Energy storage de-discretization modeling method and system considering charging and discharging efficiency

Abstract

A dediscrete modeling method and system for energy storage considering charge and discharge efficiency includes: establishing energy storage capacity constraints within a given continuous time period, and linking the upper and lower limits of the energy storage capacity constraints to the charge and discharge efficiency of the energy storage; transforming the lower limit of the energy storage capacity constraints by omitting binary variables and reducing constraint conditions; transforming the upper limit of the energy storage capacity constraints by omitting binary variables and reducing constraint conditions; simplifying the energy storage capacity constraints, thus dediscretizing the energy storage model. This invention proposes a set of sufficient conditions for energy storage capacity constraints, thereby simplifying the energy storage model, achieving dediscretization of energy storage, improving the ability of energy storage to absorb new energy sources, and providing theoretical support for the planning and operation control of energy storage in power systems.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology, specifically relating to a dediscretization modeling method for energy storage that considers charging and discharging efficiency. Background Technology

[0002] Today, the global energy landscape is undergoing a shift from fossil fuels to renewable energy, with energy storage becoming a crucial influencing factor. Energy storage not only stores excess energy during periods of oversupply but also releases energy during peak demand periods, effectively regulating the supply and demand balance of the power grid and improving the stability and reliability of the entire power system. Despite significant progress in energy storage technology, appropriately simplifying the dynamic behavior of energy storage systems, particularly the efficiency variations during charging and discharging, remains a major challenge for current research.

[0003] Existing energy storage modeling methods, in order to accurately capture the complex dynamic characteristics of energy storage devices during charging and discharging, often suffer from shortcomings such as nonlinearity and excessive simplification assumptions. This leads to significant deviations between simulation results and actual performance, resulting in poor applicability. Especially in large-scale energy storage systems or electricity market applications, such deviations can trigger a series of problems, such as improper energy dispatch, reduced economic efficiency, and even increased system safety risks.

[0004] Existing technology discloses a distribution network coordination optimization method (CN115940290A) considering linearized modeling of four-quadrant energy storage power, including the following steps: Step 1), proposing a linearized modeling method for four-quadrant energy storage power regulation; Step 2), constructing the objective function of the distribution network active-reactive power coordination optimization model; Step 3), constructing the constraints of the distribution network active-reactive power coordination optimization model including energy storage power capacity constraints and energy capacity constraints; Step 4), constructing and solving the active-reactive power coordination optimization model of the active distribution network based on the second-order cone relaxation technique. However, the shortcomings of the existing technology are that when dealing with complex energy storage systems, the existing linearization method often cannot accurately describe the constraints of energy storage capacity, leading to unrealistic schemes in the energy storage dispatch plan formulated based on this technology, such as simultaneous charging and discharging of the energy storage system. This not only makes the estimated remaining power of the energy storage model lower than the actual level, but also exacerbates the uncertainty of energy capacity constraint errors, ultimately weakening the economic efficiency of the energy storage system. Most importantly, it is physically impossible for an energy storage system to charge and discharge simultaneously. Therefore, there is a high risk of improper energy dispatch when using existing technologies for distribution network coordination and optimization. Thus, a new technological solution is needed to address these issues. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, a dediscretization modeling method for energy storage that considers charging and discharging efficiency is provided. This method simplifies the dynamic behavior of energy storage systems when considering efficiency changes during the charging and discharging process, thereby dediscretizing the energy storage model, improving its applicability, and providing theoretical support for large-scale energy storage systems and electricity market applications.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for dediscretization modeling of energy storage considering charge and discharge efficiency, comprising:

[0007] Establish energy storage capacity constraints for a given continuous time period, and link the upper and lower limits of the energy storage capacity constraints with the charging and discharging efficiency of energy storage.

[0008] The lower limit of the established energy storage capacity constraint is modified by omitting binary variables in the lower limit of the energy storage capacity constraint and reducing constraint conditions.

[0009] The upper limit of the established energy storage capacity constraint is modified by omitting the binary variables in the upper limit of the energy storage capacity constraint and reducing the constraint conditions.

[0010] Based on the above modifications, the energy capacity constraints of energy storage are simplified, and the dediscretization modeling of energy storage is realized.

[0011] Preferably, the expression for the energy storage capacity constraint is:

[0012]

[0013] In the formula, E represents the change in electrical energy at each time interval in the energy storage power station; Δt represents the time interval between adjacent decision points; ESS,Char and E ESS,Dis These represent the energy that can be absorbed and the energy that can be released, respectively. Indicates the energy storage discharge power. Indicates energy storage and discharge. Indicates energy storage charging. Discharge power during the energy storage charging period, This refers to the discharge power during the energy storage discharge period;

[0014] η dischar =1 / λ dischar η char =λ char , where λ dischar The discharge efficiency of the energy storage is defined as 0 ≤ λ. dischar ≤1, λ char The charging efficiency of energy storage is defined as 0 ≤ λ. char ≤1; Ω tLet Ω be a set of consecutive operating segments of energy storage, where t is a certain moment in the energy storage process. c and Ω d t represents the set of time periods for energy storage charging and discharging. c and t d These represent the charging and discharging times for energy storage, respectively.

[0015] Preferably, in equations (3) and (4),

[0016]

[0017] In the formula, For the state of charge of energy storage at the initial time t0, E ESS Energy storage capacity.

[0018] Preferably, the step of deforming the lower limit of the energy storage capacity constraint is as follows: according to equation (2) and η dischar ≥η char ,get:

[0019]

[0020] By rearranging equation (5) above, we can obtain:

[0021]

[0022] therefore:

[0023]

[0024] Preferably, based on equation (7), a sufficient condition for equation (3) can be obtained as follows:

[0025]

[0026] Preferably, the upper limit of the energy storage capacity constraint is modified to be equivalent to equation (2) and equation (4):

[0027]

[0028] Preferably, equation (9) is further equivalent to equations (10) and (11), respectively:

[0029]

[0030] Preferably, because Furthermore, equations (10) and (11) are both equivalent to equation (4), therefore equation (4) can be further equivalent to:

[0031]

[0032] Preferably, the maximum power of energy storage charging and discharging is S.ESS Then the following inequality holds:

[0033]

[0034] Based on (13), the right side of equation (12) is reduced:

[0035]

[0036] Preferably, from equations (12) and (14), a sufficient condition for equation (4) is obtained as follows:

[0037]

[0038] Preferably, based on equations (8) and (15), the energy storage capacity constraint in step S4 is simplified to:

[0039]

[0040] Achieve dediscretization modeling for energy storage.

[0041] A second aspect of the present invention provides an energy storage dediscretization system, based on the above-described energy storage dediscretization modeling method considering charge and discharge efficiency, including...

[0042] Capacity constraint establishment element, capacity constraint lower limit deformation element, capacity constraint upper limit deformation element, and dediscretization modeling element;

[0043] Among them, the capacity constraint establishment unit is used to establish the energy capacity constraint of energy storage;

[0044] The capacity constraint lower limit deformation element is used to deform the lower limit of the energy storage capacity constraint;

[0045] The capacity constraint upper limit deformation unit is used to deform the upper limit of the energy storage capacity constraint;

[0046] Dediscretization modeling units are used to simplify the energy capacity constraints of energy storage and realize dediscretization modeling of energy storage.

[0047] The beneficial effects of this invention are that, compared with the prior art, the dynamic behavior of the energy storage system is appropriately simplified, and the energy storage model can be dediscretized when considering the efficiency changes during the energy storage charging and discharging process, thereby improving the applicability of the energy storage model. It has important theoretical value for large-scale energy storage systems and electricity market applications.

[0048] This invention employs a dediscretization modeling method, eliminating the need to distinguish between the charging and discharging states of energy storage when predicting energy storage output, thus broadening the model's adaptability. This invention can more accurately describe the energy capacity constraints of energy storage systems.

[0049] This invention uses the interactive power of energy storage to replace the charging power and discharging power, which solves the problem of judging the charging and discharging state of the energy storage system at the model level and eliminates the risk of improper energy scheduling.

[0050] This invention links the upper and lower limits of energy storage capacity constraints with the charging and discharging efficiency of energy storage. On the one hand, this makes the error of energy capacity constraints controllable, and on the other hand, the error of energy capacity constraints decreases as the charging and discharging efficiency of energy storage increases, thereby improving the economic benefits of energy storage systems. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0052] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0053] like Figure 1 As shown, this invention provides a dediscretization modeling method for energy storage that considers charge and discharge efficiency, comprising the following steps:

[0054] Establish energy storage capacity constraints within a given continuous time period, and link the upper and lower limits of the energy storage capacity constraints with the energy storage charging and discharging efficiency.

[0055] The expression for the energy storage capacity constraint is as follows:

[0056]

[0057] In the formula, The change in electrical energy at each time period in the energy storage power station is represented, and in this embodiment of the invention, a decrease in electrical energy is defined as positive; Δt represents the time period between adjacent decision points, i.e., the decision step size;

[0058] These represent the absorbable energy and the releaseable energy stored, respectively. For the state of charge (SOC) of the stored energy at the initial time t0, E ESS Energy storage capacity; Indicates the energy storage discharge power. Indicates energy storage discharge. <0 indicates energy storage charging. Discharge power during the energy storage charging period, The discharge power during the energy storage discharge period; defined η char =λchar , where λ dischar The discharge efficiency of the energy storage is defined as 0 ≤ λ. dischar ≤1, λ char The charging efficiency of energy storage is defined as 0 ≤ λ. char ≤1; Ω t Let Ω be a set of consecutive operating segments of energy storage, where t is a certain moment in the energy storage process. c and Ω d t represents the set of time periods for energy storage charging and discharging. c and t d These represent the charging and discharging times for energy storage, respectively.

[0059] In a preferred but non-limiting embodiment of the invention, Ω t =Ω c +Ω d Ω c and Ω d They can be Ω respectively t A certain continuous set of runtime segments, or Ω t The set of multiple discontinuous time periods. Equation (2) represents the set of multiple discontinuous time periods Ω in a given continuous time period. t The total change in SOC of energy storage (including charging and discharging periods).

[0060] Based on the established energy storage capacity constraints, the lower limit of the energy storage capacity constraints is modified by omitting binary variables in the lower limit of the energy storage capacity constraints and reducing the constraint restrictions.

[0061] The method for modifying the lower limit of energy storage capacity constraints is as follows:

[0062] According to (2) and η dischar ≥η char (η dischar ≥1,η char ≤1, when equality is taken (energy storage charging and discharging efficiency is not considered), we have:

[0063]

[0064] Among them, t c t represents a specific moment during the charging period of an energy storage power station. d This indicates a specific moment during the discharge period of an energy storage power station.

[0065] It is worth mentioning that the way to deform the energy storage capacity constraint is to replace the coefficients of the minuend and the subtrahend in equation (2) to obtain equation (5); for the upper inequality of equation (5), the inequality sign is valid because the minuend is reduced; for the lower inequality of equation (5), the inequality sign is valid because the subtrahend is increased.

[0066] Equation (5) means that the lower limit of energy storage capacity considering the difference in charge and discharge efficiency is greater than the lower limit of energy storage capacity when the charge and discharge efficiencies are the same. This can be simplified to:

[0067]

[0068] therefore:

[0069]

[0070] Based on (7), a sufficient condition for (3) is:

[0071]

[0072] Based on the established energy storage capacity constraints, the upper limit of the energy storage capacity constraints is modified, the binary variables in the lower limit of the energy storage capacity constraints are omitted, and the constraint restrictions are reduced.

[0073] The method for modifying the upper limit of energy storage capacity constraints is as follows:

[0074] According to (2), (4) is equivalent to:

[0075]

[0076] Equation (9) can be further equivalent to equations (10) and (11) respectively:

[0077]

[0078] because Furthermore, equations (10) and (11) are both equivalent to equation (4), therefore equation (4) can be further equivalent to:

[0079]

[0080] The maximum power of energy storage charging and discharging is S ESS Then the following inequality holds:

[0081]

[0082] In the formula: T represents Ω t Length, S ESS This represents the maximum power for charging and discharging energy storage.

[0083] Based on (13), the right side of equation (12) is reduced:

[0084]

[0085] From equations (12, 14), a sufficient condition for equation (4) is obtained:

[0086]

[0087] Based on the above deformation, the energy capacity constraint of energy storage is simplified, and the dediscretization modeling of energy storage is realized.

[0088] After dediscretization of energy storage, the expression for the energy storage capacity constraint is as follows:

[0089]

[0090] By replacing equations (1) to (4) with equation (16), binary variables can be omitted and constraints can be reduced while considering the energy storage charging and discharging efficiency, thereby simplifying the energy storage model and solving the technical problem that the simulation results of existing energy storage models, namely equations (1) to (4), have large deviations from actual performance and poor applicability. This achieves the technical effect of improving the applicability of the energy storage model and provides theoretical support for large-scale energy storage systems and electricity market applications.

[0091] In this embodiment of the invention, dediscretization refers to the transformation of the formula in steps S2 and S3. The purpose of dediscretization is to obtain equation (16). The energy storage output of the obtained equation (16) does not need to distinguish between the charging and discharging states of the energy storage. Conventional models, on the other hand, need to introduce binary variables or complementary constraints to limit the simultaneous occurrence of energy storage charging and discharging.

[0092] Based on the above scheme, this embodiment applies and analyzes the above method, as follows:

[0093] In a preferred but non-limiting embodiment of the present invention, the parameters for energy storage are selected as shown in Table 1. The decision step size Δt = 0.25h, and the set of continuous operating periods of the energy storage power station Ω. t The length is T = 2h.

[0094] Table 1 Energy Storage Parameter Settings

[0095]

[0096] Energy storage capacity constraints are expressed as follows:

[0097]

[0098] After dediscretization of energy storage, the expression for the capacity constraint is as follows:

[0099]

[0100] The impact of the proposed method on the energy storage output operating domain was analyzed using Monte Carlo simulation. Based on equations (6)-(8), 10,000 sets of energy storage output plans were randomly generated, of which 9,554 sets, or 95.54%, satisfied equations (9)-(10). Based on equations (9)-(10), 10,000 sets of energy storage output plans were randomly generated, of which 10,000 sets, or 100%, satisfied equations (6)-(8).

[0101] The simulation results show that the energy storage dediscretization method proposed in this invention reduces the energy storage's own operating domain by a small amount. The energy storage output plans formulated based on the method proposed in this invention are all feasible, verifying the effectiveness of the energy storage dediscretization modeling method provided in this invention.

[0102] This invention also provides an energy storage dediscretization system based on the above-described energy storage dediscretization modeling method considering charge and discharge efficiency, comprising:

[0103] Capacity constraint establishment element, capacity constraint lower limit deformation element, capacity constraint upper limit deformation element, and dediscretization modeling element;

[0104] Among them, the capacity constraint establishment unit is used to establish the energy capacity constraint of energy storage;

[0105] The capacity constraint lower limit deformation element is used to deform the lower limit of the energy storage capacity constraint;

[0106] The capacity constraint upper limit deformation unit is used to deform the upper limit of the energy storage capacity constraint;

[0107] The dediscretization modeling unit is used to simplify the energy capacity constraints of energy storage, enabling dediscretization modeling of energy storage. This disclosure can be a system, method, and / or computer program product. The computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for causing a processor to implement various aspects of this disclosure.

[0108] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0109] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0110] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A dediscretization modeling method for energy storage considering charge and discharge efficiency, characterized in that, Includes the following steps: Establish energy storage capacity constraints within a given continuous time period, and link the upper and lower limits of the energy storage capacity constraints with the energy storage charging and discharging efficiency. The expression for the energy storage capacity constraint is: （1） （2） （3） （4） In the formula, This indicates the change in electrical energy in the energy storage power station at each time period; Indicates the time interval between adjacent decision points; and These represent the energy that can be absorbed and the energy that can be released, respectively. Indicates the energy storage discharge power. Indicates energy storage and discharge. <0 indicates energy storage charging. Discharge power during the energy storage charging period, This refers to the discharge power during the energy storage discharge period; , ,in The discharge efficiency of energy storage is given by a value ranging from [value range missing]. , The charging efficiency of energy storage, with a value range of [value missing]. Ωt represents a set of continuous operating segments for energy storage. Let Ωc and Ωd be the sets of time periods during which energy is charged and discharged, respectively, representing a certain moment in energy storage. and These represent the charging and discharging times of the energy storage system, respectively. The lower limit of the established energy storage capacity constraint is modified by omitting binary variables in the lower limit of the energy storage capacity constraint and reducing constraint conditions. The steps for transforming the lower limit of the energy storage capacity constraint are as follows: according to equation (2) and ,get: （5） therefore: （7）； The upper limit of the established energy storage capacity constraint is modified by omitting the binary variables in the upper limit of the energy storage capacity constraint and reducing the constraint conditions. Based on the above modifications, the energy capacity constraints of energy storage are simplified, and the dediscretization modeling of energy storage is realized.

2. The energy storage dediscretization modeling method considering charge and discharge efficiency according to claim 1, characterized in that: In equations (3) and (4), In the formula, For energy storage, the state of charge at the initial time t0, Energy storage capacity.

3. The energy storage dediscretization modeling method considering charge and discharge efficiency according to claim 1, characterized in that: Based on equation (7), a sufficient condition for equation (3) can be obtained as follows: （8）。 4. The energy storage dediscretization modeling method considering charge and discharge efficiency according to claim 3, characterized in that: The upper limit of the energy storage capacity constraint is transformed into the following equations: According to equation (2), equation (4) is equivalent to: （9）。 5. The energy storage dediscretization modeling method considering charge and discharge efficiency according to claim 4, characterized in that: Equation (9) can be further equivalent to equations (10) and (11): （10） （11）。 6. The energy storage dediscretization modeling method considering charge and discharge efficiency according to claim 5, characterized in that: because Furthermore, equations (10) and (11) are both equivalent to equation (4), therefore equation (4) can be further equivalent to: （12）。 7. The energy storage dediscretization modeling method considering charge and discharge efficiency according to claim 6, characterized in that: The maximum power of energy storage charging and discharging is Then the following inequality holds: （13） Based on (13), the right side of equation (12) is reduced: （14）。 8. The energy storage dediscretization modeling method considering charge and discharge efficiency according to claim 7, characterized in that: From equations (12) and (14), a sufficient condition for equation (4) is obtained: （15）。 9. The energy storage dediscretization modeling method considering charge and discharge efficiency according to claim 8, characterized in that: Based on equations (8) and (15), the energy storage capacity constraint is simplified to: （16） Achieve dediscretization modeling for energy storage.

10. An energy storage dediscretization system, based on the energy storage dediscretization modeling method considering charge and discharge efficiency as described in any one of claims 1-9, characterized in that, include: Capacity constraint establishment element, capacity constraint lower limit deformation element, capacity constraint upper limit deformation element, and dediscretization modeling element; Among them, the capacity constraint establishment unit is used to establish the energy capacity constraint of energy storage; The capacity constraint lower limit deformation element is used to deform the lower limit of the energy storage capacity constraint; The capacity constraint upper limit deformation unit is used to deform the upper limit of the energy storage capacity constraint; Dediscretization modeling units are used to simplify the energy capacity constraints of energy storage and realize dediscretization modeling of energy storage.

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