CN115313519A - Power distribution network energy storage optimal configuration method, device, equipment and storage medium - Google Patents

Power distribution network energy storage optimal configuration method, device, equipment and storage medium Download PDF

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Publication number
CN115313519A
CN115313519A CN202211125655.2A CN202211125655A CN115313519A CN 115313519 A CN115313519 A CN 115313519A CN 202211125655 A CN202211125655 A CN 202211125655A CN 115313519 A CN115313519 A CN 115313519A
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energy storage
distribution network
power distribution
flexibility
cost
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Inventor
安然然
杨跃
岳菁鹏
龚锐
梁晓兵
马燕君
黄振琳
魏焱
赵兵
游祥
郭浩然
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Guangdong Power Grid Co Ltd
Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Guangdong Power Grid Co Ltd
Electric Power Research Institute of Guangdong Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a power distribution network energy storage optimization configuration method, a device, equipment and a storage medium, wherein an upper-layer configuration decision model taking the minimum annual comprehensive cost of a power distribution network as a target function and a lower-layer operation optimization model taking the minimum risk cost with insufficient flexibility as the target function are constructed, a plurality of initial energy storage configuration schemes are input into the lower-layer operation optimization model, the initial energy storage configuration schemes are solved, the corresponding first risk cost with insufficient flexibility is input into the upper-layer configuration decision model, the initial energy storage configuration schemes are subjected to iterative updating based on a particle swarm optimization until a population optimal solution is obtained, and the optimal energy storage configuration scheme is output. According to the technical scheme, the risk cost with insufficient flexibility is considered, the flexibility resources of the power distribution network are fully called, the minimum comprehensive cost is taken as a target function, the effect of improving the flexibility of the power distribution network by energy storage is maximized, the capability of the power distribution network for dealing with wind and light load output fluctuation is improved, and the economical efficiency and the flexibility of the operation of the power distribution network can be guaranteed.

Description

Power distribution network energy storage optimal configuration method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a power distribution network energy storage optimal configuration method, device, equipment and storage medium.
Background
The wind power photovoltaic power generation output is influenced by natural environment factors such as wind speed, illumination, temperature and other conditions, and has the characteristics of volatility, uncertainty and the like, so that the safe and stable operation of a power system is greatly influenced, for example: abandoning wind and abandoning light, voltage instability, unbalanced tidal current distribution and the like.
At present, the academic community has a plurality of methods for researching the uncertain fluctuation of wind-solar output and load, but the methods have limitations; the probabilistic scene method requires a large amount of historical data to analyze the distribution of random variables, and as the number of input scenes increases, the model complexity and the calculation amount also increase; the robust optimization method carries out optimization processing on the uncertain parameters based on the worst condition of the uncertain parameters, but the result is usually over conservative, so that the investment and operation cost of the system are over high and do not meet the economic requirement; the interval optimization avoids over conservation of robust optimization, but the range selection of interval variables can greatly influence the result properties; if the wind-solar output upper limit and the wind-solar output lower limit are defined by a normal distribution method or a confidence interval, the information on two sides of the interval is not considered, so that the evaluation result is not comprehensive enough.
At present, scholars at home and abroad have a certain research on the economy and flexibility of the optimal configuration of the distributed energy storage system, but most of the scholars consider the optimization targets to be single or optimize based on multiple targets, and the optimization models based on comprehensive evaluation indexes of the economy and the reliability are few.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method, the device, the equipment and the storage medium for the optimal configuration of the energy storage of the power distribution network are provided, and the economical efficiency and the flexibility of the operation of the power distribution network are guaranteed through the constructed double-layer optimal configuration model of the energy storage of the power distribution network.
In order to solve the technical problem, the invention provides a power distribution network energy storage optimal configuration method, which comprises the following steps:
constructing an upper-layer configuration decision model by taking the minimum annual comprehensive cost of the power distribution network as a first objective function, and setting a first constraint condition for the upper-layer configuration decision model;
constructing a lower-layer operation optimization model by taking the minimum risk cost with insufficient flexibility as a second objective function, and setting a second constraint condition for the lower-layer operation optimization model;
setting the population scale of the particle swarm, and setting the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm to obtain a plurality of initial energy storage configuration schemes;
respectively inputting the initial energy storage configuration schemes into the lower-layer operation optimization model, so that the lower-layer operation optimization model solves each initial energy storage configuration scheme, and outputs a first insufficient flexibility risk cost corresponding to each initial energy storage configuration scheme;
inputting the first insufficient-flexibility risk cost into the upper-layer configuration decision model, iteratively updating the plurality of initial energy storage configuration schemes based on a particle swarm algorithm until a population optimal solution is obtained, and outputting an optimal energy storage configuration scheme.
In a possible implementation manner, after obtaining a plurality of initial energy storage configuration schemes, the method further includes:
acquiring network parameters of a power distribution network and wind power generation parameters, photovoltaic power generation parameters and load parameters of each typical day;
predicting the wind power generation parameters, the photovoltaic power generation parameters and the load parameters to obtain a wind power output predicted value, a photovoltaic output predicted value and a load output predicted value;
and generating a power distribution network scene based on the power distribution network parameters, the wind power output predicted value, the photovoltaic output predicted value, the load output predicted value and the initial energy storage configuration scheme.
In a possible implementation manner, the method for constructing the upper-layer configuration decision model by using the minimum annual integrated cost of the power distribution network as a first objective function specifically includes:
the minimum annual comprehensive cost of the power distribution network is taken as a first objective function, wherein the annual comprehensive cost comprises the equal annual investment cost of energy storage, the annual operation cost of the power distribution network, the annual flexibility resource calling cost of the power distribution network and the annual flexibility shortage risk cost of the power distribution network;
the first objective function is as follows:
min C total ＝C ess +C net +C fs +C risk ；
in the formula, C total The annual comprehensive cost of the power distribution network; c ess Equal annual investment cost for energy storage; c net The annual operating cost of the power distribution network is calculated; c fs The annual flexible resource calling cost of the power distribution network is saved; c risk The annual flexibility of the distribution network is insufficient for risk cost.
In a possible implementation manner, setting a first constraint condition on the upper-layer configuration decision model specifically includes:
the first constraint condition comprises an energy storage rated power constraint, a rated capacity constraint and an energy storage installation position constraint;
wherein the energy storage rated power constraint is as follows:
Figure BDA0003847901970000041
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000042
upper and lower limits of rated power of stored energy respectively
The energy storage rated power constraint is as follows:
Figure BDA0003847901970000043
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000044
respectively an upper limit and a lower limit of the rated energy storage capacity;
the energy storage mounting position constraint is as follows:
1≤L ess,j ≤N node ；
in the formula, L ess,j The jth installation location of stored energy; n is a radical of node The number of the nodes of the power distribution network.
In one possible implementation, the risk cost with insufficient flexibility is minimized to a second objective function, wherein the second objective function is as follows:
Figure BDA0003847901970000045
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000046
the risk cost of insufficient flexibility for the distribution grid on the d-th typical day.
In a possible implementation manner, setting a second constraint condition on the lower-layer operation optimization model specifically includes:
the second constraint condition comprises an energy storage constraint, a superior main network constraint and a demand response constraint;
wherein the energy storage constraint is as follows:
Figure BDA0003847901970000047
S SOC,j,min ≤S SOC,j (t)≤S SOC,j,max ；
Figure BDA0003847901970000048
S SOC,j (0)＝S SOC,j (24)；
in the formula, S SOC,j (t) is the state of charge of the jth stored energy at time t; eta is charging efficiency;
Figure BDA0003847901970000049
for the jth energy stored charging, discharging power at time t, S SOC,j,max 、 S SOC,j,min Respectively the upper and lower limits of the j-th stored energy charge state;
the upper level main network constraint is as follows:
Figure BDA0003847901970000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000052
the power supply amount of the main grid in the t period is determined;
the demand response constraint is as follows:
Figure BDA0003847901970000053
of formula (II) to (III)' DR,j (t) is the load value of the node j at the moment t when the transferable load does not participate in the demand response; p DR,j,max 、P DR,j,min And participating in the upper and lower limits of the demand response for the transferable load of the node j at the time t.
In one possible implementation, the insufficient flexibility risk cost includes an up-flexibility insufficient risk cost and a down-flexibility insufficient risk cost, where the insufficient flexibility risk cost is as follows:
C risk (t)＝f ur (t)+f dr (t)；
Figure BDA0003847901970000054
Figure BDA0003847901970000055
in the formula (f) ur (t) risk cost of insufficient flexibility for upscaling, f dr (t) Down-Regulation of insufficient flexibility Risk cost, c ur The loss coefficient is the wind-solar electricity limiting loss coefficient; p ur (t) is the expected value of the power difference under the condition of insufficient up-regulation flexibility; c. C dr Loss factor for load shedding; p dr (t) a desired value of the power difference in case of insufficient turndown flexibility,
Figure BDA0003847901970000056
the upper and lower confidence limits, P, of the net load condition confidence interval calculated by the condition risk value CVaR under the confidence level beta ub (t)、P lb (t) is the flexibility bound upper and lower limits, f, of the distribution network NL (z) is the probability density function of the payload, P NL (t) payload uncertainty power.
The invention also provides a power distribution network energy storage optimal configuration device, which comprises: the system comprises an upper-layer configuration decision model building module, a lower-layer operation optimization model building module, an initial energy storage configuration scheme obtaining module, an insufficient-flexibility risk cost solving module and an optimal energy storage configuration scheme output module;
the upper-layer configuration decision model building module is used for building an upper-layer configuration decision model by taking the minimum annual comprehensive cost of the power distribution network as a first objective function, and setting a first constraint condition for the upper-layer configuration decision model;
the lower-layer operation optimization model building module is used for building a lower-layer operation optimization model by taking the minimum risk cost due to insufficient flexibility as a second objective function, and setting a second constraint condition for the lower-layer operation optimization model;
the initial energy storage configuration scheme acquisition module is used for setting the population scale of the particle swarm, setting the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm, and obtaining a plurality of initial energy storage configuration schemes;
the insufficient flexibility risk cost solving module is used for respectively inputting the plurality of initial energy storage configuration schemes into the lower-layer operation optimization model, so that the lower-layer operation optimization model solves each initial energy storage configuration scheme, and outputs a first insufficient flexibility risk cost corresponding to each initial energy storage configuration scheme;
and the optimal energy storage configuration scheme output module inputs the first insufficient-flexibility risk cost into the upper-layer configuration decision model, iteratively updates the plurality of initial energy storage configuration schemes based on a particle swarm optimization until a population optimal solution is obtained, and outputs an optimal energy storage configuration scheme.
In a possible implementation manner, the initial energy storage configuration scheme obtaining module is further configured to obtain a power distribution network parameter and a wind power generation parameter, a photovoltaic power generation parameter, and a load parameter of each typical day, predict the wind power generation parameter, the photovoltaic power generation parameter, and the load parameter to obtain a wind power output predicted value, a photovoltaic output predicted value, and a load output predicted value, and generate a power distribution network scene based on the power distribution network parameter, the wind power output predicted value, the photovoltaic output predicted value, the load output predicted value, and the initial energy storage configuration scheme.
In a possible implementation manner, the upper-layer configuration decision model building module is configured to build an upper-layer configuration decision model with a minimum annual integrated cost of the power distribution network as a first objective function, and specifically includes:
the minimum annual comprehensive cost of the power distribution network is taken as a first objective function, wherein the annual comprehensive cost comprises the equal annual investment cost of energy storage, the annual operation cost of the power distribution network, the annual flexibility resource calling cost of the power distribution network and the annual flexibility shortage risk cost of the power distribution network;
the first objective function is as follows:
min C total ＝C ess +C net +C fs +C risk ；
in the formula, C total The annual comprehensive cost of the power distribution network; c ess Equal annual investment cost for energy storage; c net The annual operating cost of the power distribution network is calculated; c fs The annual flexible resource calling cost of the power distribution network is saved; c risk The annual flexibility of the distribution network is insufficient for risk cost.
In a possible implementation manner, the upper-layer configuration decision model building module is configured to set a first constraint condition for the upper-layer configuration decision model, and specifically includes:
the first constraint condition comprises an energy storage rated power constraint, a rated capacity constraint and an energy storage installation position constraint;
wherein the energy storage rated power constraint is as follows:
Figure BDA0003847901970000071
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000072
upper and lower limits of the rated power of the stored energy
The energy storage rated power constraint is as follows:
Figure BDA0003847901970000073
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000074
respectively an upper limit and a lower limit of the rated energy storage capacity;
the energy storage mounting position constraint is as follows:
1≤L ess,j ≤N node ；
in the formula, L ess,j The jth installation location of stored energy; n is a radical of hydrogen node The number of the nodes of the power distribution network.
In one possible implementation manner, the lower layer runs an optimization model building module, which is configured to use the least risk cost due to insufficient flexibility as a second objective function, where the second objective function is as follows:
Figure BDA0003847901970000081
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000082
there is insufficient risk cost for flexibility on the d-th typical day of the distribution grid.
In a possible implementation manner, the lower-layer operation optimization model building module is configured to set a second constraint condition for the lower-layer operation optimization model, and specifically includes:
the second constraint condition comprises energy storage constraint, superior main network constraint and demand response constraint;
wherein the energy storage constraint is as follows:
Figure BDA0003847901970000083
S SOC,j,min ≤S SOC,j (t)≤S SOC,j,max ；
Figure BDA0003847901970000084
S SOC,j (0)＝S SOC,j (24)；
in the formula, S SOC,j (t) is the state of charge of the jth stored energy at time t; eta is charging efficiency;
Figure BDA0003847901970000085
for the jth energy stored charging, discharging power at time t, S SOC,j,max 、 S SOC,j,min Respectively the upper and lower limits of the charge state of the jth stored energy;
the upper level main network constraint is as follows:
Figure BDA0003847901970000086
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000087
the power supply amount of the main network in the t period is set;
the demand response constraint is as follows:
Figure BDA0003847901970000088
of formula (II) to (III)' DR,j (t) is the load value of the node j at the moment t when the transferable load does not participate in the demand response; p is DR,j,max 、P DR,j,min And participating the upper limit and the lower limit of the demand response for the transferable load of the node j at the moment t.
In one possible implementation, the under-flexibility risk cost in the lower-layer operation optimization model building module includes an up-flexibility under-risk cost and a down-flexibility under-risk cost, where the under-flexibility risk cost is as follows:
C risk (t)＝f ur (t)+f dr (t)；
Figure BDA0003847901970000091
Figure BDA0003847901970000092
in the formula (f) ur (t) Up-Regulation insufficient flexibility Risk cost, f dr (t) Down-Regulation of insufficient flexibility Risk cost, c ur The loss coefficient is wind-light electricity limiting; p ur (t) is the expected value of the power difference under the condition of insufficient up-regulation flexibility; c. C dr Loss factor for load shedding; p dr (t) a desired value of the power difference in case of insufficient turndown flexibility,
Figure BDA0003847901970000093
the upper and lower limits of a confidence interval, P, of the net load condition obtained by calculating the condition risk value CVaR under the confidence level beta ub (t)、P lb (t) is the upper and lower limits of the flexibility boundary of the distribution network, f NL (z) probability density function of payload, P NL (t) is the payload uncertainty power.
The invention also provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the power distribution network energy storage optimization configuration method as described in any one of the above.
The invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the device where the computer-readable storage medium is located is controlled to execute the power distribution network energy storage optimization configuration method according to any item above.
Compared with the prior art, the method, the device, the equipment and the storage medium for optimizing the energy storage configuration of the power distribution network have the following beneficial effects that:
the method comprises the steps of constructing an upper-layer configuration decision model taking the minimum annual comprehensive cost of the power distribution network as a target function and a lower-layer operation optimization model taking the minimum risk cost with insufficient flexibility as the target function, inputting a plurality of initial energy storage configuration schemes into the lower-layer operation optimization model, solving and inputting the corresponding first risk cost with insufficient flexibility into the upper-layer configuration decision model, iteratively updating the plurality of initial energy storage configuration schemes based on a particle swarm algorithm until a population optimal solution is obtained, and outputting an optimal energy storage configuration scheme. Compared with the prior art, the technical scheme of the invention considers the risk cost of insufficient flexibility, fully calls the flexibility resources of the power distribution network, takes the minimum comprehensive cost as a target function, maximizes the effect of improving the flexibility of the power distribution network by energy storage, improves the capability of the power distribution network for dealing with wind and light load output fluctuation, and can ensure the economical efficiency and the flexibility of the operation of the power distribution network.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of a power distribution network energy storage optimization configuration method provided by the present invention;
fig. 2 is a schematic structural diagram of an embodiment of an energy storage optimal configuration device for a power distribution network according to the present invention;
FIG. 3 is a schematic diagram comparing a payload legacy confidence interval and a payload condition confidence interval for one embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the risk of insufficient flexibility for one embodiment of the present invention;
FIG. 5 is a schematic diagram of an IEEE33J node model according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an energy storage configuration scheme and a comprehensive cost analysis result according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Example 1
Referring to fig. 1, fig. 1 is a schematic flowchart of an embodiment of a power distribution network energy storage optimization configuration method provided by the present invention, and as shown in fig. 1, the method includes steps 101 to 105, specifically as follows:
step 101: and constructing an upper-layer configuration decision model by taking the minimum annual comprehensive cost of the power distribution network as a first objective function, and setting a first constraint condition for the upper-layer configuration decision model.
In one embodiment, the upper layer configuration decision model mainly considers configuration decisions of energy storage, and the decision variables include rated power, rated capacity and installation position of the energy storage.
In one embodiment, the upper-layer configuration decision model takes the annual integrated cost minimum of the power distribution network as a first objective function, wherein the annual integrated cost comprises the equal annual investment cost of energy storage, the annual operating cost of the power distribution network, the annual flexibility resource calling cost of the power distribution network and the annual flexibility shortage risk cost of the power distribution network.
Specifically, the first objective function is as follows:
min C total ＝C ess +C net +C fs +C risk ；
in the formula, C total The annual comprehensive cost of the power distribution network is reduced; c ess Equal annual investment cost for energy storage; c net Annual operating cost of the power distribution network; c fs The cost is called for annual flexible resources of the power distribution network; c risk The annual flexibility of the distribution network is insufficient for risk cost.
The equal annual investment costs for energy storage are as follows:
Figure BDA0003847901970000111
Figure BDA0003847901970000112
in the formula (I), the compound is shown in the specification,γ j is the t-th energy storage equal-year value coefficient; y is ess,j The operational life of the energy storage for the t-th time; r is the discount rate, N ess The quantity of stored energy is; c. C ep 、c ee Respectively the unit power of stored energy and the unit capacity investment cost;
Figure BDA0003847901970000121
the rated power and the rated capacity of the t-th stored energy are respectively.
For the annual operating costs of the distribution network, the following are shown:
Figure BDA0003847901970000122
in the formula, c loss 、c grid Respectively unit grid loss cost and unit main grid electricity purchasing cost; t is D Run time for each typical day; d is the number of selected typical days; t is the operation time in the day;
Figure BDA0003847901970000123
the electric quantity of the network loss and the electric quantity of the main network purchase at the d-th typical day time t are respectively corresponding.
For the annual flexibility resource calling cost of the power distribution network, the following is shown:
Figure BDA0003847901970000124
in the formula, c cd 、c oltc 、c DR The cost of the unit electric quantity of energy storage charge and discharge, the unit cost of OLTC gear adjustment and the unit electric quantity cost of demand response are respectively;
Figure BDA0003847901970000125
the charge and discharge power of the first stored energy at the time t in the d typical day;
Figure BDA0003847901970000126
tap position of on-load tap changer at time t of d-th typical day；P′ DR,j (t)、P DR,j And (t) load values before and after the transferable load of the node j at the moment t participates in the demand response respectively.
The annual flexibility deficiency risk cost for the distribution network is as follows:
Figure BDA0003847901970000127
in the formula:
Figure BDA0003847901970000128
the lack of flexibility for time t, the d typical day, risks the cost.
In one embodiment, the first constraint condition of the upper configuration decision model includes an energy storage rated power constraint, a rated capacity constraint and an energy storage installation position constraint.
Wherein the energy storage rated power constraint is as follows:
Figure BDA0003847901970000131
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000132
respectively an upper limit and a lower limit of the rated power of the energy storage.
The energy storage rated power constraint is as follows:
Figure BDA0003847901970000133
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000134
respectively the upper and lower limits of the rated capacity of the stored energy.
The energy storage installation position is restrained as follows:
1≤L ess,j ≤N node ；
in the formula, L ess,j The jth installation location of stored energy; n is a radical of node The number of the nodes of the power distribution network.
Step 102: and constructing a lower-layer operation optimization model by taking the minimum risk cost with insufficient flexibility as a second objective function, and setting a second constraint condition for the lower-layer operation optimization model.
In one embodiment, the lower-layer operation optimization model is an operation optimization layer, the operation optimization of the power distribution network is mainly considered by calling various flexible resources, an energy storage configuration scheme is provided by the upper-layer configuration decision model, and the decision variables are operation schemes.
In one embodiment, the objective function of the underlying operational optimization model is the least risk cost for insufficient flexibility.
Figure BDA0003847901970000135
In the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000136
the risk cost of insufficient flexibility for the distribution grid on the d-th typical day.
In one embodiment, the lower-layer operation optimization module comprises a power flow constraint, a safety constraint, an OLTC constraint, an energy storage constraint and a demand response constraint.
Specifically, the second constraint condition includes an energy storage constraint, an upper level main network constraint and a demand response constraint;
wherein the energy storage constraint is as follows:
Figure BDA0003847901970000137
S SOC,j,min ≤S SOC,j (t)≤S SOC,j,max ；
Figure BDA0003847901970000138
S SOC,j (0)＝S SOC,j (24)；
in the formula, S SOC,j (t) is the state of charge of the jth stored energy at time t; eta is charging efficiency;
Figure BDA0003847901970000141
for the jth energy stored charging, discharging power at time t, S SOC,j,max 、 S SOC,j,min Respectively the upper and lower limits of the j-th stored energy state of charge.
The upper level main network constraint is as follows:
Figure BDA0003847901970000142
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000143
the main power supply amount in the t period is supplied.
The demand response constraint is as follows:
Figure BDA0003847901970000144
of formula (II) to (III)' DR,j (t) is the load value of the node j at the time t when the transferable load does not participate in the demand response; p DR,j,max 、P DR,j,min And participating in the upper and lower limits of the demand response for the transferable load of the node j at the time t.
Specifically, the second constraint condition further includes common constraints such as a power flow constraint, a safety constraint, an OLTC constraint, and the like.
Step 103: and setting the population scale of the particle swarm, and setting the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm to obtain a plurality of initial energy storage configuration schemes.
In one embodiment, a particle swarm with a decision model configured at an upper layer is initialized, specifically, a population scale, iteration times, convergence conditions, and the like of the particle swarm are set; and generating initial particle swarm data, wherein each particle comprises an initial energy storage configuration scheme which comprises energy storage rated capacity, rated power and an installation position, and the initial energy storage scheme is energy storage data which is randomly generated by a computer and meets the energy storage constraint of an upper configuration decision model.
In one embodiment, network parameters of a power distribution network and wind power generation parameters, photovoltaic power generation parameters and load parameters of each typical day are obtained; predicting the wind power generation parameters, the photovoltaic power generation parameters and the load parameters to obtain a wind power output predicted value, a photovoltaic output predicted value and a load output predicted value; and generating a power distribution network scene based on the power distribution network parameters, the wind power output predicted value, the photovoltaic output predicted value, the load output predicted value and the initial energy storage configuration scheme.
In one embodiment, a plurality of different power distribution network scenes can be obtained based on different randomly generated initial energy storage configuration schemes; and the upper-layer configuration decision model respectively calculates the annual comprehensive cost of the power distribution network based on different power distribution network scenes.
Step 104: and respectively inputting the initial energy storage configuration schemes into the lower-layer operation optimization model, so that the lower-layer operation optimization model solves each initial energy storage configuration scheme, and outputting a first insufficient flexibility risk cost corresponding to each initial energy storage configuration scheme.
In one embodiment, the upper layer configuration decision model inputs a plurality of initial energy storage configuration schemes into the lower layer operation optimization model respectively, and the lower layer operation optimization model performs solving by using a mode of using MATLAB to call a Yamlip + Gurobi solver.
Specifically, a net load predicted value under the power distribution network scene is obtained and calculated based on a wind output predicted value, a photovoltaic output predicted value and a load output predicted value under the power distribution scene based on the power distribution network scene corresponding to the initial energy storage configuration scheme.
According to the central limit theorem in probability theory, when a certain number of indexes are influenced by a plurality of mutually independent random factors and the influence of each factor is very small, the indexes can be regarded as obeying normal distribution. The existing research shows that the output power of wind power generation
Figure BDA0003847901970000151
Output power of photovoltaic power generation
Figure BDA0003847901970000152
Load power
Figure BDA0003847901970000153
The actual power of (a) is distributed around the predicted value according to a certain mathematical rule, and can be regarded as obeying normal distribution.
Because the normal distribution has additive property, the random variables of the independent normal distribution still satisfy the normal distribution through linear random combination. The wind and light loads are independent of each other, so that P can be added by using the additivity Load (t)、-P WT (t)、-P PV (t) adding to obtain the uncertain power of the net load in each scheduling period t obeying normal distribution
Figure BDA0003847901970000154
f NL (z) is the probability density function of the payload, which is calculated as follows:
Figure BDA0003847901970000161
Figure BDA0003847901970000162
Figure BDA0003847901970000163
in the formula, mu NL Expectation of a predicted value for payload; mu.s L,k 、μ WT,i 、μ PV,j Respectively expectation of load and wind-power-volt output predicted values; k. i and j are the serial numbers of the loaded wind and light respectively; b. n and m are the number of the loaded wind and light respectively; sigma NL Standard deviation of the predicted net load value; sigma L,k 、σ WT,i 、σ PV,j Respectively the standard deviation of the predicted values of load, wind power and photovoltaic output; z is the payload prediction value.
Specifically, a net load condition confidence interval is set for the net load predicted value.
The Value at Risk (VaR) refers to the maximum loss that a certain portfolio may incur at a certain confidence level; conditional risk value (CVaR) refers to the mean of conditions that exceed the risk value at a certain confidence level; the method can reflect the average loss level, overcomes the insufficiency of the risk value to the estimation of the net load tail distribution, and is more reliable than the VaR. Therefore, in the embodiment, the traditional confidence interval optimization method is improved by using the conditional risk value, the concept of the net load power conditional confidence interval is provided, the tail risk can be reflected, and the method has excellent mathematical characteristics.
Net load fluctuations, possibly limited by net load power cap P up (t) and a lower payload power limit P low (t) Definitions, payload are described as follows with VaR and CVaR, respectively:
payload legacy confidence interval based on VaR calculation:
Figure BDA0003847901970000164
Figure BDA0003847901970000165
Figure BDA0003847901970000166
Figure BDA0003847901970000171
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000172
calculated by VaR for confidence level βUpper and lower limits of the incoming payload confidence interval; phi is a unit of up (α)、φ low And (alpha) is the probability that the power does not cross the threshold alpha at time t.
Payload condition confidence interval based on CVaR calculation:
Figure BDA0003847901970000173
Figure BDA0003847901970000174
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000175
and the upper limit and the lower limit of the net load condition confidence interval are obtained by calculating the condition risk value CVaR under the confidence level beta.
FIG. 3 is a schematic diagram of a comparison of payload conventional confidence intervals and payload condition confidence intervals, as shown in FIG. 3; as can be seen from fig. 3, the traditional confidence interval range of the payload is small, only the quantiles corresponding to the confidence level are considered, the distribution estimation of the two ends is not sufficient, and a certain limit exists; the part outside the quantile corresponding to the confidence level has lower occurrence probability, but the numerical value is larger, which means that the corresponding net load fluctuation is larger, once the net load fluctuation occurs, the influence on the power distribution network is larger, and therefore the net load fluctuation cannot be directly ignored. Compared with the traditional confidence interval of the net load, the confidence interval of the improved net load condition considers the tail risk expected values at two ends, so that the range is larger, the actual requirement of the power distribution network is better met, the serious power distribution network accident caused by the overlarge fluctuation of the net load is avoided, and the economy and the reliability are realized.
Specifically, the objective function of the lower-layer operation optimization model is the minimum risk cost with insufficient flexibility, the risk cost with insufficient flexibility includes the risk cost with insufficient up-regulation flexibility and the risk cost with insufficient down-regulation flexibility, and the risk cost with insufficient up-regulation flexibility refers to the expected value of loss caused by wind and light power limitation due to insufficient up-regulation flexibility when the net load power is lower than the lower bound of the flexibility boundary of the power distribution network. Risk cost caused by insufficient down-regulation flexibility refers to an expected value of loss caused by load shedding due to insufficient down-regulation flexibility when net load power is higher than an upper limit of a flexibility boundary of a power distribution network; as shown in fig. 4, fig. 4 is a schematic diagram of risk of insufficient flexibility, where the upper net load limit exceeds the upper flexibility limit, i.e. the lower flexibility limit is insufficient, resulting in a load shedding risk cost; the net load lower limit exceeds the flexibility lower bound, that is, the up-regulation flexibility is insufficient, and the risk cost of wind abandoning and light abandoning is caused.
Therefore, a distribution network flexibility boundary needs to be set before calculating the risk cost with insufficient flexibility. The upper and lower limits of the flexibility boundary of the power distribution network refer to the upper and lower limits of the fluctuation of the net load of the power distribution network, which can be allowed by the cooperative scheduling of the flexibility resources when the power distribution network operates safely and stably; by comprehensively considering the scheduling strategies of each device and line constraint of the power distribution network and the flexibility resource, the maximum value and the minimum value of the net load of each node are respectively superposed to obtain the flexibility boundary of the power distribution network, as follows:
Figure BDA0003847901970000181
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000182
the flexibility boundary upper limit and the flexibility boundary lower limit of the power distribution network in the time period t are set;
Figure BDA0003847901970000183
respectively representing the active load of j nodes in the t period;
Figure BDA0003847901970000184
respectively representing the active power of photovoltaic wind power generation of j nodes in a t period; and C, configuring a constraint condition set of the decision model for the upper layer.
In one embodiment, based on the upper and lower flexibility boundary limits, the insufficient flexibility risk cost is calculated as follows:
C risk (t)＝f ur (t)+f dr (t)；
Figure BDA0003847901970000185
Figure BDA0003847901970000186
in the formula (f) ur (t) Up-Regulation insufficient flexibility Risk cost, f dr (t) Down-Regulation of insufficient flexibility Risk cost, c ur The loss coefficient is the wind-solar electricity limiting loss coefficient; p is ur (t) is the expected value of the power difference under the condition of insufficient up-regulation flexibility; c. C dr Loss factor for load shedding; p dr (t) a desired value of the power difference in case of insufficient turndown flexibility,
Figure BDA0003847901970000191
the upper and lower limits of a confidence interval, P, of the net load condition obtained by calculating the condition risk value CVaR under the confidence level beta ub (t)、P lb (t) is the upper and lower limits of the flexibility boundary of the distribution network, f NL (z) probability density function of payload, P NL (t) payload uncertainty power.
Step 105: inputting the first insufficient-flexibility risk cost into the upper-layer configuration decision model, iteratively updating the initial energy storage configuration schemes based on a particle swarm algorithm until a population optimal solution is obtained, and outputting an optimal energy storage configuration scheme.
In one embodiment, after the first insufficient-flexibility risk cost is calculated by the lower-layer operation optimization model, the first insufficient-flexibility risk cost is input into the upper-layer configuration decision model again, the fitness value corresponding to each initial energy storage configuration scheme, namely the comprehensive cost of the power distribution network, is solved based on a particle swarm algorithm, the optimal solution of the population is obtained, the particle speed and the position of each particle in the particle swarm are updated, and the flexibility boundary processing is carried out; meanwhile, based on the iteration processing set in the step 103, whether the current iteration times reach a large iteration number is judged, if yes, an optimal energy storage configuration scheme is output most based on a population optimal solution, and if not, the step 104 and the step 105 are repeated, so that the upper layer model and the lower layer model are continuously optimized in an iteration mode until the maximum iteration times reach, the optimal energy storage configuration scheme is output, the flexible operation of the system is ensured at the minimum economic cost, and the renewable energy consumption capacity is improved.
The technical solution provided in this embodiment is illustrated as follows:
the technical scheme of the application is applied to an IEEE33 node model, a series of flexible resources are set, and a used power distribution network simulation model is built. As shown in fig. 5, fig. 5 is a schematic diagram of an IEEE33J node model, in the power distribution system, distributed wind power stations with installed capacities of 500kW and 1000kW are connected to nodes 3 and 17, distributed photovoltaic power stations with installed capacities of 500kW and 1000kW are connected to nodes 16 and 30, typical solar photovoltaic power generation data and load data of a power distribution network are set, and demand responses are set to nodes 5, 15, and 31, and mainly considered as transferable loads.
The investment cost of unit capacity of energy storage is 2500 yuan/kWh, the investment cost of unit power is 1000 yuan/kW, the charge and discharge charge cost of unit energy storage is 0.03 yuan/kWh, and the regulation cost of the on-load tap changer is 1.5 yuan/time. The rated capacity of the stored energy is set to be [500, 1000] kWh, and the rated power is set to be [100, 300] kW. The maximum iteration number of the particle swarm optimization is set to be 50, and the number of particles is set to be 20.
Based on the parameters, setting an energy storage configuration scheme as follows:
scheme 1: and (4) not considering an energy storage system, only considering flexible resources such as an on-load tap changing transformer and load response and facilities such as a static var compensator, and the like, and using a gurobi solver of a lower-layer operation optimization model to take the minimum operation cost as an objective function, wherein the objective function comprises the network loss cost and the main network electricity purchasing cost. And solving the objective function and analyzing the comprehensive cost condition of the scheme.
Scheme 2: the energy storage system is considered, uncertainty of flexibility requirements of the power distribution network is not considered, and the risk of insufficient flexibility is not considered. And (4) performing energy storage optimal configuration by using a particle swarm algorithm of an upper-layer configuration decision model and taking the minimum energy storage investment cost and the minimum distribution network operation cost as a target function. And solving the objective function and analyzing the comprehensive cost condition of the scheme.
Scheme 3: and considering the risk of insufficient flexibility of the power distribution network, and fully calling each flexibility resource of the power distribution network such as energy storage and the like. The energy storage double-layer optimization configuration model constructed by the embodiment, namely the upper-layer configuration decision model and the lower-layer operation optimization model, is applied, the minimum comprehensive cost is taken as an objective function, energy storage optimization configuration is carried out, the objective function is solved, and the comprehensive cost condition of the scheme is analyzed.
By solving the energy storage configuration scheme, a configuration scheme and a comprehensive cost analysis are obtained, as shown in fig. 6, fig. 6 is a schematic diagram of an energy storage configuration scheme and a comprehensive cost analysis result. Based on the solution results, it can be:
comparing scheme 1 with scheme 2, scheme 2 is configured for energy storage in the power distribution network compared with scheme 1. From the analysis of the comprehensive cost result, the scheme 2 has one more configuration cost of energy storage than the scheme 1, but in the operation cost, the electricity purchasing cost and the network loss cost are both reduced, and even the sum of the configuration cost and the operation cost of the scheme 2 is 56.87 ten thousand yuan lower than that of the scheme 1. Therefore, the method can reflect the energy storage in the aspect of peak clipping and valley filling, and has good economic benefit. In the aspect of comparing the insufficient flexibility risks of the two schemes, compared with the scheme 1, the scheme 2 has the advantages that the insufficient flexibility risk cost is greatly improved, 791.92 ten thousand yuan is reduced, only the flexible resource calling cost is improved by 37.69 ten thousand yuan, and the comprehensive cost is reduced by 811.10 ten thousand yuan.
Comparing the scheme 2 with the scheme 3, the scheme 3 considers the uncertainty of the flexibility requirement of the power distribution network compared with the scheme 2, converts the uncertainty into the risk cost with insufficient flexibility, incorporates the risk cost into an objective function, and performs optimization solution. And the case that the net load confidence interval curve of the scheme 3 crosses the flexibility boundary is improved to a certain extent compared with the scheme 2. The analysis of the comprehensive cost result shows that the installation position of the energy storage in the scheme 3 is changed compared with that in the scheme 2, and the rated power and the rated capacity are improved, so that the energy storage configuration cost is 64.54 ten thousand yuan higher. But the running cost and the risk cost of insufficient flexibility are reduced to some extent, the running cost is reduced by 38.74 ten thousand yuan, the risk cost of insufficient flexibility is reduced by 228.62 ten thousand yuan, the flexible resource calling cost is increased by 7.36 ten thousand yuan only, and the comprehensive cost is reduced by 195.46 ten thousand yuan.
According to the scheme 3, the risk cost due to insufficient flexibility is considered, the minimum comprehensive cost is taken as a target function, the effect of improving the flexibility of the power distribution network by energy storage is maximized, the capability of the power distribution network for dealing with wind and light load output fluctuation is improved, and the risk due to insufficient flexibility in each time period is improved. Under the background that the proportion of wind and light resources accessed to the power distribution network is increased day by day, the flexibility planning model constructed by the embodiment better meets the actual requirements of the power distribution network, and the safety, stability and economic benefit of the power distribution network can be better guaranteed.
In conclusion, the method for optimally configuring the energy storage of the power distribution network improves the traditional calculation method of the confidence interval by applying the condition risk value theory, provides the confidence interval of the net load power condition, and has good mathematical characteristics; by calculating the flexibility boundary and the risk cost with insufficient flexibility, the economic loss caused by insufficient flexibility of the power distribution network can be quantified, the power distribution network energy storage double-layer optimization configuration model is constructed in a matched mode, the risk cost with insufficient flexibility is considered, the flexibility resources of the power distribution network are fully called, the minimum annual comprehensive cost is used as a target function, the effect of improving the flexibility of the power distribution network by energy storage is maximized, the capability of the power distribution network for dealing with wind and light load output fluctuation is improved, and the economical efficiency and the flexibility of the power distribution network can be guaranteed.
Example 2
Referring to fig. 2, fig. 2 is a schematic structural diagram of an embodiment of an energy storage optimal configuration device for a power distribution network, as shown in fig. 2, the device includes an upper layer configuration decision model building module 201, a lower layer operation optimization model building module 202, an initial energy storage configuration scheme obtaining module 203, an insufficient flexibility risk cost solving module 204, and an optimal energy storage configuration scheme outputting module 205, which are specifically as follows:
the upper-layer configuration decision model building module 201 is configured to build an upper-layer configuration decision model by using the minimum annual integrated cost of the power distribution network as a first objective function, and set a first constraint condition for the upper-layer configuration decision model.
The lower-layer operation optimization model building module 202 is configured to build a lower-layer operation optimization model by using the minimum risk cost due to insufficient flexibility as a second objective function, and set a second constraint condition for the lower-layer operation optimization model.
The initial energy storage configuration scheme obtaining module 203 is configured to set a population scale of the particle swarm, and set the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm to obtain multiple initial energy storage configuration schemes.
The insufficient flexibility risk cost solving module 204 is configured to input the plurality of initial energy storage configuration schemes to the lower-layer operation optimization model, so that the lower-layer operation optimization model solves each initial energy storage configuration scheme, and outputs a first insufficient flexibility risk cost corresponding to each initial energy storage configuration scheme.
The optimal energy storage configuration scheme output module 205 inputs the first low-flexibility risk cost into the upper-level configuration decision model, iteratively updates the initial energy storage configuration schemes based on a particle swarm optimization until a population optimal solution is obtained, and outputs an optimal energy storage configuration scheme.
In an embodiment, the initial energy storage configuration scheme obtaining module 203 is further configured to obtain a power distribution network parameter and a wind power generation parameter, a photovoltaic power generation parameter, and a load parameter of each typical day, predict the wind power generation parameter, the photovoltaic power generation parameter, and the load parameter to obtain a wind power output predicted value, a photovoltaic output predicted value, and a load output predicted value, and generate a power distribution network scene based on the power distribution network parameter, the wind power output predicted value, the photovoltaic output predicted value, the load output predicted value, and the initial energy storage configuration scheme.
In an embodiment, the upper-layer configuration decision model building module 201 is configured to build an upper-layer configuration decision model by using the minimum annual integrated cost of the power distribution network as a first objective function, and specifically includes:
the minimum annual comprehensive cost of the power distribution network is taken as a first objective function, wherein the annual comprehensive cost comprises the equal annual investment cost of energy storage, the annual operation cost of the power distribution network, the annual flexibility resource calling cost of the power distribution network and the annual flexibility shortage risk cost of the power distribution network;
the first objective function is as follows:
min C total ＝C ess +C net +C fs +C risk ；
in the formula, C total The annual comprehensive cost of the power distribution network; c ess Equal annual investment cost for energy storage; c net The annual operating cost of the power distribution network is calculated; c fs The annual flexible resource calling cost of the power distribution network is saved; c risk The annual flexibility of the distribution network is low, and the risk cost is low.
In an embodiment, the upper layer configuration decision model building module 201 is configured to set a first constraint condition for the upper layer configuration decision model, and specifically includes:
the first constraint condition comprises an energy storage rated power constraint, a rated capacity constraint and an energy storage installation position constraint;
wherein the energy storage rated power constraint is as follows:
Figure BDA0003847901970000241
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000242
respectively an upper limit and a lower limit of the rated power of the energy storage.
The energy storage rated power constraint is as follows:
Figure BDA0003847901970000243
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000244
respectively an upper limit and a lower limit of the rated energy storage capacity;
the energy storage mounting position constraint is as follows:
1≤L ess,j ≤N node ；
in the formula, L ess,j The jth installation location of stored energy; n is a radical of node The number of the nodes of the power distribution network.
In one embodiment, the lower-layer operation optimization model building module 202 is configured to use the least risk cost of insufficient flexibility as a second objective function, where the second objective function is as follows:
Figure BDA0003847901970000245
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000246
there is insufficient risk cost for flexibility on the d-th typical day of the distribution grid.
In an embodiment, the lower-layer operation optimization model building module 202 is configured to set a second constraint condition for the lower-layer operation optimization model, and specifically includes:
the second constraint condition comprises energy storage constraint, superior main network constraint and demand response constraint;
wherein the energy storage constraint is as follows:
Figure BDA0003847901970000247
S SOC,j,min ≤S SOC,j (t)≤S SOC,j,max ；
Figure BDA0003847901970000248
S SOC,j (0)＝S SOC,j (24)；
in the formula, S SOC,j (t) is the state of charge of the jth stored energy at time t; eta is charging efficiency;
Figure BDA0003847901970000251
for the jth energy stored charging, discharging power at time t, S SOC,j,max 、 S SOC,j,min Respectively the upper and lower limits of the charge state of the jth stored energy;
the upper level main network constraint is as follows:
Figure BDA0003847901970000252
in the formula (I), the compound is shown in the specification,
Figure BDA0003847901970000253
the power supply amount of the main grid in the t period is determined;
the demand response constraint is as follows:
Figure BDA0003847901970000254
of formula (II) to (III)' DR,j (t) is the load value of the node j at the moment t when the transferable load does not participate in the demand response; p is DR,j,max 、P DR,j,min And participating in the upper and lower limits of the demand response for the transferable load of the node j at the time t.
In an embodiment, the under-flexibility risk cost in the lower-layer operation optimization model building module 202 includes an up-flexibility under-risk cost and a down-flexibility under-risk cost, where the under-flexibility risk cost is as follows:
C risk (t)＝f ur (t)+f dr (t)；
Figure BDA0003847901970000255
Figure BDA0003847901970000256
in the formula (f) ur (t) Up-Regulation insufficient flexibility Risk cost, f dr (t) is as followsAdjustment of insufficient flexibility Risk cost, c ur The loss coefficient is wind-light electricity limiting; p ur (t) is the expected value of the power difference under the condition of insufficient up-regulation flexibility; c. C dr Loss factor for load shedding; p dr (t) desired power margin for insufficient turndown flexibility,
Figure BDA0003847901970000257
the upper and lower limits of a confidence interval, P, of the net load condition obtained by calculating the condition risk value CVaR under the confidence level beta ub (t)、P lb (t) is the flexibility bound upper and lower limits, f, of the distribution network NL (z) probability density function of payload, P NL (t) payload uncertainty power.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
It should be noted that the above embodiment of the power distribution network energy storage optimization configuration apparatus is merely illustrative, where the modules described as separate components may or may not be physically separate, and the components displayed as modules may or may not be physical units, that is, may be located in one place, or may also be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
On the basis of the foregoing embodiment of the power distribution network energy storage optimization configuration method, another embodiment of the present invention provides a power distribution network energy storage optimization configuration terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the power distribution network energy storage optimization configuration method according to any embodiment of the present invention is implemented.
Illustratively, the computer program may be partitioned in this embodiment into one or more modules that are stored in the memory and executed by the processor to implement the invention. The one or more modules may be a series of instruction segments of a computer program capable of performing specific functions, and the instruction segments are used for describing the execution process of the computer program in the power distribution network energy storage optimization configuration terminal device.
The power distribution network energy storage optimal configuration terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The power distribution network energy storage optimization configuration terminal equipment can comprise, but is not limited to, a processor and a memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the power distribution network energy storage optimization configuration terminal device, and various interfaces and lines are utilized to connect various parts of the whole power distribution network energy storage optimization configuration terminal device.
The memory can be used for storing the computer programs and/or modules, and the processor realizes various functions of the power distribution network energy storage optimization configuration terminal equipment by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
On the basis of the above embodiment of the power distribution network energy storage optimization configuration method, another embodiment of the present invention provides a storage medium, where the storage medium includes a stored computer program, and when the computer program runs, a device in which the storage medium is located is controlled to execute the power distribution network energy storage optimization configuration method according to any embodiment of the present invention.
In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, an executable file or some intermediate form, and so on. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
In summary, according to the method, the device, the equipment and the storage medium for optimal configuration of energy storage of the power distribution network provided by the invention, an upper-layer configuration decision model taking the minimum annual integrated cost of the power distribution network as an objective function and a lower-layer operation optimization model taking the minimum risk cost due to insufficient flexibility as the objective function are constructed, a plurality of initial energy storage configuration schemes are input into the lower-layer operation optimization model, the initial energy storage configuration schemes are solved and the corresponding first risk cost due to insufficient flexibility is input into the upper-layer configuration decision model, the initial energy storage configuration schemes are iteratively updated based on a particle swarm algorithm until a population optimal solution is obtained, and an optimal energy storage configuration scheme is output. According to the technical scheme, the risk cost with insufficient flexibility is considered, the flexibility resources of the power distribution network are fully called, the minimum comprehensive cost is taken as a target function, the effect of improving the flexibility of the power distribution network by energy storage is maximized, the capability of the power distribution network for dealing with wind and light load output fluctuation is improved, and the economical efficiency and the flexibility of the operation of the power distribution network can be guaranteed.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.

Claims (10)

1. A power distribution network energy storage optimal configuration method is characterized by comprising the following steps:
constructing an upper-layer configuration decision model by taking the minimum annual comprehensive cost of the power distribution network as a first objective function, and setting a first constraint condition for the upper-layer configuration decision model;
constructing a lower-layer operation optimization model by taking the minimum risk cost with insufficient flexibility as a second objective function, and setting a second constraint condition for the lower-layer operation optimization model;
setting the population scale of the particle swarm, and setting the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm to obtain a plurality of initial energy storage configuration schemes;
respectively inputting the plurality of initial energy storage configuration schemes into the lower-layer operation optimization model, so that the lower-layer operation optimization model solves each initial energy storage configuration scheme, and outputting a first insufficient flexibility risk cost corresponding to each initial energy storage configuration scheme;
inputting the first insufficient-flexibility risk cost into the upper-layer configuration decision model, iteratively updating the plurality of initial energy storage configuration schemes based on a particle swarm algorithm until a population optimal solution is obtained, and outputting an optimal energy storage configuration scheme.
2. The method for optimal configuration of energy storage for a power distribution network according to claim 1, wherein after obtaining a plurality of initial energy storage configuration schemes, the method further comprises:
acquiring power distribution network parameters and wind power generation parameters, photovoltaic power generation parameters and load parameters of each typical day;
predicting the wind power generation parameters, the photovoltaic power generation parameters and the load parameters to obtain a wind power output predicted value, a photovoltaic output predicted value and a load output predicted value;
and generating a power distribution network scene based on the power distribution network parameters, the wind power output predicted value, the photovoltaic output predicted value, the load output predicted value and the initial energy storage configuration scheme.
3. The method for optimal configuration of energy storage of the power distribution network according to claim 1, wherein a minimum annual integrated cost of the power distribution network is used as a first objective function to construct an upper-level configuration decision model, and the method specifically comprises the following steps:
the minimum annual comprehensive cost of the power distribution network is taken as a first objective function, wherein the annual comprehensive cost comprises the equal annual investment cost of energy storage, the annual operation cost of the power distribution network, the annual flexibility resource calling cost of the power distribution network and the annual flexibility shortage risk cost of the power distribution network;
the first objective function is as follows:
min C total ＝C ess +C net +C fs +C risk ；
in the formula, C total The annual comprehensive cost of the power distribution network; c ess Equal annual investment cost for energy storage; c net The annual operating cost of the power distribution network is calculated; c fs The cost is called for annual flexible resources of the power distribution network; c risk The annual flexibility of the distribution network is insufficient for risk cost.
4. The method according to claim 1, wherein setting a first constraint condition for the upper layer configuration decision model specifically includes:
the first constraint condition comprises an energy storage rated power constraint, a rated capacity constraint and an energy storage installation position constraint;
wherein the energy storage rated power constraint is as follows:
Figure FDA0003847901960000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003847901960000022
upper and lower limits of the rated power of the stored energy
The energy storage rated power constraint is as follows:
Figure FDA0003847901960000023
in the formula (I), the compound is shown in the specification,
Figure FDA0003847901960000024
respectively an upper limit and a lower limit of the rated energy storage capacity;
the energy storage mounting position constraint is as follows:
1≤L ess，j ≤N node ；
in the formula, L ess，j The jth installation location of stored energy; n is a radical of node The number of the nodes of the power distribution network.
5. The method for optimally configuring energy storage of the power distribution network according to claim 1, wherein the risk cost with insufficient flexibility is minimized as a second objective function, wherein the second objective function is as follows:
Figure FDA0003847901960000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003847901960000032
for flexibility of distribution network at d typical daySufficient risk cost.
6. The method for optimally configuring energy storage of the power distribution network according to claim 1, wherein setting a second constraint condition for the lower-layer operation optimization model specifically comprises:
the second constraint condition comprises energy storage constraint, superior main network constraint and demand response constraint;
wherein the energy storage constraint is as follows:
Figure FDA0003847901960000033
S SOC，j，min ≤S SOC，j (t)≤S SOC，j，max ；
Figure FDA0003847901960000034
S SOC，j (0)＝S SOC，j (24)；
in the formula, S SOC，j (t) is the state of charge of the jth stored energy at time t; eta is charging efficiency;
Figure FDA0003847901960000035
for the jth energy stored charging, discharging power at time t, S SOC，j，max 、S SOC，j，min Respectively the upper and lower limits of the j-th stored energy charge state;
the upper level main network constraint is as follows:
Figure FDA0003847901960000036
in the formula (I), the compound is shown in the specification,
Figure FDA0003847901960000037
the power supply amount of the main grid in the t period is determined;
the demand response constraint is as follows:
Figure FDA0003847901960000038
of formula (II) to (III)' DR，j (t) is the load value of the node j at the time t when the transferable load does not participate in the demand response; p DR，j，max 、P DR，j，min And participating the upper limit and the lower limit of the demand response for the transferable load of the node j at the moment t.
7. The optimal configuration method for energy storage of power distribution network according to claim 5, wherein the insufficient flexibility risk cost includes an up-flexibility insufficient risk cost and a down-flexibility insufficient risk cost, wherein the insufficient flexibility risk cost is as follows:
C risk (t)＝f ur (t)+f dr (t)；
Figure FDA0003847901960000041
Figure FDA0003847901960000042
in the formula, f ur (t) risk cost of insufficient flexibility for upscaling, f dr (t) Down-Regulation of insufficient flexibility Risk cost, c ur The loss coefficient is wind-light electricity limiting; p ur (t) is the expected value of the power difference under the condition of insufficient up-regulation flexibility; c. C dr Loss factor for load shedding; p dr (t) a desired value of the power difference in case of insufficient turndown flexibility,
Figure FDA0003847901960000043
the upper and lower limits of a confidence interval, P, of the net load condition obtained by calculating the condition risk value CVaR under the confidence level beta ub (t)、P lb (t) is the flexibility bound upper and lower limits, f, of the distribution network NL (z) is the probability density function of the payload, P NL (t) is the payload uncertainty power.
8. The utility model provides a distribution network energy storage optimal configuration device which characterized in that includes: the system comprises an upper-layer configuration decision model building module, a lower-layer operation optimization model building module, an initial energy storage configuration scheme obtaining module, an insufficient-flexibility risk cost solving module and an optimal energy storage configuration scheme output module;
the upper-layer configuration decision model building module is used for building an upper-layer configuration decision model by taking the minimum annual comprehensive cost of the power distribution network as a first objective function, and setting a first constraint condition for the upper-layer configuration decision model;
the lower-layer operation optimization model building module is used for building a lower-layer operation optimization model by taking the minimum risk cost due to insufficient flexibility as a second objective function, and setting a second constraint condition for the lower-layer operation optimization model;
the initial energy storage configuration scheme acquisition module is used for setting the population scale of the particle swarm, setting the randomly generated initial energy storage configuration scheme as a single particle in the particle swarm, and obtaining a plurality of initial energy storage configuration schemes;
the insufficient flexibility risk cost solving module is used for respectively inputting the plurality of initial energy storage configuration schemes into the lower-layer operation optimization model so that the lower-layer operation optimization model can solve each initial energy storage configuration scheme and output a first insufficient flexibility risk cost corresponding to each initial energy storage configuration scheme;
and the optimal energy storage configuration scheme output module inputs the first insufficient flexibility risk cost into the upper-layer configuration decision model, iteratively updates the initial energy storage configuration schemes based on a particle swarm algorithm until a population optimal solution is obtained, and outputs an optimal energy storage configuration scheme.
9. A terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the power distribution network energy storage optimization configuration method according to any one of claims 1 to 7.
10. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls a device to execute the power distribution network energy storage optimization configuration method according to any one of claims 1 to 7.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115860440A (en) * 2023-02-28 2023-03-28 国网浙江电动汽车服务有限公司 Method, device, equipment and medium for generating deployment scheme of multifunctional mobile energy storage vehicle
CN118014164A (en) * 2024-04-08 2024-05-10 国网江西省电力有限公司经济技术研究院 Energy storage capacity configuration double-layer optimization method and system considering flexibility requirements

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115860440A (en) * 2023-02-28 2023-03-28 国网浙江电动汽车服务有限公司 Method, device, equipment and medium for generating deployment scheme of multifunctional mobile energy storage vehicle
CN118014164A (en) * 2024-04-08 2024-05-10 国网江西省电力有限公司经济技术研究院 Energy storage capacity configuration double-layer optimization method and system considering flexibility requirements

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