CN109378838B - Multi-energy-storage and user-side load scheduling interval optimization method for wind-solar-energy-storage combined system - Google Patents

Multi-energy-storage and user-side load scheduling interval optimization method for wind-solar-energy-storage combined system Download PDF

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CN109378838B
CN109378838B CN201811206313.7A CN201811206313A CN109378838B CN 109378838 B CN109378838 B CN 109378838B CN 201811206313 A CN201811206313 A CN 201811206313A CN 109378838 B CN109378838 B CN 109378838B
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张慧峰
吴江
岳东
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Nanjing University of Posts and Telecommunications
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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
    • 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
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Abstract

The invention discloses a multi-energy-storage and user-side load scheduling interval optimization method for a wind-solar-energy-storage combined system, and belongs to the technical field of power system automation. According to the characteristics of wind-solar-energy-storage combined operation, the dynamic change of energy storage energy in the operation process is considered, user loads in the micro-grid are divided into adjustable loads and non-adjustable loads, the adjustable loads are classified according to the properties of the adjustable loads, the higher the load grade is, the smaller the adjustable quantity is, the sum of multiple energy storage operation cost and adjustable load compensation cost is taken as a target, the self characteristics of each energy storage unit are taken as constraints, the adjustable load operation constraints are considered, the interval optimization method of the multiple energy storage and user side load scheduling of the wind-solar-energy-storage combined system is provided, the uncertainty of variables is described by using intervals, the advantages and disadvantages of objective function intervals under different interval variables are compared, the optimal load adjustment mode and the optimal interval of energy storage charging and discharging are obtained, and the operation cost of.

Description

Multi-energy-storage and user-side load scheduling interval optimization method for wind-solar-energy-storage combined system
Technical Field
The invention discloses a multi-energy-storage and user-side load scheduling interval optimization method for a wind-solar-energy-storage combined system, and belongs to the technical field of power system automation.
Background
With the large-scale application of intermittent energy such as photovoltaic and wind power, the uncertainty of the power generation process of the intermittent energy has great influence on the operation of a power grid system, the reasonable application of energy storage reduces the comprehensive power generation cost of distributed energy and a microgrid, but the investment and the operation cost of the energy storage are still considerable, and when photovoltaic and fans are used for supplying power, the output peak-valley curve of the photovoltaic and wind power and the load peak-valley curve are difficult to match. With the development of the intelligent power grid and the investment of the remote control equipment, part of the load in the micro-grid has certain adjustable characteristics, the adjustable load can relieve the charging and discharging pressure of the stored energy in the load peak period, and the operating cost of the stored energy is reduced.
The traditional wind-solar-energy-storage combined model optimization algorithm is used for searching a certain point in a solution space to serve as an optimal solution, belongs to point optimization, and when a random optimization method is adopted for point optimization, a probability density distribution function needs to be known, but an accurate probability density function is difficult to obtain; for some complicated industrial processes, the optimal point in the solution space is often difficult to find, at the moment, interval optimization is more suitable to use, and the interval variable replaces the point variable, so that the interference of other uncertain factors except wind-electricity photovoltaic uncertainty in the microgrid can be avoided in a certain precision range, more feasible schemes can be provided for the system, the interval optimization method mainly adopts a two-layer nested conversion method to convert the uncertain model into the deterministic model, and the whole optimization method is more complicated and has larger calculated amount.
Disclosure of Invention
The invention aims to provide a multi-energy-storage and user-side load scheduling interval optimization method of a wind-solar-energy-storage combined system aiming at the defects of the background technology, a wind-solar-energy-storage combined optimization model based on multi-energy-storage is established, uncertainty of variables is described by using intervals, and the advantages and disadvantages of objective function intervals under different interval variables are compared, so that a load regulation mode with optimal operation cost and adjustable load compensation cost and an optimal interval of energy storage and charge and discharge are obtained, and the problems that each energy storage unit has higher charge and discharge operation cost and photovoltaic wind power output is not matched with load are solved.
The invention adopts the following technical scheme for realizing the aim of the invention:
a multi-energy-storage and user-side load scheduling interval optimization method for a wind-solar-energy-storage combined system is characterized by establishing an optimization model of the wind-solar-energy-storage combined system, wherein the optimization model comprises an objective function considering user-side adjustable load compensation cost and user-side adjustable load constraint conditions, converting uncertain variables in the optimization model into interval variables, solving objective function value intervals under different interval variables, and taking a variable interval when the objective function value interval is optimal as an optimal decision variable interval.
Further, in the interval optimization method for multi-energy storage and user side load scheduling of the wind-solar-energy storage combined system, an objective function considering user side adjustable load compensation cost is as follows:
Figure BDA0001831297310000021
wherein T is the length of the dispatching cycle, N is the number of the energy storage units, K is the number of users providing adjustable load,
Figure BDA0001831297310000022
the discharge capacity of the ith energy storage unit and the charge capacity of the jth energy storage unit at the moment t are respectively, i is not equal to j,
Figure BDA0001831297310000023
Figure BDA0001831297310000024
the operation cost of the ith energy storage unit during discharging and the operation cost of the jth energy storage unit during charging are respectively set at the moment t,
Figure BDA0001831297310000025
for the mth user adjustable load change amount at time t,
Figure BDA0001831297310000026
the compensation cost of the adjustable load for the user at the moment t.
Further, in the interval optimization method for multi-energy storage and user side load scheduling of the wind-solar-energy storage combined system, the user side adjustable load constraint conditions are as follows:
Figure BDA0001831297310000027
wherein the content of the first and second substances,
Figure BDA0001831297310000028
rated power for the nth adjustable load of the mth user, nmFor the number of adjustable loads in the mth user,
Figure BDA0001831297310000029
for the adjustment factor of the nth adjustable load in the mth user at time t,
Figure BDA00018312973100000210
indicating that the nth adjustable load among the mth users at time t can be interrupted,
Figure BDA00018312973100000211
indicating that the nth adjustable load of the mth users at time t is not adjusted for the moment,
Figure BDA00018312973100000212
the minimum value and the maximum value of the adjustable load compensation cost are respectively, and s is a time period for adopting load adjustment.
Further, in the interval optimization method for multi-energy storage and user side load scheduling of the wind-solar-energy storage combined system, the optimization model further includes:
and (3) load balance constraint:
Figure BDA0001831297310000031
energy storage charging and discharging restraint:
Figure BDA0001831297310000032
energy storage operation cost constraint:
Figure BDA0001831297310000033
wherein the content of the first and second substances,
Figure BDA0001831297310000034
for the non-adjustable load power at time t,
Figure BDA0001831297310000035
for the adjustable load residual power, P, at time tpv,tFor photovoltaic power generation power at time t, Pw,tFor the power generated by the fan at the moment t,
Figure BDA0001831297310000036
for the charging power or discharging power of the kth energy storage unit at time t,
Figure BDA0001831297310000037
for the total amount of adjustable load on the user side at time t, Ek,min、Ek,maxAre respectively asLowest and highest reserves of the kth energy storage unit, Ek,t-1、Ek,tThe reserves, eta of the kth energy storage unit at the time t-1 and the time t respectivelyD、ηCRespectively the discharge efficiency and the charge efficiency of the energy storage unit,
Figure BDA0001831297310000038
respectively is the minimum value of the charging power of the jth energy storage unit and the minimum value of the discharging power of the ith energy storage unit,
Figure BDA0001831297310000039
respectively the maximum value of the charging power of the jth energy storage unit and the maximum value of the discharging power of the ith energy storage unit,
Figure BDA00018312973100000310
respectively the lowest running cost and the highest running cost when the ith energy storage unit is discharged,
Figure BDA00018312973100000311
and respectively charging the jth energy storage unit with the lowest operation cost and the highest operation cost.
Further, in the interval optimization method for multi-energy storage and user side load scheduling of the wind-solar-energy storage combined system, the method for converting uncertain variables in the optimization model into interval variables is as follows: and accumulating the midpoint of the uncertain variable interval in the radius range of the uncertain variable value interval by considering the tolerance of the decision maker to the uncertain level of the interval number.
Still further, in the interval optimization method for multi-energy storage and user side load scheduling of the wind-solar-energy storage combined system, the following definitions are defined:
Figure BDA00018312973100000312
AI＝<AC,AW>＝{x|AC-AW≤x≤AC+AW}，AL、AR、AC、AWas an uncertain variable AILower bound, upper bound, middle point, radius of value interval, uncertain variable AIValue intervalAny real number x in is represented as AC+(η-1)AWEta is the tolerance of a decision maker to the uncertainty level of the interval number, and the value range of eta is 0-1.
Furthermore, in the interval optimization method for multi-energy storage and user-side load scheduling of the wind-solar-energy storage combined system, after converting uncertain variables in the optimization model into interval variables, an objective function considering user-side adjustable load compensation cost is converted into:
Figure BDA0001831297310000041
wherein the content of the first and second substances,
Figure BDA0001831297310000042
Figure BDA0001831297310000043
the middle point and the radius of the value interval of the discharge electric quantity of the ith energy storage unit at the moment t,
Figure BDA0001831297310000044
the middle point and the radius of the charging electric quantity value interval of the jth energy storage unit at the moment t,
Figure BDA0001831297310000045
and the middle point and the radius of the value interval of the adjustable load variation of the mth user at the moment t.
Furthermore, in the interval optimization method for multi-energy storage and user side load scheduling of the wind-solar-energy storage combined system, after converting uncertain variables in the optimization model into interval variables, user side adjustable load constraint conditions are converted into:
Figure BDA0001831297310000046
wherein the content of the first and second substances,
Figure BDA0001831297310000047
and (3) adjusting the middle point and the radius of a load variation value-taking interval for the mth user at the time t, wherein eta is the tolerance of a decision maker to the uncertainty level of the number of the intervals, and the value-taking range of eta is 0-1.
Furthermore, in the multi-energy-storage-based wind-solar-energy-storage combined interval optimization method, after converting uncertain variables in the optimization model into interval variables, load balance constraint is converted into:
Figure BDA0001831297310000051
wherein the content of the first and second substances,
Figure BDA0001831297310000052
and (3) adjusting the middle point and the radius of a load variation value-taking interval for the mth user at the time t, wherein eta is the tolerance of a decision maker to the uncertainty level of the number of the intervals, and the value-taking range of eta is 0-1.
By adopting the technical scheme, the invention has the following beneficial effects: aiming at the problems that each energy storage unit has high charge and discharge operation cost and photovoltaic wind power output is not matched with load, a wind-solar-energy storage combined optimization model based on multiple energy storages is established, the output of intermittent energy is regarded as interval number, the charge and discharge amount and the adjustable load amount of the energy storages are regarded as interval variables, an uncertain optimization model in a microgrid can be simply and conveniently converted into a deterministic optimization model by using an interval sequence relation based on the uncertainty level tolerance of a decision maker to the interval number, an excitation compensation mechanism is adopted for the adjustable load, the charge and discharge amount and the user adjustable load amount of each energy storage unit are adjusted by using an interval optimization algorithm to realize the optimization of the charge and discharge cost and the adjustable load compensation cost of the energy storage, and the optimal interval of the charge and discharge amount and the optimal interval of the adjustable load amount of each energy storage unit are obtained under the condition that the operation cost of, and solving the optimal value meeting the optimization target by using a small calculation amount.
Drawings
Fig. 1 is a schematic diagram of interval optimization of multiple energy storage and user-side load scheduling of a wind-solar-energy storage combined system.
Detailed Description
The technical solution of the invention is explained in detail with reference to fig. 1. The interval optimization method for multi-energy storage and user side load scheduling of the wind-solar-energy storage combined system optimizes the energy storage charging and discharging cost and the adjustable load compensation cost by adjusting the charging and discharging electric quantity of each energy storage unit and the adjustable load quantity of a user, and further realizes the wind-solar-energy storage combined optimization scheme based on multi-energy storage.
Establishing an integral combined optimization model of the multi-energy storage system according to the characteristic that the micro-grid energy storage system has combined complementation:
(1) optimizing the target:
Figure BDA0001831297310000053
wherein T is the length of the scheduling period,
Figure BDA0001831297310000054
the ith energy storage discharge electric quantity and the jth energy storage charge electric quantity are respectively at the moment t, i is not equal to j, and i is not equal to j represents that the energy storage cannot execute a discharge task during charging;
Figure BDA0001831297310000055
the operation cost of the ith energy storage unit during discharging and the operation cost of the jth energy storage unit during charging are respectively set at the moment t;
Figure BDA0001831297310000061
the load variable quantity is adjustable for the mth user at the moment t;
Figure BDA0001831297310000062
and the compensation cost of the adjustable load of the user at the moment t, the number of the users capable of providing the adjustable load is K, and the number of the energy storage units is N.
(2) Constraint conditions are as follows:
load balancing constraint:
Figure BDA0001831297310000063
wherein the content of the first and second substances,
Figure BDA0001831297310000064
for the non-adjustable load power at time t,
Figure BDA0001831297310000065
for the adjustable load residual power, P, at time tpv,tFor photovoltaic power generation power at time t, Pw,tFor the power generated by the fan at the moment t,
Figure BDA0001831297310000066
for the kth stored energy charging power or discharging power at time t,
Figure BDA0001831297310000067
the total amount of the adjustable load of the user side at the time t;
energy storage charging and discharging constraint:
Figure BDA0001831297310000068
the investment cost of energy storage is very high, when the energy storage is in deep discharge or charge all the time, the service life of the energy storage can be greatly reduced, so the charge and discharge amount of the energy storage is restrained, wherein, considering that the charge and discharge properties of each energy storage unit are different, the charge and discharge restraint amount is respectively expressed, Ek,T＝Ek,0Indicating that the reserve balance is maintained at the beginning and the end of the energy storage period of the kth storage battery, Ek,min、Ek,maxRespectively the lowest and highest reserves of the kth stored energy, Ek,t-1、Ek,tRespectively storing the k-th stored energy at the moment t-1 and the moment t, etaD、ηCRespectively the discharge efficiency and the charging efficiency of the energy storage unit,
Figure BDA0001831297310000069
respectively is the minimum value of the jth energy storage charging power and the ith energy storage discharging power,
Figure BDA00018312973100000610
respectively setting the maximum value of the jth energy storage charging power and the maximum value of the ith energy storage discharging power;
energy storage operation cost constraint:
Figure BDA00018312973100000611
wherein the content of the first and second substances,
Figure BDA00018312973100000612
respectively the lowest and highest operating costs for discharging the ith energy storage unit,
Figure BDA00018312973100000613
Figure BDA0001831297310000071
respectively charging the lowest and highest operation costs of the jth energy storage unit;
adjustable load restraint:
Figure BDA0001831297310000072
wherein the content of the first and second substances,
Figure BDA0001831297310000073
is the rated power of the nth adjustable load in the mth user, nmIndicating the number of adjustable loads in the mth user,
Figure BDA0001831297310000074
is the adjustment coefficient of the nth adjustable load in the mth user at the moment t, the higher the user adjustable load grade is,
Figure BDA0001831297310000075
the larger the value is,
Figure BDA0001831297310000076
indicating that the nth adjustable load among the mth users at time t can be interrupted, in order to not affect the user experience as much as possible,
Figure BDA0001831297310000077
indicating that the nth adjustable load of the mth users at time t is not adjusted for the moment,
Figure BDA0001831297310000078
respectively, the minimum value and the maximum value of the adjustable load compensation cost, and s is the load adjustment which can be adoptedA period of time.
And (II) obtaining the optimal scheduling state between the microgrid and the multiple energy storages through optimization model conversion based on interval uncertainty, so that the energy storage operation cost is minimum and the microgrid economic benefit is maximum:
the model is very reasonable to analyze by adopting an interval uncertainty optimization method, the value range of the uncertain variable is relatively easy to know in the decision of an actual system, and the required uncertain information is greatly reduced. For uncertain information, the boundary is often determined, unknown bounded parameter variables are described according to upper and lower boundary information, and the conversion from uncertain target functions to deterministic target function optimization problems is realized. The following is a description of the contents involved in model conversion.
(1) Two different definitions of the number of intervals:
Figure BDA0001831297310000079
AI＝<AC,AW>＝{x|AC-AW≤x≤AC+AW} (7)，
wherein A isI、AL、ARRespectively representing an interval, an interval lower bound and an interval upper bound, AC、AWThe middle point and the radius of the interval are respectively shown, the middle point of the interval is used for describing the overall function of the system, the radius of the interval is used for describing the deviation degree of the upper and lower boundaries of the interval from the middle point of the interval, and the interval number of the energy storage charging and discharging amount and the interval number of the adjustable load amount are described by using the expression mode of the middle point and the radius of the interval.
(2) Interval order relationship:
in the optimization method based on the interval uncertainty, the interval sequence relation is used for judging whether one interval is superior or inferior to another interval, and the optimal interval of the charge and discharge capacity of each energy storage unit can be judged by adopting the interval sequence relation. The formula for comparing the number of intervals is introduced here:
AC+(η-1)AW＜BC+(η-1)BW(8)，
wherein eta is the tolerance of a decision maker (an actual system, namely the microgrid) to the uncertainty level of the interval number, the eta value range is 0-1, the greater the eta value, the more preference of the decision maker to the midpoint of the interval number is, and the less preference to the radius is, and the formula (8) is that the interval A is judgedIIs superior to interval BIIs a sufficient requirement.
(3) And (3) converting an interval optimization model:
conversion of an objective function:
Figure BDA0001831297310000081
inequality constraint conversion containing interval variables:
adjustable load restraint:
Figure BDA0001831297310000082
③ equality constraint conversion containing interval variables:
and (3) converting uncertain equality constraints into inequality constraints for processing:
and (3) load balance constraint:
Figure BDA0001831297310000083
adjustable load restraint:
Figure BDA0001831297310000084
after the inequality constraint is converted, the further processing mode is the same as the conversion of the uncertain inequality constraint:
and (3) load balance constraint:
Figure BDA0001831297310000091
adjustable load restraint:
Figure BDA0001831297310000092
(4) solving an interval model:
when the microgrid is in a load electricity utilization peak moment, the stored energy is in a discharge state, if the microgrid is in a load electricity utilization peak moment
Figure BDA0001831297310000093
The microgrid can selectively reduce the energy storage discharge capacity and increase the adjustable load capacity, namely
Figure BDA0001831297310000094
The number of the grooves is reduced, and the,
Figure BDA0001831297310000095
and (c) increasing the rate of, among others,
Figure BDA0001831297310000096
is time-varying if
Figure BDA0001831297310000097
Increasing user load and adjustable load capacity
Figure BDA0001831297310000098
The economic operation cost of the microgrid is increased, at the moment, the energy storage and discharge electric quantity can be increased, the adjustable quantity of the load of a user is reduced, and therefore the operation cost of the microgrid is reduced.
When the microgrid is in a load power utilization valley, if the stored energy is in a charging state and the stored energy electric quantity is sufficient, the stored energy charging cost is high, the adjustable load of a user can be selected not to be adjusted as much as possible, the stored energy charging cost is reduced as much as possible, the electric energy generated by the photovoltaic and the fan is used by the user, and more economic benefits are brought to the microgrid.
And (3) considering various conditions between the energy storage and the user adjustable load which possibly occur in the model, and optimizing the charge and discharge electric quantity of each energy storage unit and the user adjustable load quantity by comparing the advantages and disadvantages of the target function interval by using an interval optimization algorithm, so that the sum of the energy storage charge and discharge cost and the adjustable load compensation cost is the lowest. In the process of solving the interval model, the following interval number operation and interval comparison methods are used:
calculating an interval number:
interval number and scalar multiplication:
Figure BDA0001831297310000099
addition and subtraction operation among interval numbers:
Figure BDA00018312973100000910
multiplication between interval numbers:
Figure BDA00018312973100000911
interval comparison:
according to the formula for comparing the number of intervals: a. theC+(η-1)AW＜BC+(η-1)BWJudging whether the interval is good or bad:
if AW＞BWAnd A isC＜BC，AIIs superior to BI；
If AW＜BWAnd A isC＜BC，AIIs superior to BI；
If AW＜BWAnd A isC＞BC，AIIs superior to BI。
In the function optimization process, the advantages and disadvantages of the target function intervals under different interval variables are compared, so that the optimal decision variable interval, namely the variable interval when the value interval of the target function is optimal, is found. The method utilizes the interval boundary to realize the deterministic transformation of the operating parameters of the multi-energy-storage system, realizes the optimal configuration of the energy storage capacity, and utilizes the interval sequence relation to realize the solution of the wind-light-storage combined optimization model based on the multi-energy-storage. In interaction between the microgrid and the stored energy, the economic operation cost of the microgrid is minimized by adjusting the charging and discharging electric quantity of each energy storage unit and the adjustable load quantity of a user, optimizing the charging and discharging cost of the stored energy and adjusting the load compensation cost.

Claims (5)

1. A multi-energy-storage and user-side load scheduling interval optimization method of a wind-light-storage combined system is characterized in that,
establishing an optimization model of the wind-solar-energy-storage combined system, wherein the optimization model comprises an objective function considering user-side adjustable load compensation cost, user-side adjustable load constraint, load balance constraint, energy storage charging and discharging constraint and energy storage operation cost constraint;
considering the tolerance of a decision maker to the uncertain level of the interval number, converting the uncertain variable in the optimization model into the interval variable in a mode of accumulating the midpoint of the uncertain variable interval in the radius range of the uncertain variable value interval, and then converting an objective function considering the adjustable load compensation cost of the user side into the following steps:
Figure FDA0002635491990000011
the user side adjustable load constraint condition is converted into:
Figure FDA0002635491990000012
the load balancing constraint translates into:
Figure FDA0002635491990000013
wherein T is the length of the dispatching cycle, N is the number of the energy storage units, K is the number of users providing adjustable load,
Figure FDA0002635491990000014
the operation cost of the ith energy storage unit during discharging and the operation cost of the jth energy storage unit during charging are respectively set at the moment t,
Figure FDA0002635491990000015
for the compensation cost of the user adjustable load at time t,
Figure FDA0002635491990000016
rated power for the nth adjustable load of the mth user, nmFor the number of adjustable loads in the mth user,
Figure FDA0002635491990000017
for the adjustment factor of the nth adjustable load in the mth user at time t,
Figure FDA0002635491990000018
indicating that the nth adjustable load among the mth users at time t can be interrupted,
Figure FDA0002635491990000019
indicating that the nth adjustable load of the mth users at time t is not adjusted for the moment,
Figure FDA00026354919900000110
the middle point and the radius of the value interval of the discharge electric quantity of the ith energy storage unit at the moment t,
Figure FDA00026354919900000111
the middle point and the radius of the charging electric quantity value interval of the jth energy storage unit at the moment t,
Figure FDA0002635491990000021
the middle point and the radius of a variable value interval of the adjustable load of the mth user at the time t, eta is the tolerance of a decision maker to the uncertainty level of the number of intervals, the value range of eta is 0-1, s is the time period of adopting load adjustment, and P is the time period of adopting load adjustmentt adjFor the user side adjustable load residual power at time t,
Figure FDA0002635491990000022
the total amount of the adjustable load of the user side at the time t;
and solving the value intervals of the objective function under the variables in different intervals, and taking the variable interval when the value interval of the objective function is optimal as the optimal decision variable interval.
2. The interval optimization method for multi-energy storage and user-side load scheduling of the wind-solar-energy storage combined system according to claim 1, wherein an objective function considering user-side adjustable load compensation cost is as follows:
Figure FDA0002635491990000023
wherein the content of the first and second substances,
Figure FDA0002635491990000024
Figure FDA0002635491990000025
the discharge capacity of the ith energy storage unit and the charge capacity of the jth energy storage unit at the moment t are respectively, i is not equal to j,
Figure FDA0002635491990000026
the load variation is adjustable for the mth user at time t.
3. The interval optimization method for multi-energy storage and user side load scheduling of the wind, light and energy storage combined system according to claim 1, wherein the user side adjustable load constraint condition is as follows:
Figure FDA0002635491990000027
wherein the content of the first and second substances,
Figure FDA0002635491990000028
the minimum value and the maximum value of the adjustable load compensation cost are respectively, and s is a time period for adopting load adjustment.
4. The interval optimization method for multi-energy storage and user-side load scheduling of the wind, photovoltaic and energy storage combined system according to claim 1, wherein the optimization model further comprises:
and (3) load balance constraint:
Figure FDA0002635491990000029
energy storage charging and discharging restraint:
Figure FDA0002635491990000031
energy storage operation cost constraint:
Figure FDA0002635491990000032
wherein, Pt loadPower of non-adjustable load at time t, Pt adjFor the adjustable load residual power, P, at time tpv,tFor photovoltaic power generation power at time t, Pw,tFor the power generated by the fan at the moment t,
Figure FDA0002635491990000033
for the charging power or discharging power of the kth energy storage unit at time t,
Figure FDA0002635491990000034
for the total amount of adjustable load on the user side at time t, Ek,min、Ek,maxRespectively the lowest and highest reserves of the kth energy storage unit, Ek,t-1、Ek,tThe reserves, eta of the kth energy storage unit at the time t-1 and the time t respectivelyD、ηCRespectively the discharge efficiency and the charge efficiency of the energy storage unit,
Figure FDA0002635491990000035
respectively is the minimum value of the charging power of the jth energy storage unit and the minimum value of the discharging power of the ith energy storage unit,
Figure FDA0002635491990000036
respectively the maximum value of the charging power of the jth energy storage unit and the maximum value of the discharging power of the ith energy storage unit,
Figure FDA0002635491990000037
respectively the lowest running cost and the highest running cost when the ith energy storage unit is discharged,
Figure FDA0002635491990000038
lowest operation cost and highest operation when charging the jth energy storage unit respectivelyAnd (4) cost.
5. The interval optimization method for multi-energy storage and user side load scheduling of the wind-solar-energy storage combined system according to claim 1, wherein considering the tolerance of a decision maker to the uncertainty level of the number of intervals, the way of accumulating the midpoint of the uncertainty variable interval within the radius range of the uncertainty variable value interval is as follows: defining:
Figure FDA0002635491990000039
AL、AR、AC、AWas an uncertain variable AILower bound, upper bound, middle point, radius of value interval, uncertain variable AIAny real number x in the value interval is expressed as AC+(η-1)AWEta is the tolerance of a decision maker to the uncertainty level of the interval number, and the value range of eta is 0-1.
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