CN116611575A - Multi-VPP shared energy storage capacity optimal configuration method based on double-layer decision game - Google Patents

Multi-VPP shared energy storage capacity optimal configuration method based on double-layer decision game Download PDF

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CN116611575A
CN116611575A CN202310651967.5A CN202310651967A CN116611575A CN 116611575 A CN116611575 A CN 116611575A CN 202310651967 A CN202310651967 A CN 202310651967A CN 116611575 A CN116611575 A CN 116611575A
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赵锦山
林涛
陈美润
赵磊
冯华华
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Hebei University of Technology
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Abstract

The application relates to a multi-VPP shared energy storage capacity optimizing configuration method based on double-layer decision game, wherein a double-layer decision game model comprises an upper-layer capacity optimizing configuration model and a lower-layer operation optimizing scheduling model; firstly, constructing an objective function of an upper capacity optimization configuration model and a lower operation optimization scheduling model; and then, taking constraint conditions of all models into consideration, taking the lowest annual comprehensive cost of the multi-VPP and the shared energy storage power station as an optimization target by the upper-layer capacity optimization configuration model, taking the lowest annual comprehensive cost of the shared energy storage power station as an optimization target by the lower-layer operation optimization scheduling model, and respectively carrying out alternate iterative solution on the upper-layer capacity optimization configuration model and the lower-layer operation optimization scheduling model to complete the optimization configuration of the multi-VPP shared energy storage capacity. According to the method, the overall economic benefit of the multi-VPP shared energy storage system is comprehensively considered from a plurality of decision variables of each model, and the overall capacity configuration and optimal scheduling of the system are completed.

Description

Multi-VPP shared energy storage capacity optimal configuration method based on double-layer decision game
Technical Field
The application belongs to the technical field of shared energy storage capacity configuration, and particularly relates to a multi-VPP shared energy storage capacity optimization configuration method based on double-layer decision game.
Background
The shared energy storage is an energy storage business application mode which combines the traditional energy storage technology with a shared economic mode, an energy storage power station is built by investment of independent shared energy storage power station service providers, energy storage service is provided for each virtual power plant (Virtual Power Plant, VPP) at a certain price, and in the electric energy transaction process of the VPP and the shared energy storage power station, when the distributed energy generation power in the VPP is greater than a load, the residual electric energy can be sold to the shared energy storage power station, and the shared energy storage power station can sell the electric energy to the VPP needing the electric energy; when the maximum power provided by the distributed energy generation and shared energy storage power stations in the VPP can not meet the load yet, the VPP can purchase electric energy from a large power grid; the electric energy exchange between the VPPs needs to realize the transfer of the electric energy between the shared energy storage power station and different VPPs on the space level through the shared energy storage system. The shared energy storage can enable the VPP to use energy storage service under the condition that the VPP does not need to construct energy storage, and meanwhile, the high-efficiency utilization of the energy storage system can be ensured by means of the flexibility of shared economy, so that the shared energy storage power station can realize quick profit. Because the investment cost of the energy storage equipment is higher, the utilization rate is relatively lower, and compared with the independent configuration of the energy storage equipment in each VPP, the shared energy storage has incomparable advantages, and the investment and the operation cost of the energy storage system can be effectively reduced. How to reasonably carry out capacity allocation has important significance for effectively improving the level of the consumption of distributed energy and improving the economical efficiency of the energy storage system.
In the existing shared energy storage system, multiple benefit agents seek to maximize their own benefits, and the capacity allocation of each benefit agent cannot guarantee the economical efficiency of the whole operation of the shared energy storage system. Therefore, the application provides a multi-VPP shared energy storage capacity optimal configuration method based on double-layer decision game.
Disclosure of Invention
Aiming at the defects of the prior art, the application aims to provide a multi-VPP shared energy storage capacity optimal configuration method based on double-layer decision game.
The technical scheme adopted for solving the technical problems is as follows:
a multi-VPP shared energy storage capacity optimizing configuration method based on double-layer decision game uses a multi-VPP shared energy storage system comprising a shared energy storage power station and a plurality of VPPs, wherein each VPP comprises a load end and a distributed energy source consisting of wind power generation and photovoltaic power generation, and the shared energy storage power station utilizes an energy storage battery to store energy; the shared energy storage power station and the VPP exchange electric energy in two directions, and the shared energy storage power station and the VPP exchange electric energy with a large power grid at the same time; the method is characterized in that a double-layer decision game model adopted by the method comprises an upper-layer capacity optimization configuration model and a lower-layer operation optimization scheduling model, and comprises the following contents:
1. an objective function of an upper capacity optimization configuration model is constructed, and the expression is as follows:
in the method, in the process of the application,for the total investment costs of the energy storage battery +.>For the total replacement cost of the energy storage cell, +.>Total investment costs for all wind turbines for multiVPP, < >>The total investment cost of all the photovoltaic generating sets of the multi-VPP is that beta is annual rate, Y a Sharing the total design age of the energy storage system for multiple VPPs, C Grid Cost of purchasing electric energy from large grid for multiVPP, C Cut Annual penalty cost for multiple VPP wind and light abandoning, C Fl Scheduling cost for multi-VPP flexible load;
constraint conditions of the upper capacity optimization configuration model objective function comprise energy multiplying power constraint of an energy storage battery, power and capacity constraint of the energy storage battery, construction quantity constraint of wind turbines and photovoltaic generators in VPP, and power constraint of the VPP for purchasing electric energy from a large power grid;
2. constructing an objective function of a lower-layer operation optimization scheduling model, wherein the expression is as follows:
wherein C is ESS.B C for sharing cost of energy storage power station purchasing electric energy from multiple VPPs ESS.S C for sharing benefits of selling electric energy from energy storage power stations to various VPPs serv Lease service fees paid to each VPP for the shared energy storage power station;
constraint conditions of the lower-layer operation optimization scheduling model objective function comprise VPP power balance constraint, flexible load reduction and increment constraint, energy storage battery operation constraint, distributed energy climbing constraint and VPP charge and discharge power constraint;
3. the upper-layer capacity optimizing configuration model takes the lowest annual comprehensive cost of the multi-VPP and the shared energy storage power station as an optimizing target, and the lower-layer operation optimizing scheduling model takes the lowest annual comprehensive cost of the shared energy storage power station as an optimizing target, and the upper-layer capacity optimizing configuration model and the lower-layer operation optimizing scheduling model are respectively subjected to alternate iterative solution to complete the optimizing configuration of the multi-VPP shared energy storage capacity.
Compared with the prior art, the application has the beneficial effects that:
the application researches the operation and investment between the multi-VPP shared energy storage system and the large power grid, mixes the operation variable and the planning variable of the system by considering the operation condition of the system in the planning stage, establishes a double-layer optimization model under the targets of user demand response and economy, wherein the upper layer is used for solving the planning problem of the multi-VPP shared energy storage system, and the lower layer is used for solving the scheduling optimization problem of the shared energy storage power station and the multi-VPP, and considers the overall economic benefit of the multi-VPP shared energy storage system; secondly, a double-layer decision game model is provided for solving a double-layer optimization model of capacity allocation and optimization scheduling of the multi-VPP shared energy storage system, an intelligent optimization algorithm and a second-order cone programming are respectively used for an upper layer and a lower layer of the double-layer decision game model algorithm, the upper layer and the lower layer are alternately iterated and subjected to layered optimization, a plurality of decision variables in the system can be calculated by the algorithm model, the calculation accuracy is high, and a capacity allocation scheme and corresponding optimization scheduling results of the system can be effectively provided under the conditions of flexible load and economy.
Drawings
FIG. 1 is a diagram of a multi-VPP shared energy storage system;
FIG. 2 is a schematic diagram of a two-layer decision gaming model;
FIG. 3 is an optimization flow chart of a two-layer decision gaming model;
FIG. 4 is a graph of electricity prices for various schedule periods of a typical day;
FIG. 5 is a graph of charge and discharge power and state of charge of a shared energy storage power station;
FIG. 6 is a VPP A Is a power balance scheduling result diagram;
FIG. 7 is a VPP B Is a power balance scheduling result diagram;
FIG. 8 is a VPP C Is a power balance scheduling result diagram;
fig. 9 is a diagram of the waste-to-waste power of multiple VPPs during each scheduling period.
Detailed Description
The following specific embodiments are given by way of illustration only and not by way of limitation of the scope of the application.
Referring to fig. 1, the multi-VPP shared energy storage system of the present application includes a shared energy storage plant and a plurality of Virtual Power Plants (VPPs), the Virtual power plants and the shared energy storage plant exchange electric energy in both directions, and the shared energy storage plant and the Virtual power plants exchange electric energy with a large power grid at the same time; the virtual power plant comprises distributed energy sources and load ends, wherein the distributed energy sources comprise photovoltaic power generation and wind power generation, and the load ends comprise industrial power and residential power; the shared energy storage power station utilizes an energy storage battery to store electric energy; each virtual power station can purchase and sell electric energy to the shared energy storage power station, when the electric energy generated by the virtual power station is greater than the load, the rest electric energy can be sold to the shared energy storage power station, and the shared energy storage power station can sell the stored electric energy to the virtual power station needing the electric energy, so that the effect of promoting the on-site consumption of renewable energy sources is achieved; when the electric energy generated by the virtual power plant and the electric energy provided by the shared energy storage power station can not meet the load, the virtual power plant can purchase electric energy from a large power grid; the electric energy exchange between the virtual power plants is realized through the shared energy storage system, so that the electric energy transfer between the virtual power plants and the independent energy storage is realized at the space level.
The application relates to a multi-VPP shared energy storage capacity optimizing configuration method based on double-layer decision game, wherein the adopted double-layer decision game model comprises an upper-layer capacity optimizing configuration model and a lower-layer operation optimizing scheduling model, the upper-layer capacity optimizing configuration model is used for solving the problems of the installed capacity of distributed energy sources in a multi-VPP shared energy storage system and the capacity and power configuration of energy storage batteries in a shared energy storage power station, the lowest annual comprehensive cost of the multi-VPP and the shared energy storage power station is taken as an optimizing target, and decision variables comprise the power and capacity configuration of the energy storage batteries and the installed capacity of distributed energy sources in each VPP; the lower-layer operation optimization scheduling model is used for solving the economic scheduling problem of the shared energy storage power station, taking the lowest annual comprehensive cost of the shared energy storage power station as an optimization target, and the decision variables comprise the operation condition of distributed energy equipment in each VPP, the electricity purchasing condition of the VPP from a large power grid and the electricity purchasing and selling condition of each VPP and the shared energy storage power station; the method specifically comprises the following steps:
1. constructing an objective function of an upper capacity optimization configuration model;
1) Total investment cost of energy storage batteryThe method comprises the following steps:
in the method, in the process of the application,and->Respectively the rated power and rated capacity of the energy storage battery, c p And c E The unit power cost coefficient and the unit capacity cost coefficient of the energy storage battery are respectively;
2) Total replacement cost of energy storage cellsThe method comprises the following steps:
wherein Y is t For the practical service life of the energy storage battery, Y a Sharing the total design age of the energy storage system for the multi-VPP;
as can be seen from (2), the total replacement cost of the energy storage batteryAnd the actual service life Y t In the related art, based on the analysis of the experimental data of the renewable energy laboratory in the united states, the service life of the energy storage battery is mainly related to factors such as the depth of charge and discharge, the discharge rate, the number of charge and discharge, and the like, and each discharge isIrreversible damage is caused to the service life of the energy storage battery until the service life is finished; based on this, if the energy storage battery is in life cycle T 0 Including n discharge events, the actual service life of the energy storage cell can be expressed as:
wherein, Γ R The total effective throughput during discharge is expressed in units of: ampere-hours; l (L) R Represents the charge and discharge cycle times of the energy storage battery at rated discharge depth and rated discharge current, D R Represents rated depth of discharge, C R A rated capacity at rated discharge current; d, d eff (τ) represents the τ -th discharge event, discharge event d eff Expressed as:
wherein D is A For the actual depth of discharge, C A D is the actual discharge capacity act U is the ampere-hours at the actual discharge current 0 And u 1 The parameters are fitting parameters and can be obtained through the relation curve fitting of the discharging depth and the failure cycle times of the energy storage battery;
3) Total investment cost of all wind turbines of multi-VPPThe method comprises the following steps:
in the method, in the process of the application,for the total investment cost of the wind turbine generator in the ith VPP, I is the number of VPPs in the multi-VPP shared energy storage system,/for the total investment cost of the wind turbine generator in the ith VPP>The number of wind turbine generator sets built for the ith VPP, C WT1 Investment cost of a single wind turbine generator system;
4) Total investment cost of all photovoltaic power generation units of multi-VPPThe method comprises the following steps:
in the method, in the process of the application,for the total investment cost of the photovoltaic generator set in the ith VPP, < + >>The number of photovoltaic generator sets built for the ith VPP, C PV1 Investment cost of a single photovoltaic generator set;
5) Cost of purchasing electrical energy from large grid C by multiVPP Grid The method comprises the following steps:
in the method, in the process of the application,purchasing electric energy costs from a large electric grid on the M typical days for the ith VPP, M being the number of typical days per year, +.>For each VPP, buying a price matrix of unit electric energy from the large electric network in each scheduling period,/for each VPP>From large at the m-th typical day for the ith VPPElectric power purchased by the power grid;
6) Multi-VPP wind and light discarding annual penalty cost C Cut The method comprises the following steps:
in the method, in the process of the application,represents the cost, lambda, of the wind and light curtailment penalty of the ith VPP on the mth typical day cut Punishment cost coefficient for wind and light abandoning>For the (i) th VPP (virtual private plane) in each scheduling time period of the (m) th typical day, the (T) is a matrix transposition (xi) wp The wind-solar complementary power generation internet electricity price matrix is adopted;
7) Scheduling cost C of multi-VPP flexible load Fl The method comprises the following steps:
in the method, in the process of the application,scheduling costs, v for flexible load of ith VPP on mth typical day ele.down Indicating flexible load reduction compensation unit price v ele.up Indicating that the flexible load increases the price of the reward, < > and->Indicating the flexible load reduction amount of the ith VPP on the mth typical day, +.>Represents the increase in flexible load of the ith VPP on the mth typical day;
in summary, the objective function of the upper capacity optimization configuration model is:
wherein, beta is annual rate;
constraint conditions of the upper capacity optimization configuration model comprise energy multiplying power constraint of an energy storage battery, power and capacity constraint of the energy storage battery, construction quantity constraint of wind turbines and photovoltaic generators in the VPP, and power constraint of the VPP for purchasing electric energy from a large power grid;
1) Energy storage battery energy multiplying power constraint
In the method, in the process of the application,for maximum investment capacity of the energy storage battery +.>The maximum investment power of the energy storage battery is shown, and delta is the energy multiplying power of the energy storage battery;
2) Power and capacity constraints for energy storage batteries
In the method, in the process of the application,for the minimum investment power of the energy storage battery, +.>Minimum investment capacity for the energy storage battery;
3) The configuration of the number of the devices has great influence on improving the efficiency of the system, if the number is too large, the total investment in the initial stage of the project is high, otherwise, the wind and light cannot be fully utilized, and the basic operation requirement of the system cannot be met; therefore, the problem of the number of the construction of the equipment is to consider the problems of space and resources, and the constraint of the number of the construction of the wind generating set and the photovoltaic generating set in the VPP is as follows:
in the method, in the process of the application,and->The minimum number and the maximum number of wind turbine generators built in the VPP are respectively, and the wind turbine generators are in a +.>And->The minimum number and the maximum number of the photovoltaic generator sets are respectively built in the VPP;
4) Power constraint for VPP to purchase electrical energy from large grid:
wherein P is grid.min And P grid.max Purchasing electrical energy from a large grid for VPP allows for minimum and maximum electrical power to be transferred.
2. Constructing an objective function of a lower-layer operation optimization scheduling model;
1) Cost C of sharing energy storage power station to purchase electrical energy from multiple VPPs ESS.B The method comprises the following steps:
in the method, in the process of the application,expense of purchasing electric energy from the ith VPP on the mth typical day for sharing the energy storage power station,/-)>Unit price of electricity for sharing energy storage station to purchase electrical energy from individual VPPs, < >>Electric power purchased from the ith VPP on the mth typical day for the shared energy storage plant;
2) Revenue C of selling electric energy to various VPPs by sharing energy storage power station ESS.S The method comprises the following steps:
in the method, in the process of the application,to share the benefits of the energy storage plant selling electric energy to the ith VPP on the mth typical day,/>Selling a unit price of electric energy to each VPP for sharing an energy storage station, < >>Selling electric power to the ith VPP on the mth typical day for sharing the energy storage power station;
3) Rental service fee C paid by shared energy storage power station to each VPP serv The method comprises the following steps:
in the method, in the process of the application,rents for sharing energy storage power stations to collect from the ith VPP on the mth typical dayLay service fee->Coefficient matrix for unit electric energy lease service charge paid to each VPP by shared energy storage power station;
in summary, the objective function of the lower-layer operation optimization scheduling model is as follows:
constraint conditions of the lower-layer operation optimization scheduling model comprise VPP power balance constraint, flexible load reduction and increment constraint, energy storage battery operation constraint, distributed energy climbing constraint and VPP charge and discharge power constraint;
1) The VPP power balance constraint is:
in the method, in the process of the application,the actual output power of the wind power generation set and the photovoltaic power generation set of the ith VPP respectively on the mth typical day,/->Load demand on the m typical day for the i-th VPP;
the output model of the wind turbine generator is as follows:
wherein P is WT For the actual power of the wind turbine, P r The rated power of the wind turbine generator is v is the actual wind speed, v ci 、v co And v r The wind speed is cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine generator;
the output model of the photovoltaic generator set is as follows:
wherein P is PV For the actual power of the photovoltaic generator set, P STC Rated output power of the photovoltaic generator set in a standard test environment; g AC G is the actual solar radiation intensity of the photovoltaic generator set STC The unit of solar radiation intensity of the photovoltaic generator set in the standard test environment is kW/m 2 The method comprises the steps of carrying out a first treatment on the surface of the k is the influence factor of temperature on output power, and represents the influence of temperature change on the actual output power of the photovoltaic generator set, T STC T is the test temperature (25 ℃) in a standard environment C The actual ambient temperature of the photovoltaic generator set;
2) Flexible load shedding and incremental restraint
The formula (I) is shown in the specification,and->The upper limit of flexible load shedding and increasing on the m typical day for the ith VPP, respectively;
3) Energy storage battery operation constraints
When charged, the state of charge (SOC) of the energy storage battery is expressed as:
upon discharge, the state of charge of the energy storage battery is expressed as:
wherein S is OC (t)、S OC (t-1) the states of charge of the energy storage cells in the t-1 th and t-1 th schedule periods, respectively, σ being the self-discharge rate, η of the energy storage cells c 、η d Respectively charging and discharging efficiency of the energy storage battery, wherein Deltat is the time interval between two scheduling time periods;
in order to avoid damage to the service life caused by excessive charge and discharge of the energy storage battery, the state of charge should be strictly controlled, and then the state of charge satisfies the following formula:
in the method, in the process of the application,and->Respectively the minimum and maximum states of charge of the energy storage battery;
4) Distributed energy climbing constraint
In the method, in the process of the application,the actual output power of the distributed energy sources in the t-th scheduling time period and the t-1 scheduling time period is respectively, n is 0 and represents a photovoltaic generator set, and 1 represents a wind turbine set; r is R u For maximum value of climbing rate in scheduling time period, R d Minimum value of downhill climbing rate in the scheduling time period;
5) VPP charge-discharge power constraint
When any VPP exchanges electric energy with the shared energy storage power station, the same time period can not be charged and discharged at the same time, so the following constraint exists:
in the method, in the process of the application,charge and discharge identification bits of the ith VPP on the mth typical day, respectively, +.>And->The upper limit of the charge and discharge power of the energy storage battery is respectively set.
3. Solving a double-layer decision game model
Decision variables of the upper capacity optimization configuration model include rated power of the energy storage batteryAnd rated capacity->Wind turbine generator system number built by each VPP (virtual private plane)>And the number of photovoltaic generating sets +.>The decision variables of the lower layer operation optimization scheduling model comprise charge and discharge identification bits of each VPP>And->Electric power purchased from each VPP by sharing energy storage power station +.>And electric power sold to the respective VPPs +.>Electric power purchased by each VPP from a large electric network +.>And the amount of reduction of the flexible load of the respective VPPs +.>And increase amount->
Initializing decision variables of an upper-layer capacity optimizing configuration model and a lower-layer operation optimizing scheduling model, and respectively carrying out alternate iterative solution on the lower-layer operation optimizing scheduling model and the upper-layer capacity optimizing configuration model by utilizing a second-order cone optimizing algorithm and an intelligent optimizing algorithm to complete the optimal configuration of the multi-VPP shared energy storage capacity. The intelligent optimization algorithm may be whale algorithm (WOA), particle swarm algorithm, ant algorithm, etc.
Example 1
The multi-VPP shared energy storage system of the embodiment comprises 3 VPPs and a shared energy storage power station; the typical days are divided into four types according to the wind and light information of one year and 4 seasons of spring, summer, autumn and winter, 24 scheduling time periods are taken for each typical day for calculation, and the duration of each scheduling time period is one hour. The energy storage battery is selected from a lead storage battery with mature technology and high energy density, relevant parameters are shown in table 1, the initial SOC of the energy storage battery is set to be 0.5, and the wind discarding and light discarding punishment cost is set to be 0.5 times of the electricity purchasing price of the power grid.
Table 1 lead storage battery parameters
The parameters of the wind turbine generator are as follows: rated power P r =1500w, cut-in wind speed v ci =2.6m/s, rated wind speed v r Cut-out wind speed v=14m/s co =18.8m/s; the total investment cost of the fan unit is 2500 yuan/table, the maintenance coefficient cost is 0.0079 yuan/kW.h, and the service life of the design is 30 years.
The parameters of the photovoltaic generator set are as follows: rated power P r The influence factor k of the temperature on the output power is = -0.0047, the total investment cost of the photovoltaic generator set is 500 yuan/table, the maintenance coefficient cost is 0.0187 yuan/kw.h, and the design service life is 30 years.
The electricity purchase price adopts a peak-to-valley electricity price mechanism, and the electricity price of each scheduling period is shown in fig. 4. Solving the double-layer decision game model to obtain the installed capacity configuration of each VPP distributed energy source, as shown in Table 2; the optimal capacity of the shared energy storage power station is 4065.2 kW.h, and the charging and discharging rated power of the energy storage battery is 372kW.
TABLE 2 results of the capacity configuration of each VPP distributed energy source
As can be seen from Table 2, VPP C The wind resources of the location are most abundant, and the installed capacity of the wind turbine reaches 285kW; VPP (virtual private plane) B The optical resources of the place are rich, and the installed capacity of the photovoltaic generator set reaches 199.5kW.
Fig. 5 reflects the charge and discharge power and state of charge of the shared energy storage power station, and it can be known that the shared energy storage power station is participating in the power scheduling of each VPP in each time period of the whole day. The maximum discharge power 285kW is reached at 13, the maximum charge power 371kW is reached at 12, the charge and discharge power reaches 100kW in almost most of the time period of the whole day, the shared energy storage power station fully exchanges electric energy with a plurality of VPPs, and one full charge and one full discharge behaviors are achieved.
FIGS. 6-8 are, respectively, VPPs in a multi-VPP shared energy storage power station system A 、VPP B 、VPP C The electric power balance scheduling result graph of (a) for each period of time in each VPP is as followsThe figure shows the figure.
As can be seen from fig. 6, the VPPA is self-sufficient, in which the exchange power is smaller, and the self-configured wind turbine photovoltaic installed capacity basically can meet the load condition of most of the time period, except that the load is very high in 8 time periods, but if the wind turbine and the photovoltaic group are configured to be higher, the large-scale wind curtailment and the light curtailment are caused even if the wind turbine and the photovoltaic group are configured to be higher, and at the moment, the electricity purchasing cost from the large power grid is the lowest 0.37 yuan/kw.h, compared with the load supplying cost performed by energy storage and discharge, the cost of load supplying is lower, and in order to ensure economy, the scheduling result shows that the scheme of purchasing electric energy from the large power grid is adopted in the time period.
Fig. 7 shows the result of VPPB power balance optimization, which can be obtained that the VPP is power-deficient, and the capacity of the VPP wind motor and the photovoltaic installation cannot meet the load, and the VPP is purchased from either a large power grid or a shared energy storage power station in almost all time periods. The power balance image is analyzed: because the electricity price of the external large power grid is the lowest 0.37 yuan/kW.h with short time in the whole day at the moment of 1-8, the VPP is shown in the figure to not use the shared energy storage power station to supply power when the wind-solar power generation condition can not meet the load condition, but use to purchase electric energy from the external large power grid; when the electricity price of 9-12 and 18-21 reaches 1.36 yuan/kW.h, the VPP can be seen to be just opposite to the above when the wind-solar power generation condition can not meet the load condition, the shared energy storage power station is used for supplying power, but the power is not purchased from an external large power grid, and the scheduling result can be seen to reduce the running cost to the minimum.
Fig. 8 shows the result of VPPC power balance optimization, which can be obtained that the VPP is multi-electric, because of the developed wind-light resources in the area, the configured wind motor and photovoltaic installed capacity are larger, the power transmission is performed to the shared energy storage power station after the VPP load of the wind motor and photovoltaic installed capacity is satisfied, and the power transmission is performed to the shared energy storage power station almost exclusively in each time period.
From fig. 6 to 8, it can be known that there is a flexible load participation scheduling situation in each time period in each VPP. For the time periods with high loads, because the flexible load participation scheduling cost is lower than the energy storage scheduling cost, the flexible load participation scheduling is prioritized, the user side demand electricity consumption is adjusted in part of the time periods, and part of the loads are reduced through the demand response behaviors of the loads. Secondly, in order to reduce investment and operation cost of the energy storage power station as much as possible, the flexible load is preferentially considered to participate in scheduling, and when the load is lower in time period, the power consumption condition of the load after the flexible load participates in scheduling is enhanced.
In fig. 9, the wind and light discarding power of the shared energy storage power station in each time period is achieved, the wind and light utilization rate of the multi-VPP shared energy storage system reaches 96.53%, and it can be known that the wind and light utilization rate of the multi-micro-grid shared energy storage system is further improved compared with that of the energy storage power station configured by each micro-grid.
The application is applicable to the prior art where it is not described.

Claims (4)

1. A multi-VPP shared energy storage capacity optimizing configuration method based on double-layer decision game uses a multi-VPP shared energy storage system comprising a shared energy storage power station and a plurality of VPPs, wherein each VPP comprises a load end and a distributed energy source consisting of wind power generation and photovoltaic power generation, and the shared energy storage power station utilizes an energy storage battery to store energy; the shared energy storage power station and the VPP exchange electric energy in two directions, and the shared energy storage power station and the VPP exchange electric energy with a large power grid at the same time; the method is characterized in that a double-layer decision game model adopted by the method comprises an upper-layer capacity optimization configuration model and a lower-layer operation optimization scheduling model, and comprises the following contents:
1. an objective function of an upper capacity optimization configuration model is constructed, and the expression is as follows:
in the method, in the process of the application,for the total investment costs of the energy storage battery +.>For the total replacement cost of the energy storage cell, +.>Total investment costs for all wind turbines for multiVPP, < >>The total investment cost of all the photovoltaic generating sets of the multi-VPP is that beta is annual rate, Y a Sharing the total design age of the energy storage system for multiple VPPs, C Grid Cost of purchasing electric energy from large grid for multiVPP, C Cut Annual penalty cost for multiple VPP wind and light abandoning, C Fl Scheduling cost for multi-VPP flexible load;
constraint conditions of the upper capacity optimization configuration model objective function comprise energy multiplying power constraint of an energy storage battery, power and capacity constraint of the energy storage battery, construction quantity constraint of wind turbines and photovoltaic generators in VPP, and power constraint of the VPP for purchasing electric energy from a large power grid;
2. constructing an objective function of a lower-layer operation optimization scheduling model, wherein the expression is as follows:
wherein C is ESS.B C for sharing cost of energy storage power station purchasing electric energy from multiple VPPs ESS.S C for sharing benefits of selling electric energy from energy storage power stations to various VPPs serv Lease service fees paid to each VPP for the shared energy storage power station;
constraint conditions of the lower-layer operation optimization scheduling model objective function comprise VPP power balance constraint, flexible load reduction and increment constraint, energy storage battery operation constraint, distributed energy climbing constraint and VPP charge and discharge power constraint;
3. the upper-layer capacity optimizing configuration model takes the lowest annual comprehensive cost of the multi-VPP and the shared energy storage power station as an optimizing target, and the lower-layer operation optimizing scheduling model takes the lowest annual comprehensive cost of the shared energy storage power station as an optimizing target, and the upper-layer capacity optimizing configuration model and the lower-layer operation optimizing scheduling model are respectively subjected to alternate iterative solution to complete the optimizing configuration of the multi-VPP shared energy storage capacity.
2. The multi-VPP shared energy storage capacity optimal configuration method based on double-layer decision game according to claim 1, wherein decision variables of the upper-layer capacity optimal configuration model comprise rated power and rated capacity of an energy storage battery, and the number of wind turbines and photovoltaic generators built by each VPP; the decision variables of the lower-layer operation optimization scheduling model comprise charge and discharge identification bits of each VPP, electric power purchased from each VPP and electric power sold to each VPP by the shared energy storage power station, electric power purchased from a large power grid by each VPP and reduction and increase of flexible loads of each VPP.
3. The multi-VPP shared energy storage capacity optimization configuration method based on double-layer decision game according to claim 1 or 2, wherein an upper-layer capacity optimization configuration model is solved by using an intelligent optimization algorithm, and a lower-layer operation optimization scheduling model is solved by using a second-order cone optimization algorithm.
4. The multi-VPP shared energy storage capacity optimization configuration method based on double-layer decision game according to claim 1, wherein the distributed energy climbing constraint is:
in the method, in the process of the application,the actual output power of the distributed energy sources in the t-th and t-1 th scheduling time periods is respectively represented by n 0,taking 1 to represent a wind turbine generator; r is R u For maximum value of climbing rate in scheduling time period, R d For minimum downhill climbing in a scheduled time period, Δt is the time interval between two scheduled time periods.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117767375A (en) * 2024-02-22 2024-03-26 山东理工大学 shared energy storage fairness allocation strategy based on risk constraint asymmetric cooperative game
CN117767375B (en) * 2024-02-22 2024-05-14 山东理工大学 Shared energy storage fairness allocation strategy based on risk constraint asymmetric cooperative game

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