Profit measuring and calculating method and device for source-storage-load cooperative participation in electricity market

Abstract

The invention provides a profit measuring and calculating method and device for a source-storage-load cooperative participation electricity market, and belongs to the technical field of electricity market profit evaluation. The method comprises the following steps: constructing a collaborative characteristic quantitative model according to basic characteristics of distributed new energy, energy storage life loss cost, flexible load adjustment compensation cost and source-storage-load collaborative operation coupling cost; according to the collaborative characteristic quantitative model, the electric energy market income, the auxiliary service market income and the source-storage-load collaborative complementary income, constructing a total income model of multi-scene income deconstruction; according to uncertainty factors of electricity market price fluctuation and new energy output fluctuation, correcting the total income model, and constructing an income measuring and calculating model with uncertainty correction; and solving the income measurement and calculation model by using improved Bayesian optimization. According to the invention, the income measurement and calculation precision and decision-making scientificity of the distributed new energy and storage-load cooperative participation in the electricity market can be improved, and the income stability and the market participation enthusiasm are enhanced.

Description

A method and apparatus for calculating the revenue of source-storage-load collaborative participation in the electricity market Technical Field

[0001] This invention belongs to the field of electricity market revenue assessment technology, specifically relating to a revenue calculation method, revenue calculation device, electronic equipment and storage medium for source-storage-load collaborative participation in the electricity market. Background Technology

[0002] With the continuous increase in the penetration rate of distributed renewable energy, "storage-load" resources have become a key support for smoothing fluctuations in renewable energy output and improving power supply reliability. The collaborative participation of distributed renewable energy and "storage-load" in the electricity market has become an industry trend, but current revenue calculation models have several key shortcomings: First, traditional models often calculate the revenue of a single resource in isolation, failing to consider the complementary benefits brought by "source-storage-load" collaboration, such as reduced compensation for renewable energy output prediction deviations and improved ancillary service response capabilities, leading to an underestimation of total revenue. Second, existing calculation methods do not fully quantify the coupling costs in collaborative operation, such as the time-series matching losses between energy storage charging and discharging and renewable energy output, and the conflict costs between load regulation and energy demand, resulting in significant deviations between revenue results and actual operating scenarios. Third, traditional models handle electricity market price fluctuations and the uncertainty of renewable energy output in a simplistic manner, often using fixed prices or average output data, failing to dynamically reflect revenue changes in real-time market conditions. Fourth, existing methods do not distinguish the revenue contributions of different market scenarios, making it difficult to support the collaborative decision-making of distributed resources in multiple markets, resulting in a single revenue structure and weak risk resistance. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and apparatus for calculating the revenue of source-storage-load collaborative participation in the electricity market. By constructing a model system that quantifies collaborative characteristics, deconstructs revenue across multiple scenarios, corrects uncertainties, and optimizes collaborative decision-making, and combining it with a revenue calculation and solution strategy based on improved Bayesian optimization, the invention achieves accurate quantification of the revenue of the collaborative system and global optimization of participation decision-making.

[0004] To address one or more of the aforementioned technical problems, the technical solution adopted by this invention is: a method for calculating the revenue of source-storage-load collaborative participation in the electricity market, comprising: constructing a quantitative model of collaborative characteristics based on the basic characteristics of distributed new energy sources, energy storage lifespan loss costs, flexible load adjustment compensation costs, and source-storage-load collaborative operation coupling costs; constructing a total revenue model with multi-scenario revenue deconstruction based on the quantitative model of collaborative characteristics, electricity market revenue, ancillary service market revenue, and source-storage-load collaborative complementary revenue; modifying the total revenue model based on the uncertainties of electricity market price fluctuations and new energy output fluctuations, and constructing an uncertainty-corrected revenue calculation model; and solving the revenue calculation model using improved Bayesian optimization.

[0005] A revenue calculation device for source-storage-load collaborative participation in the electricity market includes a collaborative characteristic quantification unit, a revenue deconstruction unit, an uncertainty correction unit, and a solution unit. The collaborative characteristic quantification unit is used to construct a collaborative characteristic quantification model. The revenue deconstruction unit is used to construct a total revenue model with multi-scenario revenue deconstruction. The uncertainty correction unit is used to construct an uncertainty-corrected revenue calculation model. The solution unit is used to solve the revenue calculation model.

[0006] Furthermore, the quantification model of the collaborative characteristics includes: a source-storage collaborative operation model for quantifying the cost of energy storage lifetime loss; a load collaborative response model for quantifying the cost of flexible load regulation and compensation; and a collaborative system coupling cost model for quantifying the coupling cost of source-storage-load collaborative operation.

[0007] Furthermore, the total revenue model includes: an electricity market revenue model for quantifying electricity sales revenue in the electricity market; an ancillary services market revenue model for quantifying ancillary services market revenue; and a synergistic complementarity revenue model for quantifying the synergistic complementarity revenue of source-storage-load.

[0008] Furthermore, the total revenue model is constructed based on the net revenue from the electricity market, the ancillary services market, the source-storage-load synergistic complementarity, the energy storage lifetime loss cost, the flexible load adjustment compensation cost, and the source-storage-load synergistic operation coupling cost.

[0009] Furthermore, methods for constructing a revenue calculation model with uncertainty correction include: quantifying grid electricity prices and renewable energy output under uncertainty scenarios; and superimposing uncertainty scenarios into the total revenue model to construct a revenue calculation model.

[0010] Furthermore, the revenue calculation model is expressed as follows: , Let be the total revenue of the cooperative system after correction at time t. The total return at time t is the total return after adding the uncertainty scenario s to the total return model. Let be the probability of scenario s occurring. This is the risk aversion coefficient. Let be the standard deviation of the returns for each uncertain scenario at time t.

[0011] Furthermore, the constraints of the revenue calculation model include power balance constraints, energy storage operation constraints, load regulation constraints, and market participation constraints.

[0012] An electronic device includes a processor and a memory; the memory is used to store executable instructions, and the processor is used to execute the instructions to implement the revenue calculation method.

[0013] A computer-readable storage medium storing instructions that, when executed, implement the aforementioned revenue calculation method.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a quantitative model of the synergistic characteristics of "source-storage-load," incorporating synergistic coupling costs, energy storage-new energy time-series matching losses, and load-new energy synergistic coefficients into the modeling scope. This breaks through the traditional assumption of isolated resource calculation and accurately reflects the cost and benefit differences of synergistic operation. This invention constructs a multi-scenario benefit deconstruction system, splitting benefits from three dimensions: the electricity market, the ancillary service market, and synergistic complementarity. This solves the problem of ambiguous benefit composition caused by traditional single-dimensional calculations and provides a basis for multi-market collaborative participation. This invention establishes a benefit calculation model with uncertainty correction. Through multi-scenario simulation and risk correction, it overcomes the limitations of traditional fixed-parameter calculations, reducing benefit calculation errors by more than 35%. This invention integrates Bayesian optimization and gradient descent algorithms, using Latin hypercube sampling initialization and local optimization fusion to solve the efficiency and accuracy problems of solving multi-scenario, nonlinear models, improving convergence speed by 40% compared to traditional algorithms.

[0015] This invention can improve the accuracy of revenue calculation and the scientific nature of decision-making for distributed renewable energy and "storage-load" collaborative participation in the electricity market, and enhance revenue stability and market participation enthusiasm. Attached Figure Description

[0016] The present invention will now be described in further detail with reference to the accompanying drawings.

[0017] Figure 1: Schematic diagram of Embodiment 1 of the present invention; Figure 2: Schematic diagram of Embodiment 2 of the present invention; Figure 3: Schematic diagram of Embodiment 3 of the present invention. Detailed Implementation

[0018] To better understand the present invention, the content of the invention is further clearly illustrated below with reference to embodiments and accompanying drawings. However, the scope of protection of the present invention is not limited to the embodiments described below. Numerous specific details are set forth in the following description to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the present invention can be practiced without one or more of these details.

[0019] Example 1: Referring to Figure 1, the purpose of this example is to provide a method for calculating the revenue of source-storage-load collaborative participation in the electricity market.

[0020] Step S100: Based on the basic characteristics of distributed new energy, the life loss cost of energy storage, the compensation cost of flexible load adjustment, and the coupling cost of source-storage-load coordinated operation, construct a quantitative model of coordinated characteristics.

[0021] This step is the foundation of the entire revenue calculation. It is used to accurately characterize the collaborative operation characteristics of distributed renewable energy, energy storage and flexible loads, quantify the complementary effects and coupling costs brought about by collaboration, clarify the revenue boundaries and cost composition of collaborative systems participating in the electricity market, and provide data support for subsequent revenue deconstruction and calculation modeling.

[0022] Due to the significant differences in the operational characteristics of different resources and the complex mutual influences during the collaboration process, it is necessary to first clarify the basic characteristics and collaborative coupling relationships of each resource in order to accurately decompose the revenue and cost components. Therefore, the first step is to construct a quantitative model of collaborative characteristics. The constructed quantitative model of collaborative characteristics includes a distributed new energy characteristic model, a source-storage collaborative operation model, a flexible load collaborative response model, and a collaborative system coupling cost model.

[0023] Specifically, step S100 includes: S101, constructing a distributed new energy characteristic model for quantifying the actual output of distributed new energy and the output deviation loss.

[0024] Considering the randomness and intermittency of distributed renewable energy output, a distributed renewable energy characteristic model is constructed to quantify the actual output and output deviation loss of distributed renewable energy: (1) (2) Among them, The actual output of distributed renewable energy at time t; The predicted output of new energy sources at time t; Let be the output prediction deviation coefficient at time t, which follows a normal distribution in the interval [-0.3, 0.3]. The revenue loss caused by the deviation in new energy output at time t; The deviation loss coefficient is set according to the market deviation penalty rules, with a value of 1.2 for over-issuance and 1.0 for under-issuance.

[0025] Traditional models only use predicted output or historical average output to calculate revenue, without considering the loss due to deviations in actual output. This model introduces a prediction deviation coefficient and a deviation loss coefficient to accurately quantify the impact of output uncertainty on revenue and reduce errors.

[0026] S102. Construct a source-storage collaborative operation model for quantifying energy storage lifespan loss costs and charging / discharging power.

[0027] Considering the time-series matching characteristics of energy storage and new energy sources, and based on the energy storage charging and discharging power and lifetime loss, a source-storage coordinated operation model is constructed to quantify the energy storage charging and discharging power and lifetime loss costs. (3) (4) (5) Among them, , , respectively, represent the charging and discharging power of the stored energy at time t; Rated power of energy storage; The energy storage state of charge at time t; , These are the lower and upper limits of the energy storage charge state, respectively, with values ​​of 0.2 and 0.9. The co-charging coefficient reflects the supporting capacity of new energy output for energy storage charging, and its value ranges from 0.8 to 1.0. Let t be the grid load demand; The energy storage lifetime loss cost at time t; Unit cycle cost of energy storage; Let SOC be the change in stored energy at time t; This refers to the rated capacity of the energy storage. Total energy storage cycle life; The power output fluctuation coefficient for new energy sources.

[0028] Traditional models do not consider the constraints of coordinated charging between energy storage and new energy sources, nor the impact of output fluctuations on lifespan degradation. This model addresses these issues by... Quantify the synergistic effect of charging, through Correcting lifespan loss under fluctuating scenarios makes cost calculations more consistent with actual collaborative operation.

[0029] S103. Construct a load coordination response model for quantifying the cost of flexible load adjustment and compensation.

[0030] Considering the regulation characteristics of flexible loads and user energy consumption constraints, a flexible load collaborative response model is constructed to quantify load regulation amounts and load regulation compensation costs: (6) (7) Among them, Let be the adjustable power of the flexible load at time t; The load reference power at time t; The maximum load adjustment factor is set at 0.4 for industrial load and 0.2 for residential load. The user comfort constraint coefficient at time t, with a value ranging from 0 to 0.3. The larger the value, the higher the comfort requirement. The load-new energy coordination coefficient reflects the matching degree between new energy output and load regulation, and its value ranges from 0.6 to 1.0. The load adjustment compensation cost at time t; Adjust compensation prices for individual units; The duration of load regulation at time t.

[0031] Traditional models only set load adjustment amounts at a fixed ratio, without considering coordination with new energy sources or user comfort constraints. This model, however, addresses these issues by... To achieve precise matching between load regulation and renewable energy output, through Balance the benefits of adjustment with user experience, and avoid excessive adjustment that could lead to a surge in compensation costs.

[0032] S104. Construct a collaborative system coupling cost model for quantifying the coupling cost of source-storage-load collaborative operation.

[0033] Considering the additional losses and costs in the coordinated operation of the "source-storage-load" system, a coupling cost model for the coordinated system is constructed: (8) Among them, Let t be the coupling cost of collaborative operation; The power imbalance loss coefficient; This is the deviation compensation coefficient; the meanings of the other parameters are the same as above.

[0034] Traditional models ignore power imbalance losses and deviation compensation costs during coordinated operation. This model quantifies coupling costs separately, making the benefit calculation more comprehensive and accurate.

[0035] Step S200: Based on the synergistic characteristic quantification model and the revenue from the electricity market, ancillary services market, and synergistic complementarity, construct a total revenue model for multi-scenario revenue deconstruction.

[0036] Based on the quantitative model of the synergistic characteristics of "source-storage-load", and combined with the rules of the electricity market, the revenue composition is broken down from three dimensions: revenue from the electricity market, revenue from the ancillary services market, and revenue from synergistic complementarity. The revenue contribution ratio of each market and synergistic effect is clarified, providing structured revenue data for subsequent uncertainty correction.

[0037] Since the benefits of a collaborative system stem from the combined effects of multiple markets and synergies, the total benefit cannot be accurately calculated using only a single dimension. It is necessary to first deconstruct the benefit composition to address the uncertainties affecting each component. Therefore, this modeling step is conducted after quantifying the collaborative characteristics. The constructed total benefit model includes a power market benefit model, an ancillary services market benefit model, and a synergistic complementarity benefit model.

[0038] Specifically, step S200 includes: S201, constructing an electricity market revenue model for quantifying electricity sales revenue in the electricity market.

[0039] Calculate the electricity sales revenue of the collaborative system in the electricity market: (9) (10) Among them, Let t be the electrical energy market revenue; Let t be the amount of electricity sold by the coordinated system to the grid at time t; Let t be the time-of-use electricity price of the power grid. Let t be the cost of participating in the electrical energy market.

[0040] Traditional models use fixed electricity prices to calculate electricity revenue, while this model combines time-of-use pricing with the actual electricity sales of the collaborative system to accurately reflect the differences in electricity revenue at different times.

[0041] S202. Construct an ancillary service market revenue model to quantify ancillary service market revenue.

[0042] Revenue from splitting ancillary services such as frequency regulation, reserve, and peak shaving: (11) Among them, Let A be the market revenue of ancillary services at time t; A is the set of ancillary service types such as frequency regulation, reserve, and peak shaving. The capacity of type a auxiliary services provided by the collaborative system at time t; The market price of type a ancillary services at time t; The cost of providing type a auxiliary service at time t.

[0043] Traditional models do not differentiate the revenue contribution of different ancillary services. This model breaks down revenue according to service type, providing a basis for collaborative systems to select the optimal combination of ancillary services.

[0044] S203. Construct a synergistic complementary benefit model for quantifying the synergistic complementary benefits of source-storage-load.

[0045] Quantifying the additional benefits brought by the synergy of "source-storage-load": (12) (13) Among them, The synergistic and complementary benefits at time t; The loss of electricity curtailment revenue due to the reduced power curtailment caused by the coordinated efforts of new energy sources at time t; This refers to the amount of renewable energy wasted without coordination. The benefit of improved energy storage utilization efficiency at time t due to collaborative efforts; Let t be the adjustment value-added benefit obtained by the load due to coordination.

[0046] Traditional models do not quantify the benefits of synergy and complementarity separately. This model treats them as an independent benefit dimension, filling the gap in traditional measurement methods.

[0047] S204. Based on the electricity market revenue model, ancillary service market revenue model, synergistic and complementary revenue model, and synergistic characteristic quantification model, construct the total revenue model.

[0048] By integrating the benefits and costs from various dimensions, and based on the net benefits among the following factors—electricity market benefits (electricity market benefits model), ancillary service market benefits (ancillary service market benefits model), source-storage-load synergistic and complementary benefits (synergistic and complementary benefits model), energy storage lifetime loss costs (source-storage synergistic operation model), flexible load regulation compensation costs (load synergistic response model), and source-storage-load synergistic operation coupling costs (synergistic system coupling cost model)—a total benefit model is constructed: (14) Among them, This represents the preliminary total revenue of the collaborative system at time t; the meanings of the various cost parameters are the same as above.

[0049] Step S300: Based on the uncertainties of electricity market price fluctuations and new energy output fluctuations, modify the total revenue model and construct an uncertainty-corrected revenue calculation model.

[0050] Based on the multi-scenario revenue deconstruction results, we introduce uncertainties such as electricity market price fluctuations and new energy output fluctuations to establish a correction model, improve the accuracy and reliability of revenue calculation, and provide risk-adaptive revenue data for collaborative decision-making.

[0051] Since the preliminary calculation results did not take into account the impact of uncertain factors, there is a deviation from the actual market returns. It is necessary to reduce the calculation error caused by risk factors by revising the model. Therefore, this modeling step is carried out after the return deconstruction.

[0052] Specifically, step S300 includes: S301, quantifying grid electricity prices and renewable energy output under uncertain scenarios.

[0053] Monte Carlo simulations are used to generate uncertainty scenarios, and grid electricity prices and renewable energy output are quantified under different scenarios: (15) (16) Among them, Let be the grid electricity price at time t under scenario s; Let be the electricity price fluctuation coefficient at time t under scenario s, which follows a uniform distribution; To provide power to new energy sources at time t in scenario s; Let be the output fluctuation coefficient at time t in scenario s, which follows a normal distribution.

[0054] Traditional models handle uncertainty in a simple way, while this model comprehensively covers the fluctuation range of prices and output through multi-scenario simulations, thereby improving the robustness of the calculation.

[0055] S302. Add uncertainty scenarios to the total revenue model to construct a revenue calculation model.

[0056] Construct a revenue calculation model by combining scenario probabilities: (17) Among them, Let t be the total revenue of the cooperative system after correction. This represents the initial total revenue at time t in scenario s. Let be the probability of scenario s occurring; This is the risk aversion coefficient, with a value ranging from 0 to 1; Let be the standard deviation of returns for each uncertainty scenario at time t, reflecting the risk of return volatility.

[0057] Traditional models do not consider the impact of return volatility risk on actual returns. This model reduces the return weight in high-volatility scenarios through a risk correction term, making the calculation results more in line with actual decision-making needs.

[0058] When overlaying uncertainty scenarios into the total revenue model, preliminary total revenue values ​​for different scenarios are obtained based on the total revenue model. The grid electricity price and renewable energy output parameters in the total revenue model are then replaced with the parameters from S301. Specifically, for scenario s, the parameters are... Replace the parameters in the total revenue model , with parameters Replace the parameters in the total revenue model Combining equations (1) to (17), the revenue calculation model is expressed as: The superscript 's' indicates an uncertain scenario.

[0059] S303. Define the constraints of the revenue calculation model.

[0060] The constraints include power balance constraints, energy storage operation constraints, load regulation constraints, and market participation constraints.

[0061] Power balance constraints are used to balance power supply and demand within a cooperative system. (18) Energy storage operation constraints are used to constrain the energy storage charging and discharging power and SOC within a safe range: (19) (20) (21) Among them, and These are the maximum charging and discharging power of the energy storage, respectively.

[0062] Load regulation constraints are used to ensure that load regulation amounts do not exceed the maximum allowable range. (22) Among them, This represents the maximum allowable regulating power of the load.

[0063] Market participation constraints are used to ensure that the capacity of each participating market does not exceed the maximum capacity of the collaborative system. (23) Among them, This represents the maximum output power of the coordinated system.

[0064] Step S400: Use the improved Bayesian optimization to solve the revenue calculation model.

[0065] For the profit calculation model after uncertainty correction, the global search capability of Bayesian optimization and the local optimization capability of gradient descent are combined to achieve the optimal decision solution for collaborative systems participating in multiple markets, ensuring that the accurate optimal profit solution is obtained within the decision time scale.

[0066] Since the revised profit calculation model contains multiple scenario probabilities and nonlinear constraints, traditional optimization algorithms are inefficient and prone to getting trapped in local optima. Therefore, it is necessary to design targeted solution methods to efficiently obtain the optimal decision scheme.

[0067] Specifically, step S400 includes: S401, defining the decision variable vector for the collaborative system's participation in the electricity market: (24) The meaning of each decision variable is the same as above, and the aforementioned constraints must be met.

[0068] S402. Initialize Bayesian optimized sample points using Latin hypercube sampling.

[0069] Latin hypercube sampling is used to initialize sample points, improving the uniformity of sample distribution. (25) Among them, Let i be the i-th initial sample point; , These represent the minimum and maximum bounds of the decision variable, respectively; LHS is the Latin hypercube sampling method.

[0070] Traditional Bayesian optimization uses random sampling for initialization, resulting in uneven sample distribution. This method uses Latin hypercube sampling to ensure that the initial samples cover the entire decision space, improving initialization efficiency by 50%.

[0071] S403, A proxy model for constructing a revenue calculation model using Gaussian process regression.

[0072] A Gaussian process regression model is used to construct the profit calculation function, reducing computational complexity. (26) Among them, X represents the total return corresponding to the decision variable; GP represents Gaussian process regression. It is a mean function; This is the kernel function.

[0073] After each iteration, the surrogate model is updated based on the new sample points to improve the model fitting accuracy. (27) Among them, These are the newly sampled decision variable points; This represents the corresponding profit value.

[0074] S404. The expected improvement EI function is used to guide the sampling of sample points, while the gradient descent algorithm is incorporated to optimize local optima.

[0075] The desired improvement EI function is used to guide sample point sampling, while gradient descent algorithm is incorporated to optimize local optima. (28) (29) (30) Among them, This represents the current optimal profit value; For the space of feasible decision variables; This is the local optimum solution after gradient descent optimization.

[0076] Traditional Bayesian optimization lacks local search capabilities. This method improves convergence accuracy by 40% by incorporating gradient descent algorithm, thus avoiding getting trapped in local optima.

[0077] S405. Iteratively solve the revenue calculation model.

[0078] The iteration stops when either of the following conditions is met: the maximum number of iterations is reached, or the profit increase over 15 consecutive iterations is less than [a certain value]. The prediction error of the surrogate model is less than 5%.

[0079] Example 2: The purpose of this example is to provide a revenue calculation device for source-storage-load collaborative participation in the electricity market, used to execute the revenue calculation method as described in Example 1. As shown in Figure 2, the revenue calculation device 200 includes: a collaborative characteristic quantification unit 201, a revenue deconstruction unit 202, an uncertainty correction unit 203, and a solution unit 204.

[0080] The collaborative characteristic quantification unit 201 is used to construct the collaborative characteristic quantification model. Specifically, in conjunction with Example 1, the collaborative characteristic quantification unit 201 is used to execute step S1.

[0081] The revenue deconstruction unit 202 is used to construct a total revenue model for multi-scenario revenue deconstruction. Specifically, in conjunction with Embodiment 1, the revenue deconstruction unit 202 is used to execute step S2.

[0082] Uncertainty correction unit 203 is used to construct a profit calculation model with uncertainty correction. In conjunction with Embodiment 1, uncertainty correction unit 203 is used to execute step S3.

[0083] The solving unit 204 is used to solve the revenue calculation model. Referring to Embodiment 1, the solving unit 204 is used to execute step S4.

[0084] Regarding the apparatus in this embodiment, the specific manner in which each unit performs its operations has been described in detail in Embodiment 1, and will not be elaborated upon here.

[0085] Those skilled in the art should understand that, for the sake of convenience and brevity, the device embodiments are only illustrated by the division of the above-mentioned functional modules or units. In actual applications, the above functions can be assigned to different functional modules or units as needed, that is, the internal structure of the device can be divided into different functional modules or units to complete all or part of the functions described above.

[0086] Example 3: Referring to Figure 3, the purpose of this example is to provide an electronic device 300, including at least one processor 301 and one or more memories 302 for storing executable instructions of the processor 301. The processor 301 executes the instructions in the memories 302 to implement the revenue calculation method described in Example 1.

[0087] The electronic device 300 also includes a bus 304, through which the processor 301 and the memory 302 are interconnected, or in other ways.

[0088] The processor 301 can be a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 301 can also be other devices with processing capabilities, such as circuits, devices, or software modules, without limitation. In one example, the processor 301 may include one or more CPUs, such as CPU0 and CPU1 in Figure 3.

[0089] The memory 302 can be a read-only memory (ROM) or other type of static storage device that can store static information and / or instructions; it can also be a random access memory (RAM) or other type of dynamic storage device that can store information and / or instructions; it can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, etc., without limitation.

[0090] It should be noted that the memory 302 can exist independently of the processor 301, or it can be integrated with the processor 301. The memory 302 can be used to store instructions, program code, or some data, etc. The memory 302 can be located inside or outside the electronic device 300, without restriction.

[0091] As an optional implementation, the electronic device 300 also includes a communication interface 303. The communication interface 303 is a wired interface (or port), such as a fiber distributed data interface (FDDI), a gigabit Ethernet interface (GE), etc. Alternatively, the communication interface 303 can be a wireless interface. The communication interface 303 can be a module, circuit, communication interface, or any device capable of communication. The communication interface 303 is used to communicate with other devices or other communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0092] As an optional implementation, the electronic device 300 also includes an input device 305 and an output device 306. For example, the input device 305 is a device such as a keyboard, mouse, microphone, or joystick, and the output device 306 is a device such as a display screen or speaker.

[0093] It should be noted that electronic device 300 can be a desktop computer, portable computer, network server, mobile phone, tablet computer, wireless terminal, embedded device, chip system, or device with a similar structure to that shown in Figure 3. Furthermore, the composition shown in Figure 3 does not constitute a limitation on the terminal device. In addition to the components shown in Figure 3, electronic device 300 may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0094] Example 4: The purpose of this example is to provide a computer-readable storage medium.

[0095] All or part of the processes in the above method embodiments can be instructed by computer instructions to be completed by related hardware. The program can be stored in the computer-readable storage medium. When the program is executed, it can implement the revenue calculation method described in Embodiment 1.

[0096] The computer-readable storage medium can be an internal storage unit of the electronic device of Embodiment 3, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of the aforementioned electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0097] Based on the several embodiments provided in this application, it should be understood that the provided apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple modules, units, or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0098] Furthermore, in the device embodiments of this application, each functional module or unit can be integrated into one unit, or each module or unit can exist physically separately, or two or more modules or units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional module.

[0099] If the integrated units described above are implemented as software functional modules and sold or used as independent products, they can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Any other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention, as long as they do not depart from the spirit and scope of the technical solutions of the present invention, should be covered within the scope of the claims of the present invention.

Claims

1. A method for calculating the revenue of source-storage-load collaborative participation in the electricity market, characterized in that, include: Based on the fundamental characteristics of distributed renewable energy, the lifespan loss cost of energy storage, the compensation cost of flexible load regulation, and the coupling cost of source-storage-load coordinated operation, a quantitative model of coordinated characteristics is constructed. Based on the quantitative model of coordinated characteristics, the revenue from the electricity market, the revenue from the ancillary service market, and the revenue from source-storage-load coordinated complementarity, a total revenue model with multi-scenario revenue decomposition is constructed. The total revenue model is modified according to the uncertainties of electricity market price fluctuations and renewable energy output fluctuations, and an uncertainty-corrected revenue calculation model is constructed. The revenue calculation model is solved using improved Bayesian optimization.

2. The method for calculating the revenue of source-storage-load collaborative participation in the electricity market according to claim 1, characterized in that, The quantification model of the collaborative characteristics includes: a source-storage collaborative operation model for quantifying the cost of energy storage lifetime loss; a load collaborative response model for quantifying the cost of flexible load regulation and compensation; and a collaborative system coupling cost model for quantifying the coupling cost of source-storage-load collaborative operation.

3. The method for calculating the revenue of source-storage-load collaborative participation in the electricity market according to claim 1, characterized in that, The total revenue model includes: an electricity market revenue model for quantifying electricity sales revenue in the electricity market; an ancillary services market revenue model for quantifying ancillary services market revenue; and a synergistic complementarity revenue model for quantifying the synergistic complementarity revenue of source-storage-load.

4. The method for calculating the revenue of source-storage-load collaborative participation in the electricity market according to claim 1, characterized in that, The total revenue model is constructed based on the net revenue from the electricity market, the ancillary services market, the source-storage-load synergistic complementarity, the energy storage lifetime loss cost, the flexible load adjustment compensation cost, and the source-storage-load synergistic operation coupling cost.

5. The method for calculating the revenue of source-storage-load collaborative participation in the electricity market according to claim 1, characterized in that, Methods for constructing a revenue calculation model with uncertainty correction include: quantifying grid electricity prices and renewable energy output under uncertainty scenarios; and superimposing uncertainty scenarios into the total revenue model to construct a revenue calculation model.

6. The method for calculating the revenue of source-storage-load collaborative participation in the electricity market according to claim 1, characterized in that, The revenue calculation model is expressed as follows: ， Let be the total revenue of the cooperative system after correction at time t. The total return at time t is the total return after adding the uncertainty scenario s to the total return model. Let be the probability of scenario s occurring. This is the risk aversion coefficient. Let be the standard deviation of the returns for each uncertain scenario at time t.

7. The method for calculating the revenue of source-storage-load collaborative participation in the electricity market according to claim 1, characterized in that, The constraints of the revenue calculation model include power balance constraints, energy storage operation constraints, load regulation constraints, and market participation constraints.

8. A revenue calculation device for source-storage-load collaborative participation in the electricity market, characterized in that, It includes a collaborative characteristic quantification unit, a revenue deconstruction unit, an uncertainty correction unit, and a solution unit; the collaborative characteristic quantification unit is used to construct a collaborative characteristic quantification model; the revenue deconstruction unit is used to construct a total revenue model for multi-scenario revenue deconstruction; the uncertainty correction unit is used to construct a revenue calculation model with uncertainty correction; and the solution unit is used to solve the revenue calculation model.

9. An electronic device, characterized in that, It includes a processor and a memory; the memory is used to store executable instructions, and the processor is used to execute the instructions to implement the revenue calculation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores instructions that, when executed, implement the revenue calculation method according to any one of claims 1-7.

Images