CN113162083A - Two-stage coordination optimization method considering operating state of energy storage system - Google Patents

Two-stage coordination optimization method considering operating state of energy storage system Download PDF

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CN113162083A
CN113162083A CN202110234210.7A CN202110234210A CN113162083A CN 113162083 A CN113162083 A CN 113162083A CN 202110234210 A CN202110234210 A CN 202110234210A CN 113162083 A CN113162083 A CN 113162083A
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CN113162083B (en
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吴在军
路珊
李培帅
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Southeast 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
    • H02J3/28—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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention discloses a two-stage coordination optimization method considering the running state of an energy storage system, which comprises a day-ahead optimization stage considering demand response and a day-in adjustment stage. In a day-ahead optimization stage considering demand response, considering the influence of the operating condition of the energy storage element on the recession cost, and formulating the user electricity price; a two-stage interval optimization method is provided for solving a day-ahead optimization model; then, the state of charge constraint of the energy storage element is determined in a time-sharing manner; and in the day adjustment stage, solving the deterministic model by using real-time data. The resource utilization efficiency is improved and the operation cost is reduced by the two-stage coordination optimization method.

Description

Two-stage coordination optimization method considering operating state of energy storage system
Technical Field
The invention relates to the field of optimization operation of comprehensive energy systems, in particular to a two-stage coordination optimization method considering the operation state of an energy storage system.
Background
The introduction of new energy brings more uncertainty to comprehensive energy, and energy storage is one of the common means for solving the problem. However, most of the existing optimal scheduling methods are simple and inaccurate in consideration of the constraint conditions of the energy storage system, and the result still has an optimal space.
The energy storage system can store redundant energy when the output of new energy is excessive, so that resource waste is prevented; and when the energy output is insufficient and the load demand is large, the stored energy is output, so that the stability of system operation is ensured. The demand response refers to that the user adjusts an energy utilization mode according to a price signal or an incentive mechanism of the market, so that the load has a flexible characteristic, and the resource utilization efficiency is improved. The invention combines the two aspects, formulates the residential electricity price based on demand response, takes the energy storage system as flexible resource to participate in system scheduling, can combine multiple time scales and more effectively optimize the system.
Disclosure of Invention
The invention aims to provide a two-stage coordination optimization method considering the running state of an energy storage system, which considers the influence of the running working condition of an energy storage element on the recession cost in the day-ahead optimization stage considering the demand response and formulates the user electricity price; a two-stage interval optimization method is provided for solving a day-ahead optimization model; then, the state of charge constraint of the energy storage element is determined in a time-sharing manner; and in the day adjustment stage, solving the deterministic model by using real-time data. The resource utilization efficiency is improved and the operation cost is reduced by the two-stage coordination optimization method.
The purpose of the invention can be realized by the following technical scheme:
a two-stage coordination optimization method considering the running state of an energy storage system comprises the following steps:
s1: obtaining calculation data, performing day-ahead optimization by using prediction data, implementing a demand response strategy based on price, establishing a two-stage interval optimization model considering the decay cost of the storage battery, and dividing variables into a first-stage variable, a second-stage variable and an uncertain variable;
s2: decomposing the interval optimization model in the S1 into a main problem and a sub problem, and solving the main problem and the sub problem in sequence: solving the main problem to obtain an optimal solution of an objective function and optimal solutions of first and second-stage variables; solving the subproblems is divided into two conditions, wherein in one condition, the solution of the uncertain variable when the objective function is minimum is obtained, in the other condition, the solution of the uncertain variable when the objective function is maximum and the maximum value of the objective function are obtained, and the subproblems are alternately solved until the solutions of the main subproblems meet the convergence standard;
s3: optimizing the state of charge constraint of the energy storage element under the extreme condition by using the optimized solution of the first-stage variable obtained in the step S2;
s4: and reading the real-time values of the new energy output and the load data, and performing the second-stage day adjustment by using the state of charge constraint obtained in the S3.
Further, the S1 specifically includes:
s11, establishing a two-stage interval optimization model as follows:
Figure BDA0002959299420000021
s.t.g(x,y,u)＝0,h(x,y,u)≤0
s12: the decay cost of a battery is represented by the following model:
Figure BDA0002959299420000022
L(DoD)＝A·DoD-B·e-C·DoD
Figure BDA0002959299420000031
further, F (x, y, u) represents an objective function, i.e. the total operating cost C of the integrated energy systemWT+CPV+CBESS+Cgrid-Crev(ii) a Wherein, CWT，CPVRepresenting the operating and maintenance costs of wind and photovoltaic, respectively, CBESSRepresents the decay cost of the accumulator energy storage system, CgridIndicating the cost of the transaction with the main network, CrevRepresents revenue for selling electricity to the customer after PBDR is implemented; u denotes uncertaintyThe fixed variables comprise new energy output and load requirements; the remaining variables are divided into two phases: the variable x in the first stage is a PBDR related variable; the second stage variable y is a BESS scheduling related variable, including
Figure BDA0002959299420000032
Figure BDA0002959299420000033
Representing the replacement cost, η, of the accumulatorch/ηdisRespectively representing the charging and discharging efficiency of the battery; A. b and C are coefficients relating to different battery types;
Figure BDA0002959299420000034
is the variation of the charge and discharge power,
Figure BDA0002959299420000035
is the battery capacity.
Further, the S2 specifically includes:
s21, the main question is expressed as:
Figure BDA0002959299420000036
Figure BDA0002959299420000037
the sub-problem is represented as:
Figure BDA0002959299420000038
Figure BDA0002959299420000039
Figure BDA00029592994200000310
obtaining an objective function value F by solving the main problemM,kAnd optimal solution of first and second stage variables
Figure BDA00029592994200000311
Fix in subproblems
Figure BDA00029592994200000312
And
Figure BDA00029592994200000313
searching the maximum value and the minimum value of the objective function in the prediction interval of the uncertain variable, and respectively corresponding to the worst condition of the uncertain variable
Figure BDA00029592994200000314
And optimal conditions
Figure BDA00029592994200000315
S22, judging whether the algorithm converges according to the following formula:
Figure BDA00029592994200000316
if the values are converged, stopping iteration and obtaining a final first-stage variable value, and turning to S3; if not, go to S23;
and S23, updating the iteration number k to k +1, returning to S21, bringing the solution of the uncertain variable when the objective function is maximum in the subproblem into the main problem, correcting the value of the uncertain variable in the main problem, and continuing the iteration.
Further, said FM,kSolutions representing the main problem, Fpes,kRepresenting the solution when the objective function is maximal in the subproblem.
Further, the S3 specifically includes:
s31, considering the extreme case occurs when the new energy output is minimum and the load demand is maximum; optimal solution for fixed first stage variables
Figure BDA0002959299420000041
Then, optimizing the variable y in the second stage;
s32, optimizing the constraint of the state of charge of the energy storage system, and representing by the following model:
Figure BDA0002959299420000042
Figure BDA0002959299420000043
Figure BDA0002959299420000044
further, the
Figure BDA0002959299420000045
Represents the optimal solution of the variables of the first stage,
Figure BDA0002959299420000046
represents the optimal solution to the first-stage sub-problem,
Figure BDA0002959299420000047
represents the worst solution to the first stage sub-problem,
Figure BDA0002959299420000048
represents the value of the uncertain variable at the minimum of the objective function in the first stage sub-problem,
Figure BDA0002959299420000049
the value of the uncertain variable at the maximum of the objective function in the first stage sub-problem is represented.
Further, the S4 specifically includes:
s41: the objective function for deterministic optimization is:
Figure BDA00029592994200000410
s42: the constraints on the state of charge of the energy storage element in the model should instead be:
Figure BDA00029592994200000411
further, said CWT，CPVRepresenting the operating and maintenance costs of wind and photovoltaic, respectively, CrevRepresents revenue for selling electricity to customers after PBDR is implemented, CBESSRepresents the decay cost of the accumulator energy storage system, CgridRepresenting a cost of the transaction with the primary grid;
Figure BDA0002959299420000051
respectively represent the SoC constraint upper and lower limits after S3 optimization.
The invention has the beneficial effects that:
in the two-stage coordination optimization method, the influence of the operating condition of the energy storage element on the recession cost is taken into consideration in the day-ahead optimization stage of considering the demand response, and the user electricity price is formulated; a two-stage interval optimization method is provided for solving a day-ahead optimization model; then, the state of charge constraint of the energy storage element is determined in a time-sharing manner; and in the day adjustment stage, solving the deterministic model by using real-time data. The resource utilization efficiency is improved and the operation cost is reduced by the two-stage coordination optimization method.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of a two-phase coordination optimization method of the present invention;
FIG. 2 is a schematic diagram of the steps of the two-phase coordination optimization of the present invention;
FIG. 3 is a flow chart of the interval optimization solution of the present invention at the previous optimization stage.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A two-stage coordination optimization method considering the running state of an energy storage system comprises the following steps:
s1: and acquiring calculation data, and initializing variables and the calculation data.
For uncertain variables, wind power, photovoltaic output and load demand data, the values of the uncertain variables are within the upper and lower limits of the prediction data, namely:
Figure BDA0002959299420000061
Figure BDA0002959299420000062
indicates the lower limit of the predicted value,
Figure BDA0002959299420000063
representing the wind power/photovoltaic/load output value,
Figure BDA0002959299420000064
represents the lower limit of the predicted value.
The first stage is a day-ahead optimization stage, a price-based demand response (PBDR) strategy is implemented, a two-stage interval optimization (TSIO) model is established, and the expression is as follows:
Figure BDA0002959299420000065
s.t.g(x,y,u)＝0,h(x,y,u)≤0
wherein F (x, y, u) represents an objective function, namely the total operating cost of the integrated energy system, and a specific expression can be written as CWT+CPV+CBESS+Cgrid-Crev. Wherein, CWT，CPVRepresenting the operating and maintenance costs of wind and photovoltaic, respectively, CBESSRepresents the decay cost of the accumulator energy storage system, CgridIndicating the cost of the transaction with the main network, CrevRepresenting revenue for selling electricity to the customer after implementing PBDR. Specific expressions of the costs are respectively as follows:
Figure BDA0002959299420000066
Figure BDA0002959299420000067
Figure BDA0002959299420000068
Figure BDA0002959299420000069
Figure BDA00029592994200000610
Figure BDA00029592994200000611
Figure BDA00029592994200000612
wherein,
Figure BDA00029592994200000613
respectively representing the unit operation and maintenance cost of wind power and photovoltaic, tau represents the step length of scheduling time,
Figure BDA00029592994200000614
represents the accumulated decay cost of a single storage battery in a unit scheduling period,
Figure BDA00029592994200000615
respectively representing the unit price, P, of electricity purchased/sold to the main grid in each time periodt def，Pt surRespectively representing the shortage and surplus of the electric quantity of the comprehensive energy system in each time period, PrjRepresenting the price level of PBDR, αj,tIs a binary variable, L, representing the price level of the PBDRjRepresenting the demand response rate at price level j.
All variables are divided into two phases: the variable x in the first stage is a PBDR related variable; the second stage variable y is a BESS scheduling related variable, including
Figure BDA0002959299420000071
Etc.; and u represents uncertain variables including new energy output, load requirements and the like of wind power, photovoltaic and the like. Two min represent the lowest total operating cost by optimizing the variables of the first and second stages, respectively. Max and min in parentheses represent the worst and best case in an uncertain scenario.
g (x, y, u) is 0, h (x, y, u) is less than or equal to 0, and the equation and inequality condition which the variable needs to satisfy are respectively expressed as follows:
the electricity price of the user is set one day in advance, the respective electricity utilization conditions of the user can be adjusted according to the electricity price are considered, and the active and reactive load demands are met based on the strategy
Figure BDA0002959299420000072
Can be expressed as:
Figure BDA0002959299420000073
Figure BDA0002959299420000074
wherein
Figure BDA0002959299420000075
Respectively representing the active and reactive demands of the load when the PBDR strategy is not implemented,
Figure BDA0002959299420000076
Figure BDA0002959299420000077
and the active and reactive demands of the load after the PBDR strategy is implemented are represented. In the present invention, five price levels α are consideredj,tDemand response rate L corresponding theretojThe following table can be seen:
TABLE 1 price level of demand response
Price level Preferential rate Rate of demand response
1 0.6 1.21
2 0.8 1.09
3 1.0 1.0
4 1.2 0.93
5 1.4 0.88
The general BESS state expression quantities include a state of charge (SoC) and a depth of discharge (DoD), where SoC represents a ratio of a remaining capacity to a battery capacity, and DoD represents a ratio of a charge/discharge variation to the battery capacity, as shown in the following equation:
Figure BDA0002959299420000081
wherein
Figure BDA0002959299420000082
Is the amount of change in the charge/discharge power,
Figure BDA0002959299420000083
is the battery capacity.
The relationship between the life of the BESS and the DoD is:
L(DoD)＝A·DoD-B·e-C·DoD
l (dod) indicates the service life of the BESS, A, B and C are correlation coefficients related to different battery types, and specific values can be obtained through experimental data.
The relationship between the degradation cost of the battery and DoD can be expressed as:
Figure BDA0002959299420000084
Cdeg(DoD) represents the decay cost of the BESS,
Figure BDA0002959299420000085
representing the replacement cost, η, of the accumulatorch/ηdisRespectively, the efficiency of charging and discharging the battery. Frequent charge and discharge operations affect the battery life but are negligible in the short term, so the battery capacity in the above equation can be considered as a constant, or the battery capacity can be considered to be periodically updated.
Considering the continuous charge/discharge behavior of the battery, a general operation model of the storage battery is established, which is specifically expressed as follows:
Figure BDA0002959299420000086
Figure BDA0002959299420000087
Figure BDA0002959299420000088
Figure BDA0002959299420000089
Figure BDA00029592994200000810
Figure BDA00029592994200000811
Figure BDA00029592994200000812
Figure BDA00029592994200000813
wherein,
Figure BDA00029592994200000814
is a binary variable representing the charge-discharge state of the battery,
Figure BDA00029592994200000815
indicating the amount of change in charging and discharging of the battery, SoCmin，SoCmaxRespectively the minimum value and the maximum value of the SoC,
Figure BDA00029592994200000816
is the energy remaining in the BESS,
Figure BDA00029592994200000817
representing the energy accumulated by the BESS at the current time. bi,tA binary variable representing a change in the state of the BESS, the value 0 if the battery remains charged or discharged for an adjacent time interval; if the battery operating state changes, the value is recorded as 1.
bi,tThe constraint includes multiplication of two variables, and other two binary variables can be introduced
Figure BDA0002959299420000091
Performing linearization processing, wherein the expression after the linearization processing is as follows:
Figure BDA0002959299420000092
Figure BDA0002959299420000093
cumulative decay cost of BESS
Figure BDA0002959299420000094
The expression also comprises the multiplication of two variables, and a new variable can be introduced
Figure BDA0002959299420000095
Performing linearization processing, wherein the expression after the linearization processing is as follows:
Figure BDA0002959299420000096
thus, after taking into account whether battery charge-discharge behavior is continuous, the decay cost per unit time step for a single BESS can be calculated by:
Figure BDA0002959299420000097
there are also the following constraints that need to be taken into account in price-based demand response strategies. After implementing the PBDR policy, the bill of the electricity fee that the user needs to pay should not exceed the original fee when the PBDR is not implemented, and the PBDR cannot inhibit the electricity consumption behavior of the user, namely:
Figure BDA0002959299420000098
Figure BDA0002959299420000099
in terms of line flow, the following constraints need to be considered. The active power, the reactive power and the voltage of the nodes need to be kept balanced, the network loss is ignored, and a linearization model can be expressed as follows:
Figure BDA0002959299420000101
Figure BDA0002959299420000102
Figure BDA0002959299420000103
wherein P isi,tAnd Qi,tRespectively representing the active and reactive power flowing from the bus i to the main branch,
Figure BDA0002959299420000104
and
Figure BDA0002959299420000105
respectively representing the active and reactive power, R, flowing from the bus i to the external branchiAnd XiRespectively representing the branch impedance, V, of the bus i0Representing a reference voltage.
The voltage variation range in the system line and the power flow of each branch should be within the allowable range:
Figure BDA0002959299420000106
Figure BDA0002959299420000107
wherein, Vi,minAnd Vi,maxRespectively representing the minimum and maximum voltage, V, of each pointi,tRepresenting the voltage of each point; pi,tAnd Qi,tRepresenting active and reactive power, S, at each pointiRepresenting the branch power flow upper limit value.
S2: the TSIO model in the optimization before the day is decomposed into a main problem and a sub problem, and the initial iteration number is 1. The main question can be expressed as:
Figure BDA0002959299420000108
Figure BDA0002959299420000109
the main problem is to achieve the lowest operating costs under the most severe conditions at hand,
Figure BDA00029592994200001010
for the worst uncertain variable transmitted by the subproblem, the variable to be optimized is the first two-stage variable x, y, and the obtained solution is recorded as
Figure BDA00029592994200001011
And passes the solution to the sub-problem for computation.
The sub-problem can be expressed as:
Figure BDA00029592994200001012
Figure BDA00029592994200001013
Figure BDA00029592994200001014
the sub-problem is processed after fixing the solution of the main problem, with the aim of finding the optimal case with the minimum running cost, and the worst case with the maximum running cost,
Figure BDA00029592994200001015
is the optimal solution of the first two-stage variables obtained in the main problem. The variable to be optimized is an uncertain variable u, and the obtained solution is recorded
Figure BDA00029592994200001016
And passes the solution to the next iteration process of the main problem for computation.
The specific solving process can be explained as follows: solving the main problem and the sub-problems in sequence, and using the solutions obtained by the sub-problems
Figure BDA0002959299420000111
Solving the main problem, and simultaneously optimizing x and y variables to minimize the operation cost and obtain an optimal solution F of an objective functionM,kAnd first, second stage variablesOptimal solution
Figure BDA0002959299420000112
And passes it to the subproblems as a known quantity for calculation; among the subproblems, the solution obtained for the main problem
Figure BDA0002959299420000113
The solution can be regarded as a fixed variable, and is divided into two cases, wherein the solution of the variable which has the minimum objective function and is uncertain when the operation cost is the lowest is solved under one case, and the optimal case is recorded as the optimal case
Figure BDA0002959299420000114
In one case, the maximum value of the objective function is obtained, the solution of the uncertain variable and the maximum value of the objective function are obtained when the running cost is the highest, and the maximum value is the worst case and is respectively recorded as
Figure BDA0002959299420000115
And Fpes,k. The worst case of which
Figure BDA0002959299420000116
The next iteration passed to the main problem is calculated. Where the subscript k denotes the number of iterations.
Judging whether the algorithm is converged by judging the difference between the objective function values of the main problem and the subproblems, if so, stopping iteration and obtaining a final solution, and turning to S3; if the target function is not converged, updating the iteration number k to k +1, and solving the uncertain variable when the target function is maximum in the subproblem
Figure BDA0002959299420000117
And (5) returning to the main problem, correcting the value of the uncertain variable in the main problem, and continuing iteration.
For the determination of convergence, the difference between the solutions of the main problem and the sub problem is defined as
Figure BDA0002959299420000118
FM,kIs a solution of the main problem, Fpes,kFor the solution of the maximum of the objective function in the subproblem. When Δ F is less than the error limit δ, the algorithm converges. Smaller values of δ ensure the accuracy of the algorithm, but require longer time to solve, and larger values of δ do not. Considering the precision and scale of the solution, the example takes δ to be 0.001.
S3: typically, the SoC constraints used in the BESS scheduling are mostly constant values. In actual operation, the operation of the BESS can only be determined based on data in a short time period under the condition that the prediction level of an uncertain variable is limited, so that the scheduling decision is the optimal solution in the current time period, but is not necessarily the optimal solution in the time scale of the whole day. It is highly likely that the SoC margin will be insufficient to optimize the subsequent operation of the BESS. Therefore, the BESS needs to reserve a certain reserved capacity when handling uncertain situations. In order to ensure reasonable distribution and utilization of electric quantity in the whole scheduling period, the upper and lower limit values of SoC constraint are required to be optimized individually on the time scale of the whole day.
Considering that in an extreme case, the discharge of the BESS can satisfy the usage requirement, the SoC value at this time can be regarded as a limit value in the constraint. Thus, the optimized solution of the first stage variables obtained using S2
Figure BDA0002959299420000121
In an extreme case, the decision value y of the second stage variable and the energy storage system is optimized. Extreme conditions should occur when the new energy output is minimal while the load demand is maximal, and are recorded as
Figure BDA0002959299420000122
Figure BDA0002959299420000123
Figure BDA0002959299420000124
Through the formula, BESS discharge power under extreme conditions can be obtained
Figure BDA0002959299420000125
If an extreme condition occurs and the BESS is also operated in an extreme state, i.e., the state with the lowest SoC, the difference between the lower limits of SoC between two discharges should be sufficient to satisfy the discharge capacity of the battery. In addition, the difference between the upper limit and the lower limit of the SoC in the same discharge period is larger than the minimum reserved charge-discharge capacity of the storage battery:
Figure BDA0002959299420000126
Figure BDA0002959299420000127
wherein,
Figure BDA0002959299420000128
representing the minimum reserved charge-discharge capacity of the storage battery.
In addition, in the sub-problem of the optimization of the day-ahead interval, the solutions of the optimal and worst cases of the uncertain variables are obtained, and the SoC value of the BESS can be taken as the upper and lower limit values of the normal constraint in the two cases. Still taking the lowest running cost as an objective function, the following optimization model can be obtained:
Figure BDA0002959299420000129
Figure BDA00029592994200001210
Figure BDA00029592994200001211
Figure BDA00029592994200001212
is the optimal solution of the variables of the first stage,
Figure BDA00029592994200001213
for the solution of the uncertain variable in the optimal case of the subproblem,
Figure BDA00029592994200001214
for the solution of the uncertain variable in the worst case of the subproblem, y1Second-stage variables, y, to be optimized for optimal conditions2The worst case corresponds to the second stage variable to be optimized.
In the solution obtained
Figure BDA0002959299420000131
And
Figure BDA0002959299420000132
the value of SoC can be extracted, the minimum value of each hour is taken as the lower limit of SoC constraint, the maximum value is taken as the upper limit of SoC constraint, and the optimization of the upper and lower limits of SoC constraint is completed.
S4: in the in-day adjustment phase, the first-phase variable optimization solution obtained in S2
Figure BDA0002959299420000133
And the SoC constraint values obtained at S3 are known conditions. And for the new energy output and load, a more accurate time-sharing prediction value can be obtained. The in-day adjustment stage is a rolling optimization process, optimization is carried out by taking four hours as a rolling scale, only the decision value of the first hour is reserved, and subsequent values are discarded for rolling updating.
The optimization of the in-day phase can be regarded as the solution of the deterministic model:
Figure BDA0002959299420000134
each expression has the same meaning as the previous stage.
Wherein the constraints on the state of charge should instead be:
Figure BDA0002959299420000135
in the formula,
Figure BDA0002959299420000136
respectively represent the SoC constraint upper and lower limits after S3 optimization.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (9)

1. A two-stage coordination optimization method considering the running state of an energy storage system is characterized by comprising the following steps:
s1: obtaining calculation data, performing day-ahead optimization by using prediction data, implementing a demand response strategy based on price, establishing a two-stage interval optimization model considering the decay cost of the storage battery, and dividing variables into a first-stage variable, a second-stage variable and an uncertain variable;
s2: decomposing the interval optimization model in the S1 into a main problem and a sub problem, and solving the main problem and the sub problem in sequence: solving the main problem to obtain an optimal solution of an objective function and optimal solutions of first and second-stage variables; solving the subproblems is divided into two conditions, wherein in one condition, the solution of the uncertain variable when the objective function is minimum is obtained, in the other condition, the solution of the uncertain variable when the objective function is maximum and the maximum value of the objective function are obtained, and the subproblems are alternately solved until the solutions of the main subproblems meet the convergence standard;
s3: optimizing the state of charge constraint of the energy storage element under the extreme condition by using the optimized solution of the first-stage variable obtained in the step S2;
s4: and reading the real-time values of the new energy output and the load data, and performing the second-stage day adjustment by using the state of charge constraint obtained in the S3.
2. The two-stage coordination optimization method considering the operating state of the energy storage system according to claim 1, wherein the S1 specifically includes:
s11, establishing a two-stage interval optimization model as follows:
Figure FDA0002959299410000011
s.t.g(x,y,u)＝0,h(x,y,u)≤0
s12: the decay cost of a battery is represented by the following model:
Figure FDA0002959299410000012
L(DoD)＝A·DoD-B·e-C·DoD
Figure FDA0002959299410000021
3. a method for two-phase coordinated optimization taking into account the operating conditions of an energy storage system according to claim 2, characterized in that F (x, y, u) represents an objective function, i.e. synthesisTotal operating costs of energy system CWT+CPV+CBESS+Cgrid-Crev(ii) a Wherein, CWT，CPVRepresenting the operating and maintenance costs of wind and photovoltaic, respectively, CBESSRepresents the decay cost of the accumulator energy storage system, CgridIndicating the cost of the transaction with the main network, CrevRepresents revenue for selling electricity to the customer after PBDR is implemented; u represents an uncertain variable comprising new energy output and load demand; the remaining variables are divided into two phases: the variable x in the first stage is a PBDR related variable; the second stage variable y is a BESS scheduling related variable, including
Figure FDA0002959299410000022
Figure FDA0002959299410000023
Representing the replacement cost, η, of the accumulatorch/ηdisRespectively representing the charging and discharging efficiency of the battery; A. b and C are coefficients relating to different battery types;
Figure FDA0002959299410000024
is the variation of the charge and discharge power,
Figure FDA0002959299410000025
is the battery capacity.
4. The two-stage coordination optimization method considering the operating state of the energy storage system according to claim 1, wherein the S2 specifically includes:
s21, the main question is expressed as:
Figure FDA0002959299410000026
Figure FDA0002959299410000027
the sub-problem is represented as:
Figure FDA0002959299410000028
Figure FDA0002959299410000029
Figure FDA00029592994100000210
obtaining an objective function value F by solving the main problemM,kAnd optimal solution of first and second stage variables
Figure FDA00029592994100000211
Fix in subproblems
Figure FDA00029592994100000212
And
Figure FDA00029592994100000213
searching the maximum value and the minimum value of the objective function in the prediction interval of the uncertain variable, and respectively corresponding to the worst condition of the uncertain variable
Figure FDA00029592994100000214
And optimal conditions
Figure FDA00029592994100000215
S22, judging whether the algorithm converges according to the following formula:
Figure FDA0002959299410000031
if the values are converged, stopping iteration and obtaining a final first-stage variable value, and turning to S3; if not, go to S23;
and S23, updating the iteration number k to k +1, returning to S21, bringing the solution of the uncertain variable when the objective function is maximum in the subproblem into the main problem, correcting the value of the uncertain variable in the main problem, and continuing the iteration.
5. The method of claim 4, wherein F is a two-stage coordinated optimization method taking into account the operating conditions of the energy storage systemM,kSolutions representing the main problem, Fpes,kRepresenting the solution when the objective function is maximal in the subproblem.
6. The two-stage coordination optimization method considering the operating state of the energy storage system according to claim 1, wherein the S3 specifically includes:
s31, considering the extreme case occurs when the new energy output is minimum and the load demand is maximum; optimal solution for fixed first stage variables
Figure FDA0002959299410000032
Then, optimizing the variable y in the second stage;
s32, optimizing the constraint of the state of charge of the energy storage system, and representing by the following model:
Figure FDA0002959299410000033
Figure FDA0002959299410000034
Figure FDA0002959299410000035
7. a consideration store according to claim 6Method for the two-stage coordinated optimization of the operational state of a system, characterized in that
Figure FDA0002959299410000036
Represents the optimal solution of the variables of the first stage,
Figure FDA0002959299410000037
represents the optimal solution to the first-stage sub-problem,
Figure FDA0002959299410000038
represents the worst solution to the first stage sub-problem,
Figure FDA0002959299410000039
represents the value of the uncertain variable at the minimum of the objective function in the first stage sub-problem,
Figure FDA00029592994100000310
the value of the uncertain variable at the maximum of the objective function in the first stage sub-problem is represented.
8. The two-stage coordination optimization method considering the operating state of the energy storage system according to claim 1, wherein the S4 specifically includes:
s41: the objective function for deterministic optimization is:
Figure FDA0002959299410000041
s42: the constraints on the state of charge of the energy storage element in the model should instead be:
Figure FDA0002959299410000042
9. a method of accounting for energy storage system operating conditions as set forth in claim 8Is characterized in that C is the step ofWT，CPVRepresenting the operating and maintenance costs of wind and photovoltaic, respectively, CrevRepresents revenue for selling electricity to customers after PBDR is implemented, CBESSRepresents the decay cost of the accumulator energy storage system, CgridRepresenting a cost of the transaction with the primary grid;
Figure FDA0002959299410000043
respectively represent the SoC constraint upper and lower limits after S3 optimization.
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