CN114154790A - Industrial park light storage capacity configuration method based on demand management and flexible load - Google Patents
Industrial park light storage capacity configuration method based on demand management and flexible load Download PDFInfo
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Abstract
The invention provides a method for configuring optical storage capacity of an industrial park based on demand management and flexible loads, which is characterized in that aiming at flexible loads, the influence of different time-of-use electricity prices on the flexible loads is considered, a flexible load response probability model is established, and the corresponding flexible loads in a set time period are obtained; building a photovoltaic output model, calculating output intervals of different seasons of a garden to be planned, and building a garden demand electricity price calculation model; establishing a minimum power consumption cost calculation model by taking the minimum power consumption cost of users in the garden in a set time as a target function and considering a photovoltaic output model and a flexible load; the invention comprehensively considers the uncertainty of photovoltaic and flexible load, and comprehensively considers the demand electric charge, the photovoltaic uncertainty and the light storage investment cost to configure the light storage capacity from the perspective of users, thereby determining the optimal scheme for configuring the light storage capacity of the industrial park.
Description
Technical Field
The invention belongs to the technical field of light storage capacity configuration, and particularly relates to an industrial park light storage capacity configuration method based on demand management and flexible load.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the reduction of the cost of distributed photovoltaic power generation, the scale of the distributed photovoltaic grid connection at the user side is continuously enlarged. In order to relieve 'power limit + power failure', energy cost is reduced, and industrial users can also configure distributed photovoltaic cells on a large scale. However, due to the fluctuation of photovoltaic output, the access to the power grid has a certain influence on the safe and stable operation of the power system, so that large-scale photovoltaic grid connection and absorption are difficult, and the phenomenon of light abandonment is severe, so that certain energy storage is required to be configured to absorb the photovoltaic.
In the existing research, the research on energy storage configuration mainly aims at the economic benefit and the fluctuation stabilization of energy storage, energy storage capacity is configured for a photovoltaic power generation system by using statistics and a system planning operation scheme, and uncertainty of photovoltaic and flexible load is ignored in the configuration process.
Disclosure of Invention
The invention provides a method for configuring the optical storage capacity of the industrial park based on demand management and flexible load, which aims to solve the problems.
According to some embodiments, the invention adopts the following technical scheme:
an industrial park light storage capacity configuration method based on demand management and flexible load comprises the following steps:
acquiring historical load data of an industrial park, classifying loads by using a clustering algorithm according to actual production power utilization conditions, wherein the loads comprise conventional loads and flexible loads, and extracting typical daily loads in different seasons according to characteristics of peak values, valley values and average values;
aiming at the flexible load, considering the influence of different time-of-use electricity prices on the flexible load, establishing a flexible load response probability model to obtain the corresponding flexible load in a set time period;
building a photovoltaic output model, calculating output intervals of different seasons of a garden to be planned, and building a garden demand electricity price calculation model;
establishing a minimum power consumption cost calculation model by taking the minimum power consumption cost of users in the garden in a set time as a target function and considering a photovoltaic output model and a flexible load;
under the constraint condition, dynamically solving a minimum power consumption cost calculation model to obtain energy storage configuration capacity;
and calculating to obtain a typical equivalent load curve according to the photovoltaic power generation power, the load power and the energy storage charge-discharge power of a typical day in different seasons, and determining the maximum load power as the maximum demand value reported regularly.
As an alternative embodiment, the specific process of establishing the flexible load response probability model comprises fitting the flexible load probability response model into a piecewise linear function, wherein the abscissa represents the excitation level between the time intervals, the ordinate represents the responsiveness of the user, a curve of the load transfer rate from the peak time interval to the valley time interval along with the power price difference between the peak time interval and the valley time interval is drawn, a curve of the load transfer rate from the peak time interval to the flat time interval and a curve of the load transfer rate from the flat time interval to the valley time interval are drawn in a piecewise linear manner, and the fitted load of each time interval is represented according to the curves.
By way of further limitation, the load transfer rate from peak period to valley period is:
in the formula: lambda [ alpha ]pvLoad transfer rate from peak period to valley period of the user; x is the number of0Is a dead zone threshold; x is the electrovalence difference at the peak-valley time;
is a saturation region threshold; k is a radical ofpvThe slope of the linear region of the piecewise linear peak-to-valley period transfer rate curve.
As an alternative embodiment, the specific process of constructing the photovoltaic output model includes: and under the standard rated condition, determining the output power of the photovoltaic power generation system according to the test power, the illumination and the temperature.
As a further limitation, the photovoltaic output model is:
in the formula PPVThe output power of the working point of the photovoltaic cell; pSTCThe maximum test power under the standard rated condition; gCIs the light intensity of the working point; gSTCThe solar illumination intensity under the standard rated condition; k is the power temperature coefficient, and the value is-0.005/DEG C; t isCThe operating point is the photovoltaic cell temperature; t isSTCIs the temperature under standard nominal conditions.
As an alternative embodiment, the park demand electricity price calculation model is:
in the formula, C is the capacity of the transformer;
is the actual maximum demand; x is the maximum demand and cannot be lower than a certain distribution and transformation capacity; k is a threshold for charging a punitive price, p1,p2Respectively the demand price of the actual maximum demand within 1.05 times of the reported maximum demand and the demand price of the part exceeding 1.05 times of the reported maximum demand.
As an alternative embodiment, the minimum electricity cost calculation model is:
C1＝(Plt-Pvt+Pch,t-Pdis,t)·pt
C4＝α·Pdemand·Tn
in the formula, C is the total power consumption cost of the microgrid users; c1Representing the electricity purchasing cost of the microgrid users to the power grid; c2Represents the cost of photovoltaic power generation; c3Representing the energy storage and discharge cost, wherein T is the number of time periods in a research period, and delta T is the time interval of each time period; c4Representing the electricity charge required by the user; r represents the benefit of the user participating in the demand response;
Pltrepresenting the power grid load in the t-th period, and superposing a typical daily gauge load and a flexible load; pvtFor the photovoltaic power generation power in the t-th period, Pch,t、Pdis,tRespectively representing charging power and discharging power of the energy storage battery in a period of t, ptThe time-of-use electricity price of the power grid is obtained;
Cpvrepresents the overall investment cost (P) of the photovoltaicpvRepresenting installed photovoltaic capacity (kW), H representing the maximum number of photovoltaic utilization hours per year, NpvRepresenting the photovoltaic operating life;
CBESSrepresenting the overall investment cost (Yuan) of energy storage, EBESSRepresenting the rated capacity (kWh), n of the energy storage arrangementbessRepresenting the number of energy storage cycles;
Pdemandand the maximum demand of the user is shown, alpha is the unit demand electric charge, and Tn is the set period.
As an alternative embodiment, the constraints include:
the capacity at the previous moment is added with the product of the charging and discharging power and the time at the current period and is equal to the capacity at the current moment;
the residual capacity of the battery is within the upper limit and the lower limit;
the energy storage charging and discharging power does not exceed the limit value;
the maximum demand is less than or equal to a set threshold value, and the return on investment is greater than a set value.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to carry out the steps of the above-mentioned method.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the above-described method.
Compared with the prior art, the invention has the beneficial effects that:
the invention comprehensively considers the uncertainty of photovoltaic and flexible load, comprehensively considers the demand electricity charge, the photovoltaic uncertainty and the light storage investment cost from the perspective of users to configure the light storage capacity, determines the optimal scheme of the configuration of the light storage capacity of the industrial park, realizes the balance of the electricity utilization cost and the light storage investment of users of the industrial park under the conditions of price policy and market environment, reduces the electricity utilization cost of users of the micro-grid to the maximum extent, and realizes the light storage capacity evaluation and demand management of large industrial users.
The method converts the light storage investment cost into the unit power consumption cost, is different from the conventional scheme that the light storage investment is used as a determined value in the economical measurement and calculation process, can effectively calculate the investment cost of the light storage changing along with the power in the optimization solving process of the light storage capacity, improves the accuracy of the calculation of the comprehensive power consumption cost, and enables the optimization method of the light storage capacity and the scheduling strategy to be more practical.
Under the condition of photovoltaic self-utilization and without considering the power grid on photovoltaic power generation, the invention comprehensively considers the demand electric charge, photovoltaic uncertainty and light storage investment cost to configure the light storage capacity and give the maximum demand required for monthly application from the perspective of users, solves the problem of light storage optimal configuration lacking demand management in the existing scheme, and has application value.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic flow chart of at least one embodiment of the present invention;
FIG. 2 is a graphical illustration of a user response characteristic in accordance with at least one embodiment of the present invention;
FIG. 3 is a graphical illustration of a flexible load response uncertainty versus excitation level for at least one embodiment of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, a method for configuring the optical storage capacity of the microgrid in the industrial park based on demand management and flexible load includes:
step 1: the method comprises the steps of obtaining historical load data of the industrial park, classifying loads by utilizing a clustering algorithm according to actual production power utilization conditions, wherein the loads comprise conventional loads and flexible loads, and extracting typical daily loads in different seasons according to characteristics of peak values, valley values and average values.
Step 2: and aiming at the flexible load, considering the influence of different time-of-use electricity prices on the flexible load, and establishing a flexible load response probability model to obtain the flexible load corresponding to a set time period.
And step 3: and (3) building a photovoltaic output model, calculating output intervals of the park to be planned in different seasons, and building a park demand electricity price calculation model under the two electricity prices.
And 4, step 4: and (3) establishing a model with minimum electric quantity and electricity charge, demand and light storage investment cost and establishing constraints such as an energy storage battery, maximum demand and investment return by taking minimum user electricity consumption cost in a research period T as an objective function of light storage capacity configuration and considering flexible load and photovoltaic processing.
And 5: and (4) randomly generating a plurality of groups of capacity and power combination parameters according to the upper and lower boundaries of the energy storage capacity and power, establishing a dynamic programming model by combining the constraint conditions and the objective function obtained in the step (4), and solving the energy storage charge-discharge power and the daily minimum power consumption cost. And calculating to obtain the energy storage configuration capacity after considering the economic constraint.
Step 6: and calculating to obtain a typical equivalent load curve according to the photovoltaic power generation power, the load power and the energy storage charge-discharge power of a typical day in different seasons, and determining the maximum load power as the maximum demand value to be reported monthly.
The following describes the specific implementation process of each step:
in step 2, the flexible load probability response model is approximately fitted into a piecewise linear function, wherein the abscissa represents the excitation level between the periods, the ordinate represents the responsiveness of the user, and the relationship curve of the load transfer rate from the peak period to the valley period along with the peak-valley period power price difference is shown in fig. 2 and fig. 3.
The load transfer rate from peak period to valley period is:
in the formula: lambda [ alpha ]pvLoad transfer rate from peak period to valley period of the user; x is the number of0Is a dead zone threshold; x is the electrovalence difference at the peak-valley time;
is a saturation region threshold; k is a radical ofpvThe slope of the linear region of the piecewise linear peak-to-valley period transfer rate curve.
Similarly, a load transfer rate curve from a peak time period to a normal time period and a load transfer rate curve from the normal time period to a valley time period of the piecewise linearity can be drawn, and a corresponding piecewise linearity model is established.
Based on the above 3 curves, the fitting load for each time period can be expressed as:
in the formula, Tp、TfAnd TvRespectively a peak time interval, a flat time interval and a valley time interval, wherein t is any one time interval; p0(t) and P (t) are the load size of the time period before and after the implementation of the peak-valley time-of-use electricity price respectively;
and
respectively, the average value of the total load in the corresponding time interval when the front peak and the valley are implemented.
In step 3, a photovoltaic output model is constructed, and the output power of the photovoltaic power generation system is under the standard rated condition (the illumination intensity G of the sun)STCIs 1000W/m2Temperature T of batterySTCAt 25 ℃) was determined from the test power, light, temperature:
in the formula PPVThe output power of the working point of the photovoltaic cell; pSTCThe maximum test power under the standard rated condition; gCIs the light intensity of the working point; gSTCThe solar illumination intensity under rated conditions; k is the power temperature coefficient, and the value is-0.005/DEG C; t isCThe operating point is the photovoltaic cell temperature; t isSTCIs the temperature under nominal conditions.
The unit demand electric charge model is as follows:
in the formula, C is the capacity of the transformer;
is the actual maximum demand; x is the maximum demand and cannot be lower than a certain distribution and transformation capacity; k is a threshold for charging a punitive price, and in the embodiment, k is 1.05 according to the current regulation; p is a radical of1,p2The demand price of the actual maximum demand within 1.05 times of the reported maximum demand and the demand price of the part exceeding 1.05 times of the reported maximum demand are respectively, in the embodiment, according to the current regulation p2/p1＝2。
In step 4, the minimum objective function of user cost is:
in the formula, C is the total power consumption cost of the microgrid users; c1Representing the electricity purchasing cost of the microgrid users to the power grid; c2Represents the cost of photovoltaic power generation; c3Representing the energy storage and discharge cost, wherein T is the number of time periods in a research period, and delta T is the time interval of each time period; c4Representing the electricity charge required by the user; r represents the benefit of the user to participate in the demand response.
In the formula, PltRepresenting the power grid load in the t-th period, and superposing a typical daily gauge load and a flexible load; pvtFor the photovoltaic power generation power in the t-th period, Pch,t、Pdis,tRespectively representing charging power and discharging power of the energy storage battery in a period of t, ptThe power grid time-sharing price is obtained.
In the formula, CpvRepresents the overall investment cost (P) of the photovoltaicpvRepresenting installed photovoltaic capacity (kW), H representing the maximum number of photovoltaic utilization hours per year, NpvRepresenting the photovoltaic operating life.
In the formula, CBESSRepresents the overall investment cost of energy storage)，EBESSRepresenting the rated capacity (kWh), n of the energy storage arrangementbessIndicating the number of energy storage cycles.
In the formula, PdemandAnd the maximum demand of the user is shown, alpha is the unit demand electric charge, and Tn is the set time length.
The mentioned constraints, in this embodiment, include:
1) the previous time capacity + the current period charge/discharge power × time is the current time capacity.
Et＝Et-1+ηch·Pch,t·Δt-Δt·Pdis,t/ηd (3)
In the formula, EtAnd Et-1Represents the remaining battery capacity, eta, during the t period and the t-1 periodchAnd ηdThe charging efficiency and the discharging efficiency of the energy storage battery are respectively.
2) The inequality constraint of the battery residual capacity in the t-th period is as follows:
Emin≤Et≤EBESS (4)
in the formula, EBESSIndicating the rated capacity of the energy storage arrangement, EminThe lower limit of the energy storage capacity.
3) The stored energy charging and discharging power does not exceed the limit value.
0≤Pch,t≤Pmax
0≤Pdis,t≤Pmax (5)
In the formula, PmaxThe maximum charge and discharge power of the energy storage system is limited.
The energy storage grid connection condition is not considered, the configured energy storage is completely used for absorbing the photovoltaic and reducing the user load, so that the maximum charging and discharging power of the energy storage system only needs to meet the power unbalance amount within a certain period T which is not more than the assessment period T, namely the maximum charging and discharging power is the power unbalance amount
4) Maximum demand constraint
The stored energy is guided by peak-to-valley electricity price and demand response compensation, and a new load peak can be formed and even exceeds the original maximum demand, so that the demand electricity fee is increased. Therefore, the present embodiment introduces a maximum demand constraint.
max(Plt-Pvt+Pch,t-Pdis,t)≤1.05Pdemand (7)
5) Return on investment constraint
RP≥RReference to (8)
In the formula, RPFor light storage system return on investment, RReference toIs the set return on investment value.
In this embodiment, the method for calculating the return on investment is as follows:
in the formula, CGain ofThe photovoltaic power generation cost saving and the energy storage peak-valley difference profit saving electricity utilization cost are shown. CInvestment ofThe investment cost for light storage.
The above model is a dynamic programming problem, and equations (3) to (8) form constraint conditions of an objective function, including constraint of battery capacity, constraint of charging requirement, constraint of charging power, and the like. In the model, Pch,t、Pdis,t、EBESS、PdemandFor decision variables, the remaining parameters are known quantities.
The invention also provides the following product examples:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to carry out the steps of the above-mentioned method.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the above-described method.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A method for configuring the optical storage capacity of an industrial park based on demand management and flexible load is characterized by comprising the following steps: the method comprises the following steps:
acquiring historical load data of an industrial park, classifying loads by using a clustering algorithm according to actual production power utilization conditions, wherein the loads comprise conventional loads and flexible loads, and extracting typical daily loads in different seasons according to characteristics of peak values, valley values and average values;
aiming at the flexible load, considering the influence of different time-of-use electricity prices on the flexible load, establishing a flexible load response probability model to obtain the corresponding flexible load in a set time period;
building a photovoltaic output model, calculating output intervals of different seasons of a garden to be planned, and building a garden demand electricity price calculation model;
establishing a minimum power consumption cost calculation model by taking the minimum power consumption cost of users in the garden in a set time as a target function and considering a photovoltaic output model and a flexible load;
under the constraint condition, dynamically solving a minimum power consumption cost calculation model to obtain energy storage configuration capacity;
and calculating to obtain a typical equivalent load curve according to the photovoltaic power generation power, the load power and the energy storage charge-discharge power of a typical day in different seasons, and determining the maximum load power as the maximum demand value reported regularly.
2. The method for configuring the optical storage capacity of the industrial park based on the demand management and the flexible load as claimed in claim 1, wherein: the specific process of establishing the flexible load response probability model comprises the steps of fitting the flexible load probability response model into a segmented linear function, wherein the abscissa represents the excitation level among all time intervals, the ordinate represents the responsiveness of a user, a relation curve of the load transfer rate from a peak time interval to a valley time interval along with the electricity price difference of the peak time interval and the valley time interval is drawn, a load transfer rate curve from the peak time interval to a normal time interval and a load transfer rate curve from the valley time interval to the peak time interval are drawn in a segmented linear mode, and the fitted load of all the time intervals is represented according to the curves.
3. The method for configuring the optical storage capacity of the industrial park based on the demand management and the flexible load as claimed in claim 2, wherein: the load transfer rate from peak period to valley period is:
in the formula: lambda [ alpha ]pvLoad transfer rate from peak period to valley period of the user; x is the number of0Is a dead zone threshold; x is the electrovalence difference at the peak-valley time;
is a saturation region threshold; k is a radical ofpvThe slope of the linear region of the piecewise linear peak-to-valley period transfer rate curve.
4. The method for configuring the optical storage capacity of the industrial park based on the demand management and the flexible load as claimed in claim 1, wherein: the specific process for constructing the photovoltaic output model comprises the following steps: and under the standard rated condition, determining the output power of the photovoltaic power generation system according to the test power, the illumination and the temperature.
5. The method for configuring the optical storage capacity of the industrial park based on the demand management and the flexible load as claimed in claim 1 or 4, wherein: the photovoltaic output model is:
in the formula PPVThe output power of the working point of the photovoltaic cell; pSTCThe maximum test power under the standard rated condition; gCIs the light intensity of the working point; gSTCThe solar illumination intensity under the standard rated condition; k is the power temperature coefficient, and the value is-0.005/DEG C; t isCThe operating point is the photovoltaic cell temperature; t isSTCIs the temperature under standard nominal conditions.
6. The method for configuring the optical storage capacity of the industrial park based on the demand management and the flexible load as claimed in claim 1, wherein: the model for calculating the electricity price of the garden demand is as follows:
in the formula, C is the capacity of the transformer;
is the actual maximum demand; x is the maximum demand and cannot be lower than a certain distribution and transformation capacity; k is a threshold for charging a punitive price, p1,p2Respectively the demand price of the actual maximum demand within 1.05 times of the reported maximum demand and the demand price of the part exceeding 1.05 times of the reported maximum demand.
7. The method for configuring the optical storage capacity of the industrial park based on the demand management and the flexible load as claimed in claim 1, wherein: the minimum electricity cost calculation model is as follows:
C1＝(Plt-Pvt+Pch,t-Pdis,t)·pt
C4＝α·Pdemand·Tn
in the formula, C is the total power consumption cost of the microgrid users; c1Representing the electricity purchasing cost of the microgrid users to the power grid; c2Represents the cost of photovoltaic power generation; c3Representing the energy storage and discharge cost, wherein T is the number of time periods in a research period, and delta T is the time interval of each time period; c4Representing the electricity charge required by the user; r represents the benefit of the user participating in the demand response;
Pltrepresenting the power grid load in the t-th period, and superposing a typical daily gauge load and a flexible load; pvtFor the photovoltaic power generation power in the t-th period, Pch,t、Pdis,tRespectively representing charging power and discharging power of the energy storage battery in a period of t, ptThe time-of-use electricity price of the power grid is obtained;
Cpvrepresents the overall investment cost of the photovoltaic, PpvRepresents the installed photovoltaic capacity, H represents the maximum annual photovoltaic utilization hours, NpvRepresenting the photovoltaic operating life;
CBESSrepresents the overall investment cost of energy storage, EBESSRepresenting the rated capacity of the energy storage arrangement, nbessRepresenting the number of energy storage cycles;
Pdemandand the maximum demand of the user is shown, alpha is the unit demand electric charge, and Tn is the set period.
8. The method for configuring the optical storage capacity of the industrial park based on the demand management and the flexible load as claimed in claim 1, wherein: the constraint conditions include:
the capacity at the previous moment is added with the product of the charging and discharging power and the time at the current period and is equal to the capacity at the current moment;
the residual capacity of the battery is within the upper limit and the lower limit;
the energy storage charging and discharging power does not exceed the limit value;
the maximum demand is less than or equal to a set threshold value, and the return on investment is greater than a set value.
9. A computer-readable storage medium characterized by: in which a plurality of instructions are stored, said instructions being adapted to be loaded by a processor of a terminal device and to carry out the steps of the method according to any one of claims 1 to 8.
10. A terminal device characterized by: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and for performing the steps of the method according to any of claims 1-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115049251A (en) * | 2022-06-13 | 2022-09-13 | 南通沃太新能源有限公司 | Power dispatching method and device for energy storage system |
| CN116683499A (en) * | 2023-08-04 | 2023-09-01 | 国网山西电力勘测设计研究院有限公司 | Calculation method for power of user side energy storage device |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115049251A (en) * | 2022-06-13 | 2022-09-13 | 南通沃太新能源有限公司 | Power dispatching method and device for energy storage system |
| CN115049251B (en) * | 2022-06-13 | 2024-02-13 | 南通沃太新能源有限公司 | Power scheduling method and device for energy storage system |
| CN116683499A (en) * | 2023-08-04 | 2023-09-01 | 国网山西电力勘测设计研究院有限公司 | Calculation method for power of user side energy storage device |
| CN116683499B (en) * | 2023-08-04 | 2023-12-08 | 国网山西电力勘测设计研究院有限公司 | Calculation method for power of user side energy storage device |
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