Automatic decision-making method and device for energy storage unit

Abstract

The invention relates to the technical field of energy storage, in particular to an automatic decision-making method and device for an energy storage unit, and the method comprises the steps: obtaining the operation parameters of the energy storage unit and the power price data in a decision-making period; according to the operation parameters of the energy storage unit and the power price data, constructing a target function of an automatic decision-making model of the quantity-reporting non-quotation energy storage unit with maximum profit as a target; setting a constraint condition of the target function according to the actual physical condition of the energy storage unit and the market operation rule; and generating a series of optimal operation instructions in the decision-making period by using the automatic decision-making model of the quantity-reporting non-quoting energy storage unit, and forming an automatic transaction decision-making scheme. The scheme combines power market price fluctuation, energy storage system characteristics and market rules to construct an energy storage unit automatic decision-making model, the model is utilized to automatically generate a transaction decision-making scheme, and market changes are quickly responded.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, specifically to an automatic decision-making method and device for energy storage units. Background Technology

[0002] Currently, the energy storage electricity trading market is continuously developing. Energy storage systems have become an important tool for grid load balancing. Energy storage systems play a key role in peak shaving and valley filling, ensuring grid stability, and improving the absorption of renewable energy. With the deepening of electricity market trading, the optimized dispatch of energy storage based on the electricity spot market has become a core issue in the operation of energy storage systems.

[0003] Currently, the decision-making process for energy storage power charging and discharging relies on power traders to formulate charging and discharging plans for future unit operation, and there is no automated method to achieve automatic decision-making. Therefore, existing technologies face many problems in practical applications: (1) Reliance on the manual experience of power traders: Based on price scenarios, the charging and discharging plans of energy units are formulated manually. Manual decision-making relies on personal experience and judgment, and different traders may arrive at different dispatch schemes for the same price scenario, making it difficult to form a unified optimization standard. (2) Insufficient analytical capabilities: Manual dispatch usually only considers the single dimension of the current market price, making it difficult to comprehensively consider the technical constraints and economic objectives of the energy storage system, thus failing to achieve the global optimality of system revenue. (3) Difficulty in coping with complex market environments: With the deepening of electricity market trading, market rules are becoming increasingly complex, and the frequency and amplitude of price fluctuations are increasing. Traditional dispatch methods that rely on manual experience are difficult to cope with complex market environments and are prone to economic losses. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an automatic decision-making method and device for energy storage units, so as to solve the problem that the existing technology, which relies on power traders to formulate charging and discharging plans for future unit operation, is difficult to cope with complex market environments.

[0005] According to a first aspect of the present invention, an automatic decision-making method for energy storage units is provided, comprising:

[0006] Obtain operating parameters of energy storage units and electricity price data during the decision-making cycle;

[0007] Based on the operating parameters of the energy storage unit and the electricity price data, an objective function is constructed for an automatic decision-making model of energy storage unit that reports quantity but does not quote prices, with the goal of maximizing profits.

[0008] The constraints of the objective function are set according to the actual physical conditions of the energy storage unit and the market operation rules.

[0009] Using the aforementioned automatic decision-making model for energy storage units that report quantity but do not quote prices, a series of optimal operation instructions are generated within the decision-making cycle to form an automated trading decision scheme.

[0010] Preferably, the automatic decision-making method for energy storage units further includes:

[0011] The decision-making process for quantity and price quotation includes:

[0012] Set multiple tiers of charging and discharging prices; the highest charging price is lower than the lowest discharging price.

[0013] For each charging price level, set the corresponding charging power; for each discharging price level, set the corresponding discharging power.

[0014] Compare charging and discharging prices with current market prices;

[0015] If a charging price is higher than the current market price, then the charging power corresponding to the lowest charging price above the current market price will be selected for charging.

[0016] If the discharge price is lower than the current market price, then the highest discharge price below the current market price will be selected for the highest charging power.

[0017] Preferably, the objective function of the automatic decision-making model for energy storage units that reports volume but does not quote prices, with the goal of maximizing profitability, is as follows:

[0018]

[0019] Where R is the total gross profit of the energy storage unit; T is the total number at any given time point; It is the discharge amount of the unit in time period t. Pr is the amount of charge generated by the unit in time period t; + It is the maximum charging power; Pr - It is the maximum discharge power; p t It is the electricity price in period t; a t b is a 0-1 variable indicating whether or not discharge occurs in period t; t It is a 0-1 variable indicating whether charging occurs in period t.

[0020] Preferably, the constraint conditions for setting the objective function based on the actual physical conditions of the energy storage unit and market operation rules include:

[0021] Based on the losses incurred during the charging and discharging of energy storage units, a dynamic constraint on the remaining capacity of energy storage units is constructed.

[0022] Based on market operating rules, establish constraints on the number of charge / discharge cycles;

[0023] Based on the premise that energy storage units cannot be charged and discharged simultaneously, a realistic constraint is established.

[0024] Preferably, the dynamic constraint of the remaining capacity of the energy storage unit is given by the following formula:

[0025]

[0026] Among them, e t-1 It is the remaining capacity of the unit at time t-1; e t α1 is the remaining capacity of the unit at time t; α2 is the charging efficiency of the energy storage unit; α3 is the discharging efficiency of the energy storage unit.

[0027] Preferably, the limit on the number of charge / discharge cycles is defined by the following formula:

[0028]

[0029] Where U is the unit capacity, W + It is the maximum number of charging cycles, W. - It is the minimum number of discharges.

[0030] Preferably, the reality constraint is defined by the following formula:

[0031] a t +b t ≤1

[0032] e t ≤U

[0033] This indicates that the energy storage unit cannot be charged and discharged simultaneously, and the remaining capacity is less than or equal to the rated capacity.

[0034] The preferred automatic decision-making model for energy storage units that report quantity but not price is expressed by the following formula:

[0035]

[0036] Among them, C t This represents the remaining power of the unit in period t.

[0037] Preferably, the automatic decision-making method for energy storage units further includes:

[0038] Based on the automatic decision-making model for energy storage units that report quantity but not price, and combining it with the decision-making scheme of reporting quantity and price, an automatic decision-making model for energy storage units that reports quantity and prices is constructed, and the formula is expressed as follows:

[0039]

[0040] in, This indicates that the charging trigger state 0-1 is triggered in the i-th segment of period t; This indicates the discharge trigger 0-1 state of segment i in period t; This represents the charging price of the i-th layer; This represents the discharge price of the i-th layer; This represents the charging power corresponding to the charging price of the i-th layer; x i - This represents the discharge power corresponding to the discharge price of the i-th layer.

[0041] According to a second aspect of the present invention, an automatic decision-making device for an energy storage unit is provided, comprising:

[0042] The main controller and the memory connected to the main controller;

[0043] The memory stores program instructions;

[0044] The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0045] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0046] It is understood that the technical solution presented in this invention can acquire the operating parameters of the energy storage unit and the electricity price data within the decision-making period; based on the operating parameters of the energy storage unit and the electricity price data, it constructs an objective function for an automatic decision-making model of the energy storage unit that reports quantity but does not quote prices, with the goal of maximizing profitability; it sets constraints on the objective function based on the actual physical conditions of the energy storage unit and market operating rules; and it uses the automatic decision-making model of the energy storage unit that reports quantity but does not quote prices to generate a series of optimal operation instructions within the decision-making period, forming an automated trading decision scheme. This solution combines electricity market price fluctuations, energy storage system characteristics, and market rules to construct an automatic decision-making model for the energy storage unit, and uses the model to automatically generate trading decision schemes, quickly responding to market changes.

[0047] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0049] Figure 1 This is a schematic diagram illustrating the steps of an automatic decision-making method for an energy storage unit according to an exemplary embodiment. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0051] In one embodiment, Figure 1 This is a schematic diagram illustrating the steps of an automatic decision-making method for an energy storage unit according to an exemplary embodiment. See also... Figure 1 An automatic decision-making method for energy storage units is provided, including:

[0052] Step S11: Obtain the operating parameters of the energy storage unit and the electricity price data within the decision-making period.

[0053] Step S12: Based on the operating parameters of the energy storage unit and the electricity price data, construct the objective function of the automatic decision-making model for energy storage units that quote quantities but do not quote prices, with the goal of maximizing profits.

[0054] Step S13: Set the constraints of the objective function based on the actual physical conditions of the energy storage unit and the market operation rules.

[0055] Step S14: Using the automatic decision-making model for energy storage units that report quantity but do not quote prices, generate a series of optimal operation instructions within the decision cycle to form an automated transaction decision scheme.

[0056] The technical solution presented in this invention first obtains electricity price data within the decision-making period, enabling it to track electricity spot prices and dynamically adjust energy storage charging and discharging decisions based on the principle of "buying low and selling high," thus responding quickly to market changes.

[0057] During execution, the constructed automatic decision-making model for the energy storage unit incorporates multiple constraints, aiming to maximize profits while simultaneously constraining the energy storage system's capacity, charge / discharge cycles, efficiency, and power to ensure safe equipment operation. The constraints are designed in conjunction with spot market trading rules, enabling dynamic adjustments to the scheduling strategy to ensure that the optimization results meet market requirements.

[0058] When solving the automatic decision-making model of energy storage units, mixed integer linear programming (MILP) is used, and the optimal solution is obtained by combining an advanced solver. The performance of the scheme is improved through iterative optimization.

[0059] In summary, this technical solution establishes a scientific and efficient optimized dispatch scheme by combining electricity market price fluctuations, energy storage system characteristics, and market rules. This technology not only significantly improves the economic benefits of energy storage systems but also ensures the safety and reliability of equipment operation, providing theoretical and technical support for the large-scale application of energy storage systems in the future.

[0060] In practical application, the technical solution illustrated in this embodiment first constructs a strategy model for independent energy storage power stations based on a quantity-based, non-price-based approach. The profitability calculation method for independent energy storage is to subtract the charging cost from all discharge revenue, with maximizing profitability as the optimization objective of the model.

[0061] The objective function of the automatic decision-making model for energy storage units that reports volume but does not quote prices, with the goal of maximizing profitability, is as follows:

[0062]

[0063] Where R is the total gross profit of the energy storage unit; T is the total number of time points (taking Shanxi as an example, a day is divided into 96 time points with 15-minute intervals, then T = 96); It is the discharge amount of the unit in time period t. Pr is the amount of charge generated by the unit in time period t; + It is the maximum charging power; Pr - It is the maximum discharge power; p t It is the electricity price in period t; a t b is a 0-1 variable indicating whether or not discharge occurs in period t; t It is a 0-1 variable indicating whether charging occurs in period t.

[0064] Understandably, using 0-1 variables to represent the charging and discharging state avoids conflicting operations, limits the number of state transitions, and optimizes operational stability.

[0065] It should be noted that the constraints on setting the objective function based on the actual physical conditions of the energy storage unit and market operation rules include:

[0066] Based on the losses incurred during the charging and discharging of energy storage units, a dynamic constraint on the remaining capacity of the energy storage units is constructed.

[0067] In the objective function, losses during the charging and discharging of the unit are considered, i.e., the charging amount of the unit is α1Pr. + b t This indicates the actual amount of electricity purchased from the grid. The actual discharge of the generating unit is... This indicates the actual amount of electricity that reaches the power generation side.

[0068] Therefore, the dynamic constraint on the remaining capacity of the energy storage unit is given by the following formula:

[0069]

[0070] Among them, e t-1 It is the remaining capacity of the unit at time t-1; e t α1 is the remaining capacity of the unit at time t; α2 is the charging efficiency of the energy storage unit; α3 is the discharging efficiency of the energy storage unit.

[0071] According to market operating rules, independent energy storage power stations have a limit on the number of charge and discharge cycles, so it is necessary to construct a constraint on the number of charge and discharge cycles.

[0072] The limit on the number of charge / discharge cycles is defined by the following formula:

[0073]

[0074] Where U is the unit capacity, W + It is the maximum number of charging cycles, W. - It is the minimum number of discharges.

[0075] The model also needs to consider real-world constraints, including the inability to charge and discharge simultaneously, and the requirement that the remaining capacity must be less than or equal to the rated capacity to avoid overcharging.

[0076] Therefore, based on the fact that energy storage units cannot charge and discharge simultaneously, a realistic constraint is established, as shown in the following formula:

[0077] a t +b t ≤1

[0078] e t ≤U

[0079] This indicates that the energy storage unit cannot be charged and discharged simultaneously, and the remaining capacity is less than or equal to the rated capacity.

[0080] In summary, the final automatic decision-making model for independent energy storage units that report capacity but do not quote prices is expressed by the following formula:

[0081]

[0082] Among them, C t This represents the remaining power of the unit in period t.

[0083] In another embodiment, it is also possible to construct an independent energy storage power station strategy model based on reported quantity and price.

[0084] The decision-making method of reporting quantity and price is to set reasonable charging and discharging prices and power, and to position the unit in a given price scenario. When the market price is lower than the charging price, the unit will charge; when the market price is higher than the discharging price, the unit will discharge.

[0085] It should be noted that the decision-making process for reporting quantities and providing quotes includes:

[0086] The decision-making process for quantity and price quotation includes:

[0087] Set multiple tiers of charging and discharging prices; the highest charging price is lower than the lowest discharging price.

[0088] For each charging price level, set the corresponding charging power; for each discharging price level, set the corresponding discharging power.

[0089] Compare charging and discharging prices with current market prices;

[0090] If a charging price is higher than the current market price, then the charging power corresponding to the lowest charging price above the current market price will be selected for charging.

[0091] If the discharge price is lower than the current market price, then the highest discharge price below the current market price will be selected for the highest charging power.

[0092] It is understood that the technical solution shown in this embodiment, which is a decision-making scheme for reporting quantity and price, prioritizes the most economical charging and discharging operation by setting multiple price and power levels, thereby ensuring the rationality of scheduling.

[0093] In practical application, in order to enable the model to accurately trigger predetermined quotations and outputs based on the relationship between the quoted price and the market price, an automatic decision-making model for energy storage units that supports quotations and outputs in any m segments is proposed, based on the quantity-only model.

[0094] First, assume that the charging price and the discharging price satisfy the following relationship:

[0095]

[0096] arrive These are all charging prices. arrive All prices are for discharging, and the prices for charging and discharging are listed in descending order.

[0097] The charging power requirements for each stage are arranged in ascending order from smallest to largest, and the discharging power in descending order from largest to smallest, as follows:

[0098]

[0099] Set a relationship between charging and discharging prices, meaning the maximum charging price must be less than the minimum discharging price:

[0100]

[0101] If a charging price is higher than the current market price, then the charging power corresponding to the lowest charging price above the current market price will be selected for charging.

[0102] If the discharge price is lower than the current market price, then the highest discharge price below the current market price will be selected for the highest charging power.

[0103] In practical applications, for example, we now set multiple charging and discharging prices: charging prices: 0.2, 0.3, 0.4, 0.5, and discharging prices: 0.7, 0.8, 0.9, 1.0.

[0104] Assuming the current market price is 0.35, and there are charging prices higher than the current market price, namely 0.4 and 0.5, then choose the charging power corresponding to the charging price of 0.4 for charging.

[0105] Assuming the current market price is 0.85, and there are discharge prices lower than the current market price, namely 0.7 and 0.8, then the discharge power corresponding to the discharge price of 0.8 is selected for discharge.

[0106] The formula derived from the above process is as follows:

[0107] and

[0108] and

[0109] in, This indicates that the charging trigger state 0-1 is triggered in the i-th segment of period t; This indicates the discharge trigger 0-1 state of segment i in period t; This represents the charging price of the i-th layer; This represents the discharge price of the i-th layer.

[0110] It should be noted that the aforementioned automatic decision-making method for energy storage units also includes:

[0111] Based on the automatic decision-making model for energy storage units that report quantity but not price, and combining it with the decision-making scheme of reporting quantity and price, an automatic decision-making model for energy storage units that reports quantity and prices is constructed, and the formula is expressed as follows:

[0112]

[0113] in, This represents the charging power corresponding to the charging price of the i-th layer; This represents the discharge power corresponding to the discharge price of the i-th layer.

[0114] In another embodiment, an automatic decision-making device for an energy storage unit is provided, comprising:

[0115] The main controller and the memory connected to the main controller;

[0116] The memory stores program instructions;

[0117] The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0118] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0119] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0120] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0121] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0122] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0124] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0125] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0126] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An automatic decision-making method for energy storage units, characterized in that, include: Obtain operating parameters of energy storage units and electricity price data during the decision-making cycle; Based on the operating parameters of the energy storage unit and the electricity price data, an objective function is constructed for an automatic decision-making model of energy storage unit that reports quantity but does not quote prices, with the goal of maximizing profits. The constraints of the objective function are set according to the actual physical conditions of the energy storage unit and the market operation rules. Using the aforementioned automatic decision-making model for energy storage units that report quantity but do not quote prices, a series of optimal operation instructions are generated within the decision-making cycle to form an automated trading decision scheme.

2. The automatic decision-making method for energy storage units according to claim 1, characterized in that, Also includes: The decision-making process for quantity and price quotation includes: Set multiple tiers of charging and discharging prices; the highest charging price is lower than the lowest discharging price. For each charging price level, set the corresponding charging power; for each discharging price level, set the corresponding discharging power. Compare charging and discharging prices with current market prices; If a charging price is higher than the current market price, then the charging power corresponding to the lowest charging price above the current market price will be selected for charging. If the discharge price is lower than the current market price, then the highest discharge price below the current market price will be selected for the highest charging power.

3. The automatic decision-making method for energy storage units according to claim 2, characterized in that, The objective function of the automatic decision-making model for energy storage units that reports volume but does not quote prices, with the goal of maximizing profitability, is as follows: Where R is the total gross profit of the energy storage unit; T is the total number at any given time point; It is the discharge amount of the unit in time period t. Pr is the amount of charge generated by the unit in time period t; + It is the maximum charging power; Pr - It is the maximum discharge power; p t It is the electricity price in period t; a t b is a 0-1 variable indicating whether or not discharge occurs in period t; t It is a 0-1 variable indicating whether charging occurs in period t.

4. The automatic decision-making method for energy storage units according to claim 3, characterized in that, The constraints for setting the objective function based on the actual physical conditions of the energy storage unit and market operation rules include: Based on the losses incurred during the charging and discharging of energy storage units, a dynamic constraint on the remaining capacity of energy storage units is constructed. Based on market operating rules, establish constraints on the number of charge / discharge cycles; Based on the premise that energy storage units cannot be charged and discharged simultaneously, a realistic constraint is established.

5. The automatic decision-making method for energy storage units according to claim 4, characterized in that, The dynamic constraint on the remaining capacity of the energy storage unit is given by the following formula: Among them, e t-1 It is the remaining capacity of the unit at time t-1; e t α1 is the remaining capacity of the unit at time t; α2 is the charging efficiency of the energy storage unit; α3 is the discharging efficiency of the energy storage unit.

6. The automatic decision-making method for energy storage units according to claim 5, characterized in that, The limit on the number of charge / discharge cycles is defined by the following formula: Where U is the unit capacity, W + It is the maximum number of charging cycles, W. - It is the minimum number of discharges.

7. The automatic decision-making method for energy storage units according to claim 6, characterized in that, The reality constraint is defined by the following formula: a t +b t ≤1 and t ≤U This indicates that the energy storage unit cannot be charged and discharged simultaneously, and the remaining capacity is less than or equal to the rated capacity.

8. The automatic decision-making method for energy storage units according to claim 7, characterized in that, The automatic decision-making model for energy storage units that report quantity but not price is expressed by the following formula: Among them, C t This represents the remaining power of the unit in period t.

9. The automatic decision-making method for energy storage units according to claim 8, characterized in that, Also includes: Based on the automatic decision-making model for energy storage units that report quantity but not price, and combining it with the decision-making scheme of reporting quantity and price, an automatic decision-making model for energy storage units that reports quantity and prices is constructed, and the formula is expressed as follows: in, This indicates that the charging trigger state 0-1 is triggered in the i-th segment of period t; This indicates the discharge trigger 0-1 state of segment i in period t; This represents the charging price of the i-th layer; This represents the discharge price of the i-th layer; This represents the charging power corresponding to the charging price of the i-th layer; This represents the discharge power corresponding to the discharge price of the i-th layer.

10. An automatic decision-making device for an energy storage unit, characterized in that, include: The main controller and the memory connected to the main controller; The memory stores program instructions; The master controller is used to execute program instructions stored in the memory to perform the method as described in any one of claims 1 to 9.

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