CN117728474B - Energy storage capacity optimal configuration method, device, equipment and medium for clean energy base - Google Patents
Energy storage capacity optimal configuration method, device, equipment and medium for clean energy base Download PDFInfo
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Abstract
The invention relates to the technical field of energy storage optimal configuration, in particular to a clean energy base energy storage capacity optimal configuration method, a device, equipment and a medium, which comprise the following steps: performing daily correction at intervals of 15min, and constructing an inner layer model for performing rolling optimization on the output of a typical daily step hydroelectric group and a pump storage group by taking the minimum hydro-electric output deviation as a target; performing real-time regulation and control at intervals of 1min, constructing an outer model with the aim of minimizing the total output deviation, the annual equivalent cost and the attenuation degree index, and generating an energy storage planning configuration scheme and charging and discharging power; and carrying out joint solution on the inner layer model and the outer layer model by using a non-dominant sorting genetic algorithm and a large-scale mathematical programming optimizer to obtain an optimal energy storage capacity configuration scheme and optimal scheduling. According to the invention, the influence of fluctuation and uncertainty of wind and light output on the output power of the clean energy base can be reduced, the uncertainty caused by system scheduling is reduced, and the reliability of the system is improved.
Description
Technical Field
The invention relates to the technical field of energy storage optimal configuration, in particular to a clean energy base energy storage capacity optimal configuration method, device, equipment and medium.
Background
Along with the diversification of the current new energy production and supply forms, the energy industry has been in the diversification era, and the multi-energy complementation mainly based on renewable energy has become a new stream of sustainable development of energy. The water-wind-light complementary power generation system is a new energy utilization form for realizing the maximization of efficiency, but the fluctuation of the power sent out by the complementary power generation system is increased due to the uncertainty of wind-light output, so that the water-wind-light complementary power generation system is a great challenge for the safe and stable operation of a power grid. For this purpose, energy storage devices can be assembled and a reasonable operation strategy can be formulated to assist in correcting clean energy base power output fluctuations.
However, the energy storage price and the maintenance cost are high, and the energy storage configuration cost is as small as possible on the premise of meeting the smoothness requirement and other indexes in consideration of the overall economy of the system. Therefore, the optimal battery energy storage configuration scheme in the clean energy base has important research significance.
The main obstacle to new energy consumption comes from its volatility and uncontrollable nature. The large-scale wind-solar power supply is directly connected with the grid, the fluctuation of the wind-solar power supply can cause severe impact on the grid, and the safe operation of the system is threatened; the uncontrollability of the method can cause that the base is difficult to track the load of the power grid, so that the scheduling difficulty of the power grid and the resource allocation cost are increased. Most of the existing researches regulate and control units such as water and electricity with large time scale of 1h interval, but prediction accuracy of the clean energy base output at different time scales is different, and influence of volatility on system operation cannot be comprehensively considered by optimizing a single time scale, so that an optimization result may not be consistent with actual operation conditions.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a clean energy base energy storage capacity optimal configuration method, a device, equipment and a medium, thereby effectively solving the problems in the background technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: an energy storage capacity optimizing configuration method of a clean energy base comprises the following steps:
Performing daily correction at intervals of 15min to construct an inner layer model which aims at minimizing the deviation of the water and electricity output and performs rolling optimization on the output of a typical daily step water and electricity group and a pump storage group;
Performing real-time regulation and control at intervals of 1min, and constructing an outer model which aims at minimizing the total output deviation, the annual equivalent cost and the attenuation degree index to generate an energy storage planning configuration scheme and charge and discharge power;
and carrying out joint solution on the inner layer model and the outer layer model by using a non-dominant sorting genetic algorithm and a large-scale mathematical programming optimizer to obtain an optimal energy storage capacity configuration scheme and optimal scheduling under the scheme.
Further, the method further comprises the following steps:
constructing a control strategy of three battery groups, and setting a group of standby auxiliary battery groups, wherein the standby auxiliary battery groups are used as standby rechargeable battery groups when the charging capacity is insufficient; when the discharge capacity is insufficient, it serves as a spare discharge battery pack.
Further, the control strategy of the three-battery grouping comprises the following steps:
Three battery packs are arranged: BESS1, BESS2, and BESS3, wherein BESS3 is a backup secondary battery;
the BESS1 and the BESS2 are respectively charged or discharged continuously, and the two battery packs cannot work simultaneously at the same moment, and the BESS3 does not receive a calling instruction and is in a hovering closing state;
When one of the BESS1 and the BESS2 reaches the limit value of the state of charge, a calling instruction is sent to the BESS3, the BESS3 immediately starts the battery pack to operate instead of the battery pack reaching the limit, and the battery pack is matched with the other battery pack to continue to charge or discharge so as to stabilize the power fluctuation;
When one of the BESS3 and the other battery pack reaches the upper and lower limits of the charge state again, the BESS3 is immediately turned off, the BESS1 and the BESS2 are turned on, and the charge-discharge roles are exchanged to continue to cooperate to stabilize power fluctuation.
Further, the generating the energy storage planning configuration scheme and the outer layer model of the charging and discharging power comprises the following steps:
Making a power generation plan curve;
Constructing an objective function considering power deviation;
and constructing a water quantity connection constraint condition, a flow constraint condition, an output constraint condition and a start-stop constraint condition.
Further, the power generation planning curve is obtained by calculating with the minimum residual load variance of the receiving-end power grid as a target in a day-ahead power generation planning stage:
；
Wherein P t ΔL is the residual load power at time t under the condition of minimum residual load variance; p t L system load, its sampling period interval Δt=1h, 24 points a day.
Further, the constructing an objective function that considers the power deviation includes:
；
Wherein, P t unit、Pt WT and P t PV respectively represent the total power actually sent out by the clean energy base wind-light water at the moment t, and the wind power output and the photovoltaic output; The output of the ith hydroelectric generating set at the t moment; n H is the number of hydroelectric generating sets; the sampling period interval Δt=15 min, so T 1 takes 96.
Further, the construction of the water quantity connection constraint condition, the flow constraint condition, the output constraint condition and the start-stop constraint condition comprises the following steps:
water quantity connection constraint conditions:
；
；
；
wherein, The total drainage flow of the ith hydropower station in the t period; /(I)For t-/>Total drainage of the time interval i-1 level hydropower station, wherein/>Indicating the water stagnation between the step hydropower stations; /(I)Is a time interval; v i,t is the reservoir capacity of the ith hydropower station in the t period; v i,t-1 is the reservoir capacity of the ith hydropower station in t-1 period; i i,t is the warehouse-in runoff of the ith hydropower station in the t period; representation will/> Rounding down the function; /(I)For/>Total drainage flow of the time interval i-1 level hydropower station; /(I)Is thatTotal drainage flow of the time interval i-1 level hydropower station; /(I)And/>The flow rates are respectively the waste water flow rate and the power generation flow rate.
For the additional installation of the pumped storage power station, the description of the pumping working condition is added:
；
wherein, Is pumping flow;
flow constraint conditions:
；
；
；
wherein, Is the upper and lower limits of the flow;
Storage capacity constraint conditions:
The storage capacity range at the last moment is set, and the expression is as follows:
；
；
wherein, Is the upper and lower limits of the storage capacity; /(I)The target stock capacity is the last time T; /(I)Adjusting the coefficient for the range of the storage capacity;
Force constraint conditions:
；
；
；
；
wherein, ,/>Pumping power for the conventional hydropower station output and hybrid pumped storage power station; /(I)Is the upper and lower limits of power; /(I)，/>The up-down climbing capacity is respectively; u i,t is an operating state flag variable; /(I)Marking a variable for the pumping state;
start-stop constraint conditions:
；
；
；
；
；
；
；
；
wherein, ,/>The variable is the start-stop variable of the step hydropower under the power generation working condition; /(I),/>Is a start-stop variable under the pumping working condition; /(I), />, />, />Respectively minimum start-stop times.
Further, the generating the energy storage planning configuration scheme and the outer layer model of the charging and discharging power comprises the following steps:
Constructing an objective function considering the total output deviation;
constructing an equal annual investment cost objective function considering the dynamic predicted life of the stored energy;
constructing an objective function considering the average attenuation index;
And constructing an energy storage capacity constraint condition, an energy storage charge-discharge power constraint condition and a charge state constraint condition.
Further, the constructing takes into account an objective function of the total output deviation, comprising:
；
Wherein D 1,D2 is the difference between the step hydroelectric power output and the group battery control output; sampling period interval Δt=1 min, so T 2 takes 1440; p t total is the actual output power of the water-wind-solar-energy-storage combined system; p t ref is the reference output power of the clean energy base obtained by decomposing wind-light output data through a variation mode decomposition method.
Further, the constructing an annual investment cost objective function that considers dynamic predicted life of the stored energy, comprising:
Wherein, C inve is the investment cost of energy storage construction; c main is the energy storage operation maintenance cost; c reco is the recovery cost, which is generated after the project life cycle is completed, including the cost of processing the participating devices and the cost obtained by recovering the old batteries; b is the discount rate; y is the dynamic prediction life of the energy storage battery;
wherein the economic cost expressions of each part are as follows:
；
Wherein E total is the maximum rated total capacity of the energy storage battery pack; p r is the maximum rated charge-discharge power of the energy storage battery pack; c ei is the investment cost of the unit capacity battery; c pi is the investment cost of the unit power converter; c em is the operation maintenance cost of the unit capacity battery; c pm is the operation maintenance cost of the unit power battery; c rec is the rejection cost ratio;
The energy storage life adopts dynamic prediction life which changes along with different running states, and is the ratio of the total discharge capacity of the battery under the life cycle with rated discharge depth to the annual discharge capacity converted into rated discharge depth, and the expression is as follows:
；
Wherein D ODr is the rated depth of discharge; e r is the battery rated capacity; e j is the actual discharge capacity of the j-th switching stage; n r is the number of battery cycles at rated depth of discharge; d is the number of days of the year; t is the discharge phase count of the day; n j is the number of battery cycles at the actual depth of discharge in the jth discharge stage, and J is the number of switching.
Further, the constructing an objective function that considers an average attenuation index includes:
The unbalanced running state is reflected by the average attenuation degree index I, and the expression is shown as follows:
；
Wherein Y is the energy storage life; d is the number of days of the year; t is the discharge phase count of the day; epsilon 1 t, d, y、ε2 t, d, y is a life decay index of the nth switching stage of the d th day of the y th year of two battery packs, and the expression is as follows:
；
Wherein, ψ SOC1、ΨSOC1' is the charge state of a battery pack at the current switching time and the last switching time; psi SOC2、ΨSOC2' is the state of charge of the other battery at the time of the current and last switching; Δψ SOCb is the standard charge-discharge depth.
Further, the constructing the energy storage capacity constraint condition, the energy storage charge-discharge power constraint condition and the state of charge constraint condition includes:
Energy storage capacity of constraint conditions are as follows:
；
Wherein S min is the lower limit of the energy storage residual quantity; s t is the residual capacity of the battery at the moment t, and E r is the rated capacity of the battery;
Energy storage charge-discharge power constraint conditions:
；
wherein, P min is the lower limit of the battery charge-discharge power, P r is the maximum rated charge-discharge power of the energy storage battery, , Energy storage charging and discharging power is carried out at the time t;
State of charge constraints:
；
Wherein, ψ max、Ψmin is the maximum value and the minimum value of the battery SOC, The SOC of the battery at time t.
Further, the joint solution of the inner layer model and the outer layer model by using a non-dominant ranking genetic algorithm and a large-scale mathematical programming optimizer comprises the following steps:
The outer layer model is calculated by adopting a non-dominant ordering genetic algorithm;
The inner layer model is calculated by adopting a new generation large-scale mathematical programming optimizer Gurobi;
And continuously and alternately iterating the inner layer model and the outer layer model to finally obtain an optimal configuration scheme and optimal scheduling under the scheme.
The invention also comprises a clean energy base energy storage capacity optimizing configuration device, which comprises the following steps:
The inner layer model building unit is used for carrying out daily correction at intervals of 15min, and building an inner layer model which aims at minimizing the water and electricity output deviation and carries out rolling optimization on the output of a typical daily step water and electricity group and a pumped storage unit;
The outer layer model building unit is used for carrying out real-time regulation and control at intervals of 1min, constructing an outer layer model which aims at minimizing total output deviation, annual equivalent cost and attenuation degree indexes and generates an energy storage planning configuration scheme and charge and discharge power;
And the joint solving unit is used for carrying out joint solving on the inner layer model and the outer layer model by using a non-dominant sorting genetic algorithm and a large-scale mathematical programming optimizer to obtain an optimal energy storage capacity configuration scheme and optimal scheduling under the scheme.
The invention also includes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the method as described above when executing the computer program.
The invention also includes a storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.
The beneficial effects of the invention are as follows: according to the invention, the inner layer model and the outer layer model are constructed, the inner layer model is subjected to daily correction at 15min intervals, the outer layer model is subjected to real-time regulation and control at 1min intervals, the influence of fluctuation and uncertainty of wind and light output on the power output of the clean energy base can be further reduced, the operation scheduling result is closer to the actual demand, the uncertainty caused by system scheduling is reduced, and the system reliability is improved. And the annual equivalent cost and the attenuation degree index are listed in a multi-target configuration model, and the hybrid energy storage system fully utilizes the complementary advantages of high-capacity pumping and storage and quick-response electrochemical energy storage, so that the degradation of the battery can be reduced to a certain extent, and the investment cost is reduced as much as possible.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a flow chart of the method of example 1;
FIG. 2 is a schematic view of the structure of the device in example 1;
FIG. 3 is a schematic view of a step water wind-solar energy storage clean energy base in example 2;
FIG. 4 is a schematic diagram of the power generation of the clean energy base unit in example 2;
FIG. 5 is a graph showing comparison of water electric power at different time scales in example 2;
FIG. 6 is a graph showing the comparison of pumping and storing and charging and discharging power of the chemical energy storage in example 2;
Fig. 7 is a schematic diagram of the state of charge of the three battery packs in example 2;
fig. 8 is a charge/discharge power schematic diagram of the three-battery stack BESS1 in example 2;
fig. 9 is a schematic structural diagram of a computer device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
As shown in fig. 1: an energy storage capacity optimizing configuration method of a clean energy base comprises the following steps:
Performing daily correction at intervals of 15min to construct an inner layer model which aims at minimizing the deviation of the water and electricity output and performs rolling optimization on the output of a typical daily step water and electricity group and a pump storage group;
Performing real-time regulation and control at intervals of 1min, and constructing an outer model which aims at minimizing the total output deviation, the annual equivalent cost and the attenuation degree index to generate an energy storage planning configuration scheme and charge and discharge power;
and carrying out joint solution on the inner layer model and the outer layer model by using a non-dominant sorting genetic algorithm and a large-scale mathematical programming optimizer to obtain an optimal energy storage capacity configuration scheme and optimal scheduling under the scheme.
By constructing the inner layer model and the outer layer model, the inner layer model is subjected to daily correction at 15min intervals, the outer layer model is subjected to real-time regulation and control at 1min intervals, the influence of fluctuation and uncertainty of wind and light output on the power output of the clean energy base can be further reduced, the operation scheduling result is closer to the actual demand, the uncertainty caused by system scheduling is reduced, and the system reliability is improved. And the annual equivalent cost and the attenuation degree index are listed in a multi-target configuration model, and the hybrid energy storage system fully utilizes the complementary advantages of high-capacity pumping and storage and quick-response electrochemical energy storage, so that the degradation of the battery can be reduced to a certain extent, and the investment cost is reduced as much as possible.
In this embodiment, further comprising:
constructing a control strategy of three battery groups, and setting a group of standby auxiliary battery groups, wherein the standby auxiliary battery groups are used as standby rechargeable battery groups when the charging capacity is insufficient; when the discharge capacity is insufficient, it serves as a spare discharge battery pack.
The control strategy of the three-battery grouping comprises the following steps:
Three battery packs are arranged: BESS1, BESS2, and BESS3, wherein BESS3 is a backup secondary battery;
the BESS1 and the BESS2 are respectively charged or discharged continuously, and the two battery packs cannot work simultaneously at the same moment, and the BESS3 does not receive a calling instruction and is in a hovering closing state;
When one of the BESS1 and the BESS2 reaches the limit value of the state of charge, a calling instruction is sent to the BESS3, the BESS3 immediately starts the battery pack to operate instead of the battery pack reaching the limit, and the battery pack is matched with the other battery pack to continue to charge or discharge so as to stabilize the power fluctuation;
When one of the BESS3 and the other battery pack reaches the upper and lower limits of the charge state again, the BESS3 is immediately turned off, the BESS1 and the BESS2 are turned on, and the charge-discharge roles are exchanged to continue to cooperate to stabilize power fluctuation.
The method for generating the outer layer model of the energy storage planning configuration scheme and the charging and discharging power comprises the following steps:
Making a power generation plan curve;
Constructing an objective function considering power deviation;
and constructing a water quantity connection constraint condition, a flow constraint condition, an output constraint condition and a start-stop constraint condition.
The power generation planning curve is obtained by calculating by taking the minimum residual load variance of the receiving end power grid as a target in the day-ahead power generation planning stage:
；
Wherein P t ΔL is the residual load power at time t under the condition of minimum residual load variance; p t L system load, its sampling period interval Δt=1h, 24 points a day.
Constructing an objective function that accounts for power bias, comprising:
；
Wherein, P t unit、Pt WT and P t PV respectively represent the total power actually sent out by the clean energy base wind-light water at the moment t, and the wind power output and the photovoltaic output; The output of the ith hydroelectric generating set at the t moment; n H is the number of hydroelectric generating sets; the sampling period interval Δt=15 min, so T 1 takes 96.
Constructing a water quantity connection constraint condition, a flow constraint condition, an output constraint condition and a start-stop constraint condition, which comprises the following steps:
water quantity connection constraint conditions:
；
；
；
wherein, The total drainage flow of the ith hydropower station in the t period; /(I)For t-/>Total drainage of the time interval i-1 level hydropower station, wherein/>Indicating the water stagnation between the step hydropower stations; /(I)Is a time interval; v i,t is the reservoir capacity of the ith hydropower station in the t period; v i,t-1 is the reservoir capacity of the ith hydropower station in t-1 period; i i,t is the warehouse-in runoff of the ith hydropower station in the t period; representation will/> Rounding down the function; /(I)For/>Total drainage flow of the time interval i-1 level hydropower station; /(I)Is thatTotal drainage flow of the time interval i-1 level hydropower station; /(I)And/>The flow rates are respectively the waste water flow rate and the power generation flow rate.
For the additional installation of the pumped storage power station, the description of the pumping working condition is added:
；
wherein, Is pumping flow;
flow constraint conditions:
；
；
；
wherein, Is the upper and lower limits of the flow;
Storage capacity constraint conditions:
The storage capacity range at the last moment is set, and the expression is as follows:
；
；
wherein, Is the upper and lower limits of the storage capacity; /(I)The target stock capacity is the last time T; /(I)Adjusting the coefficient for the range of the storage capacity;
Force constraint conditions:
；
；
；
；
wherein, ,/>Pumping power for the conventional hydropower station output and hybrid pumped storage power station; /(I)Is the upper and lower limits of power; /(I), />The up-down climbing capacity is respectively; u i,t is an operating state flag variable; /(I)Marking a variable for the pumping state;
start-stop constraint conditions:
；
；
；
；
；
；
；
；
wherein, ,/>The variable is the start-stop variable of the step hydropower under the power generation working condition; /(I),/>Is a start-stop variable under the pumping working condition; /(I), />, />, />Respectively minimum start-stop times.
In this embodiment, an outer model of the energy storage planning configuration scheme and the charge-discharge power is generated, including the following steps:
Constructing an objective function considering the total output deviation;
constructing an equal annual investment cost objective function considering the dynamic predicted life of the stored energy;
constructing an objective function considering the average attenuation index;
And constructing an energy storage capacity constraint condition, an energy storage charge-discharge power constraint condition and a charge state constraint condition.
Constructing an objective function that accounts for the total force bias, comprising:
；
Wherein D 1,D2 is the difference between the step hydroelectric power output and the group battery control output; sampling period interval Δt=1 min, so T 2 takes 1440; p t total is the actual output power of the water-wind-solar-energy-storage combined system; p t ref is the reference output power of the clean energy base obtained by decomposing wind-light output data through a variation mode decomposition method.
Constructing an equal annual investment cost objective function that accounts for dynamic predicted life of the stored energy, comprising:
Wherein, C inve is the investment cost of energy storage construction; c main is the energy storage operation maintenance cost; c reco is the recovery cost, which is generated after the project life cycle is completed, including the cost of processing the participating devices and the cost obtained by recovering the old batteries; b is the discount rate; y is the dynamic prediction life of the energy storage battery;
wherein the economic cost expressions of each part are as follows:
；
Wherein E total is the maximum rated total capacity of the energy storage battery pack; p r is the maximum rated charge-discharge power of the energy storage battery pack; c ei is the investment cost of the unit capacity battery; c pi is the investment cost of the unit power converter; c em is the operation maintenance cost of the unit capacity battery; c pm is the operation maintenance cost of the unit power battery; c rec is the rejection cost ratio;
The energy storage life adopts dynamic prediction life which changes along with different running states, and is the ratio of the total discharge capacity of the battery under the life cycle with rated discharge depth to the annual discharge capacity converted into rated discharge depth, and the expression is as follows:
；
Wherein D ODr is the rated depth of discharge; e r is the battery rated capacity; e j is the actual discharge capacity of the j-th switching stage; n r is the number of battery cycles at rated depth of discharge; d is the number of days of the year; t is the discharge phase count of the day; n j is the number of battery cycles at the actual depth of discharge in the jth discharge stage, and J is the number of switching.
Constructing an objective function that considers an average attenuation index, comprising:
The unbalanced running state is reflected by the average attenuation degree index I, and the expression is shown as follows:
；
Wherein Y is the energy storage life; d is the number of days of the year; t is the discharge phase count of the day; epsilon 1 t, d, y、ε2 t, d, y is a life decay index of the nth switching stage of the d th day of the y th year of two battery packs, and the expression is as follows:
；
Wherein, ψ SOC1、ΨSOC1' is the charge state of a battery pack at the current switching time and the last switching time; psi SOC2、ΨSOC2' is the state of charge of the other battery at the time of the current and last switching; Δψ SOCb is the standard charge-discharge depth.
Constructing an energy storage capacity constraint condition, an energy storage charge-discharge power constraint condition and a state of charge constraint condition, comprising:
Energy storage capacity of constraint conditions are as follows:
；
Wherein S min is the lower limit of the energy storage residual quantity; s t is the residual capacity of the battery at the moment t, and E r is the rated capacity of the battery;
Energy storage charge-discharge power constraint conditions:
；
wherein, P min is the lower limit of the battery charge-discharge power, P r is the maximum rated charge-discharge power of the energy storage battery, ，Energy storage charging and discharging power is carried out at the time t;
State of charge constraints:
；
Wherein, ψ max、Ψmin is the maximum value and the minimum value of the battery SOC, The SOC of the battery at time t.
In this embodiment, the inner layer model and the outer layer model are jointly solved using a non-dominant ordered genetic algorithm and a large-scale mathematical programming optimizer, comprising the steps of:
The outer layer model is calculated by adopting a non-dominant sorting genetic algorithm;
the inner layer model adopts a new generation large-scale mathematical programming optimizer Gurobi for calculation;
and continuously and alternately iterating the inner layer model and the outer layer model to finally obtain an optimal configuration scheme and optimal scheduling under the scheme.
As shown in fig. 2, the embodiment further includes a clean energy base energy storage capacity optimizing configuration device, and the method includes:
The inner layer model building unit is used for carrying out daily correction at intervals of 15min, and building an inner layer model which aims at minimizing the water power output deviation and carries out rolling optimization on the output of a typical daily step water power unit and a pumped storage unit;
The outer layer model building unit is used for carrying out real-time regulation and control at intervals of 1min, constructing an outer layer model which aims at minimizing the total output deviation, the annual equivalent cost and the attenuation degree index and generating an energy storage planning configuration scheme and charge and discharge power;
And the joint solving unit is used for carrying out joint solving on the inner layer model and the outer layer model by using a non-dominant ordering genetic algorithm and a large-scale mathematical programming optimizer to obtain an optimal energy storage capacity configuration scheme and optimal scheduling under the scheme.
The technical scheme in the embodiment has the beneficial effects that:
1. In the embodiment, a multi-time-scale nested double-layer energy storage capacity optimal configuration model considering pumping storage and hydro-electric adjustment capacity is provided. The model can further reduce the influence of wind-light output fluctuation and uncertainty on the power sent out by the clean energy base, the operation scheduling result is closer to the actual demand, the uncertainty caused by system scheduling is reduced, and the system reliability is improved.
2. In this embodiment, a new three-battery group control operation strategy is provided, which clarifies the principle and characteristics of the operation of the main and auxiliary battery groups and establishes a specific charge and discharge model. The strategy can effectively reduce the loss of unbalanced operation of the battery and solve the problem of extreme operation possibly caused by wind power treatment fluctuation.
3. In the embodiment, a charging and discharging strategy is formulated to control the coordinated operation of a hybrid energy storage system consisting of an electrochemical energy storage unit and a pumping and storage unit, and annual equivalent cost and attenuation index are listed in a multi-target configuration model. The hybrid energy storage system fully utilizes the complementary advantages of the high-capacity pumping and storage and the quick-response electrochemical energy storage, can reduce the degradation of the battery to a certain extent, and reduces the investment cost as much as possible.
Example 2:
As shown in fig. 3, the clean energy base energy storage capacity optimizing configuration method meeting the requirement of multi-time scale nesting of the invention comprises the following steps:
Step 1: aiming at the problem of unbalanced charge and discharge of the double battery packs, a control strategy of three battery groups is adopted. The strategy sets a group of standby secondary battery packs, which serve as standby charging battery packs when the charging capacity is insufficient and serve as standby discharging battery packs when the discharging capacity is insufficient;
1.1: the power BESS1 and the power BESS2 in the three-battery-pack system are respectively charged or discharged continuously, and the two battery packs cannot work simultaneously at the same moment, and the BESS3 does not receive a calling instruction and is in a hovering and closing state;
1.2: when one of the BESS1 and the BESS2 reaches the limit value of the state of charge, a calling instruction is sent to the BESS3, the BESS3 immediately starts the battery pack to operate instead of the battery pack reaching the limit, and the battery pack is matched with the other battery pack to continue to charge or discharge so as to stabilize the power fluctuation;
1.3: when one of the BESS3 and the other battery pack reaches the upper and lower limits of the charge state again, the BESS3 is immediately turned off, the BESS1 and the BESS2 are turned on, and the charge-discharge roles are exchanged to continue to cooperate to stabilize power fluctuation.
Step 2: performing daily correction at intervals of 15min to construct an inner layer model which aims at minimizing the deviation of the hydroelectric power output and performs rolling optimization on the output of a typical daily step hydroelectric generating set and a pumped storage unit;
2.1: and (5) making a power generation plan curve. The method can be obtained by calculation with the minimum residual load variance of the receiving end power grid as a target in the day-ahead power generation planning stage:
；
Wherein P t ΔL is the residual load power at time t under the condition of minimum residual load variance; p t L system load. With a sampling period interval Δt=1h, 24 points a day.
2.2: An objective function is constructed that takes into account the power deviation. The output power during the scheduling in the day should track a power generation plan curve formulated in advance, and the expression is as follows:
；
Wherein, P t unit、Pt WT and P t PV respectively represent the total power actually sent out by the clean energy base wind-light water at the moment t, and the wind power output and the photovoltaic output; The output of the ith hydroelectric generating set at the t moment; n H is the number of hydroelectric generating sets; the sampling period interval Δt=15 min, so T 1 takes 96.
2.3: Constructing water quantity connection constraint conditions:
；
；
；
wherein, The total drainage flow of the ith hydropower station in the t period; /(I)For t-/>Total drainage of the time interval i-1 level hydropower station, wherein/>Indicating the water stagnation between the step hydropower stations; /(I)Is a time interval; v i,t is the reservoir capacity of the ith hydropower station in the t period; v i,t-1 is the reservoir capacity of the ith hydropower station in t-1 period; i i,t is the warehouse-in runoff of the ith hydropower station in the t period; representation will/> Rounding down the function; /(I)For/>Total drainage flow of the time interval i-1 level hydropower station; /(I)Is thatTotal drainage flow of the time interval i-1 level hydropower station; /(I)And/>The flow rates are respectively the waste water flow rate and the power generation flow rate.
For the additional installation of the pumped storage power station, the description of the pumping working condition is required to be added:
；
wherein, Is pumping flow.
2.4: Constructing a flow constraint condition:
；
；
；
wherein, Is the upper and lower limits of the flow.
2.5: And constructing a storage capacity constraint condition. And setting a storage capacity range at the last moment so as to provide flexibility for subsequent scheduling, wherein the expression is as follows:
；
；
wherein, Is the upper and lower limits of the storage capacity; /(I)The target stock capacity is the last time T; /(I)And adjusting the coefficient for the range of the storage capacity.
2.6: Building an output constraint condition:
；
；
；
；
wherein, ，/>Pumping power for the conventional hydropower station output and hybrid pumped storage power station; /(I)Is the upper and lower limits of power; /(I), />The up-down climbing capacity is respectively; u i,t is an operating state flag variable; /(I)And (5) marking a variable for the pumping state.
2.7: Constructing a start-stop constraint condition:
；
；
；
；
；
；
；
；
wherein, ，/>The variable is the start-stop variable of the step hydropower under the power generation working condition; /(I)，/>Is a start-stop variable under the pumping working condition; /(I), />, />, />Respectively minimum start-stop times.
Step 3: and carrying out real-time regulation and control at intervals of 1min, and constructing an outer model which aims at minimizing the total output deviation, the annual equivalent cost and the attenuation degree index to generate an energy storage planning configuration scheme and the charge and discharge power.
3.1: Constructing an objective function considering the total output deviation:
；
Wherein D 1,D2 is the difference between the step hydroelectric power output and the group battery control output; sampling period interval Δt=1 min, so T 2 takes 1440; p t total is the actual output power of the water-wind-solar-energy-storage combined system; p t ref is the reference output power of the clean energy base obtained by decomposing wind-light output data through a variation mode decomposition method.
3.2: Constructing an equal annual investment cost objective function considering the dynamic predicted life of the stored energy:
；
wherein, C inve is the investment cost of energy storage construction; c main is the energy storage operation maintenance cost; c reco is the recovery cost typically incurred after the project lifecycle has ended, including the cost of handling the participating devices and the cost of recovering the old batteries; b is the discount rate; y is the dynamic predicted lifetime of the energy storage battery.
Wherein the economic cost expressions of each part are as follows:
；
wherein E total is the maximum rated total capacity of the energy storage battery pack; p r is the maximum rated charge-discharge power of the energy storage battery pack; c ei is the investment cost of the unit capacity battery; c pi is the investment cost of the unit power converter; c em is the operation maintenance cost of the unit capacity battery; c pm is the operation maintenance cost of the unit power battery; and c rec is the rejection cost ratio.
The energy storage life adopts dynamic prediction life which changes along with different running states, and is the ratio of the total discharge capacity of the battery under the life cycle with rated discharge depth to the annual discharge capacity converted into rated discharge depth, and the expression is as follows:
；
Wherein D ODr is the rated depth of discharge; e r is the battery rated capacity; e j is the actual discharge capacity of the j-th switching stage; n r is the number of battery cycles at rated depth of discharge; d is the number of days of the year; t is the discharge phase count of the day; n j is the number of battery cycles at the actual depth of discharge in the jth discharge stage, and J is the number of switching.
3.3: An objective function is constructed that considers the average attenuation index. The unbalanced running state is reflected by an average attenuation degree index I, and the expression is shown as follows:
；
Wherein Y is the energy storage life; d is the number of days of the year; t is the discharge phase count of the day; epsilon 1 t, d, y、ε2 t, d, y is the life decay index of the nth switching stage of the d-th day of the y-th year of BESS1 and BESS 2. The expression is as follows:
；
Wherein, ψ SOC1、ΨSOC1' is the charge state of BESS1 at the current switching time and the last switching time; psi SOC2、ΨSOC2' is the state of charge of the BESS2 at the time of the current switching and the last switching; Δψ SOCb is the standard charge-discharge depth.
3.4: Constructing an energy storage capacity constraint condition:
；
Wherein S min is the lower limit of the energy storage residual quantity; s t is the remaining battery power at time t, and E r is the rated capacity of the battery.
3.5: Constructing energy storage charging and discharging power constraint conditions:
；
wherein, P min is the lower limit of the battery charge-discharge power, P r is the maximum rated charge-discharge power of the energy storage battery, , />And storing energy to charge and discharge power at the moment t.
3.6: Constructing a state of charge constraint condition:
；/>
Wherein, ψ max、Ψmin is the maximum value and the minimum value of the battery SOC, The SOC of the battery at time t.
Step 4: and adopting a non-dominant sorting genetic algorithm and a new generation large-scale mathematical programming optimizer Gurobi to carry out joint solution.
4.1: The outer layer model is calculated using a non-dominant ordering genetic algorithm.
4.2: The inner layer model is calculated using a new generation of large scale mathematical programming optimizer Gurobi.
4.3: And continuously iterating alternately the inner layer and the outer layer to finally obtain an optimal configuration scheme and optimal scheduling under the scheme.
In the embodiment, a clean energy base with pumping and accumulating step water and wind-solar complementary power generation in Sichuan is selected as an example. The base comprises a photovoltaic power station with rated installed capacity of 50MW and a wind turbine generator with rated installed capacity of 100 MW; in addition, a three-level conventional cascade hydropower station is arranged, hydropower stations 1 and 2 are daily regulation, and hydropower station 3 is diversion runoff type; and a hybrid pumped-storage power station 4. Nesting Gurobi an optimizer by adopting a non-dominant sorting genetic algorithm to solve, and obtaining an optimal configuration scheme as follows: two battery packs of 14.3MWh rated capacity and one 4.9MWh rated capacity were assembled with a DC/AC converter rated at 6.8 MW. At the moment, the output deviation coefficient is 0.56%, the annual cost of energy storage and the like is 1306.14 ten thousand yuan, and the index of the average attenuation degree of the energy storage is。
The configuration scheme can reduce the power generation fluctuation rate of the clean energy base as much as possible, and can optimize the energy storage attenuation degree and economic cost compromise. The output schematic diagram of each unit of the clean energy base is shown in fig. 4.
The step hydropower is based on a day-ahead scheduling plan, and the output of the hydropower in the day is further adjusted by using updated prediction information and taking 15min as an interval time scale. The day-ahead/day-in step hydro-electric total output curve and the corresponding base output power comparison graph are shown in fig. 5. Therefore, the rolling optimization scheduling of the short-term prediction data is an effective means for reducing the influence of wind and light prediction errors and uncertainty on the system scheduling and improving the safety and reliability of the system.
And on the basis of daily correction, the grouping energy storage batteries are further stabilized according to the real-time fluctuation of wind and light with the time scale of 1min as an interval. The comparison diagram of pumping and storing and chemical energy storage charging and discharging power is shown in fig. 6. The electrochemical energy storage regulation speed is high, the tracking power generation instruction is faster, the pump can be flexibly complemented with the pumped storage in multiple time scales, and the overall regulating capability is enhanced in all directions.
The states of charge and charge/discharge powers of the three battery packs configured in this example are shown in fig. 7 and 8. BESS1 charges, BESS2 discharges, and BESS3 hovers in a state of ψ SOC3 =0.5 at the initial time of the day. At the 45 th minute time, BESS1 charge reaches the upper SOC limit of 0.9, and the charge margin is exhausted; at this point the BESS2 still has a charge margin. The BESS3 thus acts as a rechargeable battery instead of the BESS1 at this point, and in cooperation with the BESS2, wind power stabilization is continued. And (3) when the BESS2 discharges to reach the lower limit of the SOC of 0.1 at the moment of 64 minutes, the BESS3 is closed, the BESS1 is opened, and the charging and discharging roles are exchanged with the BESS2 to continue to operate cooperatively. The two main battery packs are completely charged and discharged for switching, so that the charging and discharging switching frequency is effectively reduced, and the battery loss is reduced.
Table 1 is a comparison table of configuration results of three battery grouping charge and discharge control strategies.
Table 1 comparison of different packet control policy configurations
The table shows that the service life of the energy storage is longest under the three-battery grouping control strategy, the annual cost is minimum, and the investment cost is saved; and the average attenuation index of the battery is minimum, which shows that the operation strategy can effectively solve the problem of unbalanced group operation of the battery pack.
Please refer to fig. 9, which illustrates a schematic structure of a computer device according to an embodiment of the present application. The computer device 400 provided in the embodiment of the present application includes: a processor 410 and a memory 420, the memory 420 storing a computer program executable by the processor 410, which when executed by the processor 410 performs the method as described above.
The embodiment of the present application also provides a storage medium 430, on which storage medium 430 a computer program is stored which, when executed by the processor 410, performs a method as above.
The storage medium 430 may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The meaning of "a plurality of" is two or more, unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily for the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (8)
1. The clean energy base energy storage capacity optimizing configuration method is characterized by comprising the following steps of:
Performing daily correction at intervals of 15min to construct an inner layer model which aims at minimizing the deviation of the water and electricity output and performs rolling optimization on the output of a typical daily step water and electricity group and a pump storage group;
Performing real-time regulation and control at intervals of 1min, and constructing an outer model which aims at minimizing the total output deviation, the annual equivalent cost and the attenuation degree index to generate an energy storage planning configuration scheme and charge and discharge power;
Carrying out joint solution on the inner layer model and the outer layer model by using a non-dominant sorting genetic algorithm and a large-scale mathematical programming optimizer to obtain an optimal energy storage capacity configuration scheme and optimal scheduling under the scheme;
the generation of the energy storage planning configuration scheme and the outer layer model of the charge and discharge power comprises the following steps:
Making a power generation plan curve;
Constructing an objective function considering power deviation;
Constructing a water quantity connection constraint condition, a flow constraint condition, an output constraint condition and a start-stop constraint condition;
The power generation planning curve is obtained by calculating with the minimum residual load variance of the receiving end power grid as a target in a day-ahead power generation planning stage:
；
wherein, The residual power at the moment t is the residual power with the minimum variance; /(I)System load, its sampling period interval Δt=1h, 24 points a day;
The constructing an objective function that considers power deviation includes:
wherein, 、/>And/>The total power actually sent out by the clean energy base wind-light water at the moment t is represented by wind power output and photovoltaic output respectively; /(I)The output of the ith hydroelectric generating set at the t moment; n H is the number of hydroelectric generating sets; the sampling period interval Δt=15 min, so T 1 takes 96;
The construction of the water quantity connection constraint condition, the flow constraint condition, the output constraint condition and the start-stop constraint condition comprises the following steps:
water quantity connection constraint conditions:
；
；
；
wherein, The total drainage flow of the ith hydropower station in the t period; /(I)For/>Total drainage of the time interval i-1 level hydropower station, wherein/>Indicating the water stagnation between the step hydropower stations; /(I)Is a time interval; v i,t is the reservoir capacity of the ith hydropower station in the t period; v i,t-1 is the reservoir capacity of the ith hydropower station in t-1 period; i i,t is the warehouse-in runoff of the ith hydropower station in the t period; /(I)Representation will/>Rounding down the function; /(I)For/>Total drainage flow of the time interval i-1 level hydropower station; /(I)Is thatTotal drainage flow of the time interval i-1 level hydropower station; /(I)And/>The flow rates of the water disposal and the power generation are respectively,
For the additional installation of the pumped storage power station, the description of the pumping working condition is added:
；
wherein, Is pumping flow;
flow constraint conditions:
；
；
；
wherein, Is the upper and lower limits of the flow;
Storage capacity constraint conditions:
The storage capacity range at the last moment is set, and the expression is as follows:
；
；
wherein, Is the upper and lower limits of the storage capacity; /(I)The target stock capacity is the last time T; /(I)Adjusting the coefficient for the range of the storage capacity;
Force constraint conditions:
；
；
；
；
wherein, ,/>Pumping power for the conventional hydropower station output and hybrid pumped storage power station; /(I)，/>Is the upper and lower limits of power; /(I)And/>The up-down climbing capacity is respectively; u i,t is an operating state flag variable; /(I)Marking a variable for the pumping state;
start-stop constraint conditions:
；
；
；
；
；
；
；
；
wherein, ,/>The variable is the start-stop variable of the step hydropower under the power generation working condition; /(I),/>Is a start-stop variable under the pumping working condition;, />, />, /> Respectively minimum start-stop time;
the generation of the energy storage planning configuration scheme and the outer layer model of the charge and discharge power comprises the following steps:
Constructing an objective function considering the total output deviation;
constructing an equal annual investment cost objective function considering the dynamic predicted life of the stored energy;
constructing an objective function considering the average attenuation index;
Constructing an energy storage capacity constraint condition, an energy storage charge-discharge power constraint condition and a state of charge constraint condition;
The constructing takes into account an objective function of the total output deviation, comprising:
；
wherein D 1,D2 is the difference between the step hydroelectric power output and the group battery control output; sampling period interval Δt=1 min, so T 2 takes 1440; the actual output power of the water-wind-solar-energy-storage combined system is; /(I) The method comprises the steps that power is sent out for a reference of a clean energy base, wherein the reference is obtained by decomposing wind-light output data through a variation mode decomposition method;
The construction of an equal annual investment cost objective function taking into account the dynamic predicted lifetime of the stored energy comprises:
；
Wherein, C inve is the investment cost of energy storage construction; c main is the energy storage operation maintenance cost; c reco is the recovery cost, which is generated after the project life cycle is completed, including the cost of processing the participating devices and the cost obtained by recovering the old batteries; b is the discount rate; y is the dynamic prediction life of the energy storage battery;
wherein the economic cost expressions of each part are as follows:
；
Wherein E total is the maximum rated total capacity of the energy storage battery pack; p r is the maximum rated charge-discharge power of the energy storage battery pack; c ei is the investment cost of the unit capacity battery; c pi is the investment cost of the unit power converter; c em is the operation maintenance cost of the unit capacity battery; c pm is the operation maintenance cost of the unit power battery; c rec is the rejection cost ratio;
The energy storage life adopts dynamic prediction life which changes along with different running states, and is the ratio of the total discharge capacity of the battery under the life cycle with rated discharge depth to the annual discharge capacity converted into rated discharge depth, and the expression is as follows:
；
Wherein D ODr is the rated depth of discharge; e r is the battery rated capacity; e j is the actual discharge capacity of the j-th switching stage; n r is the number of battery cycles at rated depth of discharge; d is the number of days of the year; t is the discharge phase count of the day; n j is the battery cycle number under the actual discharge depth of the jth discharge stage, and J is the switching number;
the constructing takes into account an objective function of the average attenuation index, comprising:
The unbalanced running state is reflected by the average attenuation degree index I, and the expression is shown as follows:
；
wherein Y is the energy storage life; d is the number of days of the year; t is the discharge phase count of the day; 、/> The life decay index is the life decay index of the nth switching stage of the d th day of the y th year of two battery packs, and the expression is as follows:
；
wherein, 、/>The state of charge at the current switching time and the last switching time of a battery pack is obtained; /(I)、The state of charge at the current switching time and the last switching time of the other battery pack; /(I)Is the standard charge-discharge depth.
2. The clean energy base energy storage capacity optimization configuration method according to claim 1, further comprising:
constructing a control strategy of three battery groups, and setting a group of standby auxiliary battery groups, wherein the standby auxiliary battery groups are used as standby rechargeable battery groups when the charging capacity is insufficient; when the discharge capacity is insufficient, it serves as a spare discharge battery pack.
3. The clean energy base energy storage capacity optimization configuration method according to claim 2, wherein the control strategy of the three-battery group comprises the following steps:
Three battery packs are arranged: BESS1, BESS2, and BESS3, wherein BESS3 is a backup secondary battery;
the BESS1 and the BESS2 are respectively charged or discharged continuously, and the two battery packs cannot work simultaneously at the same moment, and the BESS3 does not receive a calling instruction and is in a hovering closing state;
When one of the BESS1 and the BESS2 reaches the limit value of the state of charge, a calling instruction is sent to the BESS3, the BESS3 immediately starts the battery pack to operate instead of the battery pack reaching the limit, and the battery pack is matched with the other battery pack to continue to charge or discharge so as to stabilize the power fluctuation;
When one of the BESS3 and the other battery pack reaches the upper and lower limits of the charge state again, the BESS3 is immediately turned off, the BESS1 and the BESS2 are turned on, and the charge-discharge roles are exchanged to continue to cooperate to stabilize power fluctuation.
4. The clean energy base energy storage capacity optimization configuration method according to claim 1, wherein the constructing the energy storage capacity constraint condition, the energy storage charge and discharge power constraint condition and the state of charge constraint condition comprises:
Energy storage capacity of constraint conditions are as follows:
；
Wherein S min is the lower limit of the energy storage residual quantity; s t is the residual capacity of the battery at the moment t, and E r is the rated capacity of the battery;
Energy storage charge-discharge power constraint conditions:
；
wherein, P min is the lower limit of the battery charge-discharge power, P r is the maximum rated charge-discharge power of the energy storage battery, , />Energy storage charging and discharging power is carried out at the time t;
State of charge constraints:
；
Wherein, ψ max、Ψmin is the maximum value and the minimum value of the battery SOC, The SOC of the battery at time t.
5. The clean energy base energy storage capacity optimization configuration method according to claim 1, wherein the joint solution of the inner layer model and the outer layer model using a non-dominant ranking genetic algorithm and a large-scale mathematical programming optimizer comprises the steps of:
The outer layer model is calculated by adopting a non-dominant ordering genetic algorithm;
The inner layer model is calculated by adopting a new generation large-scale mathematical programming optimizer Gurobi;
And continuously and alternately iterating the inner layer model and the outer layer model to finally obtain an optimal configuration scheme and optimal scheduling under the scheme.
6. A clean energy base energy storage capacity optimizing configuration apparatus characterized by using the method according to any one of claims 1 to 5, comprising:
The inner layer model building unit is used for carrying out daily correction at intervals of 15min, and building an inner layer model which aims at minimizing the water and electricity output deviation and carries out rolling optimization on the output of a typical daily step water and electricity group and a pumped storage unit;
The outer layer model building unit is used for carrying out real-time regulation and control at intervals of 1min, constructing an outer layer model which aims at minimizing total output deviation, annual equivalent cost and attenuation degree indexes and generates an energy storage planning configuration scheme and charge and discharge power;
And the joint solving unit is used for carrying out joint solving on the inner layer model and the outer layer model by using a non-dominant sorting genetic algorithm and a large-scale mathematical programming optimizer to obtain an optimal energy storage capacity configuration scheme and optimal scheduling under the scheme.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-5 when the computer program is executed.
8. A storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-5.
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| CN113361715A (en) * | 2021-06-24 | 2021-09-07 | 南通大学 | Wind-storage hybrid power station daily operation strategy optimization method based on genetic algorithm |
| WO2023274425A1 (en) * | 2021-06-28 | 2023-01-05 | 国网甘肃省电力公司电力科学研究院 | Multi-energy capacity optimization configuration method for wind-solar-water-fire storage system |
| CN114221338A (en) * | 2021-12-19 | 2022-03-22 | 东北电力大学 | Multi-energy power system optimal scheduling method considering power supply flexibility and complementarity |
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| CN116388252A (en) * | 2023-06-05 | 2023-07-04 | 河海大学 | Wind farm energy storage capacity optimal configuration method, system, computer equipment and medium |
| CN117081144A (en) * | 2023-07-19 | 2023-11-17 | 大连川禾绿能科技有限公司 | Compensation adjustment method for continuous multi-day extreme fluctuation of new energy by step hydropower |
| CN116667446A (en) * | 2023-07-28 | 2023-08-29 | 河海大学 | Capacity allocation method, device, equipment and medium of wind power and pumped storage system |
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