Optimization method and device for double-layer power unit output scheduling based on priority equilibrium

Abstract

The application relates to the technical field of scheduling, in particular to a double-layer power unit output scheduling optimization method and device based on optimal sequence balancing. The method comprises the following steps: constructing a power supplier strategic output model based on power generation data and power generation strategies of the power supplier; constructing a power demand model of a sales supplier based on power consumption data and power consumption strategies of the sales supplier; and constructing a double-layer model of unit scheduling optimization based on the power supplier strategic output model and the power demand model, so as to determine the output plan of the power supplier through the double-layer model of unit scheduling optimization. In this way, the power generation capacity is matched with the market demand by optimizing the power unit output, and the waste and loss of energy are reduced.

Description

Technical Field

[0001] This application relates to the field of dispatching-related technologies, specifically to a two-level power unit output dispatching optimization method and device based on priority equilibrium. Background Technology

[0002] In a power market environment, the uncertainty of demand behavior by both power suppliers and consumers influences their decision-making. Furthermore, the lack of mutual information between power generators and consumers in a market environment makes power balancing extremely difficult.

[0003] This approach can easily lead to insufficient power generation resulting in energy shortages, or excessive power generation resulting in energy waste. Summary of the Invention

[0004] In view of this, the embodiments of this application are committed to providing a method and apparatus for optimizing the output scheduling of two-layer power generating units based on priority balancing.

[0005] This application provides a two-level power unit output scheduling optimization method based on priority equilibrium, including: Based on the power generation data and strategies of power generators, a strategic output model for power generators is constructed. Based on the electricity consumption data and strategies of the retailers, a power demand model for the retailers is constructed. Based on the power generator's strategic output model and the power demand model, a two-layer model for unit scheduling optimization is constructed to determine the power generator's output plan.

[0006] In some embodiments, the generator's strategic output model includes: Energy production cost of each generator set The formula is as follows: (1) (2) in, It is the output of the generator set; as well as It is the cost coefficient parameter of generator unit k in a power generation enterprise; It is the total marginal operating cost of generator k, of which As a fixed cost consisting of insurance and administrative fees; , as well as It is a variable cost consisting of employee, processing materials, and maintenance costs; The pricing formula for power generators is as follows: (3) in, It is an energy bid price that takes into account all operating costs. It is the output of the generator set. and It is a strategic pricing coefficient; Each power generation company uses the following formula to calculate revenue and profit: (4) in, This is the profit belonging to generator unit k of the power generation company. That is the market price.

[0007] In some embodiments, the electricity demand model uses the following formula: (5) in, It is a demand function; It is demand; and It is the true benefit coefficient; These are the coefficients of the demand function; The consumer's quote is (6) in, It is the strategic price variable that each consumer J submits to the market; and It is the seller's strategic pricing coefficient; The revenue of each consumer firm is a function of electricity demand and transaction prices, as shown below. (7).

[0008] In some embodiments, the two-layer model for unit scheduling optimization uses a transaction order equilibrium model to determine energy supply and demand, and employs the Lagrange relaxation method to solve the priority equilibrium problem, thereby achieving unit output optimization.

[0009] In some embodiments, the formula in the two-layer model for unit scheduling optimization is as follows: (8) Where J and K represent the number of electricity consumers and the number of electricity supply units; (9) In the formula, It is a Lagrange function; They are Lagrange multipliers; The following is the solution for the Lagrange multiplier: (10) (11) (12) (13) Both generator sets and electricity demanders have become decision-makers in the scheduling of generator output. In the two-layer model of unit scheduling optimization, in the upper layer model, generators and distributors optimize their output and demand decisions based on their own strategies and market information, with the goal of maximizing their own profits. The formulas for the lower-level model include: (15) This application provides a two-layer power unit output scheduling optimization device based on priority equilibrium, comprising: The first building module is used to construct a strategic output model for power generators based on their power generation data and strategies. The second building module is used to build the electricity demand model of the retailer based on the retailer's electricity consumption data and electricity consumption strategy; The optimization module is used to construct a two-layer model for unit scheduling optimization based on the power generator's strategic output model and the power demand model, so as to determine the power generator's output plan through the two-layer model for unit scheduling optimization.

[0010] In some embodiments, the generator's strategic output model includes: Energy production cost of each generator set The formula is as follows: (1) (2) in, It is the output of the generator set; as well as It is the cost coefficient parameter of generator unit k in a power generation enterprise; It is the total marginal operating cost of generator k, of which As a fixed cost consisting of insurance and administrative fees; , as well as It is a variable cost consisting of employee, processing materials, and maintenance costs; The pricing formula for power generators is as follows: (3) in, It is an energy bid price that takes into account all operating costs. It is the output of the generator set. and It is a strategic pricing coefficient; Each power generation company uses the following formula to calculate revenue and profit: (4) in, This is the profit belonging to generator unit k of the power generation company. That is the market price.

[0011] In some embodiments, the electricity demand model uses the following formula: (5) in, It is a demand function; It is demand; and It is the true benefit coefficient; These are the coefficients of the demand function; The consumer's quote is (6) in, It is the strategic price variable that each consumer J submits to the market; and It is the seller's strategic pricing coefficient; The revenue of each consumer firm is a function of electricity demand and transaction prices, as shown below. (7).

[0012] This application provides an electronic device, including: A processor, and a memory for storing a processor-executable program; The processor is used to implement the above-described optimization method for the output scheduling of two-level power generating units based on priority balancing by running the program in the memory.

[0013] This application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the above-described optimization method for the output scheduling of two-level power generating units based on priority balancing.

[0014] This application provides a two-tiered power unit output scheduling optimization method based on priority equilibrium. First, a strategic output model for power generators is constructed based on their generation data and strategies. Then, a power demand model for retailers is constructed based on their electricity consumption data and strategies. Finally, a two-tiered model for unit scheduling optimization is built based on both the power generator's strategic output model and the power demand model. This model is used to determine the power generator's output plan. This setup optimizes power unit output, matching power generation with market demand, reducing energy waste and losses. This helps improve energy utilization efficiency, reduce energy consumption, minimize environmental impact, and promote the green development of the power industry. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a flowchart illustrating a two-layer power unit output scheduling optimization method based on priority balancing provided in one embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the structure of a two-layer power unit output scheduling optimization device based on priority balancing provided in one embodiment of this application.

[0018] Figure 3 This is a schematic diagram of an electronic device structure provided in one embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Application Overview In the context of the electricity market, the uncertainty of demand behavior by both power suppliers and consumers influences their decision-making. Agent-based system technology demonstrates good sustainability and efficiency in simulating and modeling unit dispatch optimization in a market environment, enabling participants to adjust their unit output behavior. However, in a market environment, the lack of mutual information between power generators and consumers leads to significant challenges in power balancing, which is the most realistic scenario in the electricity market. To address this gap, this paper studies the optimization decision-making of power unit output in a market environment with elastic demand using a two-level optimization problem. Based on this, this invention proposes a two-level power unit output dispatch optimization method based on pecking order equilibrium. It utilizes pecking order equilibrium and the Lagrange relaxation method to optimize unit output.

[0021] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0022] Exemplary methods Figure 1 This is a flowchart illustrating a two-tiered power unit output scheduling optimization method based on priority balancing provided in one embodiment of this application. Figure 1 As shown, the method includes the following:

[0023] Step S110: Based on the power generation data and power generation strategy of the power generator, construct a strategic output model for the power generator. First, it's necessary to collect power generation data from the power generators, including the output of each generator unit and power generation cost data. This data forms the foundation for model building and reflects the operating status and performance of the generator units. Next, it's crucial to understand the power generators' power generation strategies, including their market positioning, objectives, and competitive strategies. For example, a power generator might adopt a low-price strategy to gain more market share or a high-price strategy to achieve higher profits.

[0024] Based on the collected power generation data, analyze the energy production costs of the generator sets, including fixed costs (such as equipment depreciation, insurance, and management fees) and variable costs (such as fuel costs, employee wages, and maintenance costs). A clear understanding of the cost structure helps power generators make informed decisions when submitting bids.

[0025] Taking into account factors such as power generation data, power generation strategies, and cost structures, a strategic output model for power generators is constructed. The model needs to describe the output behavior of generator units under different conditions, and how power generators adjust output according to market conditions to maximize profits. The model may include the relationship between generator output and costs / revenue, as well as the power generator's pricing strategies.

[0026] Step S120: Based on the electricity consumption data and electricity consumption strategies of the retailers, construct the electricity demand model of the retailers; Specifically, this involves collecting electricity consumption data from retailers, including electricity consumption volume, time distribution of electricity use, and types of electrical equipment used. This data reflects the characteristics and patterns of retailers' electricity demand. Understanding retailers' electricity consumption strategies is also crucial, such as whether they implement energy-saving measures or adjust their electricity consumption under peak-valley pricing strategies. These strategies influence retailers' electricity demand patterns.

[0027] Based on electricity consumption data and strategies, an electricity demand function is established. This function describes the relationship between electricity demand and factors such as market prices and timing of electricity consumption. For example, the demand function may be a linear or nonlinear function of prices, reflecting the impact of price changes on demand. A power demand model for retailers is then constructed, including the demand function and consumer pricing strategies. The model needs to be able to predict retailers' electricity demand and pricing behavior under different market conditions, providing accurate demand information for subsequent unit scheduling optimization.

[0028] Step S130: Based on the power generator's strategic output model and the power demand model, construct a two-layer model for unit scheduling optimization, so as to determine the power generator's output plan through the two-layer model for unit scheduling optimization.

[0029] Specifically, the strategic output model of power generators and the electricity demand model are integrated to form the basic framework for unit dispatch optimization. In this framework, the output decisions of power generators and the demand decisions of electricity retailers influence each other and jointly determine the supply and demand balance of the electricity market.

[0030] A two-layer model is constructed. The upper layer focuses on maximizing the profits of generators and distributors, while the lower layer focuses on achieving optimal supply and demand equilibrium in the market. In the upper layer, generators and distributors optimize their output and demand decisions based on their own strategies and market information. The lower layer uses optimization algorithms such as the Lagrange relaxation method to find the optimal solution for market supply and demand equilibrium.

[0031] The optimization algorithm, including the Lagrange relaxation method, is used to solve the pecking order equilibrium problem in a two-level model. By introducing Lagrange multipliers, the algorithm transforms the constraints into part of the objective function, thus converting the complex constrained optimization problem into a more easily solvable form. The final Lagrange multipliers represent a uniform trading price, and based on this uniform trading price, the optimal dispatch output for each generator unit is determined.

[0032] Based on the solution results of the two-level model, the power generation operator's output plan is determined. The output plan includes the output arrangement of each generating unit in different time periods, as well as the corresponding pricing strategy. This plan enables the power generation operator to maximize its profits while meeting market demand and maintaining the stable operation of the electricity market. Specifically, a strategic output model for power generators is established. The goal of power generators is to optimize unit output and maximize profits while considering energy production costs.

[0033] Energy production cost of each generator set as follows: (1) (2) in, It is the output of the generator set; as well as It is the cost coefficient parameter of generator unit k in a power generation enterprise; It is the total marginal operating cost of generator k, of which As a fixed cost consisting of insurance and administrative fees; , as well as It is a variable cost consisting of employee, processing materials and maintenance costs.

[0034] The power supplier's quote is as follows: (3) in, It is an energy bid price that takes into account all operating costs. It is the output of the generator set. and It is a strategic pricing coefficient.

[0035] Each power generation company uses the following formula to calculate revenue and profit.

[0036] (4) in, This is the profit belonging to generator unit k of the power generation company. That is the market price.

[0037] Furthermore, a power demand model for the seller is established.

[0038] Electricity sales companies are medium to large consumers, and their demand is variable and elastic. The consumer's load demand function is an inverse linear function, and its real marginal benefit curve exhibits a negative gradient. Therefore, the consumer's benefit function is... (5) in, It is a demand function; It is demand; and It is the true benefit coefficient; These are the coefficients of the demand function.

[0039] The consumer's quote is (6) in, It is the strategic price variable that each consumer J submits to the market. and It is the seller's strategic pricing coefficient.

[0040] The revenue of each consumer firm is a function of electricity demand and transaction prices, as shown below. (7) Furthermore, a two-layer model for optimizing unit scheduling is established.

[0041] This invention uses a transaction order equilibrium model to determine energy supply and demand, thereby optimizing unit output.

[0042] (8) Here, J and K represent the number of electricity consumers and the number of electricity supply units.

[0043] This invention employs the Lagrange relaxation method to solve the pecking order equilibrium problem. The solution of the Lagrange multiplier becomes a uniform trading price, from which the optimal dispatch output of each generator unit can be derived.

[0044] (9) In the formula, It is a Lagrange function; It is a Lagrange multiplier.

[0045] Use the following formula as the Lagrange multiplier solution: (10) (11) (12) (13) Both generators and electricity demanders become decision-makers in the scheduling of generator output. This dual behavior forms a two-level mathematical problem, where the upper level is represented by the revenues obtained by generators and demanders in equations (4) and (7), while the lower level is represented by the market supply and demand pecking order equilibrium model. Therefore, the decisions of each market participant are made in a two-level mathematical optimization problem of the two functions given in equation (15).

[0046] In the upper-level model, generators and distributors optimize their output and demand decisions based on their own strategies and market information, with the goal of maximizing their own profits. Lower-level model: (15) The solutions from both the upper and lower level models ultimately provide the optimized results for unit scheduling output.

[0047] This invention, by adopting the above technical solution, has the following advantages: This invention models producers and consumers as agents with incomplete information about competitors. The agents determine the optimal dispatch output of generating units to achieve maximum profit under power supply and demand balance and Nash equilibrium. The method proposed in this invention can guide power generators to formulate optimal power supply schemes even without knowing the demand side's information. Therefore, this invention can be widely applied to power unit output dispatch.

[0048] Exemplary device The apparatus embodiments of this application can be used to execute the method embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of this application.

[0049] Figure 2 The diagram shown is a block diagram of a two-layer power unit output scheduling optimization device based on priority balancing, according to an embodiment of this application. Figure 2 As shown, the device includes: The first construction module 21 is used to construct a strategic output model for power generators based on their power generation data and strategies. The second construction module 22 is used to construct the electricity demand model of the seller based on the seller's electricity consumption data and electricity consumption strategy; The optimization module 23 is used to construct a two-layer model for unit scheduling optimization based on the power generator's strategic output model and the power demand model, so as to determine the power generator's output plan through the two-layer model for unit scheduling optimization.

[0050] In some embodiments, the generator's strategic output model includes: Energy production cost of each generator set The formula is as follows: (1) (2) in, It is the output of the generator set; as well as It is the cost coefficient parameter of generator unit k in a power generation enterprise; It is the total marginal operating cost of generator k, of which As a fixed cost consisting of insurance and administrative fees; , as well as It is a variable cost consisting of employee, processing materials, and maintenance costs; The pricing formula for power generators is as follows: (3) in, It is an energy bid price that takes into account all operating costs. It is the output of the generator set. and It is a strategic pricing coefficient; Each power generation company uses the following formula to calculate revenue and profit: (4) in, This is the profit belonging to generator unit k of the power generation company. That is the market price.

[0051] In some embodiments, the electricity demand model uses the following formula: (5) in, It is a demand function; It is demand; and It is the true benefit coefficient; These are the coefficients of the demand function; The consumer's quote is (6) in, It is the strategic price variable that each consumer J submits to the market; and It is the seller's strategic pricing coefficient; The revenue of each consumer firm is a function of electricity demand and transaction prices, as shown below. .

[0052] Exemplary electronic devices Below, for reference Figure 3 This describes an electronic device according to embodiments of the present application. Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0053] like Figure 3 As shown, the electronic device 300 includes one or more processors 310 and memory 320.

[0054] The processor 310 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0055] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may execute the program instructions to implement the dual-level power unit output scheduling optimization method based on priority balancing described in the various embodiments of this application above, and / or other desired functions. Various contents, such as category correspondence, may also be stored in the computer-readable storage medium.

[0056] In one example, the electronic device 300 may also include an input device 330 and an output device 340, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0057] In addition, the input device 330 may also include, for example, a keyboard, mouse, interface, etc. The output device 340 can output various information to the outside, including analysis results, etc. The output device 340 may include, for example, a display, speaker, printer, and communication network and its connected remote output devices, etc.

[0058] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device may include any other suitable components depending on the specific application.

[0059] Exemplary computer program products and computer-readable storage media In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the tiered power unit output scheduling optimization method based on priority balancing according to various embodiments of this application described in the "Exemplary Methods" section of this specification.

[0060] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0061] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions that, when executed by a processor, cause the processor to perform the steps in the tiered power unit output scheduling optimization method based on priority balancing according to various embodiments of this application described in the "Exemplary Methods" section above.

[0062] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0063] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A two-layer power unit output scheduling optimization method based on priority equilibrium, characterized in that, include: Based on the power generation data and strategies of the power generator, a strategic output model for the k-th generating unit is constructed; the strategic output model includes the energy production cost C. k , Power purchaser quote V k and the profits of power generation companies k ,in: q k For the generator set to output power, a k and b k For the cost coefficient parameter of generator unit k in power generation enterprises; O k Let F be the total marginal operating cost of the k-th generator unit. ck S consists of fixed costs, including insurance and administrative expenses. ck M k-1 and M k-2 α is a variable cost consisting of employee, processing materials, and maintenance costs. k and β k λ is the pricing coefficient for power generators. t Tprice Let q be the uniform trading price for time period t. k,t Let T be the output of the k-th generator unit during time period t, where T is the total number of trading periods. Based on the electricity consumption data and strategies of the retailers, an electricity demand model is constructed for the j-th retailer; the electricity demand model includes a benefit function B. j Consumer price W j and consumer business revenue Π j ,in: q j For demand, c j and d j z represents the true benefit coefficient. j γ is the coefficient of the benefit function; j and δ j q represents the seller's strategic pricing coefficient. j,t Let be the demand of the j-th seller during time period t; A two-layer model for unit scheduling optimization is constructed based on the power generator's strategic output model and the electricity demand model. In the upper layer of this two-layer model, both the power generator and the electricity seller act as decision-makers for unit scheduling output, with the power generation company's profit Π as the deciding factor. k and consumer business revenue Π j The objective is to maximize the sum, where the generator's quote V k From α k β k Confirmed, consumer quote W j By γ j δ j It is determined that power generators and distributors will optimize their output and demand decisions, respectively. In the lower layer of the two-layer model, a transaction order equilibrium model is used to determine energy supply and demand, with the total output of generating units equal to the total demand of retailers serving as the supply-demand balance constraint. The supply and demand equilibrium constraint is solved using the Lagrange relaxation method to obtain the Lagrange multiplier λ, and this Lagrange multiplier λ is used as the unified transaction price λ. t Tprice ,in: K represents the number of power supply units, and J represents the number of power consumers; based on the unified transaction price λ t Tprice Determine the output of the k-th generator set and the demand of the j-th seller, respectively: Substituting the generator set's output, the distributor's demand, and the unified transaction price into the power generation company's profit Π k and the revenue of the aforementioned consumer enterprises Π j The power generation plan of the generator is determined based on the solutions of the upper-level model and the lower-level model.

2. A two-layer power unit output scheduling optimization device based on priority equilibrium, characterized in that, It includes a first building module, a second building module, and an optimization module; The first construction module is used to construct, based on the power generation data and strategies of the power generator, a system for constructing a system including the energy production cost C for the k-th generator unit. k , Power purchaser quote V k and the profits of power generation companies k The strategic output model of power generators, in which: q k For the generator set to output power, a k and b k For the cost coefficient parameter of generator unit k in power generation enterprises; O k Let F be the total marginal operating cost of the k-th generator unit. ck For fixed costs, S ck M k-1 and M k-2 For variable costs; α k and β k λ is the pricing coefficient for power generators. t Tprice Let q be the uniform trading price for time period t. k,t Let T be the output of the k-th generator unit during time period t, where T is the total number of trading periods. The second construction module is used to construct a benefit function B for the j-th retailer based on the retailer's electricity consumption data and electricity consumption strategy. j Consumer price W j and consumer business revenue Π j The electricity demand model, in which: q j For demand, c j and d j z represents the true benefit coefficient. j γ represents the coefficients of the demand function; j and δ j q represents the seller's strategic pricing coefficient. j,t Let be the demand of the j-th seller during time period t; The optimization module is used to construct a two-layer model for unit scheduling optimization based on the power generator's strategic output model and the power demand model. In the upper layer of the two-layer model, both the power generator and the electricity seller act as decision-makers for unit scheduling output, with the power generation company's profit Π k and consumer business revenue Π j The objective is to maximize the sum, where the generator's quote V k From α k β k Confirmed, consumer quote W j By γ j δ j It is determined that power generators and retailers optimize their output and demand decisions, respectively. In the lower layer of the two-layer model, a transaction order equilibrium model is used to determine the energy supply and demand, with the total output of generator units equal to the total demand of retailers serving as the supply-demand balance constraint. The optimization module is also used to solve the supply and demand balance constraint using the Lagrange relaxation method to obtain the unified transaction price λ. t Tprice The Lagrange multiplier λ, where: And based on the unified transaction price λ t Tprice Determine the output of the k-th generator set and the demand of the j-th vendor: And substitute the output of the generator set, the demand of the distributor, and the uniform transaction price into the profit of the power generation company. k and the revenue of the aforementioned consumer enterprises Π j The power output plan of the power generator is determined based on the solutions of the upper-level model and the lower-level model; where K is the number of power supply units and J is the number of power demanders.

3. An electronic device, characterized in that, Includes a processor and memory for storing processor-executable programs; The processor is used to implement the dual-level power unit output scheduling optimization method based on priority balancing as described in claim 1 by running the program in the memory.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the dual-level power unit output scheduling optimization method based on priority balancing as described in claim 1.

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