CN113159412A - Multi-level energy storage optimal configuration method and device based on user demand response - Google Patents

Multi-level energy storage optimal configuration method and device based on user demand response Download PDF

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CN113159412A
CN113159412A CN202110410823.1A CN202110410823A CN113159412A CN 113159412 A CN113159412 A CN 113159412A CN 202110410823 A CN202110410823 A CN 202110410823A CN 113159412 A CN113159412 A CN 113159412A
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李先允
张效言
倪喜军
王书征
何鸿天
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Nanjing Institute of Technology
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Abstract

In order to meet different requirements of energy storage system application under different voltage levels, the invention discloses a multi-level energy storage double-layer planning model based on user demand response. Constructing an optimal configuration model of a medium-low voltage distribution network side energy storage system of 10KV and above on the upper layer, working out an optimal time-of-use electricity price by taking the minimum running cost of the distribution network as a target function, and adding load peak-valley difference limit into a constraint condition; an 380/220V user side energy storage system optimization configuration model is established at the lower layer, the minimum operation cost of a user is taken as an objective function, optimization is carried out under the time-of-use electricity price given by the upper layer, and the light abandonment amount limitation is added into the constraint condition. The upper layer model and the lower layer model are alternately iterated by taking time-of-use electricity price and interaction power with a superior power grid as coupling variables, and peak clipping and valley filling at a power distribution side and a user side and new energy consumption requirements are promoted by optimally selecting rated power, installation capacity and grid-connected positions of the centralized high-capacity energy storage system and the distributed and small-capacity energy storage systems at different voltage levels.

Description

Multi-level energy storage optimal configuration method and device based on user demand response
Technical Field
The invention belongs to the technical field of energy storage optimal configuration, and particularly relates to a multi-level energy storage optimal configuration method and device based on user demand response.
Background
The wide application of new energy sources such as wind energy, photovoltaic energy and the like also has many problems, such as light and wind abandonment, active power transmission and the like.
The energy storage technology can reduce power fluctuation and enhance system flexibility, and is an effective method for solving the problems. At present, the optimal configuration of an energy storage system is mostly under a single voltage level, only single requirements are considered, a centralized and distributed multidimensional combined application mode of the energy storage system is not fully developed, and the site selection and the capacity configuration of the energy storage system with multiple application requirements on a distribution network side and a user side are not properly solved. In the prior art, only the requirement response of a user is considered to have a lot of defects, such as the uninterruptible load in the response period and the unsuitable cost of large-scale industrial user adjustment production, the peak clipping and valley filling effect is poor due to the excessive uninterruptible load, the benefit of using the time-of-use electricity price is greatly reduced due to the excessive unsuitable cost, the peak-valley electricity price and the unreasonable formulation of the peak-valley period lead to the low participation of the user, and the like.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a multi-level energy storage optimal configuration method and device based on user demand response, and can enable the configuration of multi-level energy storage to be more scientific and reasonable and have strong applicability.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the invention provides a multi-level energy storage optimization configuration method based on user demand response, which comprises the following steps:
constructing a multi-level energy storage double-layer optimization configuration model; the multi-level energy storage double-layer optimization configuration model takes the optimal economy of an active power distribution network and a user as a target and comprises an upper-layer optimization model on the power distribution network side and a lower-layer optimization model on the user side;
acquiring a historical user load value, a historical photovoltaic output value and an urban power distribution network structure;
inputting a historical user load value, a historical photovoltaic output value and an urban distribution network structure into an upper-layer optimization model of a multi-level energy storage double-layer optimization configuration model, and performing optimization calculation by taking the minimum running cost of the distribution network as a target function according to an upper-layer constraint condition to obtain the centralized energy storage capacity and the time-of-use electricity price;
inputting the time-of-use electricity price into a lower-layer optimization model; under the time-of-use electricity price, optimizing and calculating according to a lower-layer constraint condition by taking the minimum running cost of the user as an objective function to obtain the load demand of the user and the charge-discharge state of the distributed energy storage, and returning the optimized interaction power of the upper-layer power grid of the user to the upper-layer optimization model;
alternately iterating the upper and lower layers of optimization models by taking time-of-use electricity price and interaction power of a user and a superior power grid as coupling variables until the upper and lower layers of objective functions are converged, and obtaining an optimal configuration scheme at the moment; the optimal configuration scheme comprises the optimal configurations of centralized energy storage, distributed energy storage capacity, charging and discharging power configuration and time-of-use electricity price.
Further, the multi-level energy storage double-layer optimization configuration model is a Mixed Integer Second-order Cone Programming (MI-SOCP) problem, and is solved by adopting a Yalmip and Cpelx solver; the construction method of the multi-level energy storage double-layer optimization configuration model comprises the following steps:
aiming at the optimal annual comprehensive cost of the power distribution network, constructing an upper-layer optimization model by considering linear DistFlow constraint, substation power second-order cone constraint, line power constraint, centralized energy storage constraint and user demand response constraint;
and constructing a lower-layer optimization model by taking the annual comprehensive cost optimization of the user as a target and considering power balance constraint, distributed energy storage constraint, photovoltaic output constraint and photovoltaic absorption constraint.
Further, the upper-layer optimization model aims at minimizing annual comprehensive cost of the active power distribution network, and an objective function of the upper-layer optimization model is as follows:
minF1＝C1+C2+C3-C4
wherein, F1For annual integrated cost of active distribution network, C1The electricity purchasing cost is carried out on the power distribution network to a superior power grid; c2Initial investment cost for centralized energy storage; c3Operating and maintaining costs for centralized energy storage; c4For the benefit of selling electricity to users for the power distribution network, the following expressions are used for each sub-target:
Figure BDA0003023996600000021
Figure BDA0003023996600000022
Figure BDA0003023996600000023
Figure BDA0003023996600000024
in the formula, Pk,tThe method comprises the following steps of (1) purchasing electric quantity from a power distribution network to a superior power grid at a time T, wherein T is the hours of a selected typical day; ckThe purchase price of electricity; ddxSelecting the number of typical days; cpIn order to keep the unit power cost of the energy storage battery,
Figure BDA0003023996600000025
rated power for the centralized energy storage battery; ceIn order to provide the cost per unit capacity of the energy storage battery,
Figure BDA0003023996600000026
the rated capacity is the energy storage battery; ccRenting cost for a centralized energy storage place, and gamma is the discount rate; d1The service life of the centralized energy storage device is prolonged; crIs a centralized energy storage unitCost of power operation, CwCost of maintenance for centralized energy storage per power; psold,tThe amount of electricity sold to the user for the distribution network at time t, CsoldIs the price for selling electricity.
Further, the lower layer optimization model aims at minimizing the annual integrated cost of the user, and the objective function is as follows:
F2＝C5+C6+C7+C8
wherein, F2For the annual combined cost of the user, C5The electricity purchasing cost is carried out on the superior power grid for the user; c6Investment cost for distributed energy storage; c7Operating costs for distributed energy storage; c8Photovoltaic maintenance costs; the following are expressions for each sub-target:
Figure BDA0003023996600000031
Figure BDA0003023996600000032
Figure BDA0003023996600000033
Figure BDA0003023996600000034
in the formula, Pb,tThe interactive power of the user and the superior power grid at the time t is determined as positive when the user purchases power from the power distribution network and negative when the user sells the power; cbThe price of buying/selling electricity at the time t; cpIn order to keep the unit power cost of the energy storage battery,
Figure BDA0003023996600000035
the rated power of the distributed energy storage battery is set; ceIn order to provide the cost per unit capacity of the energy storage battery,
Figure BDA0003023996600000036
the rated capacity is the energy storage battery; d2The service life of the distributed energy storage device is prolonged; (ii) a CrOperating costs per unit power for decentralized energy storage, CwThe cost of unit power maintenance for distributed energy storage; kpvAnd the photovoltaic operation maintenance coefficient is obtained.
Further, the method for obtaining the optimal configuration scheme of the centralized and distributed energy storage collaborative planning comprises the following steps:
based on the multi-level energy storage double-layer planning model, the multi-level energy storage double-layer planning model is a Mixed Integer Second-order Cone planning (MI-SOCP) problem, a Yalmip and Cpelx solver is adopted to solve the problem, and the optimal time-of-use electricity price, the interaction power of a user and a superior distribution network and the optimal capacity of centralized and distributed energy storage are obtained through iterative solution of an upper layer model and a lower layer model.
Further, the upper-layer constraint conditions comprise linear DistFlow power flow constraint, transformer substation second-order cone constraint, line transmission power constraint, centralized energy storage constraint and user demand corresponding constraint;
the linear DistFlow flow constraint adopts a DistFlow flow model:
Figure BDA0003023996600000041
in the formula, Pij，QijRespectively representing the active and reactive power transfer between line i and line j, rij，xijRespectively representing the resistance and reactance between the bus i and the bus j, and m represents a father node of the node i;
Figure BDA0003023996600000042
respectively the active power consumed and flowing out on the line i,
Figure BDA0003023996600000043
respectively the reactive power consumed and flowed out on the line i;
the second-order cone constraint of the transformer substation is that the square of active power and reactive power of the transformer substation should be smaller than the square of rated capacity of the transformer substation:
Figure BDA0003023996600000044
in the formula:
Figure BDA0003023996600000045
respectively providing active power and reactive power of the transformer substation i at the moment t;
Figure BDA0003023996600000046
rated capacity for the existing substation i; n is a radical ofsubThe number of the transformer substations is;
the line transmission power constraint is:
SLi，t≤SLimax i＝1,2,……NL
in the formula, SLi，tThe transmission capacity of the branch i at the moment t; sLimaxMaximum transmission capacity for branch i; n is a radical ofLThe total number of branches;
the centralized energy storage constraint comprises a number constraint, an energy storage power, a capacity constraint and an SOC constraint;
1) number constraint
In the invention, the centralized energy storage is arranged in the transformer substation, so the number of the centralized energy storage is less than that of the transformer substation:
Njz≤Nsub
wherein N isjzNumber of energy stored in a centralized manner, NsubNumber of transformer stations
2) Energy storage power and capacity constraints
The centralized energy storage power and capacity are less than set values:
Figure BDA0003023996600000047
in the formula (I), the compound is shown in the specification,
Figure BDA0003023996600000048
rated charging power and discharging power of centralized energy storage installed for the node i respectively;
Figure BDA0003023996600000049
Figure BDA00030239966000000410
upper limits of rated charging and discharging power of centralized energy storage installed for the node i respectively; omegajzInstalling a centralized energy storage node set;
Figure BDA0003023996600000051
in the formula, Ejz,iCentralized energy storage capacity installed for node i, EJZA centralized total energy storage capacity for installation;
3) SOC constraints
Figure BDA0003023996600000052
Wherein soc (state of charge) is the state of charge of the stored energy, which is defined as the ratio of the current capacity to the rated capacity of the system; ejz,tIs the centralized energy storage capacity, Soc, at time tjz,t、
Figure BDA0003023996600000053
Respectively the state of charge and the upper and lower limits of the centralized energy storage i at the moment t; etach、ηdischRespectively the charge and discharge efficiency of the stored energy;
the user response expression of the user demand response constraint comprises:
Figure BDA0003023996600000054
in the formula (d)o(t)、dTOU(t) load requirements before and after time-of-use electricity prices are adopted; ρ (t)0) And rho (t) are respectively time-of-use electricity priceFront and rear electricity prices; e.g. of the typei,i(t) is a required elastic coefficient matrix at time t; gamma-shapedn、ΓmRespectively representing an excitation demand ratio and a punishment demand ratio; a (t), pen (t) respectively adjust the income after the power consumption for the user and the punishment of not adjusting the power consumption according to the contract;
the following equation is the total demand response limit;
-DTOU≤dTOU(t)≤DTOU
in the formula, DTOUA total volume limit for demand response;
to ensure that the demand response electricity price does not fall below the limit of the average power cost of the system,
p(t)≥Cav
in the formula, CavThe average power cost of the system.
Further, the lower layer constraint conditions comprise a power balance constraint, a distributed energy storage constraint, a photovoltaic output constraint and a photovoltaic absorption constraint;
the expression of the power balance constraint is:
Ppv(t)+Pbess,fs(t)-Pload(t)-Ptrans(t)＝0
in the formula, Ppv(t) photovoltaic output at time t; pbess,fs(t) is the distributed energy storage charging and discharging power at the moment t, the value is positive when the energy storage is discharged, and the value is negative when the energy storage is charged; pload(t) is the load at time t; ptrans(t) the interaction power between the user and the superior power grid at the moment t;
the distributed energy storage constraint comprises a number constraint, an energy storage power, a capacity constraint and an energy storage SOC constraint;
1) number constraint
The invention installs the distributed energy storage at the load node or the corresponding feeder, so the number of the distributed energy storage should be less than the sum of the number of the loads and the number of the feeders:
Nfs≤Nload+Nline
wherein N isfsNumber of distributed energy storage, NloadIs the number of load nodes, NlineThe number of the feeder lines is;
2) energy storage power and capacity constraints
The distributed energy storage power and capacity are less than set values:
Figure BDA0003023996600000061
in the formula (I), the compound is shown in the specification,
Figure BDA0003023996600000062
rated charging and discharging powers of distributed energy storage installed at the node j are respectively set;
Figure BDA0003023996600000063
rated charging and discharging power upper limits of distributed energy storage installed at the node j are respectively set; omegafsInstalling a distributed energy storage node set;
Figure BDA0003023996600000064
in the formula, Efs,jFor distributed energy storage capacity installed at node j, EFSTotal capacity for installed distributed energy storage;
3) energy storage SOC constraint
Figure BDA0003023996600000065
In the formula, Efs,tFor distributed energy storage capacity at time t, Socfs,i,t、
Figure BDA0003023996600000066
Respectively the state of charge and the upper and lower limits of the distributed energy storage j at the time t; etach、ηdischRespectively the charge and discharge efficiency of the stored energy;
the photovoltaic output constraint expression is:
Ppv,o,t≤PG,o，o∈Ωpv
in the formula, Ppv,o,tThe photovoltaic output of the o node at the time t; pG,oThe installed photovoltaic capacity of the o node; omegapvA collection of installed photovoltaic nodes;
the expression of the photovoltaic absorption constraint is:
Figure BDA0003023996600000071
in the formula, CpvThe photovoltaic absorption rate is the lowest expected value.
In a second aspect, the present invention provides a multi-level energy storage optimization configuration apparatus based on user demand response, the apparatus comprising:
a model construction module: constructing a multi-level energy storage double-layer optimization configuration model; the multi-level energy storage double-layer optimization configuration model takes the optimal economy of an active power distribution network and a user as a target and comprises an upper-layer optimization model on the power distribution network side and a lower-layer optimization model on the user side;
an information acquisition module: acquiring a historical user load value, a historical photovoltaic output value and an urban power distribution network structure;
an upper layer optimization module: inputting a historical user load value, a historical photovoltaic output value and an urban distribution network structure into an upper-layer optimization model of a multi-level energy storage double-layer optimization configuration model, and performing optimization calculation by taking the minimum running cost of the distribution network as a target function according to an upper-layer constraint condition to obtain the centralized energy storage capacity and the time-of-use electricity price;
the lower layer optimization module: inputting the time-of-use electricity price into a lower-layer optimization model; under the time-of-use electricity price, optimizing and calculating according to a lower-layer constraint condition by taking the minimum running cost of the user as an objective function to obtain the load demand of the user and the charge-discharge state of the distributed energy storage, and returning the optimized interaction power of the upper-layer power grid of the user to the upper-layer optimization model;
a scheme acquisition module: alternately iterating the upper and lower layers of optimization models by taking time-of-use electricity price and interaction power of a user and a superior power grid as coupling variables until the upper and lower layers of objective functions are converged, and obtaining an optimal configuration scheme at the moment; the optimal configuration scheme comprises the optimal configurations of centralized energy storage, distributed energy storage capacity, charging and discharging power configuration and time-of-use electricity price.
In a third aspect, the present invention provides a multi-level energy storage optimization configuration device based on user demand response, including a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps according to the above-described method.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method.
Compared with the prior art, the invention has the following beneficial effects:
1. the energy storage device and the user demand response principle are combined to be modeled, so that the defects of the prior art, such as the non-interruptible load in the response time period and the non-adaptive cost of adjustment and production of large industrial users, the low participation of the users caused by unreasonable formulation of the peak-valley electricity price and the peak-valley time period, and the like, are well overcome, and peak clipping and valley filling are better performed and the stability of a power system is promoted;
2. according to the invention, by constructing an energy storage double-layer optimization configuration model, the upper layer model and the lower layer model are alternately iterated by taking time-of-use electricity price and interaction power with a higher-level power grid as coupling variables, and by optimally selecting rated power, installation capacity and grid-connected position of a centralized high-capacity energy storage system and a distributed low-capacity energy storage system under different voltage levels, the peak clipping and valley filling at a power distribution side and a user side and the requirement for promoting new energy consumption are realized;
3. the method jointly considers different voltage levels and different energy storage scales to carry out optimal configuration, jointly applies distributed energy storage and centralized energy storage, makes the two energy storage modes respectively make the best of each other and avoid the disadvantages, brings out the best in each other, fully exploits the application value of the energy storage system in high-permeability distributed power generation, improves the economy of the energy storage system, provides a new application mode for the popularization of energy storage, and promotes the rapid development and application of the energy storage in a power system.
Drawings
FIG. 1 is a model structure diagram of an energy storage double-layer optimization configuration method participating in power grid peak shaving;
FIG. 2 is a flow chart for solving a two-layer model.
FIG. 3 historical user load values
FIG. 4 historical photovoltaic output values
FIG. 5 interaction curve between historical users and upper-level power grid
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
the embodiment provides a multi-level energy storage optimal configuration method based on user requirements.
Referring to fig. 1 and fig. 2, the method for optimizing and configuring multi-level energy storage according to user requirements provided by the present invention includes the following steps:
1) construction of a two-layer optimal configuration model structure
Firstly, obtaining a historical user and superior power grid interaction power negative curve according to a historical user load value and a historical photovoltaic output value, and sending the historical user and superior power grid interaction power negative curve to an upper-layer optimization model.
The historical user load curve and the historical photovoltaic curve are researched according to typical enterprises in the area, user load and photovoltaic output values in four seasons of spring, summer, autumn and winter and in four days of sunny, cloudy, rainy and cloudy are respectively counted, a power interaction curve of a user and a superior power grid can be obtained by overlapping the load curve and the photovoltaic curve by taking a certain city in Zhejiang province as an example, as shown in figures 3, 4 and 5, the horizontal coordinate in the figure represents time, and the vertical coordinate in the figure represents a per unit value.
And secondly, in the upper model, the annual comprehensive cost of the active power distribution network is optimized, the electricity purchasing cost, the centralized energy storage investment cost and the operation cost of the power distribution network to the upper power grid are considered, the electricity purchasing income of the power distribution network to users is optimized, the optimal time-of-use electricity price and the centralized energy storage capacity in 24 time periods are optimized, and the time-of-use electricity price is transmitted to the lower model.
And thirdly, the lower model optimizes the user requirements and the distributed energy storage charging and discharging states in 24 time intervals according to the optimal time-of-use electricity price output by the upper model and the optimal annual comprehensive cost of the user as a target, and considers the electricity purchasing cost, the distributed energy storage investment and operating cost and the photovoltaic maintenance cost of the user from the upper power grid, and transmits the optimal time-of-use electricity price back to the upper model. And the upper layer model and the lower layer model alternately iterate by taking the time-of-use electricity price and the interaction power of the user and the power distribution network as coupling variables until the target functions are converged, and the centralized and distributed energy storage values at the moment are selected as the result of the optimal configuration of the energy storage system.
2) Double-layer model objective function
(a) Upper layer model objective function
The upper layer model aims at minimizing annual comprehensive cost of the active power distribution network
minF1＝C1+C2+C3-C4 (1)
Wherein, C1The electricity purchasing cost is carried out on the power distribution network to a superior power grid; c2Initial investment cost for centralized energy storage; c3Operating and maintaining costs for centralized energy storage; c4For the benefit of selling electricity to users by the power distribution network, expressions of all sub-targets are listed as follows:
Figure BDA0003023996600000091
Figure BDA0003023996600000092
Figure BDA0003023996600000093
Figure BDA0003023996600000094
in the formula, Pk,tThe method comprises the following steps of (1) purchasing electric quantity from a power distribution network to a superior power grid at a time T, wherein T is the hours of a selected typical day; ckThe purchase price of electricity; ddxSelecting the number of typical days; cpIn order to keep the unit power cost of the energy storage battery,
Figure BDA0003023996600000095
rated power for the centralized energy storage battery; ceIn order to provide the cost per unit capacity of the energy storage battery,
Figure BDA0003023996600000096
the rated capacity is the energy storage battery; ccRenting cost for a centralized energy storage place, and gamma is the discount rate; d1The service life of the centralized energy storage device is prolonged; crFor operating costs per unit power of centralized energy storage, CwCost of maintenance for centralized energy storage per power; psold,tThe amount of electricity sold to the user for the distribution network at time t, CsoldIs the price for selling electricity.
(b) Lower layer model objective function
The lower model aims at minimizing the annual integrated cost of the user:
F2＝C5+C6+C7+C8 (6)
wherein, C5The electricity purchasing cost is carried out on the superior power grid for the user; c6Investment cost for distributed energy storage; c7Operating costs for distributed energy storage; c8For photovoltaic maintenance costs, expressions for each sub-objective are listed below:
Figure BDA0003023996600000101
Figure BDA0003023996600000102
Figure BDA0003023996600000103
Figure BDA0003023996600000104
in the formula, Pb,tThe interactive power of the user and the superior power grid at the time t is determined as positive when the user purchases power from the power distribution network and negative when the user sells the power; cbThe price of buying/selling electricity at the time t; cpIn order to keep the unit power cost of the energy storage battery,
Figure BDA0003023996600000105
the rated power of the distributed energy storage battery is set; ceIn order to provide the cost per unit capacity of the energy storage battery,
Figure BDA0003023996600000106
the rated capacity is the energy storage battery; d2The service life of the distributed energy storage device is prolonged; crOperating costs per unit power for decentralized energy storage, CwThe cost of unit power maintenance for distributed energy storage; kpvAnd the photovoltaic operation maintenance coefficient is obtained.
3) Optimization model solving method
A multi-level energy storage double-layer optimization configuration model based on user requirements is a Mixed Integer Second-order Cone Programming (MI-SOCP) problem, and a Yalmip and Cplex solver is adopted to solve the problem, so that the centralized and distributed energy storage capacity and the charging and discharging power under different time-of-use electricity prices and interactive powers are obtained.
4) Constraint conditions
(a) Upper layer model constraints
Linear power flow constraint
Considering a radial running structure of a power distribution network, a DistFlow power flow model is supposed to be adopted; further, in order to reduce the calculation cost and improve the calculation efficiency, the invention uses a linear DistFlow power flow model:
Figure BDA0003023996600000111
in the formula, Pij，QijRespectively representing the active and reactive power transfer between line i and line j, rij，xijRespectively, the resistance and reactance between bus i and bus j, and m represents the parent node of node i.
Figure BDA0003023996600000112
Respectively the active power consumed and flowing out on the line i,
Figure BDA0003023996600000113
respectively the reactive power consumed and flowing out on the line i.
Second order cone constraint of transformer substation
The output of the transformer substation needs to meet certain limits, and in the part of the limits, the square of the active power and the reactive power of the transformer substation should be smaller than the square of the rated capacity of the transformer substation.
Figure BDA0003023996600000114
In the formula:
Figure BDA0003023996600000115
respectively providing active power and reactive power of the transformer substation i at the moment t;
Figure BDA0003023996600000116
rated capacity for the existing substation i; n is a radical ofsubThe number of the transformer substations is.
Third, line transmission power constraint
SLi，t≤SLimax i＝1,2,……NL
In the formula, SLi，tThe transmission capacity of the branch i at the moment t; sLimaxMaximum transmission capacity for branch i; n is a radical ofLIs the total number of branches.
Central energy storage restraint
1) Number constraint
In the invention, the centralized energy storage is arranged in the transformer substation, so the number of the centralized energy storage is less than that of the transformer substation:
Njz≤Nsub (12)
wherein N isjzNumber of energy stored in a centralized manner, NsubThe number of the transformer substations is.
2) Energy storage power and capacity constraints
The centralized energy storage power and capacity are less than set values:
Figure BDA0003023996600000121
in the formula (I), the compound is shown in the specification,
Figure BDA0003023996600000122
and rated charging power and discharging power of centralized energy storage installed for the node i respectively.
Figure BDA0003023996600000123
Figure BDA0003023996600000124
And the upper limits of rated charging and discharging power of centralized energy storage installed for the node i respectively. OmegajzA centralized set of energy storage nodes is installed.
Figure BDA0003023996600000125
In the formula, Ejz,iCentralized energy storage capacity installed for node i, EJZThe total capacity of centralized energy storage for installation.
3) SOC constraints
Figure BDA0003023996600000126
In the formula, soc (state of charge) is the state of charge of the stored energy, which is defined as the current capacity and the rated capacity of the systemThe ratio of (A) to (B); ejz,tIs the centralized energy storage capacity, Soc, at time tjz,t、
Figure BDA0003023996600000127
Respectively, the state of charge and the upper and lower limits of the centralized energy storage i at the time t. Etach、ηdischRespectively the charge-discharge efficiency of stored energy.
User demand response constraint
The invention adopts Price-based Demand Response (Price Demand Response) to analyze, and the modeling method adopts a power electricity Price elastic matrix and only considers the relation between the user Response and the electricity Price in the time period.
Figure BDA0003023996600000128
In the formula (d)o(t)、dTOU(t) load requirements before and after time-of-use electricity prices are adopted; ρ (t)0) Rho (t) is the price of electricity before and after the time-of-use price of electricity is adopted; e.g. of the typei,i(t) is a required elastic coefficient matrix at time t; gamma-shapedn、ΓmRespectively representing an excitation demand ratio and a punishment demand ratio; a (t), pen (t) respectively adjust the income after the power consumption for the user and the punishment of not adjusting the power consumption according to the contract. Further, the present invention, regardless of the relationship between the user and the operator, can obtain the following models:
Figure BDA0003023996600000129
-DTOU≤dTOU(t)≤DTOU (18)
in the formula, DTOUIs a total amount limit of demand response. If the user excessively responds to the electricity price, the situation of inversion of the peak and the valley occurs. The maximum response limit of the TOU is set to 10% of the peak load in this model.
p(t)≥Cav (19)
In the formula, CavThe average power cost of the system. This constraint ensures that demand response electricity prices are not less than the system average power cost.
(2) Lower layer constraint conditions
Power balance constraint
Ppv(t)+Pbess,fs(t)-Pload(t)-Ptrans(t)＝0 (20)
In the formula, Ppv(t) photovoltaic output at time t; pbess,fs(t) is the distributed energy storage charging and discharging power at the moment t, the value is positive when the energy storage is discharged, and the value is negative when the energy storage is charged; pload(t) is the load at time t; ptransAnd (t) the interaction power of the user and the superior power grid at the moment t. Therefore, the interaction power of the user and the superior distribution network is obtained as follows:
Ptrans(t)＝Ppv(t)+Pbess,fs(t)-Pload(t) (21)
② distributed energy storage constraint
The distributed energy storage arranged on the load nodes or the corresponding feeders should satisfy the following constraints:
1) number constraint
The invention installs the distributed energy storage at the load node or the corresponding feeder, so the number of the distributed energy storage should be less than the sum of the number of the loads and the number of the feeders:
Nfs≤Nload+Nline (22)
in the formula, NfsNumber of distributed energy storage, NloadIs the number of load nodes, NlineThe number of the feeder lines is.
2) Energy storage power and capacity constraints
The distributed energy storage power and capacity are less than set values:
Figure BDA0003023996600000131
in the formula (I), the compound is shown in the specification,
Figure BDA0003023996600000132
respectively the rated charging and discharging power of the distributed energy storage installed at the node j.
Figure BDA0003023996600000133
And the upper limits of rated charging and discharging power of distributed energy storage installed at the node j are respectively. OmegafsTo install a set of distributed energy storage nodes.
Figure BDA0003023996600000141
In the formula, Efs,jFor distributed energy storage capacity installed at node j, EFSThe total capacity of the installed distributed energy storage is increased.
3) Energy storage SOC constraint
Figure BDA0003023996600000142
In the formula, Efs,tFor distributed energy storage capacity at time t, Socfs,i,t、
Figure BDA0003023996600000143
Respectively, the state of charge and the upper and lower limits of the distributed energy storage j at the time t. Etach、ηdischRespectively the charge-discharge efficiency of stored energy.
Photovoltaic output constraint
Ppv,o,t≤PG,o，o∈Ωpv
In the formula, Ppv,o,tThe photovoltaic output of the o node at the time t; pG,oThe installed photovoltaic capacity of the o node; omegapvA collection of photovoltaic nodes is installed.
Photovoltaic absorption constraint
Figure BDA0003023996600000144
In order to improve the photovoltaic utilization rate of the user side, the photovoltaic consumption rate is setGreater than the lowest expected value. In the formula, CpvThe photovoltaic absorption rate is the lowest expected value.
In the prior art, only the requirement response of a user is considered, and often, a plurality of defects are pointed out, for example, the uninterruptible load in a response period and the unsuitable cost of adjustment and production of a large industrial user, the peak clipping and valley filling effect is poor due to excessive uninterruptible load, the benefit of using time-of-use electricity price is greatly reduced due to excessive unsuitable cost, the peak-valley electricity price and the peak-valley period are unreasonably formulated, and the user participation is low. And the energy storage device and the user demand response principle are jointly modeled, so that the defects can be well overcome, peak clipping and valley filling are better performed, and the stability of the power system is promoted.
By constructing an energy storage double-layer optimization configuration model, the upper layer model and the lower layer model alternately iterate by taking time-of-use electricity price and power interacted with a higher-level power grid as coupling variables, and by optimally selecting rated power, installation capacity and grid-connected positions of a centralized high-capacity energy storage system and a distributed low-capacity energy storage system at different voltage levels, peak clipping and valley filling at a power distribution side and a user side are realized, and new energy consumption requirements are promoted. According to the method, optimization configuration is carried out by jointly considering different voltage levels and different energy storage scales, distributed energy storage and centralized energy storage are jointly applied, two energy storage modes are respectively made good at the best and are kept away from the shortest, and the best is brought out, meanwhile, the application value of the energy storage system in high-permeability distributed power generation is fully exploited, the economy of the energy storage system is improved, a new application mode is provided for popularization of energy storage, and rapid development and application of energy storage in a power system are promoted.
According to the method, an upper-layer optimized configuration model with optimal comprehensive cost of the power distribution network side is constructed, peak clipping and valley filling of the power distribution network side are realized through the construction of the model, and safe and stable operation of a power system of the power distribution network side is promoted; meanwhile, the application of the centralized energy storage system can make up the defect of rough demand response adjustment, further promote peak clipping and valley filling of the power distribution network side and improve the economical efficiency of power network operation.
According to the embodiment, a lower-layer optimal configuration model with optimal comprehensive cost on the user side is constructed, and due to the fact that output of distributed energy sources such as photovoltaic energy has the characteristics of being high in randomness, strong in fluctuation and the like, the capacity of the distributed energy sources accessed into a power grid is limited due to a lot of influences caused after the distributed energy sources are accessed into the power distribution network. The distributed energy storage system is matched with distributed energy sources with strong volatility, such as photovoltaic and the like, so that photovoltaic consumption of users in areas with high photovoltaic permeability is promoted, and photovoltaic resources are fully utilized; the photovoltaic power generation system can also play the quick charge-discharge characteristic of the energy storage system to stabilize the fluctuation, improve the power output quality of the photovoltaic, greatly improve the quality of electric energy, achieve the aim of adjusting the total output power on a certain time scale, and further enable the photovoltaic power generation system to have a certain degree of adjustability.
The energy storage double-layer optimization configuration model of the embodiment is a multi-layer energy storage joint planning model, and the centralized energy storage has the characteristics of high energy density, high conversion efficiency and strong reliability, and can meet the application requirements of high power and high capacity; the distributed energy storage has the characteristics of flexible scale, various forms, strong station site adaptability and the like, and can meet the requirements of common users. However, the centralized energy storage occupies a large area, natural conditions are limited, and equipment cost and operation and maintenance cost are high; and the distributed energy storage single-point access capacity is small, the accumulated scale is limited, and the supporting effect on the power system is not obvious. Therefore, a layered energy storage system combining centralized energy storage and distributed energy storage is developed, the flexible layout and aggregation effect of the distributed energy storage and the large-scale advantages of the centralized energy storage are fully exerted, the advantages of the energy storage of different levels are complemented, the application value of the energy storage system in high-permeability distributed power generation is fully excavated, the economy and the operation efficiency of the energy storage system are improved, a new application mode is provided for the popularization of the energy storage, and the rapid development of the energy storage in a power system is promoted.
Example two:
a multi-level energy storage double-layer optimization configuration method based on user demand response comprises the following steps
(1) The method comprises the steps of taking annual comprehensive cost optimization of the power distribution network as a target, and constructing an upper-layer optimization model by considering linear DistFlow constraint, substation power second-order cone constraint, line power constraint, centralized energy storage constraint, user demand response constraint and the like.
(2) And constructing a lower-layer optimal configuration model by taking the annual comprehensive cost optimization of the user as a target and considering power balance constraint, distributed energy storage constraint, photovoltaic output constraint and photovoltaic absorption constraint.
(3) The upper layer model is optimized by taking hours as a unit on the basis of the past electricity prices according to the historical load data and the historical photovoltaic data and combining the electricity and power demand elastic matrix, when the electricity consumption of the next hour is larger than that of the previous hour, the electricity price is adjusted upwards, otherwise, the electricity price is adjusted downwards, and the time-of-use electricity price of the next day is finally obtained.
In the step (1), the specific implementation method is as follows:
step (1-1) comprehensively considering the annual comprehensive cost of setting centralized energy storage and purchasing electricity to the upper-level power grid of the power distribution network, and obtaining an objective function expressed as follows:
F1＝C1+C2+C3-C4 (27)
Figure BDA0003023996600000161
Figure BDA0003023996600000162
Figure BDA0003023996600000166
Figure BDA0003023996600000163
in the formula, C1For purchasing electricity from the upper grid, C2For centralized energy storage equal annual investment costs, C3For centralized energy storage operating costs, C4Selling revenue to users for a distribution network, Pk,tThe power purchasing quantity from the power distribution network to a superior power grid at the moment t; ckThe purchase price of electricity; ddxTo pick the number of typical days. CpIn order to keep the unit power cost of the energy storage battery,
Figure BDA0003023996600000164
rated power for the centralized energy storage battery; ceIn order to provide the cost per unit capacity of the energy storage battery,
Figure BDA0003023996600000165
the rated capacity is the energy storage battery; ccAnd renting expenses for the centralized energy storage site. Gamma is the discount rate; d1The service life of the centralized energy storage device is prolonged; crFor operating costs per unit power of centralized energy storage, CwCost is maintained for centralized energy storage per power. In the formula, Ps,tWhen the power distribution network sells electricity to the power distribution network, the value is positive and represents the electricity purchasing expense; cs,tTrading prices for the amount of electricity at time t.
Step (1-2): constraint conditions considered during the establishment of a multi-level energy storage double-layer optimization configuration model based on user demand response comprise linear DistFlow constraint, substation power second-order cone constraint, line power constraint, centralized energy storage constraint and user demand response constraint, and are as follows:
and (1) using linear DistFlow power flow constraint of the active power distribution network.
Figure BDA0003023996600000171
In the formula, Pij，QijRespectively representing the active and reactive power transfer between line i and line j, rij，xijRespectively, the resistance and reactance between bus i and bus j, and m represents the parent node of node i.
Figure BDA0003023996600000172
Respectively the active power consumed and flowing out on the line i,
Figure BDA0003023996600000173
respectively the reactive power consumed and flowing out on the line i.
Step (1-4) considering transformer substation power second-order cone constraint
Figure BDA0003023996600000174
In the formula:
Figure BDA0003023996600000175
respectively providing active power and reactive power of the transformer substation i at the moment t;
Figure BDA0003023996600000176
rated capacity for the existing substation i; n is a radical ofsubThe number of the transformer substations is.
Step (1-5) takes into account line transmission power constraints
SLi，t≤SLimax i＝1,2,……NL (34)
In the formula, SLi，tThe transmission capacity of the branch i at the moment t; sLimaxMaximum transmission capacity for branch i; n is a radical ofLIs the total number of branches.
Step (1-3) considers centralized energy storage constraint
Step (1-3-1) considers the centralized energy storage number constraint
Njz≤Nsub (35)
Wherein N isjzNumber of energy stored in a centralized manner, NsubNumber of transformer stations
Step (1-3-2) considers the constraint of centralized energy storage charging and discharging power
Figure BDA0003023996600000177
In the formula (I), the compound is shown in the specification,
Figure BDA0003023996600000178
centralization of installation for nodes i respectivelyRated charging power and discharging power of the formula energy storage.
Figure BDA0003023996600000179
Figure BDA00030239966000001710
And the upper limits of rated charging and discharging power of centralized energy storage installed for the node i respectively. OmegajzA centralized set of energy storage nodes is installed.
Step (1-3-3) considers the constraint of centralized energy storage capacity
Figure BDA0003023996600000181
In the formula, Ejz,iCentralized energy storage capacity installed for node i, EJZThe total capacity of centralized energy storage for installation.
Step (1-3-4) considers centralized energy storage SOC constraint
Figure BDA0003023996600000182
In the formula, Ejz,tIs the centralized energy storage capacity, Soc, at time tjz,t、
Figure BDA0003023996600000183
Respectively, the state of charge and the upper and lower limits of the centralized energy storage i at the time t. Etach、ηdischRespectively the charge-discharge efficiency of stored energy.
Step (1-4) considering user demand response constraint
Figure BDA0003023996600000184
In the formula (d)o(t)、dTOU(t) load requirements before and after time-of-use electricity prices are adopted; ρ (t)0) Rho (t) is the price of electricity before and after the time-of-use price of electricity is adopted; e.g. of the typei,i(t) required elasticity at time tA coefficient matrix;
-DTOU≤dTOU(t)≤DTOU (40)
in the formula, DTOUIs a total amount limit of demand response. If the user excessively responds to the electricity price, the situation of inversion of the peak and the valley occurs. The maximum response limit of the TOU is set to 10% of the peak load in this model.
p(t)≥Cav (41)
In the formula, CavThe average power cost of the system.
In the step (2), the specific implementation method is as follows:
and (2-1) calculating the lower-layer optimization model under the condition that the upper-layer optimization model obtains the optimal time-of-use electricity price and centralized energy storage capacity scheme, wherein the optimization target is the optimal annual comprehensive cost of the user side.
The objective function of the underlying model is:
the lower model aims at minimizing the annual integrated cost of the user:
F2＝C5+C6+C7+C8 (42)
wherein, C5The electricity purchasing cost for the superior power grid; c6Investment cost for distributed energy storage; c7Operating costs for distributed energy storage; c8For photovoltaic maintenance costs, expressions for each sub-objective are listed below:
Figure BDA0003023996600000191
Figure BDA0003023996600000192
Figure BDA0003023996600000196
Figure BDA0003023996600000193
in the formula, Pb,tThe interactive power of the user and the superior power grid at the time t is determined as positive when the user purchases power from the power distribution network and negative when the user sells the power; cbThe price of buying/selling electricity at the time t; cpIn order to keep the unit power cost of the energy storage battery,
Figure BDA0003023996600000194
the rated power of the distributed energy storage battery is set; ceIn order to provide the cost per unit capacity of the energy storage battery,
Figure BDA0003023996600000195
the rated capacity is the energy storage battery; d2The service life of the distributed energy storage device is prolonged; crOperating costs per unit power for decentralized energy storage, CwThe cost of unit power maintenance for distributed energy storage; kpvAnd the photovoltaic operation maintenance coefficient is obtained.
The constraint condition considered by the lower layer model in the step (2-2) is
Step (2-3) considers power balance constraints
Ppv(t)+Pbess,fs(t)-Pload(t)-Ptrans(t)＝0 (47)
In the formula, Ppv(t) photovoltaic output at time t; pbess,fs(t) is the distributed energy storage charging and discharging power at the moment t, the value is positive when the energy storage is discharged, and the value is negative when the energy storage is charged; pload(t) is the load at time t; ptransAnd (t) the interaction power of the user and the superior power grid at the moment t.
Step (2-4) considering distributed energy storage constraint
Step (2-4-1) considering the number constraint of distributed energy storage
The distributed energy stores will be installed at the load nodes or at the respective feeders, so the number of distributed energy stores should be less than the sum of the number of loads and the number of feeders:
Nfs≤Nload+Nline (48)
in the formula, NfsNumber of distributed energy storage, NloadIs the number of load nodes, NlineThe number of the feeder lines is.
Step (2-4-2) considering distributed energy storage power constraint
The distributed energy storage power and capacity are less than set values:
Figure BDA0003023996600000201
in the formula (I), the compound is shown in the specification,
Figure BDA0003023996600000202
respectively the rated charging and discharging power of the distributed energy storage installed at the node j.
Figure BDA0003023996600000203
And the upper limits of rated charging and discharging power of distributed energy storage installed at the node j are respectively. OmegafsTo install a set of distributed energy storage nodes.
Step (2-4-3) considering the constraint of distributed energy storage capacity
Figure BDA0003023996600000204
In the formula, Efs,jFor distributed energy storage capacity installed at node j, EFSThe total capacity of the installed distributed energy storage is increased.
Step (2-4-4) considering distributed energy storage SOC constraint
Figure BDA0003023996600000205
In the formula, Efs,tFor distributed energy storage capacity at time t, Socfs,i,t、
Figure BDA0003023996600000206
Respectively, the state of charge and the upper and lower limits of the distributed energy storage j at the time t. Etach、ηdischAre respectively energy storageThe charge and discharge efficiency of the cell.
Step (2-5) considering photovoltaic output constraint
Ppv,o,t≤PG,o，o∈Ωpv (52)
In the formula, Ppv,o,tThe photovoltaic output of the o node at the time t; pG,oThe installed photovoltaic capacity of the o node; omegapvA collection of photovoltaic nodes is installed.
Step (2-6) consider photovoltaic absorption constraint
Figure BDA0003023996600000207
In order to improve the photovoltaic utilization rate of the user side, the photovoltaic consumption rate is set to be larger than the lowest expected value. In the formula, CpvThe photovoltaic absorption rate is the lowest expected value.
In the step (3), the specific implementation method is
And the optimal time-of-use electricity price obtained by solving the upper layer model is brought into the lower layer model, the lower layer model adjusts the load demand according to the time-of-use electricity price optimized by the upper layer model, and sends the time-of-use electricity price and the exchange power curve of the upper layer power grid to the upper layer model, and the upper layer model and the lower layer model carry out iterative solution by taking the time-of-use electricity price and the exchange power as coupling variables. And the capacity of centralized and distributed energy storage under the optimal condition can be obtained.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Example three:
the embodiment provides a multi-level energy storage optimal configuration device based on user demand response, which is characterized by comprising:
a model construction module: constructing a multi-level energy storage double-layer optimization configuration model; the multi-level energy storage double-layer optimization configuration model takes the optimal economy of an active power distribution network and a user as a target and comprises an upper-layer optimization model on the power distribution network side and a lower-layer optimization model on the user side;
an information acquisition module: acquiring historical user and superior power grid exchange power values, historical photovoltaic output values and urban power distribution network structures;
an upper layer optimization module: inputting the historical user and upper-level power grid exchange power value, the historical photovoltaic output value and the urban power distribution network structure into an upper-layer optimization model of a multi-layer energy storage double-layer optimization configuration model, and performing optimization calculation by taking the minimum running cost of the power distribution network as a target function according to upper-layer constraint conditions to obtain the centralized energy storage capacity and the time-of-use electricity price;
the lower layer optimization module: inputting the time-of-use electricity price into a lower-layer optimization model; under the time-of-use electricity price, optimizing and calculating according to a lower-layer constraint condition by taking the minimum running cost of the user as an objective function to obtain the load demand of the user and the charge-discharge state of the distributed energy storage, and returning the optimized interaction power of the upper-layer power grid of the user to the upper-layer optimization model;
a scheme acquisition module: alternately iterating the upper and lower layers of optimization models by taking time-of-use electricity price and interaction power of a user and a superior power grid as coupling variables until the upper and lower layers of objective functions are converged, and obtaining an optimal configuration scheme at the moment; the optimal configuration scheme comprises the optimal configurations of centralized energy storage, distributed energy storage capacity, charging and discharging power configuration and time-of-use electricity price.
The device needs to obtain the power value of the historical user and the superior power grid exchange, the photovoltaic historical output curve and the urban power distribution network structure, and brings the historical user and the superior power grid exchange power value into a multi-level energy storage optimization configuration model. The established multi-level energy storage optimization configuration model is a Mixed Integer Second-order Cone Programming (MI-SOCP)) problem, and is optimally configured by using Yalmip and Cplex; and outputting a new interactive power curve and distributed energy storage charging and discharging power of the user and the superior power grid.
Example four:
the embodiment of the invention also provides a multi-level energy storage optimal configuration device based on user demand response, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of embodiment two.
Example five:
the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method according to the second embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A multi-level energy storage optimal configuration method based on user demand response is characterized by comprising the following steps:
constructing a multi-level energy storage double-layer optimization configuration model; the multi-level energy storage double-layer optimization configuration model takes the optimal economy of an active power distribution network and a user as a target and comprises an upper-layer optimization model on the power distribution network side and a lower-layer optimization model on the user side;
acquiring a historical user load value, a historical photovoltaic output value and an urban power distribution network structure;
inputting a historical user load value, a historical photovoltaic output value and an urban distribution network structure into an upper-layer optimization model of a multi-level energy storage double-layer optimization configuration model, and performing optimization calculation by taking the minimum running cost of the distribution network as a target function according to an upper-layer constraint condition to obtain the centralized energy storage capacity and the time-of-use electricity price;
inputting the time-of-use electricity price into a lower-layer optimization model; under the time-of-use electricity price, optimizing and calculating according to a lower-layer constraint condition by taking the minimum running cost of the user as an objective function to obtain the load demand of the user and the charge-discharge state of the distributed energy storage, and returning the optimized interaction power of the upper-layer power grid of the user to the upper-layer optimization model;
alternately iterating the upper and lower layers of optimization models by taking time-of-use electricity price and interaction power of a user and a superior power grid as coupling variables until the upper and lower layers of objective functions are converged, and obtaining an optimal configuration scheme at the moment; the optimal configuration scheme comprises the optimal configurations of centralized energy storage, distributed energy storage capacity, charging and discharging power configuration and time-of-use electricity price.
2. The multi-level energy storage optimization configuration method based on user demand response according to claim 1, wherein the multi-level energy storage double-layer optimization configuration model is a Mixed Integer Second-order Cone Programming (MI-SOCP) problem, and is solved by a Yalmip and Cpelx solver; the construction method of the multi-level energy storage double-layer optimization configuration model comprises the following steps:
aiming at the optimal annual comprehensive cost of the power distribution network, constructing an upper-layer optimization model by considering linear DistFlow constraint, substation power second-order cone constraint, line power constraint, centralized energy storage constraint and user demand response constraint;
and constructing a lower-layer optimization model by taking the annual comprehensive cost optimization of the user as a target and considering power balance constraint, distributed energy storage constraint, photovoltaic output constraint and photovoltaic absorption constraint.
3. The user demand response-based multi-level energy storage optimization configuration method according to claim 2, wherein the upper-level optimization model aims at minimizing annual comprehensive cost of the power distribution network, and an objective function of the upper-level optimization model is as follows:
F1＝C1+C2+C3-C4
wherein, F1For annual integrated cost of active distribution network, C1The electricity purchasing cost is carried out on the power distribution network to a superior power grid; c2Initial investment cost for centralized energy storage; c3Operating and maintaining costs for centralized energy storage; c4For the benefit of selling electricity to users for the power distribution network, the following expressions are used for each sub-target:
Figure FDA0003023996590000021
Figure FDA0003023996590000022
Figure FDA0003023996590000023
Figure FDA0003023996590000024
in the formula, Pk,tThe method comprises the following steps of (1) purchasing electric quantity from a power distribution network to a superior power grid at a time T, wherein T is the hours of a selected typical day; ckThe purchase price of electricity; ddxSelecting the number of typical days; cpIn order to keep the unit power cost of the energy storage battery,
Figure FDA0003023996590000025
rated power for the centralized energy storage battery; ceIn order to provide the cost per unit capacity of the energy storage battery,
Figure FDA0003023996590000026
the rated capacity is the energy storage battery; ccRenting cost for a centralized energy storage place, and gamma is the discount rate; d1The service life of the centralized energy storage device is prolonged; crFor operating costs per unit power of centralized energy storage, CwCost of maintenance for centralized energy storage per power; psold,tThe amount of electricity sold to the user for the distribution network at time t, CsoldIs the price for selling electricity.
4. The user demand response-based multi-level energy storage optimization configuration method according to claim 2, wherein the lower-layer optimization model aims at minimizing user annual integrated cost, and an objective function is as follows:
F2＝C5+C6+C7+C8
wherein, F2For the annual combined cost of the user, C5The electricity purchasing cost is carried out on the superior power grid for the user; c6Investment cost for distributed energy storage; c7Operating costs for distributed energy storage; c8Photovoltaic maintenance costs; the following are expressions for each sub-target:
Figure FDA0003023996590000031
Figure FDA0003023996590000032
Figure FDA0003023996590000033
Figure FDA0003023996590000034
in the formula, Pb,tThe interactive power of the user and the superior power grid at the time t is determined as positive when the user purchases power from the power distribution network and negative when the user sells the power; cbThe price of buying/selling electricity at the time t; cpIn order to keep the unit power cost of the energy storage battery,
Figure FDA0003023996590000035
the rated power of the distributed energy storage battery is set; ceIn order to provide the cost per unit capacity of the energy storage battery,
Figure FDA0003023996590000036
the rated capacity is the energy storage battery; d2The service life of the distributed energy storage device is prolonged; crOperating costs per unit power for decentralized energy storage, CwThe cost of unit power maintenance for distributed energy storage; kpvAnd the photovoltaic operation maintenance coefficient is obtained.
5. The user demand response-based multi-level energy storage optimization configuration method according to claim 1, wherein the method for obtaining the optimal configuration scheme of the centralized and decentralized energy storage collaborative planning comprises:
based on the multi-level energy storage double-layer planning model, the optimal time-of-use electricity price, the interaction power of the user and the superior distribution network and the optimal capacity of centralized and distributed energy storage are obtained through iterative solution of the upper layer model and the lower layer model.
6. The user demand response-based multi-level energy storage optimization configuration method according to claim 1, wherein the upper-level constraint conditions include a linear DistFlow power flow constraint, a substation second-order cone constraint, a line transmission power constraint, a centralized energy storage constraint, and a user demand corresponding constraint;
the linear DistFlow flow constraint adopts a DistFlow flow model:
Figure FDA0003023996590000041
in the formula, Pij，QijRespectively representing the active and reactive power transfer between line i and line j, rij，xijRespectively representing the resistance and reactance between the bus i and the bus j, and m represents a father node of the node i; pi L、Pi GRespectively the active power consumed and flowing out on the line i,
Figure FDA0003023996590000042
respectively the reactive power consumed and flowed out on the line i;
the second-order cone constraint of the transformer substation is that the square of active power and reactive power of the transformer substation should be smaller than the square of rated capacity of the transformer substation:
Figure FDA0003023996590000043
in the formula:
Figure FDA0003023996590000044
respectively providing active power and reactive power of the transformer substation i at the moment t;
Figure FDA0003023996590000045
rated capacity for the existing substation i; n is a radical ofsubThe number of the transformer substations is;
the line transmission power constraint is:
SLi，t≤SLimax i＝1,2,……NL
in the formula, SLi，tThe transmission capacity of the branch i at the moment t; sLimaxMaximum transmission capacity for branch i; n is a radical ofLThe total number of branches;
the centralized energy storage constraint comprises a number constraint, an energy storage power, a capacity constraint and an SOC constraint;
the number constraint is that the number of the centralized energy storage is smaller than the number of the transformer substations:
Njz≤Nsub
wherein N isjzNumber of energy stored in a centralized manner, NsubThe number of the transformer substations is;
the energy storage power and the capacity are constrained to be centralized energy storage power, and the capacity is smaller than a set value:
Figure FDA0003023996590000051
in the formula (I), the compound is shown in the specification,
Figure FDA0003023996590000052
rated charging power and discharging power of centralized energy storage installed for the node i respectively;
Figure FDA0003023996590000053
upper limits of rated charging and discharging power of centralized energy storage installed for the node i respectively; omegajzInstalling a centralized energy storage node set;
Figure FDA0003023996590000054
in the formula, Ejz,iCentralized energy storage capacity installed for node i, EJZA centralized total energy storage capacity for installation;
the SOC constraint is:
Figure FDA0003023996590000055
wherein soc (state of charge) is the state of charge of the stored energy, which is defined as the ratio of the current capacity to the rated capacity of the system; ejz,tIs the centralized energy storage capacity, Soc, at time tjz,t、
Figure FDA0003023996590000056
Respectively the state of charge and the upper and lower limits of the centralized energy storage i at the moment t; etach、ηdischRespectively the charge and discharge efficiency of the stored energy;
the user response expression of the user demand response constraint comprises:
Figure FDA0003023996590000057
in the formula (d)o(t)、dTOU(t) load requirements before and after time-of-use electricity prices are adopted; ρ (t)0) Rho (t) is the price of electricity before and after the time-of-use price of electricity is adopted; e.g. of the typei,i(t) is a required elastic coefficient matrix at time t; gamma-shapedn、ΓmRespectively representing an excitation demand ratio and a punishment demand ratio; a (t), pen (t) andrespectively adjusting the income of the user after the electricity consumption and punishment of the electricity consumption which is not adjusted according to the contract;
the following equation is the total demand response limit;
-DTOU≤dTOU(t)≤DTOU
in the formula, DTOUA total volume limit for demand response;
to ensure that the demand response electricity price does not fall below the limit of the average power cost of the system,
p(t)≥Cav
in the formula, CavThe average power cost of the system.
7. The user demand response-based multi-level energy storage optimization configuration method according to claim 1, wherein the lower-layer constraint conditions include a power balance constraint, a decentralized energy storage constraint, a photovoltaic output constraint and a photovoltaic absorption constraint;
the expression of the power balance constraint is:
Ppv(t)+Pbess,fs(t)-Pload(t)-Ptrans(t)＝0
in the formula, Ppv(t) photovoltaic output at time t; pbess,fs(t) is the distributed energy storage charging and discharging power at the moment t, the value is positive when the energy storage is discharged, and the value is negative when the energy storage is charged; pload(t) is the load at time t; ptrans(t) the interaction power between the user and the superior power grid at the moment t;
the distributed energy storage constraint comprises a number constraint, an energy storage power, a capacity constraint and an energy storage SOC constraint;
the number constraint is that the number of the distributed energy storage is smaller than the sum of the load number and the feeder line number:
Nfs≤Nload+Nline
wherein N isfsNumber of distributed energy storage, NloadIs the number of load nodes, NlineThe number of the feeder lines is;
the energy storage power and the capacity are constrained to be distributed energy storage power and the capacity is smaller than a set value:
Figure FDA0003023996590000071
in the formula (I), the compound is shown in the specification,
Figure FDA0003023996590000072
rated charging and discharging powers of distributed energy storage installed at the node j are respectively set;
Figure FDA0003023996590000073
Figure FDA0003023996590000074
rated charging and discharging power upper limits of distributed energy storage installed at the node j are respectively set; omegafsInstalling a distributed energy storage node set;
Figure FDA0003023996590000075
in the formula, Efs,jFor distributed energy storage capacity installed at node j, EFSTotal capacity for installed distributed energy storage;
the energy storage SOC constraint is as follows:
Figure FDA0003023996590000076
in the formula, Efs,tFor distributed energy storage capacity at time t, Socfs,i,t、
Figure FDA0003023996590000077
Respectively the state of charge and the upper and lower limits of the distributed energy storage j at the time t; etach、ηdischRespectively the charge and discharge efficiency of the stored energy;
the photovoltaic output constraint expression is:
Ppv,o,t≤PG,o，o∈Ωpv
in the formula, Ppv,o,tThe photovoltaic output of the o node at the time t; pG,oThe installed photovoltaic capacity of the o node; omegapvA collection of installed photovoltaic nodes;
the expression of the photovoltaic absorption constraint is:
Figure FDA0003023996590000078
in the formula, CpvThe photovoltaic absorption rate is the lowest expected value.
8. A multi-tier energy storage optimal configuration apparatus based on user demand response, the apparatus comprising:
a model construction module: constructing a multi-level energy storage double-layer optimization configuration model; the multi-level energy storage double-layer optimization configuration model takes the optimal economy of an active power distribution network and a user as a target and comprises an upper-layer optimization model on the power distribution network side and a lower-layer optimization model on the user side;
an information acquisition module: acquiring a historical user load value, a historical photovoltaic output value and an urban power distribution network structure;
an upper layer optimization module: inputting a historical user load value, a historical photovoltaic output value and an urban distribution network structure into an upper-layer optimization model of a multi-level energy storage double-layer optimization configuration model, and performing optimization calculation by taking the minimum running cost of the distribution network as a target function according to an upper-layer constraint condition to obtain the centralized energy storage capacity and the time-of-use electricity price;
the lower layer optimization module: inputting the time-of-use electricity price into a lower-layer optimization model; under the time-of-use electricity price, optimizing and calculating according to a lower-layer constraint condition by taking the minimum running cost of the user as an objective function to obtain the load demand of the user and the charge-discharge state of the distributed energy storage, and returning the optimized interaction power of the upper-layer power grid of the user to the upper-layer optimization model;
a scheme acquisition module: alternately iterating the upper and lower layers of optimization models by taking time-of-use electricity price and interaction power of a user and a superior power grid as coupling variables until the upper and lower layers of objective functions are converged, and obtaining an optimal configuration scheme at the moment; the optimal configuration scheme comprises the optimal configurations of centralized energy storage, distributed energy storage capacity, charging and discharging power configuration and time-of-use electricity price.
9. A multi-level energy storage optimal configuration device based on user demand response is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
10. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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CN114142460A (en) * 2021-11-17 2022-03-04 浙江华云电力工程设计咨询有限公司 Energy storage double-layer target optimization configuration method and terminal in comprehensive energy system
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CN114142460A (en) * 2021-11-17 2022-03-04 浙江华云电力工程设计咨询有限公司 Energy storage double-layer target optimization configuration method and terminal in comprehensive energy system
CN114142460B (en) * 2021-11-17 2024-03-15 浙江华云电力工程设计咨询有限公司 Energy storage double-layer target optimal configuration method and terminal in comprehensive energy system
CN114741834A (en) * 2021-12-02 2022-07-12 华北电力大学 Comprehensive energy flow optimization method and device based on space-time expansion network flow
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