Industrial park light storage capacity configuration method based on demand management and flexible load

Abstract

The application provides an industrial park light storage capacity configuration method based on demand management and flexible load, for the flexible load, considering the influence of different time-of-use electricity prices on the flexible load, a flexible load response probability model is established to obtain the corresponding flexible load in a set time period; a photovoltaic output model is constructed to calculate the output interval of the to-be-planned park in different seasons, and a park demand electricity price calculation model is established; taking the minimum electricity cost of the park users in a set time period as an objective function, considering the photovoltaic output model and the flexible load, a minimum electricity cost calculation model is established; the application comprehensively considers the uncertainty of photovoltaic and flexible load, comprehensively considers the demand electricity fee, photovoltaic uncertainty and light storage investment cost from the user's point of view to configure the light storage capacity, and determines the optimal scheme of the industrial park light storage capacity configuration.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic and energy storage capacity configuration technology, specifically relating to a method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible loads. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As the cost of distributed photovoltaic (PV) power generation decreases, the scale of distributed PV grid connection on the user side continues to expand. To alleviate power curtailment and outages and reduce energy costs, industrial users are also deploying distributed PV on a large scale. However, due to the volatility of PV output, grid connection has a certain impact on the safe and stable operation of the power system. Therefore, large-scale grid connection and absorption of PV is difficult, and curtailment is quite serious. Thus, a certain amount of energy storage is needed to absorb PV power.

[0004] Existing research on energy storage configuration mainly focuses on the economic benefits of energy storage and the mitigation of fluctuations. It uses statistics and system planning operation schemes to configure energy storage capacity for photovoltaic power generation systems, but ignores the uncertainties of photovoltaic and flexible loads in the configuration process. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible loads. This invention comprehensively considers the uncertainties of photovoltaics and flexible loads, and configures photovoltaic and energy storage capacity from the user's perspective by comprehensively considering demand electricity costs, photovoltaic uncertainties, and photovoltaic and energy storage investment costs, thereby determining the optimal scheme for configuring photovoltaic and energy storage capacity in industrial parks.

[0006] According to some embodiments, the present invention adopts the following technical solution:

[0007] A method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible loads includes the following steps:

[0008] Historical load data of industrial parks are obtained, and the loads are classified using clustering algorithms based on actual production electricity consumption, including conventional loads and flexible loads. Typical daily loads for different seasons are extracted based on peak, valley, and average characteristics.

[0009] For flexible loads, considering the impact of different time-of-use electricity prices on flexible loads, a flexible load response probability model is established to obtain the corresponding flexible load within a set time period;

[0010] Construct a photovoltaic power output model, calculate the power output range of the planned industrial park in different seasons, and establish a demand electricity price calculation model for the industrial park.

[0011] With the objective function of minimizing the electricity cost for users in the park within a set time period, and considering the photovoltaic power output model and flexible load, a minimum electricity cost calculation model is established.

[0012] Under constraints, the minimum electricity cost calculation model is dynamically solved to obtain the energy storage configuration capacity.

[0013] Based on the photovoltaic power generation, load power, and energy storage charging and discharging power of typical days in different seasons, a typical equivalent load curve is calculated, and the maximum load power is determined as the maximum demand value to be reported periodically.

[0014] As an alternative implementation method, the specific process of establishing a flexible load response probability model includes fitting the flexible load probability response model to a piecewise linear function, where the horizontal axis represents the excitation level between different time periods and the vertical axis represents the user's responsiveness; plotting the relationship curve between the load transfer rate from peak to valley time and the peak-valley electricity price difference; plotting the piecewise linear load transfer rate curves from peak to normal time and from normal to valley time; and representing the fitted load for each time period based on the above curves.

[0015] As a further limitation, the load transfer rate from peak to off-peak hours is:

[0016]

[0017] In the formula: λ pv x is the load transfer rate from peak to valley hours for users; x0 is the dead zone threshold; x is the electricity price difference between peak and valley hours. k is the threshold for the saturation region. pv This represents the slope of the linear region of the piecewise linear peak-valley transition rate curve.

[0018] As an alternative implementation method, the specific process of constructing a photovoltaic output model includes: determining the output power of the photovoltaic power generation system based on test power, illumination, and temperature under standard rated conditions.

[0019] As a further limitation, the photovoltaic power output model is as follows:

[0020]

[0021] In the formula P PV P represents the output power at the operating point of the photovoltaic cell. STC The maximum test power under standard rated conditions; G C G represents the illuminance at the working point. STC The solar irradiance is under standard rated conditions; k is the power temperature coefficient, with a value of -0.005 / ℃; T C The operating point of the photovoltaic cell is its temperature; T STC Temperature under standard rated conditions.

[0022] As an alternative implementation method, the demand electricity price calculation model for the industrial park is as follows:

[0023]

[0024] In the formula, C is the transformer capacity; is the actual maximum demand; x is the maximum demand, which cannot be lower than a certain transformer capacity; k is the threshold for charging a penalty price; p1 and p2 are the demand prices for the actual maximum demand within 1.05 times the reported maximum demand and the demand prices for the portion exceeding 1.05 times the reported maximum demand, respectively.

[0025] As an alternative implementation method, the minimum electricity cost calculation model is as follows:

[0026]

[0027] C1=(P lt -P vt +P ch,t -P dis,t )·p t

[0028]

[0029]

[0030] C4=α·P demand ·T n

[0031] In the formula, C represents the total electricity cost for microgrid users; C1 represents the cost of purchasing electricity from the grid for microgrid users; C2 represents the cost of photovoltaic power generation; C3 represents the cost of energy storage discharge; T represents the number of time periods in a research period; Δt represents the time interval of each time period; C4 represents the user's demand electricity cost; and R represents the user's benefit from participating in demand response.

[0032] P lt P represents the grid load in time period t, which is obtained by superimposing the typical daily routine load and flexible load; vt Let P be the photovoltaic power generation in time period t. ch,t P dis,t p represents the charging power and discharging power of the energy storage battery during time period t, respectively. t Time-of-use pricing for the power grid;

[0033] C pv P represents the total investment cost of photovoltaic power (in yuan). pv H represents the photovoltaic installed capacity (kW), H represents the maximum annual utilization hours of photovoltaic power, and N represents the photovoltaic installed capacity (kW). pv Indicates the number of years the photovoltaic system has been in operation;

[0034] C BESS E represents the total investment cost of energy storage (in yuan). BESS Indicates the rated capacity (kWh) of the energy storage configuration, n bess Indicates the number of energy storage cycles;

[0035] P demand This represents the user's maximum demand, α is the unit demand electricity cost, and Tn is the set period.

[0036] As an alternative implementation, the constraints include:

[0037] The capacity at the previous moment plus the product of the charging / discharging power and time in the current period equals the capacity at the current moment.

[0038] The remaining battery power is within its upper and lower limits;

[0039] The energy storage charging and discharging power shall not exceed the limit.

[0040] The constraints include a maximum demand that is less than or equal to a set threshold, and a return on investment that is greater than a set value.

[0041] A computer-readable storage medium storing a plurality of instructions adapted for loading by a processor of a terminal device and executing the steps of the method described above.

[0042] A terminal device includes a processor and a computer-readable storage medium, the processor being configured to implement various instructions; the computer-readable storage medium being configured to store a plurality of instructions adapted to be loaded by the processor and executed in accordance with the steps of the method described above.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] This invention comprehensively considers the uncertainties of photovoltaics and flexible loads, and from the user's perspective, it comprehensively considers demand electricity costs, photovoltaic uncertainties, and photovoltaic-storage investment costs to configure photovoltaic and storage capacity. It determines the optimal scheme for photovoltaic and storage capacity configuration in industrial parks, and achieves a balance between electricity costs and photovoltaic and storage investment for industrial park users under pricing policies and market conditions. It also minimizes electricity costs for microgrid users and enables photovoltaic and storage capacity assessment and demand management for large industrial users.

[0045] This invention converts the investment cost of photovoltaic and energy storage into the cost per kilowatt-hour, which differs from conventional schemes where the investment cost of photovoltaic and energy storage is a fixed value in economic calculations. In the process of optimizing the photovoltaic and energy storage capacity, this invention can effectively calculate the investment cost of photovoltaic and energy storage as the power changes, improve the accuracy of the calculation of the overall electricity cost, and make the optimization method of photovoltaic and energy storage capacity and scheduling strategy more in line with reality.

[0046] This invention addresses the issue of optimizing photovoltaic and energy storage configuration by comprehensively considering demand costs, photovoltaic uncertainties, and photovoltaic-energy storage investment costs from the user's perspective, without considering photovoltaic power generation and grid transmission, under the condition of self-consumption of photovoltaic power generation and without considering photovoltaic power generation and grid transmission. It also provides the maximum monthly demand that needs to be applied for. This solves the problem of photovoltaic-energy storage optimization configuration that lacks demand management in existing solutions and has application value.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0048] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0049] Figure 1 This is a flowchart illustrating at least one embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of user response characteristic curves according to at least one embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of the variation curve of the flexible load response uncertainty with the excitation level in at least one embodiment of the present invention. Detailed implementation method:

[0052] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0053] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, 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 pertains.

[0054] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0055] like Figure 1 As shown, a method for configuring photovoltaic and energy storage capacity in industrial park microgrids based on demand management and flexible loads includes:

[0056] Step 1: Obtain historical load data of the industrial park, classify the load according to the actual production electricity consumption using clustering algorithms, including regular load and flexible load, and extract typical daily loads for different seasons based on peak, valley and average characteristics.

[0057] Step 2: For flexible loads, consider the impact of different time-of-use electricity prices on flexible loads, establish a flexible load response probability model, and obtain the flexible load corresponding to the set time period.

[0058] Step 3: Construct a photovoltaic power output model, calculate the power output range of the planned park in different seasons, and establish a demand electricity price calculation model for the park under the two-part tariff.

[0059] Step 4: Using the minimum user electricity cost within the research period T as the objective function for configuring photovoltaic and energy storage capacity, and considering flexible loads and photovoltaic processing, establish a model to minimize electricity costs, demand costs, and photovoltaic and energy storage investment costs, and establish constraints such as energy storage batteries, maximum demand, and return on investment.

[0060] Step 5: Based on the upper and lower boundaries of energy storage capacity and power, randomly generate multiple sets of capacity and power combination parameters. Combined with the constraints and objective function obtained in Step 4, establish a dynamic programming model to solve for the energy storage charging and discharging power and the minimum daily electricity cost. After considering economic constraints, calculate the energy storage configuration capacity.

[0061] Step 6: Based on the photovoltaic power generation, load power and energy storage charging and discharging power of typical days in different seasons, calculate the typical equivalent load curve and determine the maximum load power as the maximum demand value to be reported monthly.

[0062] The specific implementation process of each step is explained in detail below:

[0063] In step 2, the flexible load probabilistic response model is approximately fitted to a piecewise linear function, where the horizontal axis represents the incentive level between different time periods, and the vertical axis represents the user's responsiveness. The relationship between the load transfer rate from peak to valley periods and the peak-valley electricity price difference is shown in the curve below. Figure 2 , Figure 3 As shown.

[0064] The load transfer rate from peak to off-peak hours is:

[0065]

[0066] In the formula: λ pv x is the load transfer rate from peak to valley hours for users; x0 is the dead zone threshold; x is the electricity price difference between peak and valley hours. k is the threshold for the saturation region. pv This represents the slope of the linear region of the piecewise linear peak-valley transition rate curve.

[0067] Similarly, piecewise linear load transfer rate curves from peak to normal periods and from normal to valley periods can be plotted, and corresponding piecewise linear models can be established.

[0068] Based on the above three curves, the fitted load for each time period can be expressed as:

[0069]

[0070] In the formula, T p T f and T v They represent peak time, normal time, and valley time, respectively, with t being any of these time periods; P0(t) and P(t) represent the load size during time period t before and after the implementation of peak-valley time-of-use pricing, respectively. and These represent the average total load during the peak and off-peak periods before implementation, respectively, within the corresponding time periods.

[0071] In step 3, a photovoltaic output model is constructed. The output power of the photovoltaic power generation system is under standard rated conditions (solar irradiance G). STC 1000W / m 2 Battery temperature T STC The test power, illumination, and temperature (at 25℃) were used to obtain the following results:

[0072]

[0073] In the formula P PV P represents the output power at the operating point of the photovoltaic cell. STC The maximum test power under standard rated conditions; G C G represents the illuminance at the working point. STC is the solar irradiance under rated conditions; k is the power temperature coefficient, with a value of -0.005 / ℃; T C The operating point of the photovoltaic cell is its temperature; T STC The temperature is the temperature under rated conditions.

[0074] The unit demand electricity price model is as follows:

[0075]

[0076] In the formula, C is the transformer capacity; is the actual maximum demand; x is the maximum demand, which cannot be lower than a certain transformer capacity; k is the threshold for charging a punitive price. In this embodiment, according to current regulations, k = 1.05; p1 and p2 are the demand prices for the actual maximum demand within 1.05 times the reported maximum demand and the demand prices for the portion exceeding 1.05 times the reported maximum demand, respectively. In this embodiment, according to current regulations, p2 / p1 = 2.

[0077] In step 4, the objective function for minimizing user cost is:

[0078]

[0079] In the formula, C represents the total electricity cost for microgrid users; C1 represents the cost of purchasing electricity from the grid for microgrid users; C2 represents the cost of photovoltaic power generation; C3 represents the cost of energy storage discharge; T represents the number of time periods in a research period; Δt represents the time interval of each time period; C4 represents the electricity cost for user demand; and R represents the benefits of user participation in demand response.

[0080] In the formula, P lt P represents the grid load in time period t, which is obtained by superimposing the typical daily routine load and flexible load; vt Let P be the photovoltaic power generation in time period t. ch,t P dis,t p represents the charging power and discharging power of the energy storage battery during time period t, respectively. t This refers to the time-of-use electricity price for the power grid.

[0081] In the formula, C pv P represents the total investment cost of photovoltaic power (in yuan). pv H represents the photovoltaic installed capacity (kW), H represents the maximum annual utilization hours of photovoltaic power, and N represents the photovoltaic installed capacity (kW). pv Indicates the number of years the photovoltaic system has been in operation.

[0082] In the formula, C BESS E represents the total investment cost of energy storage (in yuan). BESS Indicates the rated capacity (kWh) of the energy storage configuration, n bess This indicates the number of energy storage cycles.

[0083] In the formula, P demand This represents the user's maximum demand, α is the unit demand electricity cost, and Tn is the set duration.

[0084] The constraints mentioned, in this embodiment, include:

[0085] 1) Capacity at the previous moment + charging / discharging power at the current time period * time = capacity at the current moment.

[0086] E t =E t-1 +η ch ·P ch,t ·Δt-Δt·P dis,t / η d (3)

[0087] In the formula, E t and E t-1 η represents the remaining battery capacity during time period t and time period t-1. ch and η dThese refer to the charging efficiency and discharging efficiency of the energy storage battery, respectively.

[0088] 2) The inequality constraint for the remaining battery charge in time period t is:

[0089] E min ≤E t ≤E BESS (4)

[0090] In the formula, E BESS E represents the rated capacity of the energy storage configuration. min This represents the lower limit of energy storage capacity.

[0091] 3) The energy storage charging and discharging power shall not exceed the limit.

[0092] 0≤P ch,t ≤P max

[0093] 0≤P dis,t ≤P max (5)

[0094] In the formula, P max This limits the maximum charging and discharging power of the energy storage system.

[0095] Without considering grid connection of energy storage, if the energy storage is configured solely to absorb photovoltaic loads and reduce user load, then the maximum charging and discharging power of the energy storage system only needs to meet the requirement of not exceeding the power imbalance within a certain time period t within the assessment period T.

[0096]

[0097] 4) Maximum demand constraint

[0098] Energy storage, guided by peak-valley electricity pricing and demand response compensation, may generate new load spikes, even exceeding the original maximum demand, leading to an increase in demand-based electricity costs. Therefore, this embodiment introduces a maximum demand constraint.

[0099] max(P lt -P vt +P ch,t -P dis,t ≤1.05P demand (7)

[0100] 5) Investment return constraints

[0101] R P ≥R 参考 (8)

[0102] In the formula, R P For the return on investment of photovoltaic and energy storage systems, R 参考 The set rate of return on investment.

[0103] In this embodiment, the return on investment is calculated as follows:

[0104]

[0105] In the formula, C 收益 This indicates the cost savings from photovoltaic power generation and the electricity cost savings from peak-valley arbitrage using energy storage. (C) 投资 This refers to the investment cost of photovoltaic energy storage.

[0106] The above model is a dynamic programming problem. Equations (3) to (8) constitute the constraints of the objective function, including constraints on battery capacity, charging demand, and charging power. In the model, P ch,t P dis,t E BESS P demand The variable is the decision variable, and all other parameters are known quantities.

[0107] The present invention also provides the following product examples:

[0108] A computer-readable storage medium storing a plurality of instructions adapted for loading by a processor of a terminal device and executing the steps of the method described above.

[0109] A terminal device includes a processor and a computer-readable storage medium, the processor being configured to implement various instructions; the computer-readable storage medium being configured to store a plurality of instructions adapted to be loaded by the processor and executed in accordance with the steps of the method described above.

[0110] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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, etc.) containing computer-usable program code.

[0111] This 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 block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible loads, characterized in that: Includes the following steps: Historical load data of industrial parks are obtained, and the loads are classified using clustering algorithms based on actual production electricity consumption, including conventional loads and flexible loads. Typical daily loads for different seasons are extracted based on peak, valley, and average characteristics. For flexible loads, considering the impact of different time-of-use electricity prices on flexible loads, a flexible load response probability model is established to obtain the corresponding flexible load within a set time period; The specific process of establishing a flexible load response probability model includes fitting the flexible load probability response model into a piecewise linear function, where the horizontal axis represents the incentive level between different time periods and the vertical axis represents the user's responsiveness. The relationship curve between the load transfer rate from peak to valley time and the peak-valley electricity price difference is plotted. Plotting the piecewise linear load transfer rate curves from peak to normal time and from normal time to valley time, and representing the fitted load for each time period based on the above curves. Construct a photovoltaic power output model, calculate the power output range of the planned industrial park in different seasons, and establish a demand electricity price calculation model for the industrial park. With the objective function of minimizing the electricity cost for users in the park within a set time period, and considering the photovoltaic power output model and flexible load, a minimum electricity cost calculation model is established. Under constraints, the minimum electricity cost calculation model is dynamically solved to obtain the energy storage configuration capacity. Based on the photovoltaic power generation, load power, and energy storage charging and discharging power of typical days in different seasons, a typical equivalent load curve is calculated, and the maximum load power is determined as the maximum demand value to be reported periodically.

2. The method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible load as described in claim 1, characterized in that: The load transfer rate from peak to off-peak hours is: In the formula: This represents the load transfer rate from peak to off-peak hours for users. This is the dead zone threshold; This refers to the price difference between peak and off-peak hours for electricity; This is the threshold for the saturation region; This represents the slope of the linear region of the piecewise linear peak-valley transition rate curve.

3. The method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible load as described in claim 1, characterized in that: The specific process of constructing a photovoltaic output model includes: determining the output power of the photovoltaic power generation system based on test power, illumination, and temperature under standard rated conditions.

4. A method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible loads as described in claim 1 or 3, characterized in that: The photovoltaic power output model is as follows: In the formula P PV This refers to the output power at the operating point of the photovoltaic cell; P STC This is the maximum test power under standard rated conditions; G C The light intensity at the working point; G STC The solar radiation intensity under standard rated conditions; k This is the power temperature coefficient, with a value of -0.005 / ℃; T C The operating point temperature of the photovoltaic cell; T STC Temperature under standard rated conditions.

5. The method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible load as described in claim 1, characterized in that: The electricity demand pricing model for the industrial park is as follows: In the formula, C is the transformer capacity; This represents the actual maximum demand. x To meet maximum demand, the transformer capacity must not be lower than a certain level. k The threshold for imposing punitive prices, p 1 , p 2 represents the demand price for the portion of the actual maximum demand within 1.05 times the reported maximum demand, and the demand price for the portion exceeding 1.05 times the reported maximum demand.

6. The method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible load as described in claim 1, characterized in that: The minimum electricity cost calculation model is as follows: In the formula, C represents the total electricity cost for microgrid users; This represents the cost of electricity purchased from the grid by microgrid users; Indicates the cost of photovoltaic power generation; This represents the cost of energy storage and discharge, where T is the number of time periods within a research period. The duration of each period; R represents the electricity cost for user demand; R represents the revenue for user participation in demand response. The load in time period t represents the grid load, which is obtained by superimposing the typical daily routine load and flexible load. Let be the photovoltaic power generation in time period t. They represent t The charging and discharging power of the energy storage battery during different time periods. Time-of-use pricing for the power grid; This indicates the overall investment cost of photovoltaic power. H represents the installed capacity of photovoltaic power, and H represents the maximum annual utilization hours of photovoltaic power. Indicates the number of years the photovoltaic system has been in operation; This indicates the overall investment cost of energy storage. Indicates the rated capacity of the energy storage configuration. Indicates the number of energy storage cycles; Indicates the user's maximum demand. The unit demand electricity cost is Tn, which is the set period.

7. The method for configuring photovoltaic and energy storage capacity in industrial parks based on demand management and flexible load as described in claim 1, characterized in that: The constraints include: The capacity at the previous moment plus the product of the charging / discharging power and time in the current period equals the capacity at the current moment. The remaining battery charge is within its upper and lower limits; The energy storage charging and discharging power shall not exceed the limit. The constraints include a maximum demand that is less than or equal to a set threshold, and a return on investment that is greater than a set value.

8. A computer-readable storage medium, characterized in that: It stores multiple instructions adapted for loading by the processor of a terminal device and executing the steps of the method according to any one of claims 1-7.

9. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor being used to implement various instructions; the computer-readable storage medium being used to store a plurality of instructions adapted to be loaded by the processor and to execute the steps of the method of any one of claims 1-7.

Images