CN113378374A - Optimal configuration method for park comprehensive energy system - Google Patents

Optimal configuration method for park comprehensive energy system Download PDF

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CN113378374A
CN113378374A CN202110635079.5A CN202110635079A CN113378374A CN 113378374 A CN113378374 A CN 113378374A CN 202110635079 A CN202110635079 A CN 202110635079A CN 113378374 A CN113378374 A CN 113378374A
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李妍
王球
陈昌铭
王青山
王鑫
李泽森
张群
诸晓骏
王琼
黄亦昕
林振智
杨莉
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Zhejiang University ZJU
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses an optimal configuration method of a park comprehensive energy system, which comprises the following steps: constructing a construction time sequence set of the park comprehensive energy system; based on the established construction time sequence set, constructing a park comprehensive energy system double-layer optimization configuration model by adopting an optimal construction time sequence method and a cloud energy storage mechanism; and solving the double-layer optimization configuration model of the park comprehensive energy system based on the KKT condition and a large M method to obtain an optimal configuration scheme of the park comprehensive energy system.

Description

Optimal configuration method for park comprehensive energy system
The technical field is as follows:
the invention belongs to the field of power systems, and particularly relates to an optimal configuration method of a park comprehensive energy system.
Background art:
with the increasing exhaustion of traditional fossil energy, the development of multi-energy complementation and integrated optimization technology is urgently needed to improve the utilization efficiency of energy. A park-integrated energy system (PIES) is one of typical applications of multi-energy coupling and supply at a user side, which can improve the renewable energy consumption rate and the energy utilization efficiency, and the optimal configuration of the system is widely concerned by scholars at home and abroad.
The existing PIES planning strategy mostly assumes that the load level of the PIES is unchanged in the whole planning period, and the equipment to be put into operation is put into operation once in the initial stage of the construction, which may cause resource waste due to the idle equipment in the initial stage of the construction of the PIES, and increase the load in the later stage of the construction of the PIES, but the equipment is aged and the capacity is insufficient. Aiming at the problems, a construction time sequence can be considered in PIES planning, a planning period is divided into a plurality of stages, and equipment investment is increased at the initial stage of each planning stage so as to meet the requirement of load increase in the whole planning period. However, different construction timing schemes have a certain influence on the optimal configuration result of the PIES.
For small-scale integrated energy systems such as PIES, investing in small-capacity solid energy storage requires high unit investment costs. In addition, the energy storage device is often configured to meet the peak demand of energy consumption, and may be idle during the peak period of non-energy consumption, resulting in waste of resources. In recent years, with the development of shared economy, the concept of Cloud Energy Storage (CES) has been proposed, and a new solution is brought to solve the above problems. The cloud energy storage means that energy storage devices originally dispersed on a user side are concentrated on a cloud end, and the virtual energy storage capacity of the cloud end is used for replacing entity energy storage of the user side, so that the user does not have difference between the cloud energy storage and the entity energy storage. The cloud energy storage equipment is built by a cloud energy storage provider, and is mainly supported by large-scale energy storage equipment and assisted by distributed energy storage resources. Cloud energy storage providers may benefit by providing distributed energy storage services for a large number of users. Cloud energy storage is an effective application means of shared economy in the energy field, and the introduction of the cloud energy storage into PIES (particle image energy storage) optimization configuration can reduce higher investment cost and maintenance cost brought by the construction of small-scale entity energy storage equipment, and can also reduce resource waste caused by the fact that entity energy storage is idle in an idle energy peak period.
The invention content is as follows:
the invention mainly solves the technical problem of providing an optimal configuration method of a park comprehensive energy system by adopting an optimal construction time sequence method and a cloud energy storage mechanism.
The technical scheme of the invention is as follows:
a park integrated energy system optimal configuration method comprises the following steps:
constructing a construction time sequence set of the park comprehensive energy system;
based on the established construction time sequence set, constructing a park comprehensive energy system double-layer optimization configuration model by adopting an optimal construction time sequence method and a cloud energy storage mechanism;
and solving the double-layer optimization configuration model of the park comprehensive energy system based on the KKT condition and a large M method to obtain an optimal configuration scheme of the park comprehensive energy system.
Preferably, the first and second electrodes are formed of a metal,and the construction time sequence set of the park comprehensive energy system is recorded as omegaPIESThe method comprises a plurality of construction phase sets formed by dividing a planning period of the park integrated energy system, wherein the total number of years and the divided construction phase number in the planning period of the park integrated energy system are respectively NyAnd NsThe number of the construction time sequences of the park comprehensive energy system is NSCE。
Preferably, the park integrated energy system double-layer optimization configuration model comprises an upper-layer park integrated energy system optimization configuration model and a lower-layer park integrated energy system optimization operation model.
Preferably, based on the established construction time sequence set, an optimal construction time sequence method and a cloud energy storage mechanism are adopted to construct a double-layer optimization configuration model of the park comprehensive energy system, and the method comprises the following steps:
under the construction time sequence scene s, the objective function of the optimization configuration model of the upper-layer park integrated energy system is the life cycle cost of the minimum park integrated energy system
Figure BDA0003104908340000021
As follows:
Figure BDA0003104908340000022
Figure BDA0003104908340000023
in the formula, the superscript s represents the s-th construction time sequence scene, the same applies below;
Figure BDA0003104908340000024
and
Figure BDA0003104908340000025
respectively the total construction cost and the total operation cost of the park comprehensive energy system,
Figure BDA0003104908340000026
the method is an objective function of an optimized operation model of the comprehensive energy system of the lower park;
Figure BDA0003104908340000027
and
Figure BDA0003104908340000028
respectively setting up equipment investment cost, cloud energy storage lease cost and equipment residual value of the park comprehensive energy system;
Figure BDA0003104908340000029
and
Figure BDA00031049083400000210
decision variable sets of an upper layer model and a lower layer model are respectively set;
equipment commissioning cost of park integrated energy system
Figure BDA00031049083400000211
Expressed as:
Figure BDA00031049083400000212
Figure BDA0003104908340000031
in the formula, NdevThe number of equipment to be built is counted;
Figure BDA0003104908340000032
the investment cost of the ith equipment;
Figure BDA0003104908340000033
the cost is built for the unit capacity of the ith equipment;
Figure BDA0003104908340000034
newly building capacity for the ith equipment in the jth construction stage; r is the discount rate; y iss,jIs the jthInitial years of the construction phase;
cloud energy storage lease cost
Figure BDA0003104908340000035
Relating to the lease capacity and the lease power of the cloud energy storage, the method is expressed as follows:
Figure BDA0003104908340000036
in the formula, the superscript y represents the y year of the construction stage of the park comprehensive energy system, which is the same as the following;
Figure BDA0003104908340000037
and
Figure BDA0003104908340000038
the method comprises the following steps that a lease capacity matrix and a lease power matrix of the cloud energy storage device are respectively provided, upper marks M and P respectively represent capacity and power, upper marks E and H respectively represent the cloud energy storage and the cloud heat storage device, and the same is carried out below;
Figure BDA0003104908340000039
and
Figure BDA00031049083400000310
respectively representing a unit capacity and a unit power lease cost matrix of the cloud energy storage device;
according to the average method of age, the residual value of the equipment of the park comprehensive energy system
Figure BDA00031049083400000311
Expressed as:
Figure BDA00031049083400000312
in the formula, Ts,iThe total number of years for the ith equipment to run from the time of commissioning to the time of the end of the planning period; gamma rayiThe net residual value rate of the ith equipment;
the constraint conditions of the upper park comprehensive energy system optimization configuration model comprise the upper limit constraint of the built-in capacity of all equipment, and are as follows:
Figure BDA00031049083400000313
in the formula (I), the compound is shown in the specification,
Figure BDA00031049083400000314
establishing an upper capacity limit for the ith equipment;
decision variables of upper park comprehensive energy system optimization configuration model
Figure BDA00031049083400000315
Including commissioning device capacity configuration scheme matrix
Figure BDA00031049083400000316
Cloud energy storage capacity lease scheme matrix
Figure BDA00031049083400000317
And cloud energy storage power lease scheme matrix
Figure BDA0003104908340000041
Wherein
Figure BDA0003104908340000042
Figure BDA0003104908340000043
A capacity matrix representing equipment i put into operation at the initial stage of each construction stage of the park integrated energy system;
Figure BDA0003104908340000044
wherein
Figure BDA0003104908340000045
And
Figure BDA0003104908340000046
respectively expressed in the parkThe method comprises the steps that a system plans a lease capacity matrix of a cloud electricity storage device and a cloud heat storage device in each year in a period;
Figure BDA0003104908340000047
wherein
Figure BDA0003104908340000048
And
Figure BDA0003104908340000049
respectively representing lease power matrixes of the cloud electricity storage device and the cloud heat storage device in each year in a planning period of the park comprehensive energy system;
under the construction time sequence scene s, the objective function of the optimized operation model of the lower-layer park integrated energy system based on the stochastic programming method is used for minimizing the total operation and maintenance cost of the park integrated energy system
Figure BDA00031049083400000410
As follows:
Figure BDA00031049083400000411
Figure BDA00031049083400000412
in the formula, the superscript n represents the nth typical daily operation scene, the same applies below;
Figure BDA00031049083400000413
the daily energy purchase cost for the park integrated energy system;
Figure BDA00031049083400000414
the cloud energy storage daily operating cost of the park comprehensive energy system;
Figure BDA00031049083400000415
the daily maintenance cost of the equipment of the park comprehensive energy system;
daily energy purchase cost of park integrated energy system
Figure BDA00031049083400000416
Expressed as:
Figure BDA00031049083400000417
in the formula (I), the compound is shown in the specification,
Figure BDA00031049083400000418
and
Figure BDA00031049083400000419
the price of electricity and the price of natural gas at the t moment are respectively;
Figure BDA00031049083400000420
and
Figure BDA00031049083400000421
electric power purchased from the EUC and natural gas purchased from the NGUC by the campus integrated energy system at the time t, respectively; t and delta T are the number of scheduling moments in one day and the time interval of adjacent scheduling moments respectively;
cloud energy storage daily operating cost of park comprehensive energy system
Figure BDA00031049083400000422
Expressed as:
Figure BDA00031049083400000423
Figure BDA0003104908340000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003104908340000052
charging power of the cloud power storage device purchased to the EUC at the time t;
Figure BDA0003104908340000053
and
Figure BDA0003104908340000054
the total charging power of the cloud electricity storage device at the t moment and the PV generating power of the garden PV stored in the cloud electricity storage device are respectively set;
daily maintenance cost of equipment of park integrated energy system
Figure BDA0003104908340000055
Expressed as:
Figure BDA0003104908340000056
in the formula (I), the compound is shown in the specification,
Figure BDA0003104908340000057
the unit maintenance cost of the ith equipment; ps,i,y,n,tThe output of the ith equipment at the t moment; the constraint conditions of the comprehensive energy system optimized operation model of the lower park comprise electric power balance constraint, thermal power balance constraint, natural gas balance constraint, energy coupling equipment operation constraint and cloud energy storage equipment operation constraint;
decision variables of lower-layer park comprehensive energy system optimization operation model
Figure BDA0003104908340000058
Wherein
Figure BDA0003104908340000059
And
Figure BDA00031049083400000510
respectively obtaining optimal operation strategy matrixes of equipment to be built and cloud energy storage in the park comprehensive energy system;
Figure BDA00031049083400000511
wherein
Figure BDA00031049083400000512
Figure BDA00031049083400000513
Representing an operation strategy matrix of the equipment i at each moment in each typical day operation scene every year;
Figure BDA00031049083400000514
wherein
Figure BDA00031049083400000515
And
Figure BDA00031049083400000516
respectively representing charge and discharge strategy matrixes of the cloud electricity storage device and the cloud heat storage device at each moment in each typical daily operation scene of each year;
optimizing configuration model of upper park comprehensive energy system for determining the above decision variables
Figure BDA00031049083400000517
The value of the central data is transmitted to an optimized operation model of the lower-layer park integrated energy system, and the lower layer solves the optimal operation strategy of the park integrated energy system according to the configuration scheme of the upper-layer park integrated energy system
Figure BDA00031049083400000518
And the minimum operation and maintenance cost of the park comprehensive energy system
Figure BDA00031049083400000519
Feedback to the upper layer, thereby obtaining the life cycle cost of the park comprehensive energy system
Figure BDA00031049083400000520
Decision variables transmitted by the upper layer model after the optimization operation model of the comprehensive energy system of the lower layer park receives
Figure BDA00031049083400000521
Then, processing uncertainty of photovoltaic output and load prediction in the park comprehensive energy system by using a random planning method; firstly, a typical scene generation method is used for generating N of each yeardIndividual photovoltaic and load typical daily operating scenarios and their corresponding probabilities; then, solving the optimized operation model of the lower-layer park integrated energy system under each typical daily operation scene every year to obtain the optimized operation strategy of the park integrated energy system
Figure BDA0003104908340000061
And operating maintenance costs
Figure BDA0003104908340000062
Finally, for each typical daily operating scenario of each year
Figure BDA0003104908340000063
According to the corresponding probability ps,y,nWeighting to obtain the total operation and maintenance cost of the park comprehensive energy system
Figure BDA0003104908340000064
And feeding back the data to the comprehensive energy system optimal configuration model of the upper park so as to complete one iteration.
Preferably, the solving of the double-layer optimization configuration model of the park integrated energy system based on the KKT condition and the large M method to obtain the optimal configuration scheme of the park integrated energy system includes:
firstly, a general expression of a double-layer optimization model of the park comprehensive energy system is given, namely:
Figure BDA0003104908340000065
ql∈argminf(qu,ql)
F(qu,ql):B(qu,ql)≤0,D(qu,ql)＝0
f(qu,ql):b(qu,ql)≤0,d(qu,ql)＝0
wherein F (q)u,ql) And f (q)u,ql) Respectively are the target functions of an upper layer model and a lower layer model; q. q.suDecision variables for upper layer models, which correspond to the invention
Figure BDA0003104908340000066
qlIs a decision variable of the underlying model,
Figure BDA0003104908340000067
B(qu,ql) And D (q)u,ql) Respectively an inequality constraint set and an equality constraint set of the upper model; b (q)u,ql) And d (q)u,ql) Respectively an inequality constraint set and an equality constraint set of the lower layer model;
then, constructing a Lagrange function of the comprehensive energy system optimization operation model of the lower park, namely:
Figure BDA0003104908340000068
wherein λ ═ λiJ (I ═ 1,2, …, I) and μ ═ μjJ (1, 2, …, J) are lagrangian multiplier sets of inequality constraints and equality constraints in the lower model, respectively, and I and J are the number of inequality constraints and equality constraints in the lower model, respectively;
according to the Lagrange function and the KKT condition, the lower layer model is converted into an additional constraint condition of the upper layer model, so that the park comprehensive energy system double-layer optimization configuration model is converted into a single-layer park comprehensive energy system optimization configuration model, namely:
Figure BDA0003104908340000071
s.t.B(qu,ql)≤0,D(qu,ql)＝0
b(qu,ql)≤0,d(qu,ql)＝0
Figure BDA0003104908340000072
λi≥0,i＝1,2,...,I
λibi(qu,ql)＝0,i＝1,2,...,I
the optimal configuration model of the single-layer park comprehensive energy system obtained based on the KKT condition comprises a nonlinear constraint condition; the nonlinear constraint condition is linearized by applying a large M method, and the result after linearization is shown as follows;
Figure BDA0003104908340000073
Figure BDA0003104908340000074
Figure BDA0003104908340000075
Figure BDA0003104908340000076
0≤λi≤MBi,i＝1,2,...,I
0≤bi(qu,ql)≤M(1-Bi),i＝1,2,...,I
wherein M is a constant; b isiIs an auxiliary binary variable;
modeling the linearized model on a Matlab platform by using a Yalmip tool box, and calling a Gurobi solver to solve, so that the optimal configuration scheme of the park comprehensive energy system can be obtained.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a park comprehensive energy system optimal configuration method by adopting an optimal construction time sequence method and a cloud energy storage mechanism, wherein a double-layer optimal configuration model is converted into a single-layer mixed integer linear programming model by applying a KKT condition and a large M method, a Gurobi solver is called for solving the model under each construction time sequence scene, and the optimal construction time sequence of PIES is screened out according to economic indexes, so that the optimal configuration scheme and the operation strategy of the PIES under the optimal construction time sequence are obtained.
The PIES optimal configuration model provided by the invention considers the optimal construction time sequence, and has higher PIES planning economy and equipment utilization rate compared with a model based on a construction time sequence clustering partition method and a model based on a construction time sequence average partition method.
The PIES optimal configuration model provided by the invention introduces cloud energy storage in PIES planning, and has lower PIES life cycle cost compared with a model for building entity energy storage.
Description of the drawings:
FIG. 1 is a schematic overall flow chart of the present invention.
Figure 2 is a block diagram of a park energy system.
Fig. 3 is a diagram of an operation scenario of the integrated energy system.
Fig. 4 is a diagram of the maximum power increase of the electrical and thermal loads in the planning period.
Fig. 5 is a cloud energy storage rental capacity and rental power diagram.
The specific implementation mode is as follows:
in order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The first embodiment is as follows:
the drawings are for illustrative purposes only and are not to be construed as limiting the patent; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
The invention provides a campus-based comprehensive energy system optimal configuration method, as shown in fig. 1, the implementation flow of the optimal configuration method comprises the following detailed steps:
step 1, constructing a building time sequence set of park-level integrated energy systems (PIES).
Let NyAnd NsRespectively the total number of years and the number of divided construction stages contained in the planning period; omegaPIESA set of construction sequences for PIES, wherein the number of PIES construction sequences contained therein is NSCE. The invention converts N intoyThe method is set according to the shortest service life of the equipment to be put into operation so as to avoid the situation that the equipment is replaced in a planning period. N is a radical ofSCENumber of construction stages NsRelated to, and NsDepending on the rate of load increase in the PIES planning cycle, i.e. the period of time during which the load increases faster, the number of phase divisions should be larger, whereas it should be smaller, NsThe larger the value is, the finer the construction time sequence division is, but the longer the corresponding model solving time is. For the fineness of the time sequence division of the comprehensive construction and the model solving efficiency, taking N s3. N is illustrated belows3 th omegaPIESMethod for determining (A) and (N)SCEThe calculating method of (2):
the years of the three stages of the PIES plan are respectively set as
Figure BDA0003104908340000091
And
Figure BDA0003104908340000092
PIES plans the number of years in the first phase
Figure BDA0003104908340000093
In that
Figure BDA0003104908340000094
If determined, the PIES plans the number of years in the second phase
Figure BDA0003104908340000095
In that
Figure BDA0003104908340000096
And
Figure BDA0003104908340000097
all determined, the PIES plans the number of years of the third phase
Figure BDA0003104908340000098
From the above analysis, the set of construction timings Ω of PIESPIESBy
Figure BDA0003104908340000099
And
Figure BDA00031049083400000910
sequentially arranged and combined to form a building time sequence set, wherein the number N of PIES building time sequences in the building time sequence setSCECan be obtained by summing the series of arithmetic differences:
Figure BDA00031049083400000911
step 2, constructing a park comprehensive energy system double-layer optimization configuration model by adopting an optimal construction time sequence method and a cloud energy storage mechanism based on the established construction time sequence set;
the PIES double-layer optimization configuration model considering the optimal construction time sequence method and the cloud energy storage mechanism comprises an upper-layer PIES optimization configuration model and a lower-layer PIES optimization operation model, wherein the optimal construction time sequence is in omegaPIESThe construction schedule minimizes the overall life cycle cost of the PIES project.
After considering the optimal construction timing sequence of PIES, the method is applied to omegaPIESSolving the PIES double-layer optimization configuration model under each construction time sequence scene to obtain NSCEOptimizing a configuration scheme and an operation strategy for the PIES; to NSCESorting the life cycle cost corresponding to the PIES optimal configuration scheme and the operation strategy, and selecting the total life cycle costAnd taking the construction time sequence corresponding to the PIES optimal configuration scheme and the operation strategy with the minimum life cycle cost as the PIES optimal construction time sequence, wherein the energy conversion equipment capacity configuration scheme, the cloud energy storage leasing scheme and the equipment operation strategy under the optimal construction time sequence are the optimal configuration scheme and the operation strategy of the PIES.
Under the condition of constructing a time sequence scene s, the objective function of the upper-layer PIES optimization configuration model is to minimize the cost of the PIES full life cycle
Figure BDA00031049083400000912
As follows:
Figure BDA00031049083400000913
Figure BDA00031049083400000914
in the formula, the superscript s represents the s-th construction time sequence scene, the same applies below;
Figure BDA00031049083400000915
and
Figure BDA00031049083400000916
the total commissioning cost and the total operating cost of the PIES respectively,
Figure BDA00031049083400000917
is an objective function of a lower-layer PIES optimization operation model;
Figure BDA00031049083400000918
and
Figure BDA0003104908340000101
respectively setting up equipment investment cost, cloud energy storage leasing cost and equipment residual value for the PIES;
Figure BDA0003104908340000102
and
Figure BDA0003104908340000103
the decision variables of the upper model and the lower model are respectively set.
Equipment commissioning cost of PIES
Figure BDA0003104908340000104
Can be expressed as:
Figure BDA0003104908340000105
Figure BDA0003104908340000106
in the formula, NdevThe number of equipment to be built is counted;
Figure BDA0003104908340000107
the investment cost of the ith equipment;
Figure BDA0003104908340000108
the cost is built for the unit capacity of the ith equipment;
Figure BDA0003104908340000109
newly building capacity for the ith equipment in the jth construction stage; r is the discount rate; y iss,jThe starting year of the jth construction phase.
Cloud energy storage lease cost
Figure BDA00031049083400001010
Related to the lease capacity and lease power of the cloud energy storage, it can be expressed as:
Figure BDA00031049083400001011
in the formula, the upper mark y represents the y-th year of the PIES construction stage, which is the same as the following;
Figure BDA00031049083400001012
and
Figure BDA00031049083400001013
the method comprises the following steps that a lease capacity matrix and a lease power matrix of the cloud energy storage device are respectively provided, upper marks M and P respectively represent capacity and power, upper marks E and H respectively represent the cloud energy storage and the cloud heat storage device, and the same is carried out below;
Figure BDA00031049083400001014
and
Figure BDA00031049083400001015
the unit capacity and the unit power lease cost matrix of the cloud energy storage device are respectively.
When the PIES planning cycle is over, some of the devices are still available, and their device residuals at the end of the planning period need to be calculated and subtracted from the life cycle cost. Equipment residual value of PIES according to age average method
Figure BDA00031049083400001016
Can be expressed as:
Figure BDA00031049083400001017
in the formula, Ts,iThe total number of years for the ith equipment to run from the time of commissioning to the time of the end of the planning period; gamma rayiIs the net residual rate of the ith plant.
The constraint conditions of the upper-layer PIES optimization configuration model comprise the upper limit constraint of the built capacity of all the equipment, and are as follows:
Figure BDA0003104908340000111
in the formula (I), the compound is shown in the specification,
Figure BDA0003104908340000112
and establishing an upper limit of the capacity of the ith equipment.
Decision variables of upper-layer PIES optimization configuration model
Figure BDA0003104908340000113
Including commissioning device capacity configuration scheme matrix
Figure BDA0003104908340000114
Cloud energy storage capacity lease scheme matrix
Figure BDA0003104908340000115
And cloud energy storage power lease scheme matrix
Figure BDA0003104908340000116
Figure BDA0003104908340000117
Wherein
Figure BDA0003104908340000118
Figure BDA0003104908340000119
A capacity matrix representing equipment i put into operation at the initial stage of each construction stage of the PIES;
Figure BDA00031049083400001110
wherein
Figure BDA00031049083400001111
And
Figure BDA00031049083400001112
respectively representing the lease capacity matrixes of the cloud electricity storage device and the cloud heat storage device in each year in the PIES planning period;
Figure BDA00031049083400001113
wherein
Figure BDA00031049083400001114
And
Figure BDA00031049083400001115
the lease power matrices of the cloud electricity storage and the cloud heat storage are respectively represented for each year during the PIES planning period.
The upper-layer PIES optimizing configuration model enables the decision variables to be changed
Figure BDA00031049083400001116
The value of (A) is transmitted to a lower-layer PIES optimization operation model, and the lower layer solves the optimal operation strategy of the PIES according to the upper-layer PIES configuration scheme
Figure BDA00031049083400001117
And the minimum operation maintenance cost of the PIES
Figure BDA00031049083400001118
Feedback to the upper layer to obtain the full life cycle cost of the PIES
Figure BDA00031049083400001119
Decision variables transmitted by the lower-layer PIES optimization operation model after receiving the upper-layer model
Figure BDA00031049083400001120
And finally, processing uncertainty of photovoltaic output and load prediction in the PIES by using a stochastic programming method. Firstly, a typical scene generation method is used for generating N of each yeardIndividual photovoltaic and load typical daily operating scenarios and their corresponding probabilities; then, solving the lower-layer PIES optimal operation model under each typical daily operation scene every year to obtain a PIES optimal operation strategy
Figure BDA00031049083400001121
And operating maintenance costs
Figure BDA00031049083400001122
Finally, for each typical daily operating scenario of each year
Figure BDA00031049083400001123
According toCorresponding probability ps,y,nWeighting to obtain the total operation maintenance cost of the PIES
Figure BDA00031049083400001124
And feeding back the data to the upper-layer PIES optimization configuration model so as to complete one iteration.
Under the condition of constructing a time sequence scene s, the objective function of a lower-layer PIES optimization operation model based on a stochastic programming method is to minimize the total operation maintenance cost of the PIES
Figure BDA00031049083400001125
As follows:
Figure BDA0003104908340000121
Figure BDA0003104908340000122
in the formula, the superscript n represents the nth typical daily operation scene, the same applies below;
Figure BDA0003104908340000123
daily energy purchase cost for PIES;
Figure BDA0003104908340000124
the cloud energy storage daily operating cost of the PIES is calculated;
Figure BDA0003104908340000125
the daily maintenance cost of the equipment for PIES.
Daily energy purchase cost of PIES
Figure BDA0003104908340000126
Can be expressed as:
Figure BDA0003104908340000127
in the formula (I), the compound is shown in the specification,
Figure BDA0003104908340000128
and
Figure BDA0003104908340000129
the price of electricity and the price of natural gas at the t moment are respectively;
Figure BDA00031049083400001210
and
Figure BDA00031049083400001211
electric power purchased from the EUC and natural gas purchased from the NGUC for the PIES at time t, respectively; t and Δ T are the number of scheduling instants in a day and the time interval of adjacent scheduling instants, respectively.
The charging of the cloud power storage device by the PIES requires payment of electricity fee according to the electricity price at the current moment, and the discharging by using the cloud power storage device does not require payment of fee; the heat storage cost of the cloud heat storage device is reflected in the electricity cost and the natural gas cost, and repeated calculation is not needed. Therefore, the daily operating cost of the cloud energy storage of the PIES is the daily charging cost of the cloud electricity storage device. It should be noted that the charging power of the cloud power storage device is derived from the power selling company and the park PV, and the latter is used for charging the cloud power storage device without paying the electricity fee. To sum up, the cloud energy storage daily operating cost of the PIES
Figure BDA00031049083400001212
Can be expressed as:
Figure BDA00031049083400001213
Figure BDA00031049083400001214
in the formula (I), the compound is shown in the specification,
Figure BDA00031049083400001215
charging power of the cloud power storage device purchased to the EUC at the time t;
Figure BDA00031049083400001216
and
Figure BDA00031049083400001217
the total charging power of the cloud power storage device at the t-th moment and the park PV power stored in the cloud power storage device are respectively.
Daily maintenance cost of PIES
Figure BDA00031049083400001218
Can be expressed as:
Figure BDA0003104908340000131
in the formula (I), the compound is shown in the specification,
Figure BDA0003104908340000132
the unit maintenance cost of the ith equipment; ps,i,y,n,tThe output of the ith equipment at the t moment. It should be noted that since the actual controller of the cloud energy storage device is the cloud energy storage provider, the PIES does not need to pay for the device maintenance cost of the cloud energy storage.
The constraint conditions of the lower-layer PIES optimization operation model comprise electric power balance constraint, thermal power balance constraint, natural gas balance constraint, energy coupling equipment operation constraint and cloud energy storage equipment operation constraint.
Decision variables of lower-layer PIES optimization operation model
Figure BDA0003104908340000133
Wherein
Figure BDA0003104908340000134
And
Figure BDA0003104908340000135
and respectively obtaining the optimal operation strategy matrixes of the equipment to be built and the cloud energy storage in the PIES.
Figure BDA0003104908340000136
Wherein
Figure BDA0003104908340000137
Figure BDA0003104908340000138
Representing an operation strategy matrix of the equipment i at each moment in each typical day operation scene every year;
Figure BDA0003104908340000139
wherein
Figure BDA00031049083400001310
And
Figure BDA00031049083400001311
and respectively representing the charge and discharge strategy matrixes of the cloud electricity storage device and the cloud heat storage device at each moment in each typical daily operation scene every year.
And 3, solving the double-layer optimization configuration model of the park comprehensive energy system based on the KKT condition and the large M method to obtain the optimal configuration scheme of the park comprehensive energy system.
The upper layer model and the lower layer model of the PIES double-layer optimization configuration model provided by the invention have a coupling relation, and are difficult to directly solve. Analyzing the model provided by the invention, the optimization operation problem of the lower layer PIES is convex continuous and differentiable, so that a Lagrangian function is constructed for the optimization operation model of the lower layer PIES, the lower layer model is converted into a constraint condition of an upper layer optimization configuration model based on a KKT condition of the lower layer model, and a nonlinear term in the model is linearized by applying a large M method, so that the PIES double-layer optimization configuration model provided by the invention can be converted into a single-layer mixed integer linear programming model which is easy to solve.
For convenience of description, a general expression of the PIES two-layer optimization model is first given, namely:
Figure BDA00031049083400001312
ql∈argminf(qu,ql)
F(qu,ql):B(qu,ql)≤0,D(qu,ql)＝0
f(qu,ql):b(qu,ql)≤0,d(qu,ql)＝0
wherein F (q)u,ql) And f (q)u,ql) Respectively are the target functions of an upper layer model and a lower layer model; q. q.suDecision variables for upper layer models, which correspond to the invention
Figure BDA0003104908340000141
qlDecision variables for the underlying model, corresponding in the invention
Figure BDA0003104908340000142
B(qu,ql) And D (q)u,ql) Respectively an inequality constraint set and an equality constraint set of the upper model; b (q)u,ql) And d (q)u,ql) Respectively, an inequality constraint set and an equality constraint set of the lower layer model.
Next, constructing a lagrangian function of the lower-layer PIES optimization operation model, namely:
Figure BDA0003104908340000143
wherein λ ═ λiJ (I ═ 1,2, …, I) and μ ═ μjJ (1, 2, …, J) are lagrange multiplier sets of inequality constraints and equality constraints in the underlying model, respectively, and I and J are the number of inequality constraints and equality constraints in the underlying model, respectively.
According to the Lagrange function and the KKT condition, the lower layer model can be converted into an additional constraint condition of the upper layer model, so that the PIES double-layer optimization configuration model is converted into a single-layer PIES optimization configuration model, namely:
Figure BDA0003104908340000144
s.t.B(qu,ql)≤0,D(qu,ql)＝0
b(qu,ql)≤0,d(qu,ql)＝0
Figure BDA0003104908340000145
λi≥0,i＝1,2,...,I
λibi(qu,ql)＝0,i＝1,2,...,I
the single-layer PIES optimal configuration model obtained based on the KKT condition comprises the nonlinear constraint condition, so that the nonlinear constraint condition is linearized by using a large M method, and the result after linearization is shown as follows.
Figure BDA0003104908340000146
Figure BDA0003104908340000147
Figure BDA0003104908340000148
Figure BDA0003104908340000151
0≤λi≤MBi,i＝1,2,...,I
0≤bi(qu,ql)≤M(1-Bi),i＝1,2,...,I
Wherein M is a sufficiently large constant; b isiIs an auxiliary binary variable.
Therefore, the PIES double-layer optimization configuration model provided by the invention has been converted into a single-layer mixed integer linear programming model, the model can be modeled by using a Yalmip tool box on a Matlab platform, and a Gurobi solver is called to solve, so that the optimal configuration scheme of the park comprehensive energy system can be obtained.
The first application embodiment:
for further understanding of the present invention, the present example implements the optimal configuration scheme of the present invention for a campus complex energy system to explain the practical application of the present invention.
The structure of the park integrated energy system is shown in fig. 2. The park comprehensive energy system consists of an energy supply side, an energy concentrator and a load side, wherein the energy supply side comprises an electricity selling company, a park photovoltaic and natural gas company; the equipment in the energy concentrator comprises an electric heater, a ground source heat pump, a gas boiler and a cogeneration unit; the load side includes the campus electrical and thermal loads. The park integrated energy system leases the cloud electricity storage device and the cloud heat storage device to a cloud energy storage provider.
The main parameters of the calculation are set as follows: t is 24; n is a radical ofs＝3；Ny＝15；N d3, including 3 typical days in summer, winter and transition seasons, wherein each typical day scene is shown in fig. 3; the conversion rate r is 0.08; the net residual value rate gamma of the equipment is 0.06; the increase of the pie load maximum during the planning period is shown in fig. 4.
In Ns＝3，NyIn the case of 15, the number N of pieces construction sequencesSCE91. The PIES double-layer optimization configuration model under 91 construction time sequences is solved, each construction time sequence is sequenced from small to large according to the whole life cycle cost, and the result is shown in table 1. As can be seen from table 1, when the construction time sequence is 2+4+9 (i.e., PIES are put into service in 3 rd period, the first period is 1-2 years, the second period is 3-6 years, and the third period is 7-15 years, new devices are put into service in the first year, the third year, and the seventh year, respectively), the total life cycle cost of the PIES is the minimum, which is 1632.8 ten thousand yuan, and thus, 2+4+9 is the optimal construction time sequence of the PIES. As can be seen from Table 1, the construction time sequences at the top of the rank are the first stage with the least years, the second stage with the third stage far greater than the first stageAnd the second stage, and the construction time sequence after ranking is that the first stage is much longer than the second stage and the third stage, because the load of the PIES is increased faster in the early stage and the increase rate is reduced in the later stage, therefore, the economy of PIES planning can be improved by repeatedly putting new equipment in the early stage of PIES planning.
TABLE 1 construction sequences ordered from small to large cost of full life cycle
Figure BDA0003104908340000161
In order to better verify the effectiveness of the pie optimal configuration Model (MOC) considering the optimal construction timing sequence method and the cloud energy storage mechanism, the MOC, the pie optimal configuration Model (MCC) based on the construction timing sequence clustering partition method, and the pie optimal configuration Model (MAC) based on the construction timing sequence average partition method are compared in terms of economy and equipment utilization, as shown in table 2, where the equipment utilization is defined as the percentage weighting of the average output of all equipment in the pie in the whole planning period to the capacity thereof. As can be seen from Table 2, the total life cycle cost of the MOC is reduced by 11.1 ten-thousand yuan and 21.7 ten-thousand yuan compared with the total life cycle cost of the MCC and the MAC, and the ranking of the total life cycle cost of the three models in Table 1 is 1 st, 15 th and 45 th in sequence; compared with the equipment utilization rate of MCC and MAC, the equipment utilization rate of MOC is respectively improved by 1.8% and 4.8%. This is because the MOC considers the optimal construction timing and can find a more optimal PIES configuration scheme than the MCC and MAC. Therefore, the PIES optimal configuration scheme obtained by the MOC provided by the invention is better than the other two models in the aspects of economy and equipment utilization rate, and the effectiveness of the model provided by the invention in improving the economy and the equipment utilization rate of the PIES optimal configuration is verified.
TABLE 2 comparison of PIES configurations derived from MOC, MCC and MCC for economics and equipment utilization
Figure BDA0003104908340000162
The optimal placement of PIES at optimal construction timing is shown in table 3. As can be seen from Table 3, after the optimal construction time sequence is considered, PV is built for 1000kW in the first two stages, and the upper limit of the built capacity is reached; the CHP is built by 3 stages for 415.6kW in total, and the single built-in capacity of the CHP in the third stage is the maximum and accounts for 54.7 percent of the total built-in capacity; HP is similarly built in 3 stages with a total capacity of 104.7kW, unlike CHP, HP has a maximum capacity per time built in the first stage, accounting for 46.4% of the total built capacity. It should be noted that the pie is not configured with EH and GB during the planning period, because the energy conversion efficiency of EH and GB is lower than that of CHP and HP, respectively, so in the case that the projected capacity of CHP and HP does not reach the upper limit, the preferential projection of CHP and HP can improve the energy utilization efficiency and planning economy of the pie.
Table 3 optimal pie allocation scheme under optimal construction timing
Figure BDA0003104908340000171
The life cycle cost of the PIES optimal configuration scheme at the optimal build timing is shown in table 4. As can be seen from table 4, in the optimal construction timing sequence, the equipment commissioning costs of the PIES in 3 stages respectively account for 44.4%, 35.4% and 20.2%, because if the equipment is once commissioned in the initial planning stage, there will be more capacity left idle and waste when the load is low in the early stage, and the residual value of the equipment will decrease with the increase of the service time; the purchase energy cost of the PIES accounts for 73% of the total life cycle cost, wherein the purchase energy cost of the third stage accounts for 79.6% of the total purchase energy cost, which reflects that the electricity and heat load level is low in the early stage of the programming of the PIES, most of the load requirements can be met by using the built park PV and the energy conversion equipment, and the electricity and heat load level of the PIES is high in the later stage of the programming of the PIES, so that more electricity and natural gas need to be purchased from electricity and gas selling companies besides the energy supply by using the park PV and the energy conversion equipment.
TABLE 4 Overall Life cycle cost of PIES optimal configuration scheme under optimal construction timing
Figure BDA0003104908340000172
Under the optimal construction timing sequence, the cloud energy storage capacity and power lease scheme for each year in the PIES planning period is shown in fig. 5. As can be seen from fig. 5, in the first and second phases of the PIES program, the annual average lease power of the cloud electricity storage device and the cloud heat storage device is only 144.2kW and 109.7kW, respectively, due to the low levels of electricity and heat load; in the third stage of the pie planning, the annual average lease capacity of the cloud power storage device and the cloud heat storage device reaches 500kWh, and the annual average lease power reaches 205.7kW and 208.9kW respectively, so as to deal with the higher electric and heat load peaks in the third stage of the pie planning.
In order to better verify the effectiveness of introducing cloud energy storage in the pie planning, the MOC provided by the invention is compared with a pie configuration scheme obtained by a pie optimal configuration Model (MOA) considering an optimal construction time sequence but putting on entity energy storage in economic terms, as shown in table 5, and the pie entity energy storage putting scheme obtained by the MOA is as shown in table 6. As can be seen from table 5 and table 6, the life cycle cost of the PIES optimal configuration scheme obtained by the MOC proposed by the present invention is 84.7 ten thousand yuan lower than that of the MOA, because the unit price of the entity energy storage is higher, the MOA selects to only respectively set up the electricity and heat storage devices of 131.3kWh and 239.1kWh in the whole PIES planning cycle, so that although the cost of the entity energy storage setting of the MOA is 17 ten thousand yuan lower than that of the cloud energy storage lease of the MOC, the flexibility of the planning and operation of the PIES is limited by the entity electricity and heat storage devices with small capacity, which causes the equipment setting cost and energy purchase cost of the MOA to be 6.9 ten thousand yuan and 74.6 ten thousand yuan higher than that of the MOC, and the equipment residual value of the MOA to be 20.3 ten thousand yuan lower than that of the MOC. Therefore, compared with the method of building entity energy storage, the cloud energy storage introduced into the PIES planning can further reduce the life cycle cost of the PIES.
Table 5 cloud energy storage capacity and power lease scheme for each year in PIES planning period under optimal construction timing sequence
Figure BDA0003104908340000181
Table 6 PIES entity energy storage project scheme obtained by MOA
Figure BDA0003104908340000182

Claims (5)

1. A park comprehensive energy system optimal configuration method is characterized by comprising the following steps: the method comprises the following steps:
constructing a construction time sequence set of the park comprehensive energy system;
based on the established construction time sequence set, constructing a park comprehensive energy system double-layer optimization configuration model by adopting an optimal construction time sequence method and a cloud energy storage mechanism;
and solving the double-layer optimization configuration model of the park comprehensive energy system based on the KKT condition and a large M method to obtain an optimal configuration scheme of the park comprehensive energy system.
2. The optimal configuration method for the park integrated energy system according to claim 1, wherein the set of the construction timing sequences of the park integrated energy system is recorded as ΩPIESThe method comprises a plurality of construction phase sets formed by dividing a planning period of the park integrated energy system, wherein the total number of years and the divided construction phase number in the planning period of the park integrated energy system are respectively NyAnd NsThe number of the construction time sequences of the park comprehensive energy system is NSCE。
3. The optimal configuration method for the campus energy grid system according to claim 2, wherein: the park comprehensive energy system double-layer optimization configuration model comprises an upper-layer park comprehensive energy system optimization configuration model and a lower-layer park comprehensive energy system optimization operation model.
4. The optimal configuration method for the campus energy grid system according to claim 3, wherein:
based on the established construction time sequence set, an optimal construction time sequence method and a cloud energy storage mechanism are adopted to construct a park comprehensive energy system double-layer optimization configuration model, which comprises the following steps:
under the construction time sequence scene s, the objective function of the optimization configuration model of the upper-layer park integrated energy system is the life cycle cost of the minimum park integrated energy system
Figure FDA0003104908330000011
As follows:
Figure FDA0003104908330000012
Figure FDA0003104908330000013
in the formula, the superscript s represents the s-th construction time sequence scene, the same applies below;
Figure FDA0003104908330000014
and
Figure FDA0003104908330000015
respectively the total construction cost and the total operation cost of the park comprehensive energy system,
Figure FDA0003104908330000016
the method is an objective function of an optimized operation model of the comprehensive energy system of the lower park;
Figure FDA0003104908330000017
and
Figure FDA0003104908330000018
respectively setting up equipment investment cost, cloud energy storage lease cost and equipment residual value of the park comprehensive energy system;
Figure FDA0003104908330000019
and
Figure FDA00031049083300000110
decision variable sets of an upper layer model and a lower layer model are respectively set;
equipment commissioning cost of park integrated energy system
Figure FDA00031049083300000111
Expressed as:
Figure FDA0003104908330000021
Figure FDA0003104908330000022
in the formula, NdevThe number of equipment to be built is counted;
Figure FDA0003104908330000023
the investment cost of the ith equipment;
Figure FDA0003104908330000024
the cost is built for the unit capacity of the ith equipment;
Figure FDA0003104908330000025
newly building capacity for the ith equipment in the jth construction stage; r is the discount rate; y iss,jThe initial year of the jth construction phase;
cloud energy storage lease cost
Figure FDA0003104908330000026
Relating to the lease capacity and the lease power of the cloud energy storage, the method is expressed as follows:
Figure FDA0003104908330000027
in the formula, the superscript y represents the y year of the construction stage of the park comprehensive energy system, which is the same as the following;
Figure FDA0003104908330000028
and
Figure FDA0003104908330000029
the method comprises the following steps that a lease capacity matrix and a lease power matrix of the cloud energy storage device are respectively provided, upper marks M and P respectively represent capacity and power, upper marks E and H respectively represent the cloud energy storage and the cloud heat storage device, and the same is carried out below;
Figure FDA00031049083300000210
and
Figure FDA00031049083300000211
respectively representing a unit capacity and a unit power lease cost matrix of the cloud energy storage device;
according to the average method of age, the residual value of the equipment of the park comprehensive energy system
Figure FDA00031049083300000212
Expressed as:
Figure FDA00031049083300000213
in the formula, Ts,iThe total number of years for the ith equipment to run from the time of commissioning to the time of the end of the planning period; gamma rayiThe net residual value rate of the ith equipment;
the constraint conditions of the upper park comprehensive energy system optimization configuration model comprise the upper limit constraint of the built-in capacity of all equipment, and are as follows:
Figure FDA00031049083300000214
in the formula (I), the compound is shown in the specification,
Figure FDA00031049083300000215
establishing an upper capacity limit for the ith equipment;
decision variables of upper park comprehensive energy system optimization configuration model
Figure FDA00031049083300000216
Including commissioning device capacity configuration scheme matrix
Figure FDA0003104908330000031
Cloud energy storage capacity lease scheme matrix
Figure FDA0003104908330000032
And cloud energy storage power lease scheme matrix
Figure FDA0003104908330000033
Figure FDA0003104908330000034
Wherein
Figure FDA0003104908330000035
Figure FDA0003104908330000036
A capacity matrix representing equipment i put into operation at the initial stage of each construction stage of the park integrated energy system;
Figure FDA0003104908330000037
wherein
Figure FDA0003104908330000038
And
Figure FDA0003104908330000039
respectively representing each year in the planning period of the park integrated energy systemThe cloud electricity storage device and the lease capacity matrix of the cloud heat storage device;
Figure FDA00031049083300000310
wherein
Figure FDA00031049083300000311
And
Figure FDA00031049083300000312
respectively representing lease power matrixes of the cloud electricity storage device and the cloud heat storage device in each year in a planning period of the park comprehensive energy system;
under the construction time sequence scene s, the objective function of the optimized operation model of the lower-layer park integrated energy system based on the stochastic programming method is used for minimizing the total operation and maintenance cost of the park integrated energy system
Figure FDA00031049083300000313
As follows:
Figure FDA00031049083300000314
Figure FDA00031049083300000315
in the formula, the superscript n represents the nth typical daily operation scene, the same applies below;
Figure FDA00031049083300000316
the daily energy purchase cost for the park integrated energy system;
Figure FDA00031049083300000317
the cloud energy storage daily operating cost of the park comprehensive energy system;
Figure FDA00031049083300000318
the daily maintenance cost of the equipment of the park comprehensive energy system;
daily energy purchase cost of park integrated energy system
Figure FDA00031049083300000319
Expressed as:
Figure FDA00031049083300000320
in the formula (I), the compound is shown in the specification,
Figure FDA00031049083300000321
and
Figure FDA00031049083300000322
the price of electricity and the price of natural gas at the t moment are respectively;
Figure FDA00031049083300000323
and
Figure FDA00031049083300000324
electric power purchased from the EUC and natural gas purchased from the NGUC by the campus integrated energy system at the time t, respectively; t and delta T are the number of scheduling moments in one day and the time interval of adjacent scheduling moments respectively;
cloud energy storage daily operating cost of park comprehensive energy system
Figure FDA00031049083300000325
Expressed as:
Figure FDA0003104908330000041
Figure FDA0003104908330000042
in the formula (I), the compound is shown in the specification,
Figure FDA0003104908330000043
charging power of the cloud power storage device purchased to the EUC at the time t;
Figure FDA0003104908330000044
and
Figure FDA0003104908330000045
the total charging power of the cloud electricity storage device at the t moment and the PV generating power of the garden PV stored in the cloud electricity storage device are respectively set;
daily maintenance cost of equipment of park integrated energy system
Figure FDA0003104908330000046
Expressed as:
Figure FDA0003104908330000047
in the formula (I), the compound is shown in the specification,
Figure FDA0003104908330000048
the unit maintenance cost of the ith equipment; ps,i,y,n,tThe output of the ith equipment at the t moment; the constraint conditions of the comprehensive energy system optimized operation model of the lower park comprise electric power balance constraint, thermal power balance constraint, natural gas balance constraint, energy coupling equipment operation constraint and cloud energy storage equipment operation constraint;
decision variables of lower-layer park comprehensive energy system optimization operation model
Figure FDA0003104908330000049
Wherein
Figure FDA00031049083300000410
And
Figure FDA00031049083300000411
respectively obtaining optimal operation strategy matrixes of equipment to be built and cloud energy storage in the park comprehensive energy system;
Figure FDA00031049083300000412
wherein
Figure FDA00031049083300000413
Figure FDA00031049083300000414
Representing an operation strategy matrix of the equipment i at each moment in each typical day operation scene every year;
Figure FDA00031049083300000415
wherein
Figure FDA00031049083300000416
And
Figure FDA00031049083300000417
respectively representing charge and discharge strategy matrixes of the cloud electricity storage device and the cloud heat storage device at each moment in each typical daily operation scene of each year;
optimizing configuration model of upper park comprehensive energy system for determining the above decision variables
Figure FDA00031049083300000418
The value of the central data is transmitted to an optimized operation model of the lower-layer park integrated energy system, and the lower layer solves the optimal operation strategy of the park integrated energy system according to the configuration scheme of the upper-layer park integrated energy system
Figure FDA00031049083300000419
And the minimum operation and maintenance cost of the park comprehensive energy system
Figure FDA00031049083300000420
FeedbackTo the upper layer, thereby obtaining the life cycle cost of the park comprehensive energy system
Figure FDA00031049083300000421
Decision variables transmitted by the upper layer model after the optimization operation model of the comprehensive energy system of the lower layer park receives
Figure FDA0003104908330000051
Then, processing uncertainty of photovoltaic output and load prediction in the park comprehensive energy system by using a random planning method; firstly, a typical scene generation method is used for generating N of each yeardIndividual photovoltaic and load typical daily operating scenarios and their corresponding probabilities; then, solving the optimized operation model of the lower-layer park integrated energy system under each typical daily operation scene every year to obtain the optimized operation strategy of the park integrated energy system
Figure FDA0003104908330000052
And operating maintenance costs
Figure FDA0003104908330000053
Finally, for each typical daily operating scenario of each year
Figure FDA0003104908330000054
According to the corresponding probability ps,y,nWeighting to obtain the total operation and maintenance cost of the park comprehensive energy system
Figure FDA0003104908330000055
And feeding back the data to the comprehensive energy system optimal configuration model of the upper park so as to complete one iteration.
5. The optimal configuration method for the campus energy complex system as set forth in claim 4,
the method comprises the following steps of solving a double-layer optimization configuration model of the park comprehensive energy system based on a KKT condition and a large M method to obtain an optimal configuration scheme of the park comprehensive energy system, wherein the optimal configuration scheme comprises the following steps:
firstly, a general expression of a double-layer optimization model of the park comprehensive energy system is given, namely:
Figure FDA0003104908330000056
ql∈argminf(qu,ql)
F(qu,ql):B(qu,ql)≤0,D(qu,ql)＝0
f(qu,ql):b(qu,ql)≤0,d(qu,ql)＝0
wherein F (q)u,ql) And f (q)u,ql) Respectively are the target functions of an upper layer model and a lower layer model; q. q.suDecision variables for upper layer models, which correspond to the invention
Figure FDA0003104908330000057
qlIs a decision variable of the underlying model,
Figure FDA0003104908330000058
B(qu,ql) And D (q)u,ql) Respectively an inequality constraint set and an equality constraint set of the upper model; b (q)u,ql) And d (q)u,ql) Respectively an inequality constraint set and an equality constraint set of the lower layer model;
then, constructing a Lagrange function of the comprehensive energy system optimization operation model of the lower park, namely:
Figure FDA0003104908330000059
wherein λ ═ λi1,2, …, I and μ ═ μ }jJ1, 2, …, J respectivelyThe lagrange multiplier set is an inequality constraint and an equality constraint in the lower model, and I and J are the number of inequality constraints and equality constraints in the lower model respectively;
according to the Lagrange function and the KKT condition, the lower layer model is converted into an additional constraint condition of the upper layer model, so that the park comprehensive energy system double-layer optimization configuration model is converted into a single-layer park comprehensive energy system optimization configuration model, namely:
Figure FDA0003104908330000061
s.t.B(qu,ql)≤0,D(qu,ql)＝0
b(qu,ql)≤0,d(qu,ql)＝0
Figure FDA0003104908330000062
λi≥0,i＝1,2,...,I
λibi(qu,ql)＝0,i＝1,2,...,I
the optimal configuration model of the single-layer park comprehensive energy system obtained based on the KKT condition comprises a nonlinear constraint condition; the nonlinear constraint condition is linearized by applying a large M method, and the result after linearization is shown as follows;
Figure FDA0003104908330000063
Figure FDA0003104908330000064
Figure FDA0003104908330000065
Figure FDA0003104908330000066
0≤λi≤MBi,i＝1,2,...,I
0≤bi(qu,ql)≤M(1-Bi),i＝1,2,...,I
wherein M is a constant; b isiIs an auxiliary binary variable;
modeling the linearized model on a Matlab platform by using a Yalmip tool box, and calling a Gurobi solver to solve, so that the optimal configuration scheme of the park comprehensive energy system can be obtained.
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