A method and system for optimizing the new energy consumption capacity of an energy storage system

Abstract

The present invention discloses a method and system for optimizing the new energy consumption capacity of an energy storage system, belonging to the technical field of power system operation. The method of the present invention includes: establishing an optimization model for the energy storage system for the energy storage system; solving the optimization model; solving the optimization model to determine the actual lower limit of the energy storage power of the energy storage system; establishing a linear optimization model for the energy storage system when the actual lower limit of the energy storage power is satisfied, solving the linear optimization model, obtaining an optimization strategy, and controlling the operation of the energy storage system through the optimization strategy. The present invention first decouples the energy storage operation constraints for improving RES consumption, simplifying the non-linear constraints related to power flow and voltage constraints in the original model in the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation, and more specifically, to a method and system for optimizing the new energy consumption capacity of an energy storage system. Background Art

[0002] With the rapid development of new energy power generation, when the output characteristics are quite different from the load characteristics, large-scale new energy power generation has encountered power curtailment problems in actual operation, which hinders the realization of the dual-carbon goal. Summary of the Invention

[0003] In view of the above problems, the present invention proposes a method for optimizing the new energy consumption capacity of an energy storage system, including:

[0004] Establish an optimization model for the energy storage system;

[0005] Solve the optimization model;

[0006] Solving the optimization model to determine the actual lower limit of the energy storage power of the energy storage system;

[0007] Establish a linear optimization model for the energy storage system when the actual lower limit of the energy storage power is satisfied, solve the linear optimization model to obtain an optimization strategy, and control the operation of the energy storage system through the optimization strategy.

[0008] Optionally, establishing the optimization model for the energy storage system includes:

[0009] Establish an objective function for the optimization model and establish conditional constraints for the objective function;

[0010] The conditional constraints include: grid power balance constraint, energy storage system power constraint, energy storage system power quantity constraint, grid security constraint, and new energy power source power curtailment variable constraint.

[0011] Optionally, determining the actual lower limit of the energy storage power of the energy storage system specifically includes:

[0012] Determine the theoretical lower limit of the energy storage power of the energy storage system;

[0013] Correct the theoretical lower limit of the energy storage power of the energy storage system to determine the actual lower limit of the energy storage power of the energy storage system.

[0014] Optionally, determining the theoretical lower limit of the energy storage power of the energy storage system includes:

[0015] Calculate the output consumption ratio of the energy storage system;

[0016] Calculate the ideal charging power required for the energy storage system to complete 100% consumption during the period when the RES output is large;

[0017] Calculate the lower limit of the discharge power of the energy storage system during the time periods when RES output does not need to be shared.

[0018] Optionally, determine the actual lower limit of the energy storage power of the energy storage system, specifically including:

[0019] Perform correction of the energy storage power constraint.

[0020] Perform correction of the energy storage energy constraint.

[0021] Optionally, obtain an optimization strategy, including:

[0022] Obtain the actual lower limit of the energy storage power of the energy storage system with the goal of improving the RES accommodation capacity;

[0023] Simplify the constraint conditions into energy storage power constraints and capacity constraints.

[0024] The present invention also proposes a system for optimizing the new energy accommodation capacity of an energy storage system, including:

[0025] An optimization model establishment module, which establishes an optimization model of the energy storage system for the energy storage system;

[0026] Solve the optimization model;

[0027] A solution module, which solves the optimization model to determine the actual lower limit of the energy storage power of the energy storage system;

[0028] A control module, which establishes a linear optimization model of the energy storage system when the actual lower limit of the energy storage power is satisfied, solves the linear optimization model to obtain an optimization strategy, and controls the operation of the energy storage system through the optimization strategy.

[0029] Optionally, establishing the optimization model of the energy storage system includes:

[0030] Establish an objective function of the optimization model and establish conditional constraints for the objective function;

[0031] The conditional constraints include: grid power balance constraints, energy storage system power constraints, energy storage system power quantity constraints, grid safety constraints, and new energy power source power curtailment variable constraints.

[0032] Optionally, determining the actual lower limit of the energy storage power of the energy storage system specifically includes:

[0033] Determine the theoretically lower limit of the energy storage power of the energy storage system;

[0034] Correct the theoretically lower limit of the energy storage power of the energy storage system to determine the actual lower limit of the energy storage power of the energy storage system.

[0035] Optionally, determining the theoretical lower limit of the energy storage power of the energy storage system includes:

[0036] Calculating the output consumption ratio of the energy storage system;

[0037] Calculating the ideal charging power required for the energy storage system to achieve 100% consumption during the period when the RES output is large;

[0038] Calculating the lower limit of the discharge power of the energy storage system during the period when the RES output does not need to be shared.

[0039] Optionally, determining the actual lower limit of the energy storage power of the energy storage system specifically includes:

[0040] Modifying the energy storage power constraint.

[0041] Modifying the energy storage energy constraint.

[0042] Optionally, obtaining an optimization strategy includes:

[0043] Obtaining the actual lower limit of the energy storage power of the energy storage system with the goal of improving the RES consumption capacity;

[0044] Simplifying the constraint conditions into energy storage power constraints and capacity constraints.

[0045] The present invention first decouples the energy storage operation constraints for improving RES consumption, simplifying the non-linear constraints related to power flow and voltage constraints in the original model in the power grid. Then, according to the capacity and power constraints of the energy storage system, the lower limit of the power is further corrected. Finally, under the condition of satisfying this actual lower limit of the power, a linear programming model for the optimal operation of the energy storage is established. This method maximally consumes the RES output and improves the comprehensive benefits of the RES and the energy storage system under the conditions of satisfying the operation of the energy storage system and voltage safety constraints. Description of the Drawings

[0046] Figure 1 Is the flowchart of the method of the present invention;

[0047] Figure 2 Is the flowchart of the enumerated optimization solution of the embodiment of the present invention;

[0048] Figure 3 Is the flowchart of the system of the present invention. Detailed Embodiments

[0049] Reference is now made to the accompanying drawings to describe exemplary embodiments of the present invention. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely, and to fully convey the scope of the present invention to those skilled in the art. The terms in the exemplary embodiments shown in the drawings are not intended to limit the present invention. In the drawings, the same units / components are denoted by the same reference numerals.

[0050] Unless otherwise specified, the terms used herein (including technical terms) have the ordinary meaning understood by those skilled in the art. In addition, it can be understood that the terms defined in the commonly used dictionary should be understood to have a meaning consistent with the context of their relevant fields, and should not be understood as idealized or overly formal meanings.

[0051] The present invention proposes a method for optimizing the new energy consumption capacity of an energy storage system, as Figure 1 shown, including:

[0052] For the energy storage system, establish an optimization model of the energy storage system;

[0053] Solve the optimization model;

[0054] Solve the optimization model to determine the actual lower limit of the energy storage power of the energy storage system;

[0055] Establish a linear optimization model for the energy storage system when the actual lower limit of the energy storage power is satisfied, solve the linear optimization model to obtain an optimization strategy, and control the operation of the energy storage system through the optimization strategy.

[0056] The present invention will be further described below with reference to embodiments:

[0057] Including: Step 1: Establish an optimized operation model of the energy storage system for improving the new energy consumption capacity.

[0058] Step 1-1: Establish an objective function of the optimization model. The energy storage optimization model takes the minimum total cost of new energy (RES) and energy storage (ES) as the objective function, as shown in Equation (1), including the new energy curtailment cost and the ES charging cost. The curtailment cost intuitively reflects the goal of the optimized operation strategy to reduce the curtailment amount and improve the new energy consumption capacity. Reducing the energy storage charging cost is a direct requirement for energy storage operation.

[0059]

[0060] In the formula, Δt is the time interval, T is the number of scheduling periods, n1 is the number of new energy power sources, C RES (t) and The limited power cost and limited power quantity of the i-th new energy power source unit in the t period are respectively, n2 is the number of energy storage systems, C ES (t) is the charging and discharging electricity price of the energy storage system in the t period, is the charging and discharging power of the energy storage system at node i. The value is positive during charging and negative during discharging.

[0061] Step 1-2: Establish the power balance constraint of the power grid.

[0062]

[0063] In the formula, n3 is the number of nodes, G ij , B ij and δ ij are the conductance, susceptance and phase angle difference between nodes i and j respectively.

[0064] Step 1-3: Establish the power constraint of the energy storage system.

[0065] The charging and discharging power of the energy storage system cannot exceed the rated power:

[0066]

[0067] In the formula, is the maximum charging and discharging power of the i-th energy storage system.

[0068] Step 1-4: Energy storage system power quantity constraint

[0069] The power quantity constraint requires that the power quantity of the energy storage system at each moment is within the upper and lower limits:

[0070]

[0071]

[0072] In the formula, is the power quantity of the i-th energy storage system in the t period, and are the maximum and minimum allowable power quantities of the energy storage system respectively, is the initial power quantity of the i-th energy storage system.

[0073] Step 1-5: Power grid security constraint

[0074] The inequality constraint mainly considers the node voltage constraint and branch power flow constraint required for the safe operation of the power grid.

[0075]

[0076] In the formula, U i (t), and They are the voltage amplitude, lower amplitude limit, and upper amplitude limit of node i respectively.

[0077] Step 1-6: Constraint on the curtailment variable of new energy power sources

[0078] There is the following constraint relationship between the curtailment amount of new energy power sources and their real-time maximum output:

[0079]

[0080] From this, the combined optimal operation model of the photovoltaic and energy storage systems established by the control variables and can be obtained.

[0081] This model is a non-linear optimization model. When the power grid scale is large and the number of RES and energy storage is large, its solution is difficult and slow. In order to meet the rapid development needs of RES and energy storage systems, a simpler and more stable solution method needs to be found.

[0082] Step 2: Decouple and simplify the energy storage system optimization model.

[0083] Step 2-1: Use power flow calculation to determine the lower limit of the ideal charge and discharge power of the energy storage system to ensure the safe operation of the voltage.

[0084] Step 2-1-1: Calculate the output absorption ratio of each energy storage system.

[0085] In a distribution network containing multiple RES and energy storage systems, the distribution of the RES curtailment power and the absorption of RES output by the energy storage system follows the fairness principle. The RES absorption power is in the same proportion p RES (t), then the curtailment power is:

[0086]

[0087] The output absorption ratio of each energy storage system is:

[0088]

[0089] Step 2-1-2: Calculate the ideal charging power required for each energy storage system to complete 100% absorption during the period when the RES output is large.

[0090] At this time, in the power flow calculation, the output of each RES is the maximum output of each period, and the power of each energy storage system is set to share the part of the output that the power grid cannot absorb:

[0091]

[0092]

[0093] Considering the only variable p in this sectionRES (t) is a value in the interval [0, 1]. Therefore, with 2 as the initial value of the variable and 0.01 as the step size, an enumeration optimization solution is carried out, and the process is as shown in the appendix. Figure 2 as shown.

[0094] At this time, the ideal charging power of the energy storage at each time period obtained by sharing the RES output that cannot be absorbed by the power grid can be obtained from Equation (12).

[0095] Step 2-1-3: Calculate the lower limit of the discharge power of the energy storage system during the time period when RES output does not need to be shared.

[0096] When the energy storage system does not need to share the RES output time period, in order to maintain voltage security, it is also necessary to calculate the lower limit of the discharge power of the energy storage system at this time. At this time, the energy storage power is:

[0097]

[0098] In the formula, s ES (t) is the discharge ratio of the energy storage system at time period t, and it also belongs to the interval [0, 1].

[0099] After p RES (t) is solved, s ES (t) can be solved by a similar method. At this time, the ideal power curve of each energy storage system under the condition of safe voltage operation can be obtained.

[0100] Step 2-2: Calculate the actual lower limit of the energy storage power according to the capacity and power of the energy storage system.

[0101] Step 2-2-1: Correct the energy storage power constraint.

[0102] If the energy storage power in a certain time period exceeds the charging power, then correct the energy storage power in this time period to the charging power:

[0103]

[0104] Step 2-2-2: Correct the energy storage energy constraint.

[0105] Considering that the capacity constraint is a problem related to time sequence, the cumulative power method is proposed for verification and correction. The specific process is as follows:

[0106] (1) Find The time periods in the curve with positive power, mark this time period as the start time period, and shift the time periods before it to after this original curve time period to obtain a new recombined curve.

[0107] (2) In the curve after time period recombination calculate the cumulative power of each time period. Whether it exceeds the energy storage capacity :

[0108]

[0109] (3) If the cumulative electricity of a time period exceeds the energy storage capacity, when the energy storage power of each time period is positive, set the same consumption ratio k ES , and its cumulative electricity at this time is:

[0110]

[0111] Ratio k ES There is a maximum value within the interval [0,1], and the enumeration optimization method can also be used to solve it to make it satisfy:

[0112]

[0113] After the correction of power constraint and capacity constraint, it can be obtained:

[0114]

[0115] (4) Perform time period restoration.

[0116] After restoration is the lower limit of the power to improve RES consumption that the energy storage system can achieve. And the total limited power of RES at each time period is shown in formula (18). The limited power of each RES is allocated according to the RES capacity.

[0117]

[0118] Step 2-3: Establish a simplified linear programming model for the optimal operation of energy storage and calculate the optimal operation strategy of energy storage.

[0119] The obtained actual power lower limit of the energy storage system aiming at improving the RES consumption capacity has fully considered the impact on voltage security. Therefore, the goal of improving RES consumption and the non-linear power flow voltage constraint can no longer be considered in the decoupled optimal operation model of the energy storage system. The main optimization goal at this time is to reduce the energy storage operation cost:

[0120]

[0121] The constraint conditions are simplified to energy storage power constraint and capacity constraint. The lower limit of the power constraint condition is corrected to as shown in formula (20). The capacity constraint is still formulas (5) and (6).

[0122]

[0123] The present invention also provides a system 200 for optimizing the new energy consumption capacity of an energy storage system, as Figure 3 shown, including:

[0124] An optimization model establishment module 201, which establishes an optimization model of the energy storage system for the energy storage system;

[0125] A solution module 202, which solves the optimization model;

[0126] And solve the optimization model to determine the actual lower limit of the energy storage power of the energy storage system;

[0127] A control module 203, which establishes a linear optimization model of the energy storage system when the actual lower limit of the energy storage power is satisfied, solves the linear optimization model, obtains an optimization strategy, and controls the operation of the energy storage system through the optimization strategy.

[0128] Among them, establishing the optimization model of the energy storage system includes:

[0129] Establishing an objective function of the optimization model and establishing conditional constraints for the objective function;

[0130] The conditional constraints include: grid power balance constraint, energy storage system power constraint, energy storage system power quantity constraint, grid safety constraint, and new energy power source power curtailment variable constraint.

[0131] Among them, determining the actual lower limit of the energy storage power of the energy storage system specifically includes:

[0132] Determining the theoretically lower limit of the energy storage power of the energy storage system;

[0133] Correcting the theoretically lower limit of the energy storage power of the energy storage system to determine the actual lower limit of the energy storage power of the energy storage system.

[0134] Among them, determining the theoretically lower limit of the energy storage power of the energy storage system includes:

[0135] Calculating the output consumption ratio of the energy storage system;

[0136] Calculating the ideal charging power required for the energy storage system to complete 100% consumption during the period when the RES output is large;

[0137] Calculating the lower limit of the discharge power of the energy storage system during the period when the RES output does not need to be shared.

[0138] Among them, determining the actual lower limit of the energy storage power of the energy storage system specifically includes:

[0139] Performing correction of the energy storage power constraint.

[0140] Performing correction of the energy storage energy constraint.

[0141] Among them, obtaining the optimization strategy includes:

[0142] Obtaining the actual power lower limit of the energy storage system with the goal of improving the RES accommodation capacity;

[0143] Simplifying the constraint conditions into energy storage power constraints and capacity constraints.

[0144] The present invention first decouples the energy storage operation constraints for improving RES accommodation, simplifies the non-linear constraints related to power flow and voltage constraints in the original model in the power grid. Then, it further corrects the power lower limit according to the capacity and power constraints of the energy storage system. Finally, under the condition of meeting this actual power lower limit, a linear programming model for the optimal operation of the energy storage is established. This method maximally accommodates the RES output under the conditions of meeting the operation of the energy storage system and voltage safety constraints, and improves the comprehensive benefits of the RES and the energy storage system.

[0145] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can adopt the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented 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. The solutions in the embodiments of the present invention can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0146] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0147] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1The functions specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one Figure 1 process or more processes and / or boxes Figure 1 or more boxes.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0150] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the new energy consumption capacity of an energy storage system, the method comprising: For the energy storage system, establish an optimization model of the energy storage system, the optimization model taking the minimum total cost of new energy and energy storage as the objective function; Solve the optimization model; The solving of the optimization model determines the actual lower limit of the energy storage power of the energy storage system; Wherein, the actual lower limit of the energy storage power of the energy storage system is the actual lower limit of the energy storage power of the energy storage system aiming at improving the new energy consumption capacity, fully considering the impact on voltage security, and the goal of improving new energy consumption and non-linear power flow voltage constraints can no longer be considered in the decoupled optimal operation model of the energy storage system; Establish a linear optimization model when the energy storage system meets the actual lower limit of the energy storage power, solve the linear optimization model, obtain an optimization strategy, and control the operation of the energy storage system through the optimization strategy; The linear optimization model is as follows: The constraint conditions of the linear optimization model are simplified to the energy storage power constraint and the capacity constraint, and the lower limit of the energy storage power constraint condition is corrected to The Satisfy the following: where Δt is the time interval, T is the number of scheduling periods, n2 is the number of energy storage systems, and C ES (t) is the charging and discharging electricity price of the energy storage system in period t, is the charging and discharging power of the energy storage system at node i, is the maximum charging and discharging power of the i-th energy storage system.

2. According to the method described in claim 1, the establishment of the optimization model of the energy storage system includes: Establish the objective function of the optimization model and establish conditional constraints for the objective function; The conditional constraints include: grid power balance constraint, energy storage system power constraint, energy storage system power quantity constraint, grid security constraint, and new energy power source power curtailment variable constraint.

3. According to the method described in claim 1, the determination of the actual lower limit of the energy storage power of the energy storage system specifically includes: Determine the theoretically lower limit of the energy storage power of the energy storage system; Correct the theoretically lower limit of the energy storage power of the energy storage system to determine the actual lower limit of the energy storage power of the energy storage system.

4. According to the method described in claim 3, the determination of the theoretically lower limit of the energy storage power of the energy storage system includes: Calculate the output consumption ratio of the energy storage system; Calculate the ideal charging power required for the energy storage system to complete 100% consumption during the period of large new energy output; Calculate the lower limit of the discharging power of the energy storage system during the period when new energy output does not need to be shared.

5. According to the method described in claim 3, the determination of the actual lower limit of the energy storage power of the energy storage system specifically includes: Perform correction of the energy storage power constraint; Perform correction of the energy storage energy constraint.

6. According to the method described in claim 1, the obtaining of the optimization strategy includes: Obtain the actual lower limit of the energy storage power of the energy storage system aiming at improving the new energy consumption capacity; Simplify the constraint conditions into energy storage power constraint and capacity constraint.

7. A system for optimizing the new energy consumption capacity of an energy storage system, the system comprising: An optimization model establishment module, which, for the energy storage system, establishes an optimization model of the energy storage system, the optimization model taking the minimum total cost of new energy and energy storage as the objective function; Solve the optimization model; A solving module, which solves the optimization model to determine the actual lower limit of the energy storage power of the energy storage system; Wherein, the actual lower limit of the energy storage power of the energy storage system is the actual lower limit of the energy storage power of the energy storage system aiming at improving the new energy consumption capacity, fully considering the impact on voltage security, and the goal of improving new energy consumption and non-linear power flow voltage constraints can no longer be considered in the decoupled optimal operation model of the energy storage system; The control module establishes a linear optimization model for the energy storage system when the actual lower limit of the energy storage power is met, solves the linear optimization model to obtain an optimization strategy, and controls the operation of the energy storage system through the optimization strategy; The linear optimization model is as follows: The constraint conditions of the linear optimization model are simplified to the energy storage power constraint and the capacity constraint, and the lower limit of the energy storage power constraint condition is corrected to The Satisfy the following: Among them, Δt is the time interval, T is the number of scheduling periods, n2 is the number of energy storage systems, and C ES (t) is the charging and discharging electricity price of the energy storage system in period t, is the charging and discharging power of the energy storage system at node i, is the maximum charging and discharging power of the i-th energy storage system.

8. The system according to claim 7, wherein establishing the optimization model of the energy storage system includes: Establishing an objective function of the optimization model and establishing conditional constraints for the objective function; The conditional constraints include: grid power balance constraint, energy storage system power constraint, energy storage system power quantity constraint, grid security constraint, and new energy power source power curtailment variable constraint.

9. The system according to claim 7, wherein determining the actual lower limit of the energy storage power of the energy storage system specifically includes: Determining the theoretical lower limit of the energy storage power of the energy storage system; Correcting the theoretical lower limit of the energy storage power of the energy storage system to determine the actual lower limit of the energy storage power of the energy storage system.

10. The system according to claim 9, wherein determining the theoretical lower limit of the energy storage power of the energy storage system includes: Calculating the output absorption ratio of the energy storage system; Calculating the ideal charging power required for the energy storage system to complete 100% absorption during the period of large new energy output; Calculating the lower limit of the discharge power of the energy storage system during the period when new energy output does not need to be shared.

11. The system according to claim 9, wherein determining the actual lower limit of the energy storage power of the energy storage system specifically includes: Performing correction of the energy storage power constraint; Performing correction of the energy storage energy constraint.

12. The system according to claim 7, wherein obtaining the optimization strategy includes: Obtaining the actual lower limit of the energy storage power of the energy storage system with the goal of improving new energy absorption capacity; Simplifying the constraint conditions into an energy storage power constraint and a capacity constraint.

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