of the scale variation factor is shown in the following formula:
Figure BDA0003392186540000091
(3) cooperative and competitive coaching strategies; setting a fixed value A _ constant, updating A in each iteration, and executing global search when A is larger than or equal to A _ constant algorithm and executing local search when A is smaller than or equal to A _ constant algorithm; where a _ constant is usually set to 0.5, a is called a line-of-sight factor, and its magnitude is related to the number of iterations and gradually decreases to 0 as the number of iterations increases, and the specific calculation is as follows:
Figure BDA0003392186540000092
in the formula, N is the maximum iteration number of the improved whale algorithm, and t represents the current iteration number of the algorithm;
in the global search and the local search, spiral search and linear search are respectively carried out, and the updating mode is as follows:
Figure BDA0003392186540000093
Figure BDA0003392186540000094
in the formula, Xt rand1、Xt rand2、Xt rand3、Xt rand4、Xt rand5The random candidate solutions are 5 random candidate solutions different from each other in the candidate solution population.
The improved whale algorithm processes dynamic relaxation of calculation infeasible solution violating equation constraint degree and simultaneous adjustment of time period variables for power balance constraint, operation and start-stop state variable logic relationship constraint and the like in a strategy type game model in S6.
The improved whale algorithm can convert other constraints in the policy type game model in the S6 into a boundary constraint mode, and the boundary constraint mode is directly processed by using a meta-heuristic algorithm.
In the above S9, the payment and policy of each participant in the different policy type game models are their respective payments and policies in different competition and cooperation relationships of the shared energy storage and multi-terminal system.
The method uses renewable energy sources to predict the output and optimize the running state of the thermoelectric generator set in each sending end system in the market at the present; the real-time market considers the uncertainty of renewable energy power generation, and a shared energy storage configuration scheme meeting the frequency modulation requirements of each sending end system is obtained under the extreme condition of power generation deviation. The invention provides a capacity allocation method for shared energy storage of a transmitting-end system service accessed for a plurality of large-scale renewable energy sources in a business mode of shared energy storage, and the method allocates energy storage capacity under extreme power of large-scale renewable energy source power generation to obtain a capacity allocation scheme of a shared energy storage system which meets the balance of frequency modulation requirements and power supply and demand; the flexible adjustment and operation capacity of the renewable energy access system are improved, and on the other hand, the business mode of sharing the energy storage makes up the defect that the initial investment cost of the traditional energy storage power station is too large due to the fact that the fluctuation of the requirements of a single user is not fully utilized, and the sustainable development of the energy sharing mode is promoted.
Drawings
FIG. 1 is a diagram showing the prediction of daily load, wind speed, irradiation intensity and temperature in the embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating an electric energy interaction mode between a renewable energy source sending-end system and shared stored energy according to an embodiment of the present invention.
Fig. 3 is a solving process of the strategic gaming model according to the embodiment of the invention.
FIG. 4 shows the operation state result of the thermoelectric generation unit in the transmission-side power grid according to the embodiment of the invention.
Fig. 5 is a curve of the power generation disturbance amount, the allowable power deviation and the frequency modulation demand in the transmission-side power grid according to the embodiment of the invention.
In fig. 1, a is a time-of-use electricity price and an elastic load scheduling price; b, forecasting the wind speed and the atmospheric temperature; c, predicting the load of each sending end system; d is the solar irradiation intensity prediction.
In the attached figure 2, a is a market in the day ahead, b is a real-time market, c is a thermal power generating unit, d is a load, e is a large-scale wind power plant group, and f is a large-scale photovoltaic power plant group.
In fig. 4, a is the number of the thermal power unit in the sending end system a; and B is the number of the thermal power unit in the sending end system B.
In fig. 5, a is the power generation disturbance amount of each transmitting end system; b is the allowable power loss of each sending end system; c is the frequency modulation requirement of each sending end system.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments may be determined according to the technical solutions and practical situations of the present invention.
The invention is further described below with reference to the following examples:
example (b): the shared energy storage optimal configuration method of the large-scale renewable energy source sending-end system performs energy storage configuration on shared energy storage facing a plurality of large-scale renewable energy source access systems based on the reference data shown in figure 1, and comprises the following steps:
s1: constructing an energy interaction mode between a sending end system accessed by large-scale renewable energy sources and shared energy storage;
s2: on the basis of an energy interaction mode among multiple participants of S1, analyzing a cooperative game relation between a shared energy storage and a sending-end system accessed by multiple large-scale renewable energy sources, and determining main elements of a cooperative game;
s3: on the basis of the S2 cooperative game elements, a strategy type game model between a transmitting-end system sharing stored energy and a plurality of large-scale renewable energy sources is constructed;
s4: based on a robust optimization theory, constructing an uncertainty set of uncertain variables of the power generation power in the policy type game model S3;
s5: constructing a payment of the sending-end system of the S2 cooperative game element on the basis of the uncertain set of S4;
s6: constructing payouts of the shared stored energy in the S2 cooperative gaming elements;
s7: deducing the adjustment satisfied by the Nash balance results of different modes in the strategic game model of S3;
s8: solving the strategic gambling model of S3 by using an improved whale algorithm;
s9: and analyzing the payment and the strategy of each participant under different strategy type game models based on the solving result of the S8.
In the step S1, the day-ahead market of the sending-end system optimizes the running state of the thermal power generating unit by taking the minimum power generation cost in the renewable energy access system as a target without considering the randomness of the renewable energy power generation and the energy storage participation on the basis of predicting the wind speed, the irradiation intensity and the temperature. The real-time market considers the power generation randomness of renewable energy source station groups in each sending end system, and under the unit running state optimized in the day-ahead, the frequency modulation requirement and the inertia support requirement caused by large-scale renewable energy source grid connection in each sending end system are met by utilizing the shared energy storage and the thermal power generating unit to run cooperatively. A capacity allocation scheme of the shared energy storage system, which meets the balance between frequency modulation requirements and power supply and demand under the extreme condition of the new energy power generation deviation of each sending end system, is obtained by utilizing robust optimization, and is shown in figure 2.
The complex interactivity caused by the shared energy storage due to the frequent change of the allocation task makes the dynamic allocation of the shared energy storage difficult to realize. The invention makes the assumption of the static allocation of the shared energy storage, allocates a fixed energy storage unit to each user in a virtual state, considers that the allocation is fixed in the whole planning and operation stage, and can be more intuitively expressed that a plurality of users access a plurality of energy storage units in the shared energy storage, the shared energy storage allows different users to charge and discharge simultaneously, and the same user cannot charge and discharge simultaneously.
The relevant cost parameters of the shared energy storage are shown in table 1.
In S2, the main elements of the cooperative game include participants, payments, and policies, which are specifically as follows:
the method comprises the following steps that S201, the participants comprise a sending end system A, a sending end system B and a shared energy storage, DA, DB and SES are used for representing the three participants respectively, and a participant set N is recorded as { DA, DB and SES };
s202, based on the electric energy transaction of the day-ahead scale and the day-inside scale in the spot market, taking 24h as a total scheduling period, and in order to ensure sustainable development of shared energy storage, requiring the sum of the charging and discharging amounts in the total scheduling period to be 0, designing payment of each participant on the premise that the sum of the charging and discharging amounts is 0, wherein the payment is the difference between the income and the cost of the participant in the total scheduling period and is respectively marked as IDA、IDB、ISES；
S203, the strategies include a strategy of the sending end system A, B and a strategy of sharing energy storage, the strategy of the sending end system A, B is the output of the thermal power generating units in their respective jurisdiction areas and their participating frequency modulation power, and the strategy of sharing energy storage is the configured capacity and the charging and discharging power limits of the sending end system a and the sending end system B (DA and DB), which are expressed as follows:
Figure BDA0003392186540000111
wherein the content of the first and second substances,
Figure BDA0003392186540000112
the output of the fire motor set i in DA and DB at the time t is obtained;
Figure BDA0003392186540000113
representing the number of thermal power generating units;
Figure BDA0003392186540000114
indicating that the thermal power generating unit participates in frequency modulation power;
Figure BDA0003392186540000115
representing a shared energy storage rated capacity;
Figure BDA0003392186540000116
representing the charge-discharge power limit of the shared energy storage pair DA and DB; decision variables are continuous values within a certain range, and all participants have continuous decision spaces.
All information related to the strategy type game model is disclosed, and the strategy type game is complete information. In the decision of the shared energy storage configuration for the new energy sending end system, there are 5 possible policy modes in the DA, DB, and SES, which are specifically shown in table 2.
In the step S4, based on the robust optimization theory, the constructed uncertainty set is a box-type uncertainty set, the uncertainty set of the wind power output is expressed as follows,
Figure BDA0003392186540000121
in the formula, Pt WIs windActual force of electricity, Pt W_sIs a predicted value of wind power output, delta Pt WIs the maximum deviation of the wind power output,
Figure BDA0003392186540000122
the value of the deviation coefficient of the wind power output is between (0, 1);
in S5, the payment of the sending end system is the cost of the sending end system, including the cost of the two stages of the day-ahead market and the real-time market, which is specifically as follows:
s501, the sending end system accessed by the large-scale renewable energy sources participates in optimization of a day-ahead market and a real-time market at the same time, wherein the day-ahead market does not relate to games, expressions of the sending end system A and the sending end system B in the first stage of the day-ahead market are the same, and the cost of the sending end system A in the first stage of the day-ahead market is as follows:
Figure BDA0003392186540000123
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000124
fuel cost and threshold effect coefficient;
Figure BDA0003392186540000125
in order to provide a future market contribution plan,
Figure BDA0003392186540000126
the lower limit of its output;
Figure BDA0003392186540000127
indicating the running state of the generator i (1 is running and 0 is off);
Figure BDA0003392186540000128
a unit start-stop cost coefficient;
Figure BDA0003392186540000129
is a boolean changeThe quantity is the starting and stopping state of the thermal power generating unit; the unit is changed from shutdown to startup
Figure BDA00033921865400001210
If the number is 1 or not 0, the unit is changed from starting to stopping
Figure BDA00033921865400001211
1, otherwise 0.
S502, the first-stage optimization of the day-ahead market in which the sending-end system participates also needs to meet the running constraint of a sending-end power grid and the running constraint of a medium-voltage generator set; the cost expression, the transmission end power grid operation constraint expression and the thermal power generating unit operation constraint expression of the transmission end system B in the first stage of the day-ahead market are the same as those of the transmission end system A;
the operation constraint of the power grid at the sending end ensures that the power supply and demand in the power grid at the sending end is balanced, and the following expression is provided:
Figure BDA00033921865400001212
in the formula, Pt L-DARepresenting a load in the sending-end system;
the thermal power unit operation constraint comprises a thermal power output constraint, a climbing constraint, a minimum stop-start time constraint and a logical relation of operation/stop-start state variables;
the output constraint expression of the thermal power generating unit is as follows:
Figure BDA0003392186540000131
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000132
representing the minimum and maximum limits of the output force of the thermal power generating unit i at the moment t;
the thermal power generating unit climbing constraint expression is as follows:
Figure BDA0003392186540000133
Figure BDA0003392186540000134
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000135
the maximum upward and downward climbing power of the thermal power generating unit i is obtained;
wherein the expression of the minimum stop-start time constraint is as follows:
Figure BDA0003392186540000136
Figure BDA0003392186540000137
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000138
the minimum starting time and the minimum stopping time of the unit are set;
wherein, the expression of the logical relation of the run/stop state variables is as follows:
Figure BDA0003392186540000139
Figure BDA00033921865400001310
s503, the sending end system accessed by the large-scale renewable energy sources participates in the second stage of the real-time market, the expressions of the sending end system A and the sending end system B in the second stage of the real-time market are the same, and the cost of the sending end system A comprises the power generation cost of the thermal power generating unit under extreme conditions
Figure BDA00033921865400001311
Frequency modulation cost of thermal power generating unit
Figure BDA00033921865400001312
Shared energy storage usage cost
Figure BDA00033921865400001313
The following expression is given:
Figure BDA00033921865400001314
in the formula (I), the compound is shown in the specification,
Figure BDA00033921865400001315
representing the output of a thermal power generating unit in a real-time market;
Figure BDA00033921865400001316
representing the frequency modulation cost coefficient and the participating frequency modulation power of the thermal power generating unit;
Figure BDA00033921865400001317
for the state of charging and discharging the shared stored energy to DA, Pt DA_dis、Pt DA_chRepresents the charge and discharge power thereof;
Figure BDA0003392186540000141
representing the time-sharing electricity set by the government, and alpha represents a subsidy coefficient;
s504, performing second-stage optimization of a real-time market in which the sending-end system participates, wherein the second-stage optimization also needs to meet the running safety constraint of a sending-end power grid, and the running safety constraint of the sending-end power grid comprises a power balance constraint, a frequency modulation capacity requirement constraint, a thermal power unit dynamic frequency output constraint, a thermal power unit running constraint and a dynamic frequency constraint; the cost expression and the transmission-end power grid operation safety constraint expression of the transmission-end system B in the second stage of the real-time market are the same as those of the transmission-end system A;
wherein the expression of the power balance constraint of the second stage is as follows:
Figure BDA0003392186540000142
in the formula, Pt WThe output of the wind power under the extreme condition of the prediction deviation,
Figure BDA0003392186540000143
the allowable power of the sending end system is lost at the moment t;
the frequency modulation capacity requirement constraint is that the thermal power generating unit and the shared energy storage participate in frequency modulation and should meet a primary frequency modulation (PFR) capacity requirement, and the expression is as follows:
Figure BDA0003392186540000144
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000145
Δ P for PFR capacity requirementRN-DAThe disturbance quantity of the predicted output of the wind power is obtained;
the thermal power generating unit dynamic frequency output constraint comprises the following expressions:
Figure BDA0003392186540000146
Figure BDA0003392186540000147
in the formula,. DELTA.fmaxThe maximum deviation of the frequency is indicated,
Figure BDA0003392186540000148
is a frequency modulation dead zone of the generator set i,
Figure BDA0003392186540000149
the power frequency static characteristic coefficient is the power frequency static characteristic coefficient of the thermal power generating unit i;
the operation constraint expression of the thermal power generating unit is the same as the expressions (5) to (11) in the S502;
the dynamic frequency constraint satisfies a limit constraint formula of a frequency change rate and a dynamic frequency lowest point constraint formula during the safe operation of the power grid, the limit constraint of the frequency change rate is as shown in a formula (17), and the dynamic frequency lowest point constraint is as shown in a formula (18);
Figure BDA00033921865400001410
in the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000151
the maximum rate of change of frequency required for the system,
Figure BDA0003392186540000152
is the rate of change of frequency at time t.
Figure BDA0003392186540000153
Is the equivalent inertia constant of the system,
Figure BDA0003392186540000154
is the system capacity, f0In order to be the initial frequency of the frequency,
Figure BDA0003392186540000155
power loss at time t;
Figure BDA0003392186540000156
in the formula (f)UFLSTo trigger the frequency limit for the UFLS action,
Figure BDA0003392186540000157
is the lowest point reached before frequency recovery,
Figure BDA0003392186540000158
the gain coefficient of the thermal power generating unit i at the moment t is obtained;
gain factor
Figure BDA0003392186540000159
Represented by formula (19):
Figure BDA00033921865400001510
in the formula (I), the compound is shown in the specification,
Figure BDA00033921865400001511
is the power frequency static characteristic coefficient, T, of the thermal power generating unit iiIs its inertial time constant;
Figure BDA00033921865400001512
time to lowest point for frequency;
time to lowest point of frequency
Figure BDA00033921865400001513
Represented by formula (20):
Figure BDA00033921865400001514
the payment of the shared storage energy is specifically as follows:
s601, paying the shared energy storage as the cost of the shared energy storage, wherein the cost comprises the initial investment daily chemical cost, the daily maintenance daily chemical cost and the electricity purchasing cost, and the cost is expressed as follows:
Figure BDA00033921865400001515
in the formula, xiSES_P、ξSES_EIs the cost per unit power and per unit capacity; gamma denotes capital discount rate; trt represents the full life cycle of the shared storage;
Figure BDA0003392186540000161
annual average maintenance cost factor for shared energy storage；
S602, the payment of the shared energy storage further needs to satisfy the operation constraint of the shared energy storage, which is specifically as follows:
the same user sharing the stored energy can not be charged and discharged at the same time, and the expression is as follows:
Figure BDA0003392186540000162
in order to ensure sustainable development of shared energy storage, the sum of the charging and discharging amounts in the total scheduling period is 0;
Figure BDA0003392186540000163
wherein the charge constraints for sharing stored energy are expressed as follows:
Figure BDA0003392186540000164
where ρ and ηch、ηdisIs the self-discharge/charge/discharge rate;
wherein, the rated power constraint of the shared energy storage is expressed as follows:
Figure BDA0003392186540000165
in S8, the improved whale algorithm specifically includes:
the improved whale algorithm specifically comprises the following steps:
(1) based on a cubic chaotic mapping initialization strategy, the mathematical expression of cubic mapping is as follows:
Figure BDA0003392186540000166
in the formula, n represents the current self-iteration times of the cubic mapping, and t represents the current iteration times of the whole algorithm; setting a D-dimensional decision variable as an optimization problem to be solved, firstly, producing a single D as a vector by utilizing a mode of generating random numbers, wherein each element in the vector is between [ -1,1], and is called as an individual 1, then, completing the rest n-1 iterations by utilizing an equation (26), mapping the normalized chaotic sequence to a feasible domain interval of the decision variable as a chaotic variable, and referring to an equation (27):
Figure BDA0003392186540000167
in the formula, xminDenotes the minimum value, x, of the decision variablemaxRepresents the maximum value of the decision variable, [ x ]min,xmax]Representing a feasible domain range of the decision variable; y istRepresents the normalized chaotic variable, x, produced by equation (26)tExpressing the chaotic variables in the feasible domain range of the decision variables after mapping on the solution interval, namely utilizing a cubic chaotic sequence to generate an initial population and then mapping the initial population to a candidate solution space;
(2) the variable scale chaotic variation strategy is to utilize the characteristic of better randomness of a chaotic sequence to carry out chaotic disturbance on the optimal individual generated in each iteration process in a population, and the Logistic mixed sequence mathematical expression is as follows:
yi(n+1)＝μyi(n)(1-yi(n))，n＝1,2,…,logistic.max (28)
in the formula, the logistic.max is the maximum iteration number of the Logistic perturbation sequence; the value of x gradually enters a chaotic state with the change of the mu value. When 0 is present<When mu is less than or equal to 1, the dynamic form of Logistic chaotic mapping is very simple, and the fixed point y is removed0There are no other cycle points than 0. When 1 is<μ<3, the dynamic form is relatively simple, and the fixed point y0＝0、y01- (1/. mu.) is the only two cycle points. When mu is more than or equal to 3 and less than or equal to 4, the dynamic characteristics of the system are more complex and gradually lead to chaos from a double period. When mu is>4, the dynamics of the system are more complex, and the model is completely open to chaos. Therefore, the optimal candidate solution is disturbed and set in a complete chaotic statePut mu-4. The value is generated by the equation (28) at [0, 1] as in the idea at initialization]The chaos sequence between, and x is mapped to the search space of the actual candidate solution by using the formula (30)i；
Figure BDA0003392186540000171
xi(n+1)＝(1-λg)xi(n)+λg·L (30)
In the formula (I), the compound is shown in the specification,
Figure BDA0003392186540000172
the maximum value and the minimum value on the corresponding dimension are obtained; if the fitness value of the individuals after the chaos sequence disturbance is superior to the fitness value of the individuals before the disturbance, the disturbed individuals are used for replacing the original individuals, and the process is repeated for a time of logic.max and lambdagThe scale variation factor is determined by a variable beta for controlling the contraction speed of the variation scale and is also related to the current iteration times;
the expression of the scale variation factor is shown in the following formula:
Figure BDA0003392186540000173
(3) cooperative and competitive coaching strategies; setting a fixed value A _ constant, updating A in each iteration, and executing global search when A is larger than or equal to A _ constant algorithm and executing local search when A is smaller than or equal to A _ constant algorithm; where a _ constant is usually set to 0.5, a is called a line-of-sight factor, and its magnitude is related to the number of iterations and gradually decreases to 0 as the number of iterations increases, and the specific calculation is as follows:
Figure BDA0003392186540000181
in the formula, N is the maximum iteration number of the improved whale algorithm, and t represents the current iteration number of the algorithm;
in the global search and the local search, spiral search and linear search are respectively carried out, and the updating mode is as follows:
Figure BDA0003392186540000182
Figure BDA0003392186540000183
in the formula, Xt rand1、Xt rand2、Xt rand3、Xt rand4、Xt rand5The random candidate solutions are 5 random candidate solutions different from each other in the candidate solution population.
The solving process of the strategic gaming model is shown in figure 3.
In S9, the payment and policy of each participant under the different policy type game models are their respective payments and policies under different competition and cooperation relationships of the shared energy storage and multi-terminal system.
According to the data, the implemented capacity configuration result of the shared energy storage is 300 MW. Under the capacity configuration result, the operation state of the thermoelectric generator set in the sending end system is shown in fig. 4, and the power generation disturbance quantity, the allowable power deviation and the frequency modulation requirement of the sending end system are shown in fig. 5.
The invention is characterized in that the novel business mode of sharing energy storage, namely the business mode of sharing energy storage, can optimize energy storage resources on a plurality of power generation sides, enables users to use the resources on the premise of not owning the ownership of the resources, and improves the utilization rate of the energy storage to make up for the defect of overlarge investment cost at the initial stage of energy storage. In addition, the method of the invention considers the capacity allocation under the extreme condition of large-scale renewable energy power generation deviation, and can improve the flexible operation scheduling capability of the power grid on the premise of ensuring the safe and reliable operation of the accessed power grid.
The invention provides a sharing energy storage optimal configuration method of a large-scale renewable energy source sending end system, which optimizes the running state of a thermoelectric generator set in each sending end system by predicting the output of renewable energy sources in the market at the day before; the real-time market considers the uncertainty of renewable energy power generation, and under the extreme condition of power generation deviation, a shared energy storage configuration scheme meeting the frequency modulation requirement of each sending end system is obtained. The invention provides a capacity allocation method for shared energy storage of a transmitting-end system service accessed for a plurality of large-scale renewable energy sources in a business mode of shared energy storage. The flexible adjustment and operation capacity of the renewable energy access system are improved, and on the other hand, the business mode of sharing the energy storage makes up the defect that the initial investment cost of the traditional energy storage power station is too large due to the fact that the fluctuation of the requirements of a single user is not fully utilized, and the sustainable development of the energy sharing mode is promoted.
The technical characteristics form an embodiment of the invention, which has strong adaptability and implementation effect, and unnecessary technical characteristics can be increased or decreased according to actual needs to meet the requirements of different situations.
TABLE 1 shared energy storage related cost parameters
Figure BDA0003392186540000191
TABLE 2 strategic gaming mode for shared energy storage configuration
Game mode Means of Degree of cooperation
{DA},{DB},{SES} DA, DB, SES completely independent decision Non-cooperative
{DA,DB,SES} DA, DB, SES complete co-decision Full cooperation
{DA,DB},{SES} DA, DB constitute alliance common decision, SES independent decision Partial collaboration
{DA},{DB,SES} DB, SES form a union common decision, DA independent decision Partial collaboration
{DA,SES},{DB} DA, SES form a union decision, DB independent decision Partial collaboration

Claims (7)

1. A shared energy storage optimal configuration method of a large-scale renewable energy source sending-end system is characterized by comprising the following steps:
s1: constructing an energy interaction mode between a sending end system accessed by large-scale renewable energy sources and shared energy storage;
s2: on the basis of an energy interaction mode among multiple participants of S1, analyzing a cooperative game relation between a shared energy storage and a sending-end system accessed by multiple large-scale renewable energy sources, and determining main elements of a cooperative game;
s3: on the basis of the S2 cooperative game elements, a strategy type game model between a transmitting-end system sharing stored energy and a plurality of large-scale renewable energy sources is constructed;
s4: based on a robust optimization theory, constructing an uncertainty set of uncertain variables of the power generation power in the policy type game model S3;
s5: constructing a payment of the sending-end system of the S2 cooperative game element on the basis of the uncertain set of S4;
s6: constructing payouts of the shared stored energy in the S2 cooperative gaming elements;
s7: deducing the adjustment satisfied by the Nash balance results of different modes in the strategic game model of S3;
s8: solving the strategic gambling model of S3 by using an improved whale algorithm;
s9: and analyzing the payment and the strategy of each participant under different strategy type game models based on the solving result of the S8.
2. The method according to claim 1, wherein in S2, the main elements of the cooperative game include participants, payments, and strategies, and specifically the following are provided:
the method comprises the following steps that S201, the participants comprise a sending end system A, a sending end system B and a shared energy storage, DA, DB and SES are used for representing the three participants respectively, and a participant set N is recorded as { DA, DB and SES };
s202, based on the electric energy transaction of the day-ahead scale and the day-inside scale in the spot market, taking 24h as a total scheduling period, and in order to ensure sustainable development of shared energy storage, requiring the sum of the charging and discharging amounts in the total scheduling period to be 0, designing payment of each participant on the premise that the sum of the charging and discharging amounts is 0, wherein the payment is the difference between the income and the cost of the participant in the total scheduling period and is respectively marked as IDA、IDB、ISES；
S203, the strategies include a strategy of the sending end system A, B and a strategy of sharing energy storage, the strategy of the sending end system A, B is the output of the thermal power generating units in their respective jurisdictions and the power participating in frequency modulation, and the strategy of sharing energy storage is the configured capacity and the charging and discharging power limits of the sending end system a and the sending end system B, which are expressed as follows:
Figure FDA0003392186530000011
wherein the content of the first and second substances,
Figure FDA0003392186530000012
the output of the fire motor set i in DA and DB at the time t is obtained;
Figure FDA0003392186530000013
representing the number of thermal power generating units;
Figure FDA0003392186530000014
indicating that the thermal power generating unit participates in frequency modulation power;
Figure FDA0003392186530000015
representing a shared energy storage rated capacity;
Figure FDA0003392186530000021
and the charge-discharge power limit of the shared energy storage pair DA and DB is shown.
3. The method for optimal configuration of shared energy storage of a large-scale renewable energy source sending end system according to claim 1 or 2, wherein in S4, based on robust optimization theory, the uncertainty set is constructed as box type uncertainty set, the uncertainty set of wind power output is expressed as follows,
Figure FDA0003392186530000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000023
the actual output of the wind power is the actual output of the wind power,
Figure FDA0003392186530000024
is a predicted value of the wind power output,
Figure FDA0003392186530000025
is the maximum deviation of the wind power output,
Figure FDA0003392186530000026
and the deviation coefficient of the wind power output is obtained.
4. The method according to claim 3, wherein in step S5, the payment paid by the sending end system is the cost of the sending end system, including the cost of the two stages of the day-ahead market and the real-time market, and specifically includes:
s501, the large-scale renewable energy source accessed transmitting end system participates in optimization of a day-ahead market and a real-time market at the same time, wherein the day-ahead market does not relate to games, and the cost of the transmitting end system A in the first stage of the day-ahead market is as follows:
Figure FDA0003392186530000027
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000028
fuel cost and threshold effect coefficient;
Figure FDA0003392186530000029
in order to provide a future market contribution plan,
Figure FDA00033921865300000210
the lower limit of its output;
Figure FDA00033921865300000211
representing the operating state of the generator i;
Figure FDA00033921865300000212
a unit start-stop cost coefficient;
Figure FDA00033921865300000213
the Boolean type variable is the starting and stopping state of the thermal power generating unit;
s502, optimizing a first stage of a day-ahead market in which the sending end system participates, and meeting the running constraint of a sending end power grid and the running constraint of a thermal power generating unit, wherein a cost expression, a sending end power grid running constraint expression and a running constraint expression of the thermal power generating unit of the sending end system B in the first stage of the day-ahead market are the same as those of the sending end system A;
the operation constraint of the power grid at the sending end ensures that the power supply and demand in the power grid at the sending end is balanced, and the following expression is provided:
Figure FDA00033921865300000214
in the formula (I), the compound is shown in the specification,
Figure FDA00033921865300000215
representing a load in the sending-end system;
the thermal power unit operation constraint comprises a thermal power output constraint, a climbing constraint, a minimum stop-start time constraint and a logical relation of operation/stop-start state variables;
the output constraint expression of the thermal power generating unit is as follows:
Figure FDA0003392186530000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000032
representing the minimum and maximum limits of the output force of the thermal power generating unit i at the moment t;
the thermal power generating unit climbing constraint expression is as follows:
Figure FDA0003392186530000033
Figure FDA0003392186530000034
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000035
the maximum upward and downward climbing power of the thermal power generating unit i is obtained;
wherein the expression of the minimum stop-start time constraint is as follows:
Figure FDA0003392186530000036
Figure FDA0003392186530000037
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000038
the minimum starting time and the minimum stopping time of the unit are set;
wherein, the expression of the logical relation of the run/stop state variables is as follows:
Figure FDA0003392186530000039
Figure FDA00033921865300000310
s503, in the second stage of the large-scale renewable energy source accessed sending end system participating in the real-time market, the cost of the sending end system A comprises the power generation cost of the thermal power generating unit under the extreme condition
Figure FDA00033921865300000311
Frequency modulation cost of thermal power generating unit
Figure FDA00033921865300000312
Shared energy storage usage cost
Figure FDA00033921865300000313
The following expression is given:
Figure FDA00033921865300000314
in the formula (I), the compound is shown in the specification,
Figure FDA00033921865300000315
representing the output of a thermal power generating unit in a real-time market;
Figure FDA00033921865300000316
representing the frequency modulation cost coefficient and the participating frequency modulation power of the thermal power generating unit;
Figure FDA00033921865300000317
in order to share the state of charging and discharging the stored energy to the DA,
Figure FDA00033921865300000318
represents the charge and discharge power thereof;
Figure FDA00033921865300000319
representing the time-sharing electricity set by the government, and alpha represents a subsidy coefficient;
s504, performing second-stage optimization of a real-time market in which the sending-end system participates, wherein the second-stage optimization also needs to meet the running safety constraint of a sending-end power grid, and the running safety constraint of the sending-end power grid comprises a power balance constraint, a frequency modulation capacity requirement constraint, a thermal power unit dynamic frequency output constraint, a thermal power unit running constraint and a dynamic frequency constraint; the cost expression and the transmission-end power grid operation safety constraint expression of the transmission-end system B in the second stage of the real-time market are the same as those of the transmission-end system A;
wherein the expression of the power balance constraint of the second stage is as follows:
Figure FDA0003392186530000041
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000042
the output of the wind power under the extreme condition of the prediction deviation,
Figure FDA0003392186530000043
the allowable power of the sending end system is lost at the moment t;
the frequency modulation capacity requirement constraint is that the thermal power generating unit and the shared energy storage participate in frequency modulation and should meet the primary frequency modulation capacity requirement, and the expression is as follows:
Figure FDA0003392186530000044
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000045
Δ P for PFR capacity requirementRN-DAThe disturbance quantity of the predicted output of the wind power is obtained;
the thermal power generating unit dynamic frequency output constraint comprises the following expressions:
Figure FDA0003392186530000046
Figure FDA0003392186530000047
in the formula,. DELTA.fmaxThe maximum deviation of the frequency is indicated,
Figure FDA0003392186530000048
is a frequency modulation dead zone of the generator set i,
Figure FDA0003392186530000049
the power frequency static characteristic coefficient is the power frequency static characteristic coefficient of the thermal power generating unit i;
the operation constraint expression of the thermal power generating unit is the same as the expressions (5) to (11) in the S502;
the dynamic frequency constraint satisfies a limit constraint formula of a frequency change rate and a dynamic frequency lowest point constraint formula during the safe operation of the power grid, the limit constraint of the frequency change rate is as shown in a formula (17), and the dynamic frequency lowest point constraint is as shown in a formula (18);
Figure FDA00033921865300000410
in the formula (I), the compound is shown in the specification,
Figure FDA00033921865300000411
the maximum rate of change of frequency required for the system,
Figure FDA00033921865300000412
is the rate of change of frequency at time t;
Figure FDA00033921865300000413
is the equivalent inertia constant of the system,
Figure FDA00033921865300000414
is the system capacity, f0In order to be the initial frequency of the frequency,
Figure FDA00033921865300000415
power loss at time t;
Figure FDA00033921865300000416
in the formula (f)UFLSTo trigger the frequency limit for the UFLS action,
Figure FDA0003392186530000051
is the lowest point reached before frequency recovery,
Figure FDA0003392186530000052
the gain coefficient of the thermal power generating unit i at the moment t is obtained;
gain factor
Figure FDA0003392186530000053
Represented by formula (19):
Figure FDA0003392186530000054
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000055
is the power frequency static characteristic coefficient, T, of the thermal power generating unit iiIs its inertial time constant;
Figure FDA0003392186530000056
time to lowest point for frequency;
time to lowest point of frequency
Figure FDA0003392186530000057
Represented by formula (20):
Figure FDA0003392186530000058
5. the method for optimally configuring the shared stored energy of the large-scale renewable energy source sending-end system according to claim 4, wherein the payment of the shared stored energy is specifically as follows:
s601, paying the shared energy storage as the cost of the shared energy storage, wherein the cost comprises the initial investment daily chemical cost, the daily maintenance daily chemical cost and the electricity purchasing cost, and the cost is expressed as follows:
Figure FDA0003392186530000059
in the formula, xiSES_P、ξSES_EIs the cost per unit power and per unit capacity; gamma denotes capital discount rate; trt represents the full life cycle of the shared storage;
Figure FDA00033921865300000510
maintaining cost factors for the year-averaged shared energy storage;
s602, the payment of the shared energy storage further needs to satisfy the operation constraint of the shared energy storage, which is specifically as follows:
the same user sharing the stored energy can not be charged and discharged at the same time, and the expression is as follows:
Figure FDA00033921865300000511
in order to ensure sustainable development of shared energy storage, the sum of the charging and discharging amounts in the total scheduling period is 0;
Figure FDA0003392186530000061
wherein the charge constraints for sharing stored energy are expressed as follows:
Figure FDA0003392186530000062
where ρ and ηch、ηdisIs the self-discharge/charge/discharge rate;
wherein, the rated power constraint of the shared energy storage is expressed as follows:
Figure FDA0003392186530000063
6. the method of claim 5, wherein in step S8, the improved whale algorithm specifically includes:
(1) based on a cubic chaotic mapping initialization strategy, the mathematical expression of cubic mapping is as follows:
Figure FDA0003392186530000064
in the formula, n represents the current self-iteration times of the cubic mapping, and t represents the current iteration times of the whole algorithm; setting a D-dimensional decision variable as an optimization problem to be solved, firstly, producing a single D as a vector by utilizing a mode of generating random numbers, wherein each element in the vector is between [ -1,1], and is called as an individual 1, then, completing the rest n-1 iterations by utilizing an equation (26), mapping the normalized chaotic sequence to a feasible domain interval of the decision variable as a chaotic variable, and referring to an equation (27):
Figure FDA0003392186530000065
in the formula, xminDenotes the minimum value, x, of the decision variablemaxRepresents the maximum value of the decision variable, [ x ]min,xmax]Representing a feasible domain range of the decision variable; y istRepresents the normalized chaotic variable, x, produced by equation (26)tRepresenting the chaotic variable in the feasible domain range of the decision variable after mapping on the solution interval;
(2) the variable scale chaotic variation strategy is to utilize the characteristic of better randomness of a chaotic sequence to carry out chaotic disturbance on the optimal individual generated in each iteration process in a population, and the Logistic mixed sequence mathematical expression is as follows:
yi(n+1)＝μyi(n)(1-yi(n))，n＝1,2,…,logistic.max (28)
in the formula, the logistic.max is the maximum iteration number of the Logistic perturbation sequence; generated at [0, 1] by the formula (28)]The chaos sequence between, and x is mapped to the search space of the actual candidate solution by using the formula (30)i；
Figure FDA0003392186530000066
xi(n+1)＝(1-λg)xi(n)+λg·L (30)
In the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000067
the maximum value and the minimum value on the corresponding dimension are obtained; if the fitness value of the individuals after the chaos sequence disturbance is superior to the fitness value of the individuals before the disturbance, the disturbed individuals are used for replacing the original individuals, and the process is repeated for a time of logic.max and lambdagIs a scale variation factor;
the expression of the scale variation factor is shown in the following formula:
Figure FDA0003392186530000071
(3) cooperative and competitive coaching strategies; setting a fixed value A _ constant, updating A in each iteration, and executing global search when A is larger than or equal to A _ constant algorithm and executing local search when A is smaller than or equal to A _ constant algorithm; wherein, A is called a sight distance factor, and the specific calculation mode is as follows:
Figure FDA0003392186530000072
in the formula, N is the maximum iteration number of the improved whale algorithm, and t represents the current iteration number of the algorithm;
in the global search and the local search, spiral search and linear search are respectively carried out, and the updating mode is as follows:
Figure FDA0003392186530000073
Figure FDA0003392186530000074
in the formula (I), the compound is shown in the specification,
Figure FDA0003392186530000075
the random candidate solutions are 5 random candidate solutions different from each other in the candidate solution population.
7. The method of claim 5 or 6, wherein in step S9, the payment and policy of each participant under the different policy type game model are their respective payments and policies under different competition and cooperation relationships between the shared energy storage and the multi-terminal system.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115276099A (en) * 2022-08-26 2022-11-01 中国华能集团清洁能源技术研究院有限公司 Wind power plant energy storage system flexible control method and device based on artificial intelligence technology
CN116594971A (en) * 2023-07-17 2023-08-15 山东天意装配式建筑装备研究院有限公司 BIM-based assembly type building data optimal storage method
CN115276099B (en) * 2022-08-26 2024-06-25 中国华能集团清洁能源技术研究院有限公司 Flexible control method and device for wind farm energy storage system based on artificial intelligence technology

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115276099A (en) * 2022-08-26 2022-11-01 中国华能集团清洁能源技术研究院有限公司 Wind power plant energy storage system flexible control method and device based on artificial intelligence technology
CN115276099B (en) * 2022-08-26 2024-06-25 中国华能集团清洁能源技术研究院有限公司 Flexible control method and device for wind farm energy storage system based on artificial intelligence technology
CN116594971A (en) * 2023-07-17 2023-08-15 山东天意装配式建筑装备研究院有限公司 BIM-based assembly type building data optimal storage method
CN116594971B (en) * 2023-07-17 2023-09-29 山东天意装配式建筑装备研究院有限公司 BIM-based assembly type building data optimal storage method

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