Energy storage capacity optimal configuration method and device under green power proportion constraint

Abstract

The invention provides an energy storage capacity optimal configuration method and device under green power proportion constraint, and belongs to the field of planning and operation of a power system. The method comprises the steps that an energy storage capacity optimal configuration model is constructed, the objective function of the model is to minimize the energy storage capacity of a to-be-planned object, and constraint conditions comprise power generation power and load power constraint, energy balance constraint, energy exchange constraint, energy storage operation constraint and green power proportion constraint; and after bilinear constraints in the energy exchange constraints and the energy storage operation constraints are converted into mixed integer linear constraints, solving the updated energy storage capacity optimal configuration model to obtain an optimal value of the target function, namely an optimal result of energy storage capacity configuration. According to the invention, matching between new energy power generation and a load time sequence can be realized, the green power consumption proportion of a park or a user equipped with distributed new energy power generation is improved, and the environmental protection is improved while the power demand is satisfied.

Description

Technical Field

[0001] The present invention belongs to the field of planning and operation of power systems, and in particular relates to a method and device for optimal configuration of energy storage capacity under green electricity ratio constraints. Background Art

[0002] With the development of energy storage technology, the application of integrated photovoltaic storage and charging in urban charging stations, highway service areas, industrial parks and other scenarios is becoming more and more widespread. The reasonable configuration of energy storage capacity is an important technical issue. The current energy storage capacity configuration problem mainly focuses on economic benefits, and does not pay attention to the constraints on the proportion of green electricity. For example, the invention patent with application number 202310977153.0 provides an optimal configuration method for the capacity of energy storage devices in an integrated photovoltaic storage and charging system under the condition of optimal economic benefits. However, the optimal configuration of energy storage capacity also needs to pay attention to carbon emission-related requirements.

[0003] "Green electricity" generally refers to electricity generated by non-fossil energy and does not produce carbon emissions. For users or parks equipped with a certain proportion of distributed photovoltaics and distributed wind turbines, since photovoltaic and wind power generation have strong randomness and volatility, if energy storage is configured to store excess electricity and release it during peak load periods, it can solve the problem of mismatch between wind power and photovoltaic output and load timing, reduce external electricity purchases, and increase the proportion of green electricity used in the park. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and propose a method and device for optimal configuration of energy storage capacity under the constraint of green electricity ratio. The present invention can achieve the matching of new energy power generation and load timing when certain distributed photovoltaic and distributed wind turbines are equipped in industrial parks, etc., and at the same time increase the green electricity consumption ratio of parks or users equipped with distributed new energy power generation, thereby improving environmental protection while meeting electricity demand.

[0005] The first embodiment of the present invention provides a method for optimal configuration of energy storage capacity under green electricity ratio constraints, including:

[0006] Constructing an objective function of an optimal configuration model for energy storage capacity, wherein the objective function is to minimize the energy storage capacity of the object to be planned;

[0007] The constraints for constructing the optimal configuration model of the energy storage capacity include: power generation and load power constraints, energy balance constraints, energy exchange constraints, energy storage operation constraints, and green electricity ratio constraints;

[0008] Converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints to obtain an updated optimal configuration model of the energy storage capacity;

[0009] Solving the updated optimal configuration model of the energy storage capacity and obtaining the optimal value of the objective function is the optimal result of the energy storage capacity configuration.

[0010] In a specific implementation of the present invention, the objective function expression is as follows:

[0011] min e ES (1)

[0012] Among them, e ES The energy storage capacity to be configured.

[0013] In a specific implementation of the present invention, it also includes:

[0014] The power generation and load power constraint expressions are as follows:

[0015]

[0016]

[0017] Wherein, Υ is the set of all calculation moments in the planning period; It represents the renewable energy power generation of the object to be planned at time t. is the maximum power generation of the new energy source to be planned at time t; The power demand of the object to be planned at time t; is the power consumption of the object to be planned at time t, exist After fluctuating according to the set ratio, δ - , δ + are the corresponding downward and upward fluctuation ratios respectively;

[0018] The energy balance constraint expression is as follows:

[0019]

[0020] in, is the non-new energy power used by the object to be planned at time t; is the power returned to the grid by the object to be planned at time t. If the object to be planned is not allowed to access the grid, They are respectively the charging power and discharging power of the energy storage at time t;

[0021] The energy exchange constraint expression is as follows:

[0022]

[0023] The energy storage operation constraint expression is as follows:

[0024]

[0025]

[0026] in, are binary variables representing the current charging and discharging states of the energy storage at time t; if the energy storage is in the charging state at time t, then If the energy storage is in the discharge state at time t, then If the energy storage is neither charged nor discharged at time t, then are the energy storage charging and discharging state conversion variables at time t; T c 、T dc are the charging time and discharging time required for the energy storage to reach the maximum storage capacity from 0; N is the maximum number of energy storage charge and discharge conversions within the planning period; SOC min , SOC max are the minimum and maximum charge states to ensure safe operation of energy storage, η c , η dc They are the charging efficiency and discharging efficiency of energy storage respectively; SOC0 is the initial charge state of energy storage;

[0027] The green electricity ratio constraint expression is as follows:

[0028]

[0029] Among them, σ req It is the preset green electricity ratio, which is the proportion of renewable energy in the electricity consumption of the planned object.

[0030] In a specific implementation of the present invention, the converting of the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints includes:

[0031] For the energy exchange constraint in equation (6), the binary variable In order to indicate whether the planned object has purchased electricity, the following conversion is adopted:

[0032]

[0033] Among them, M is a positive number, which is greater than the maximum power consumption of the object to be planned;

[0034] For the energy storage operation constraint in equation (7), an auxiliary variable is introduced: And convert it as follows:

[0035]

[0036] Among them, M' is a positive number, which is greater than the maximum capacity required for energy storage.

[0037] The second embodiment of the present invention provides a device for optimally configuring energy storage capacity under green electricity ratio constraints, including:

[0038] An objective function construction module is used to construct an objective function of the energy storage capacity optimal configuration model, wherein the objective function is to minimize the energy storage capacity of the object to be planned;

[0039] A constraint condition construction module is used to construct the constraint conditions of the energy storage capacity optimal configuration model, including: power generation and load power constraints, energy balance constraints, energy exchange constraints, energy storage operation constraints, and green electricity ratio constraints;

[0040] A constraint condition conversion module, used for converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints to obtain an updated optimal configuration model of the energy storage capacity;

[0041] The energy storage configuration module is used to solve the updated optimal configuration model of the energy storage capacity, and the optimal value of the objective function is obtained, which is the optimal result of the energy storage capacity configuration.

[0042] In a specific implementation of the present invention, the objective function expression is as follows:

[0043] min e ES (1)

[0044] Among them, e ES The energy storage capacity to be configured.

[0045] In a specific implementation of the present invention, it also includes:

[0046] The power generation and load power constraint expressions are as follows:

[0047]

[0048] Wherein, Υ is the set of all calculation moments in the planning period; It represents the renewable energy power generation of the object to be planned at time t. is the maximum power generation of the new energy source to be planned at time t; The power demand of the object to be planned at time t; is the power consumption of the object to be planned at time t, exist After fluctuating according to the set ratio, δ - , δ + are the corresponding downward and upward fluctuation ratios respectively;

[0049] The energy balance constraint expression is as follows:

[0050]

[0051] in, is the non-new energy power used by the object to be planned at time t; is the power returned to the grid by the object to be planned at time t. If the object to be planned is not allowed to access the grid, They are respectively the charging power and discharging power of the energy storage at time t;

[0052] The energy exchange constraint expression is as follows:

[0053]

[0054] The energy storage operation constraint expression is as follows:

[0055]

[0056] in, are binary variables representing the current charging and discharging states of the energy storage at time t; if the energy storage is in the charging state at time t, then If the energy storage is in the discharge state at time t, then If the energy storage is neither charged nor discharged at time t, then are the energy storage charging and discharging state conversion variables at time t; T c 、T dc are the charging time and discharging time required for the energy storage to reach the maximum storage capacity from 0; N is the maximum number of energy storage charge and discharge conversions within the planning period; SOC min , SOC max are the minimum and maximum charge states to ensure safe operation of energy storage, η c , η dc They are the charging efficiency and discharging efficiency of energy storage respectively; SOC0 is the initial charge state of energy storage;

[0057] The green electricity ratio constraint expression is as follows:

[0058]

[0059] Among them, σ req It is the preset green electricity ratio, which is the proportion of renewable energy in the electricity consumption of the planned object.

[0060] In a specific implementation of the present invention, the converting of the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints includes:

[0061] For the energy exchange constraint in equation (6), the binary variable In order to indicate whether the planned object has purchased electricity, the following conversion is adopted:

[0062]

[0063] Among them, M is a positive number, which is greater than the maximum power consumption of the object to be planned;

[0064] For the energy storage operation constraint in equation (7), an auxiliary variable is introduced: And convert it as follows:

[0065]

[0066] Among them, M' is a positive number, which is greater than the maximum capacity required for energy storage.

[0067] A third aspect of the present invention provides an electronic device, including:

[0068] at least one processor; and a memory communicatively coupled to the at least one processor;

[0069] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above-mentioned method for optimal configuration of energy storage capacity under the green electricity ratio constraint.

[0070] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned method for optimal configuration of energy storage capacity under the green electricity ratio constraint.

[0071] Features and beneficial effects of the present invention:

[0072] The present invention can combine the energy conservation and carbon reduction needs of the park or users, and provide the optimal configuration plan of energy storage capacity under the preset green electricity ratio. In the present invention, by transforming the constraint conditions, the efficiency of model selection can be accelerated, while saving the equipment investment cost as much as possible, the green electricity ratio requirements of the park or users can be met; the present invention can be used in the expansion planning field of the park or users, while meeting the power demand and improving the protection of the environment, and has a high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 The present invention is an overall flow chart of a method for optimal configuration of energy storage capacity under green electricity ratio constraints according to an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The present invention proposes a method and device for optimal configuration of energy storage capacity under green electricity ratio constraints, which is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0075] The embodiment of the present invention provides a method for optimal configuration of energy storage capacity under green electricity ratio constraints, including:

[0076] Constructing an objective function of an optimal configuration model for energy storage capacity, wherein the objective function is to minimize the energy storage capacity of the object to be planned;

[0077] The constraints for constructing the optimal configuration model of the energy storage capacity include: power generation and load power constraints, energy balance constraints, energy exchange constraints, energy storage operation constraints, and green electricity ratio constraints;

[0078] Converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints to obtain an updated optimal configuration model of the energy storage capacity;

[0079] Solving the updated optimal configuration model of the energy storage capacity and obtaining the optimal value of the objective function is the optimal result of the energy storage capacity configuration.

[0080] In a specific embodiment of the present invention, the method for optimal configuration of energy storage capacity under green electricity ratio constraint has the following overall process: Figure 1 As shown, the following steps are included:

[0081] 1) Construct the objective function of the optimal configuration model of energy storage capacity.

[0082] In this embodiment, the objective function is to minimize the energy storage capacity of the object to be planned (i.e., the park or user):

[0083] min e ES (1)

[0084] Among them, e ES The energy storage capacity to be configured.

[0085] 2) The constraints for constructing the optimal configuration model of energy storage capacity include:

[0086] 2-1) Power generation and load power constraints:

[0087]

[0088] Wherein, Y is a set of all calculation moments in the planning period. In the embodiment of the present invention, the planning period may include historical data of a certain period of time, and may also include future prediction data of a selected period of time; It represents the renewable energy power generation of the object to be planned at time t. is the maximum power generation of the new energy source to be planned at time t, which can be obtained from historical data or prediction; is the power demand of the object to be planned at time t, which can be obtained from historical data or predictions. is the power consumption of the object to be planned at time t, which can be calculated based on the actual industrial production or the power consumption characteristics of the user. There is a certain fluctuation (translation) around it, but its total load remains unchanged, δ - , δ + are the corresponding downward and upward fluctuation ratios, respectively. In a typical embodiment of the present invention, for a certain industrial park, δ - , δ + All are taken as 10%;

[0089] 2-2) Energy balance constraints:

[0090]

[0091] in, The non-renewable energy power used by the planning object at time t, which may be the power purchased from the power grid or generated by its own non-renewable energy generator (such as a diesel generator); is the power returned to the grid by the object to be planned at time t. If the object to be planned is not allowed to access the grid, then They are respectively the charging power and discharging power of the energy storage at time t;

[0092] 2-3) Energy exchange constraints:

[0093]

[0094] In this embodiment, equations (5) and (6) ensure that the object to be planned cannot purchase electricity or use non-new energy for power generation while returning power.

[0095] 2-4) Energy storage operation constraints:

[0096]

[0097] In this embodiment, formula (7) is the charging and discharging power constraint, formula (8) is the physical constraint that the energy storage cannot be charged and discharged at the same time, formula (9) is the energy storage charging and discharging state conversion constraint, formula (10) is the energy storage charging and discharging conversion number constraint, and formula (11) is the energy storage energy constraint;

[0098] in, are binary variables representing the current charging and discharging states of the energy storage at time t; if the energy storage is in the charging state at time t, then If the energy storage is in the discharge state at time t, then If the energy storage is neither charged nor discharged at time t, then are the energy storage charging and discharging state conversion variables at time t; T c 、T dc are the charging time and discharging time required for the energy storage to reach the maximum storage capacity from 0; N is the maximum number of energy storage charge and discharge conversions within the planning period; SOC min , SOC maxare the minimum and maximum charge states to ensure safe operation of energy storage, η c , η dc They are respectively the charging efficiency and discharging efficiency of energy storage; the above parameters can be obtained from the energy storage manual.

[0099] SOC0 is the initial state of charge of the energy storage. When historical data is selected for the planning period, this value can be selected based on the historical operating value; when predicted data is selected for the planning period, this value can be selected from 0 to 1. The longer the planning period, the smaller the impact of this value on the planning result. In a specific embodiment of the present invention, this value is selected as 0.5.

[0100] 2-5) Green electricity ratio constraints:

[0101]

[0102] Among them, σ req is the preset green electricity ratio, which is defined as the proportion of renewable energy in the electricity consumption of the planned object. Formula (12) can ensure that the green electricity ratio meets the requirements by constraining non-renewable energy electricity to be less than the total electricity consumption.

[0103] 3) Transform the constraints of step 2).

[0104] In this embodiment, equations (6) and (7) contain bilinear constraints, which need to be converted into mixed integer linear constraints.

[0105] Among them, for the bilinear constraint formed by the product of continuous variables in formula (6), the binary variable Its meaning is to indicate whether the object to be planned has purchased electricity, and it is converted in the following way:

[0106]

[0107] Wherein, M is a large positive number, which must be greater than the maximum power consumption of the object to be planned; in a specific embodiment of the present invention, M is taken as 10MW;

[0108] For the bilinear constraint formed by the product of binary variables and continuous variables in formula (7), an auxiliary variable is introduced And convert it as follows:

[0109]

[0110] Wherein, M' is a large positive number, which needs to be larger than the maximum capacity that may be required for energy storage; in a specific embodiment of the present invention, M' is taken as 100 MWh.

[0111] After the above constraints are transformed, the updated optimal configuration model of energy storage capacity is obtained.

[0112] 4) Use the branch and bound method to solve the optimal configuration model of energy storage capacity composed of equations (1)-(5) and (8)-(14). The model is a mixed integer linear stochastic programming model. The optimal value of the objective function obtained by solving the solution is the optimal energy storage capacity obtained by the configuration.

[0113] To implement the above embodiment, a second embodiment of the present invention proposes a device for optimal configuration of energy storage capacity under green electricity ratio constraints, including:

[0114] An objective function construction module is used to construct an objective function of the energy storage capacity optimal configuration model, wherein the objective function is to minimize the energy storage capacity of the object to be planned;

[0115] A constraint condition construction module is used to construct the constraint conditions of the energy storage capacity optimal configuration model, including: power generation and load power constraints, energy balance constraints, energy exchange constraints, energy storage operation constraints, and green electricity ratio constraints;

[0116] A constraint condition conversion module, used for converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints to obtain an updated optimal configuration model of the energy storage capacity;

[0117] The energy storage configuration module is used to solve the updated optimal configuration model of the energy storage capacity, and the optimal value of the objective function is obtained, which is the optimal result of the energy storage capacity configuration.

[0118] In a specific embodiment of the present invention, the objective function expression is as follows:

[0119] min e ES (1)

[0120] Among them, e ES The energy storage capacity to be configured.

[0121] In a specific embodiment of the present invention, it also includes:

[0122] The power generation and load power constraint expressions are as follows:

[0123]

[0124]

[0125] Wherein, Υ is the set of all calculation moments in the planning period; It represents the renewable energy power generation of the object to be planned at time t. is the maximum power generation of the new energy source to be planned at time t; The power demand of the object to be planned at time t; is the power consumption of the object to be planned at time t, exist After fluctuating according to the set ratio, δ - , δ + are the corresponding downward and upward fluctuation ratios respectively;

[0126] The energy balance constraint expression is as follows:

[0127]

[0128] in, is the non-new energy power used by the object to be planned at time t; is the power returned to the grid by the object to be planned at time t. If the object to be planned is not allowed to access the grid, They are respectively the charging power and discharging power of the energy storage at time t;

[0129] The energy exchange constraint expression is as follows:

[0130]

[0131] The energy storage operation constraint expression is as follows:

[0132]

[0133] in, are binary variables representing the current charging and discharging states of the energy storage at time t; if the energy storage is in the charging state at time t, then If the energy storage is in the discharge state at time t, then If the energy storage is neither charged nor discharged at time t, then are the energy storage charging and discharging state conversion variables at time t; T c 、T dc are the charging time and discharging time required for the energy storage to reach the maximum storage capacity from 0; N is the maximum number of energy storage charge and discharge conversions within the planning period; SOC min , SOC max are the minimum and maximum charge states to ensure safe operation of energy storage, η c , η dc They are the charging efficiency and discharging efficiency of energy storage respectively; SOC0 is the initial charge state of energy storage;

[0134] The green electricity ratio constraint expression is as follows:

[0135]

[0136] Among them, σ req It is the preset green electricity ratio, which is the proportion of renewable energy in the electricity consumption of the planned object.

[0137] In a specific embodiment of the present invention, converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints includes:

[0138] For the energy exchange constraint in equation (6), the binary variable In order to indicate whether the planned object has purchased electricity, the following conversion is adopted:

[0139]

[0140] Among them, M is a positive number, which is greater than the maximum power consumption of the object to be planned;

[0141] For the energy storage operation constraint in equation (7), an auxiliary variable is introduced: And convert it as follows:

[0142]

[0143] Among them, M' is a positive number, which is greater than the maximum capacity required for energy storage.

[0144] This can achieve the matching of new energy power generation and load timing when industrial parks are equipped with certain distributed photovoltaics and distributed wind turbines, while increasing the proportion of green electricity consumption in parks or users equipped with distributed new energy power generation, thereby improving environmental protection while meeting electricity demand.

[0145] To implement the above embodiment, a third aspect of the present invention provides an electronic device, including:

[0146] at least one processor; and a memory communicatively coupled to the at least one processor;

[0147] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the above-mentioned method for optimal configuration of energy storage capacity under the green electricity ratio constraint.

[0148] To implement the above embodiment, the fourth aspect of the present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above method for optimal configuration of energy storage capacity under the green electricity ratio constraint.

[0149] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0150] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method for optimal configuration of energy storage capacity under green electricity ratio constraints of the above embodiment.

[0151] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 through 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., through the Internet using an Internet service provider).

[0152] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0153] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0154] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0155] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0156] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0157] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0158] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0159] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimal configuration of energy storage capacity under green electricity ratio constraints, characterized in that: include: Constructing an objective function of an optimal configuration model for energy storage capacity, wherein the objective function is to minimize the energy storage capacity of the object to be planned; The constraints for constructing the optimal configuration model of the energy storage capacity include: power generation and load power constraints, energy balance constraints, energy exchange constraints, energy storage operation constraints, and green electricity ratio constraints; Converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints to obtain an updated optimal configuration model of the energy storage capacity; Solving the updated optimal configuration model of the energy storage capacity and obtaining the optimal value of the objective function is the optimal result of the energy storage capacity configuration.

2. The method according to claim 1, characterized in that The objective function expression is as follows: my email ES (1) Among them, e ES The energy storage capacity to be configured.

3. The method according to claim 2, characterized in that Also includes: The power generation and load power constraint expressions are as follows: Wherein, Υ is the set of all calculation moments in the planning period; It represents the renewable energy power generation of the object to be planned at time t. is the maximum power generation of the new energy source to be planned at time t; The power demand of the object to be planned at time t; is the power consumption of the object to be planned at time t, exist After fluctuating according to the set ratio, δ - ,δ + are the corresponding downward and upward fluctuation ratios respectively; The energy balance constraint expression is as follows: in, is the non-new energy power used by the object to be planned at time t; is the power returned to the grid by the object to be planned at time t. If the object to be planned is not allowed to access the grid, They are respectively the charging power and discharging power of the energy storage at time t; The energy exchange constraint expression is as follows: The energy storage operation constraint expression is as follows: in, are binary variables representing the current charging and discharging states of the energy storage at time t; if the energy storage is in the charging state at time t, then If the energy storage is in the discharge state at time t, then If the energy storage is neither charged nor discharged at time t, then are the energy storage charging and discharging state conversion variables at time t; T c , T dc are the charging time and discharging time required for the energy storage to reach the maximum storage capacity from 0; N is the maximum number of energy storage charge and discharge conversions within the planning period; SOC min , SOC max are the minimum and maximum charge states to ensure safe operation of energy storage, η c , η dc They are the charging efficiency and discharging efficiency of energy storage respectively; SOC0 is the initial charge state of energy storage; The green electricity ratio constraint expression is as follows: Among them, σ req It is the preset green electricity ratio, which is the proportion of renewable energy in the electricity consumption of the planned object.

4. The method according to claim 3, characterized in that The converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints includes: For the energy exchange constraint in equation (6), the binary variable In order to indicate whether the planned object has purchased electricity, the following conversion is adopted: Among them, M is a positive number, which is greater than the maximum power consumption of the object to be planned; For the energy storage operation constraint in equation (7), an auxiliary variable is introduced: And convert it as follows: Among them, M' is a positive number, which is greater than the maximum capacity required for energy storage.

5. A device for optimal configuration of energy storage capacity under green electricity ratio constraints, characterized in that: include: An objective function construction module is used to construct an objective function of the energy storage capacity optimal configuration model, wherein the objective function is to minimize the energy storage capacity of the object to be planned; A constraint condition construction module is used to construct the constraint conditions of the energy storage capacity optimal configuration model, including: power generation and load power constraints, energy balance constraints, energy exchange constraints, energy storage operation constraints, and green electricity ratio constraints; A constraint condition conversion module, used for converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints to obtain an updated optimal configuration model of the energy storage capacity; The energy storage configuration module is used to solve the updated optimal configuration model of the energy storage capacity, and the optimal value of the objective function is obtained, which is the optimal result of the energy storage capacity configuration.

6. The device according to claim 5, characterized in that The objective function expression is as follows: my email ES (1) Among them, e ES The energy storage capacity to be configured.

7. The device according to claim 6, characterized in that Also includes: The power generation and load power constraint expressions are as follows: Wherein, Υ is the set of all calculation moments in the planning period; It represents the renewable energy power generation of the object to be planned at time t. is the maximum power generation of the new energy source to be planned at time t; The power demand of the object to be planned at time t; is the power consumption of the object to be planned at time t, exist After fluctuating according to the set ratio, δ - ,δ + are the corresponding downward and upward fluctuation ratios respectively; The energy balance constraint expression is as follows: in, is the non-new energy power used by the object to be planned at time t; is the power returned to the grid by the object to be planned at time t. If the object to be planned is not allowed to access the grid, They are respectively the charging power and discharging power of the energy storage at time t; The energy exchange constraint expression is as follows: The energy storage operation constraint expression is as follows: in, are binary variables representing the current charging and discharging states of the energy storage at time t; if the energy storage is in the charging state at time t, then If the energy storage is in the discharge state at time t, then If the energy storage is neither charged nor discharged at time t, then are the energy storage charging and discharging state conversion variables at time t; T c , T dc are the charging time and discharging time required for the energy storage to reach the maximum storage capacity from 0; N is the maximum number of energy storage charge and discharge conversions within the planning period; SOC min , SOC max are the minimum and maximum charge states to ensure safe operation of energy storage, η c , η dc They are the charging efficiency and discharging efficiency of energy storage respectively; SOC0 is the initial charge state of energy storage; The green electricity ratio constraint expression is as follows: Among them, σ req It is the preset green electricity ratio, which is the proportion of renewable energy in the electricity consumption of the planned object.

8. The device according to claim 7, characterized in that The converting the bilinear constraints in the energy exchange constraint and the energy storage operation constraint into mixed integer linear constraints includes: For the energy exchange constraint in equation (6), the binary variable In order to indicate whether the planned object has purchased electricity, the following conversion is adopted: Among them, M is a positive number, which is greater than the maximum power consumption of the object to be planned; For the energy storage operation constraint in equation (7), an auxiliary variable is introduced: And convert it as follows: Among them, M' is a positive number, which is greater than the maximum capacity required for energy storage.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 47.

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