Wind and light storage optimal configuration method and system considering spot market and capacity electricity price

Abstract

The invention provides a wind and light storage optimal configuration method and system considering spot market and capacity electricity price. The method comprises the following steps: acquiring technical and economic parameters of each element in project planning, and acquiring market operation environment data of the project planning; based on the technical economic parameters and the market operation environment data, constructing and solving a total factor optimization model; in response to an optimization result which is obtained by solving and meets the optimization target, determining an investment return rate of project planning in combination with the market operation environment data; and in response to the fact that the investment return rate does not meet the preset threshold requirement, executing a solving cycle by taking the adjustment scale parameter as a start until the investment return rate meeting the preset threshold requirement is obtained, and taking an optimization result obtained by current cycle solving as a configuration result of project planning. According to the invention, economic optimal configuration of wind and light storage cluster energy storage under the conditions of adapting to spot market fluctuation and obtaining capacity electricity price income is realized, and the reliability and economical efficiency of project planning are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning and operation, in particular to a wind-solar-storage optimization configuration method and system considering spot market and capacity price. BACKGROUND

[0002] With the rapid increase of new energy generation proportion, the power system is facing the structural contradiction of increasingly prominent periodical supply and demand tension and new energy consumption difficulty. Under this background, the wind-solar-storage cluster as a development mode that can smooth fluctuations and improve controllability of output, has become an important direction to promote efficient use of new energy. At the same time, the in-depth promotion of power marketization reform makes it inevitable for new energy projects to participate in spot market transactions, and its revenue mode changes from fixed on-grid price to market supply and demand, and the uncertainty of project investment and operation increases significantly.

[0003] The existing energy storage optimization configuration method focuses on the joint operation of single energy type and energy storage, or only considers the price factor in local scenarios, and is difficult to be directly applied to the complex application scenario of wind-solar-storage cluster. Especially in the current market environment, the existing method has two limitations: one is that it fails to comprehensively consider the comprehensive influence of spot market price signal and capacity compensation mechanism on energy storage configuration, and ignores the key role of capacity price income on project economy; the other is that the optimization model lacks effective description of market transaction rules, resulting in that the configuration scheme lacks technical and economic efficiency in the actual market environment, and cannot realize investment benefit maximization while ensuring system reliability.

[0004] Therefore, the present application provides a wind-solar-storage optimization configuration method and system considering spot market and capacity price to solve one of the above technical problems. SUMMARY

[0005] The present application aims to provide a wind-solar-storage optimization configuration method and system considering spot market and capacity price, which can solve at least one of the above technical problems. The specific scheme is as follows: According to the specific embodiment of the present application, in a first aspect, the present application provides a wind-solar-storage optimization configuration method considering spot market and capacity price, comprising: Obtaining technical and economic parameters of each element in a project planning, and obtaining market operation environment data of the project planning; wherein, the elements include wind power, photovoltaic and electric energy storage, and a scale parameter of the technical and economic parameters of the electric energy storage is obtained based on a preset; based on the technical and economic parameters and the market operation environment data, a full-element optimization model is constructed and solved; wherein, the full-element optimization model takes minimization of total cost of the project planning as an optimization objective, and contains operation constraints of each element in the project planning; in response to an optimization result obtained by solving and meeting the optimization objective, an investment return rate of the project planning is determined in combination with the market operation environment data; in response to the investment return rate not meeting a preset threshold requirement, a solving loop is executed starting from adjustment of the scale parameter, until an investment return rate meeting the preset threshold requirement is obtained, and an optimization result obtained by solving in a current loop is taken as a configuration result of the project planning.

[0006] In an implementation manner, an economic optimization objective of the full-element optimization model is to minimize total cost, and a calculation formula is as follows: ; wherein, l N represents a number of elements involved in the project planning, including wind power, photovoltaic, and electric energy storage, Cf represents a fixed operation cost of each element, Cv represents a unit variable operation cost of each element, P represents actual output of each element, I represents initial investment of each element.

[0007] In an implementation manner, operation constraints of the full-element optimization model include wind power output constraints, photovoltaic output constraints, and electric energy storage constraints; the wind power output constraints include: 0 p w [t] p ideal w [t] ; wherein, p w Pw represents actual wind power, p ideal w Pw represents ideal wind power output; the photovoltaic output constraints include: 0 p p [t] p ideal p [t] ; wherein, p p Pp represents actual photovoltaic power, p ideal p[t] represents the ideal output of photovoltaic power generation; the energy storage constraint consists of upper and lower limits of charge / discharge power constraint and upper and lower limits of energy storage capacity constraint; the upper and lower limits of charge / discharge power constraint includes: p ch b ch [t] *p ch + ; p dc [t] b dc [t] *p dc + b ch [t]+b dc [t]<=1; where, p ch [t] represents the energy storage power station in t Charging power at any time p dc [t] represents the energy storage power station in t Discharge power at any given time p ch + This refers to the upper limit of the energy storage charging power of an energy storage power station. p dc + b is the upper limit of the energy storage discharge power of the energy storage power station. ch [t] represents the energy storage system in t The charging status at any time, b dc [t] represents the energy storage system in t Discharge state at time b ch [t] and b dc [t] takes the value of 0 or 1; the upper and lower limits of the energy storage capacity include: E [t]= E [t0]+ ; E [t] ;in, E [t] represents the energy storage power station in t Battery level at any moment For energy storage charging efficiency, For the discharge efficiency of energy storage, This is the upper limit of the energy storage capacity. This is the lower limit of the energy storage capacity.

[0008] In one embodiment, the electricity market operating environment data includes peak load periods, spot price curves, market trading rules, and capacity pricing; the variable operating costs of each element are calculated based on the market trading rules, the spot price curves, the capacity pricing, and operation and maintenance costs.

[0009] In one implementation, the operational constraints of the total factor optimization model further include confidence output constraints, which are determined based on the peak load period and are expressed as follows: P out , l A; ;in, P out Indicates the wind-solar-storage cluster at any time l The total output power, Let A represent the minimum output level, A be the set of peak load periods, and count(A) represent the number of elements in set A. Indicates the confidence level.

[0010] In one embodiment, the spot price curve is obtained as follows: historical electricity price data, regional electricity load, and meteorological factors of the project plan are acquired; the hyperparameters of the long short-term memory network model are optimized using a Bayesian optimization method; wherein, the hyperparameters include the number of hidden layer neurons, the learning rate, and the batch size; the long short-term memory network model is used to perform time-series analysis and prediction on the historical electricity price data, the regional electricity load, and the meteorological factors to obtain the spot price curve.

[0011] In one embodiment, the calculation formula of the Bayesian optimization method includes: ; ;in, Hyperparameters The probability distribution of the performance function, This represents the hyperparameters in the Long Short-Term Memory (LSTM) network model. , Let these represent the mean function and the variance function, respectively. Expressing expectations, This represents the expected promotion function. This represents the current optimal hyperparameter configuration. This indicates taking the maximum value of the set; the calculation formula for the Long Short-Term Memory network model includes: ; ; ; ; ; ;in, This represents the predicted spot price curve. i , f , o , c These represent the input gate, forget gate, output gate, and memory unit in a long short-term memory network model. W , U and b Let represent the input weight matrix, hidden state matrix, and bias term for each of the above steps, and t represent the time corresponding to the parameters.

[0012] In one implementation, the market trading rules are expressed as follows: ;in, This represents the total annual electricity market revenue, with EN indicating the total electricity output of the project throughout the year. This represents the project's average power output per hour. , These represent medium- to long-term prices and spot prices, respectively.

[0013] In one implementation, the determination of the project's planned rate of return, based on the market operating environment data, is achieved using the following formula: Where IRR represents the rate of return on investment, and s represents the number of periods. Let represent the net cash flow in period s; where, = - ;in, This represents the total annual electricity market revenue, and 'l' represents the number of elements involved in the project planning. This represents the initial investment cost of the i-th element. This represents the total initial investment cost of all elements in the project plan.

[0014] According to a specific embodiment of this application, in a second aspect, this application provides a wind-solar-storage optimized configuration system that considers the spot market and capacity pricing, comprising: The system includes a parameter acquisition module for acquiring the technical and economic parameters of each element in the project plan, as well as the market operation environment data of the project plan; wherein the elements include wind power, photovoltaic power, and energy storage, and the scale parameter of the energy storage is obtained based on a preset technical and economic parameter; an optimization model construction module for constructing a full-factor optimization model based on the technical and economic parameters and the market operation environment data; wherein the full-factor optimization model aims to minimize the total cost of the project plan and includes the operational constraints of each element in the project plan; a model solving module for solving the full-factor optimization model to obtain an optimization result that satisfies the optimization objective; an economic evaluation module for determining the rate of return on investment of the project plan by combining the market operation environment data when an optimization result that satisfies the optimization objective is obtained; and an iterative configuration module for executing a solution loop starting with adjusting the scale parameter when the rate of return on investment does not meet the preset threshold requirement, until an rate of return on investment that meets the preset threshold requirement is obtained, and then using the optimization result obtained in the current loop as the configuration result of the project plan.

[0015] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects: On the one hand, this application effectively solves the problem of energy storage optimization configuration in wind-solar-storage cluster scenarios by constructing a full-factor optimization model and solving iteratively. Specifically, addressing the limitation of existing technologies that are only applicable to the joint configuration of a single energy type and energy storage, this application simultaneously obtains the technical and economic parameters of wind power, photovoltaics, and energy storage, and comprehensively considers the operational constraints and complementary characteristics of each element in a unified full-factor optimization model, thereby achieving overall coordinated optimization of wind-solar-storage clusters and breaking through the applicability limitations of single-energy type configuration schemes.

[0016] On the other hand, to address the issue that existing technologies do not fully consider the electricity market environment, this method introduces market operation environment data that includes spot price curves, capacity tariffs, and market trading rules. It incorporates market revenue and operating costs into the optimization objective and adopts an iterative adjustment mechanism based on the rate of return on investment to ensure that the configuration scheme can both adapt to spot market fluctuations and obtain capacity tariff revenue. This significantly improves the project's economic efficiency while ensuring system reliability. Attached Figure Description

[0017] Figure 1 A flowchart is shown for an optimal allocation method of wind, solar and energy storage that takes into account the spot market and capacity pricing. Figure 2 A flowchart illustrating the implementation of a specific solution is shown; Figure 3 A block diagram of a wind-solar-storage optimized configuration system considering spot market and capacity pricing, according to an embodiment of this application, is shown. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0022] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0023] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0024] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0025] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0026] The embodiments provided in this application are embodiments of a wind, solar and energy storage optimization allocation method that takes into account the spot market and capacity pricing.

[0027] The following is combined with Figure 1 The embodiments of this application will be described in detail.

[0028] Figure 1 A flowchart illustrating an optimal allocation method for wind, solar, and energy storage that considers the spot market and capacity pricing is shown, such as... Figure 1 As shown, the procedure includes steps S101 to S104.

[0029] S101. Obtain the technical and economic parameters of each element in the project plan, and obtain the market operation environment data of the project plan.

[0030] In this application, the elements may include wind power, photovoltaic power and energy storage, and the electricity market operating environment data may include peak load periods, spot price curves, market trading rules and capacity pricing.

[0031] Among the technical and economic parameters of electric energy storage, the scale parameter is obtained based on a preset value.

[0032] S102. Based on technical and economic parameters and market operating environment data, construct and solve the total factor optimization model.

[0033] The total factor optimization model aims to minimize the total cost of project planning and includes operational constraints on each factor in the project planning.

[0034] For example, operational constraints may include wind power output constraints, photovoltaic power output constraints, energy storage constraints, and confidence output constraints. S103. In response to obtaining the optimization result that satisfies the optimization objective, and combined with market operation environment data, determine the investment return rate of the project plan.

[0035] S104. In response to the fact that the rate of return on investment does not meet the preset threshold requirement, the solution loop is executed starting with adjusting the scale parameter until the rate of return on investment that meets the preset threshold requirement is obtained. Then, the optimization result obtained from the current loop is used as the configuration result of the project planning.

[0036] In this application, the project planning configuration results are a specially designed wind-solar-storage cluster with optimized energy storage configuration, which enables the current system to maximize both reliability and investment benefits.

[0037] The method provided in this application effectively solves the energy storage optimization configuration problem in wind-solar-storage cluster scenarios by constructing a comprehensive optimization model and solving iteratively. Specifically, addressing the limitation of existing technologies that are only applicable to the joint configuration of a single energy type and energy storage, this application simultaneously obtains the technical and economic parameters of wind power, photovoltaics, and energy storage, and comprehensively considers the operational constraints and complementary characteristics of each element in a unified comprehensive optimization model, thereby achieving overall coordinated optimization of wind-solar-storage clusters and breaking through the applicability limitations of single-energy type configuration schemes.

[0038] On the other hand, to address the issue that existing technologies do not fully consider the electricity market environment, this method introduces market operation environment data that includes spot price curves, capacity tariffs, and market trading rules. It incorporates market revenue and operating costs into the optimization objective and adopts an iterative adjustment mechanism based on the rate of return on investment to ensure that the configuration scheme can both adapt to spot market fluctuations and obtain capacity tariff revenue. This significantly improves the project's economic efficiency while ensuring system reliability.

[0039] In some embodiments, the economic optimization objective of the total factor optimization model is to minimize the total cost, calculated using the following formula: ; in, l This indicates the number of elements involved in the project plan, including wind power, solar power, and energy storage. This represents the fixed operating costs of each factor. This represents the variable operating cost per unit of each factor. This indicates the actual output of each element. This indicates the initial investment in each factor.

[0040] In this application, in step S102, the operational constraints of the total factor optimization model include operational constraints of each factor, such as wind power output constraints, photovoltaic power output constraints, and energy storage constraints, as well as confidence output constraints designed with confidence output targets.

[0041] Among them, wind power output constraints include: 0 p w [t] p ideal w [t]; in, p w [t] represents the actual power generated by wind power. p ideal w [t] represents the ideal output of wind power generation; this constraint indicates that... t At any given time, the actual power output of wind power generation is less than its ideal output.

[0042] Solar power output constraints include: 0 p p [t] p ideal p [t]; in, p p [t] represents the actual power of photovoltaic power generation. p ideal p [t] represents the ideal output of photovoltaic power generation, and this constraint indicates that... t At any given time, the actual power output of photovoltaic power generation is less than its ideal output.

[0043] Among them, regarding photovoltaic power output constraints and wind power output constraints, ideal output means the maximum power that a wind turbine or photovoltaic array can theoretically generate under the natural conditions at a specific moment, without considering any artificial power curtailment or system constraints.

[0044] In some embodiments, the energy storage constraints consist of upper and lower limits for charge and discharge power and upper and lower limits for stored energy capacity.

[0045] The upper and lower limits of charging and discharging power constraints include: p ch b ch [t] *p ch + ; p dc [t] b dc [t] *p dc + ; b ch [t]+b dc [t]<=1; in, p ch [t] represents the energy storage power station in t Charging power at any time p dc [t] represents the energy storage power station in t Discharge power at any given time p ch + This refers to the upper limit of the energy storage charging power of an energy storage power station. p dc +b is the upper limit of the energy storage discharge power of the energy storage power station. ch [t] represents the energy storage system in t The charging status at any time, b dc [t] represents the energy storage system in t Discharge state at time b ch [t] and b dc [t] can take the value 0 or 1.

[0046] The upper and lower limits of energy storage capacity constraints include: E [t]= E [t0]+ ; E [t] ; in, E [t] represents the energy storage power station in t Battery level at any moment For energy storage charging efficiency, For the discharge efficiency of energy storage, This is the upper limit of the energy storage capacity. This is the lower limit of the energy storage capacity.

[0047] In some specific embodiments, the confidence output constraint is determined based on peak load periods, as shown below: P out , l A; ; in, P out Indicates the wind-solar-storage cluster at any time l The total output power, Let A represent the minimum output level, A be the set of peak load periods, and count(A) represent the number of elements in set A. Indicates the confidence level.

[0048] In this application, in step S101, the electricity market operating environment data includes peak load periods, spot price curves, market trading rules, and capacity pricing.

[0049] In the above embodiments, the variable operating costs of each element are calculated based on market trading rules, spot price curves, capacity electricity prices, and operation and maintenance costs.

[0050] In some specific embodiments, the variable operating costs of elements such as wind power, photovoltaics, and energy storage are first dynamically assessed based on the acquired market trading rules, predicted spot price curves, and capacity electricity price data.

[0051] For wind power and solar power, variable operating costs mainly consider deviation assessment risks. That is, when the actual output deviates from the medium- and long-term contract plan, the settlement cost or benefit of the deviation electricity is calculated based on the spot price curve, which is then indirectly reflected as variable costs.

[0052] For energy storage, the variable operating cost includes the cost of purchasing electricity calculated based on the spot price curve during charging, and the opportunity cost incurred by forgoing future market opportunities during discharging. This opportunity cost is obtained by inversely solving the total factor optimization model under the conditions of satisfying the confidence output target and market rules.

[0053] Based on the calculations in the above embodiments, the fixed maintenance costs of each element are subsequently added to obtain the final variable operating costs of each element.

[0054] In some embodiments, the spot price curve is obtained as follows: historical electricity price data, regional electricity load, and meteorological factors are obtained for the project plan; the hyperparameters of the long short-term memory network model are optimized using the Bayesian optimization method; and the long short-term memory network model is used to perform time-series analysis and prediction on the historical electricity price data, regional electricity load, and meteorological factors to obtain the spot price curve.

[0055] The hyperparameters of the Long Short-Term Memory (LSTM) network model include the number of neurons in the hidden layer, the learning rate, and the batch size.

[0056] In some specific embodiments, the calculation formula of the Bayesian optimization method includes: ; ; in, Hyperparameters The probability distribution of the performance function, This represents the hyperparameters in the Long Short-Term Memory (LSTM) network model. , Let these represent the mean function and the variance function, respectively. Expressing expectations, This represents the expected promotion function. This represents the current optimal hyperparameter configuration. This indicates taking the maximum value of the set.

[0057] The calculation formulas for the Long Short-Term Memory (LSTM) network model include: ; ; ; ; ; ; in, This represents the predicted spot price curve. i , f , o , c These represent the input gate, forget gate, output gate, and memory unit in a long short-term memory network model. W , U and b Let represent the input weight matrix, hidden state matrix, and bias term for each of the above steps, and t represent the time corresponding to the parameters.

[0058] In some specific embodiments, the market trading rules are based on typical electricity markets, including medium- and long-term markets and spot markets, and adopt deviation electricity settlement rules, as shown below: ; in, This represents the total annual electricity market revenue, with EN indicating the total electricity output of the project throughout the year. This represents the project's average power output per hour. , These represent medium- to long-term prices and spot prices, respectively.

[0059] In this application, step S103 determines the project's planned rate of return (ROI) by combining market operating environment data. Specifically, firstly, a confidence output target for the wind-solar-storage cluster is set based on the acquired peak load period and capacity price data. This confidence output target is then embedded as a key constraint into the total factor optimization model. During the model solving process, the energy storage charging and discharging strategy is optimized in conjunction with the spot price curve to achieve a balance between spot market arbitrage and capacity revenue protection. Further, after the optimal output plan is generated by solving the total factor optimization model, the actual spot market revenue is calculated based on market trading rules and the spot price curve. Simultaneously, capacity price revenue is confirmed based on the actual output performance during peak load periods. Based on this, the two revenue components are finally aggregated to form the project's full life-cycle cash flow, and the RPI is calculated using discounted cash flow.

[0060] In the above embodiments, the following formula is used to calculate the project's planned rate of return on investment: ; Where IRR represents the rate of return on investment, and s represents the number of periods. This represents the net cash flow in period s, where inputs are positive and returns are negative.

[0061] in, = - ; in, This represents the total annual electricity market revenue, and 'l' represents the number of elements involved in the project planning. This represents the initial investment cost of the i-th element. This represents the total initial investment cost of all elements in the project plan.

[0062] Figure 2 A flowchart illustrating the implementation of a specific solution is shown.

[0063] In the specific implementation process, such as Figure 2 As shown, the first step involves acquiring various types of data. Specifically, this includes acquiring wind power technical and economic parameters, photovoltaic technical and economic parameters, pre-setting energy storage scale parameters and acquiring other technical and economic parameters, acquiring market trading rules and predicting spot price curves, acquiring capacity tariffs, and acquiring peak load periods. When acquiring wind power technical and economic parameters, an ideal 8760-hour wind power output curve is generated based on historical power generation data of wind power projects in the project location and meteorological tower measurement data, and the installed capacity, unit cost, and unit fixed operating cost of wind power are obtained. Similarly, when acquiring photovoltaic technical and economic parameters, an ideal 8760-hour photovoltaic output curve is generated based on historical power generation data of photovoltaic projects in the project location, and the installed capacity, unit cost, and unit fixed operating cost of photovoltaic power are obtained. When pre-setting energy storage scale parameters, the installed capacity and storage duration of energy storage are set, and the unit cost, unit fixed operating cost, efficiency, charging loss, available depth ratio, and initial charging ratio of energy storage are obtained. When acquiring market trading rules and predicting spot price curves, a pre-trained Bayesian optimization-long short-term memory network prediction model is used to perform time-series analysis on historical electricity price data, regional electricity load, and meteorological factors, based on the market trading rules of the project location, to generate spot price curves. When acquiring capacity tariffs, the capacity tariff level of the project location is recorded. When acquiring peak load periods, the duration of peak load periods is determined based on historical load data analysis of the project area.

[0064] After acquiring the data required for modeling, the total factor optimization model is constructed and solved. Specifically, while adjusting the energy storage scale parameters, a confidence output target for the wind-solar-storage cluster is set based on local wind and solar resources and load levels. A total factor optimization model is constructed based on all acquired technical and economic parameters and market operation environment data. This model takes minimizing the total system cost as the economic optimization objective and includes operational constraints for wind power, photovoltaics, and energy storage. The model is solved using a commercial solver.

[0065] During the solution process of the all-factor optimization model, the iterative optimization and decision-making stage begins. Based on the solution results, combined with market trading rules, predicted spot price curves, and capacity electricity prices, the investment return rate (ROR) threshold is calculated, and it is determined whether this indicator meets the threshold requirements for project approval. If the ROR does not meet the threshold requirements, the process returns to adjusting the energy storage scale parameters and repeats the above optimization solution process. If the ROR meets the threshold requirements, the current optimization result is determined as the final optimized configuration scale of the wind-solar-storage cluster energy storage, completing the configuration process.

[0066] This application also provides system embodiments that follow the above embodiments, for implementing the method steps of the above embodiments. The interpretation of the same names is the same as that of the above embodiments, and they have the same technical effects as those of the above embodiments, so they will not be repeated here.

[0067] like Figure 3 As shown, this application provides a wind-solar-storage optimized configuration system 300 that considers the spot market and capacity pricing, including: The parameter acquisition module 301 is used to acquire the technical and economic parameters of each element in the project plan, as well as the market operation environment data of the project plan. These elements include wind power, photovoltaic power, and energy storage. Among the technical and economic parameters of energy storage, the scale parameter is obtained based on preset parameters.

[0068] The optimization model construction module 302 is used to construct a total factor optimization model based on technical and economic parameters and market operating environment data. The total factor optimization model aims to minimize the total project planning cost and includes operational constraints for each element in the project planning.

[0069] The model solving module 303 is used to solve the total factor optimization model and obtain optimization results that meet the optimization objectives.

[0070] The economic evaluation module 304 is used to determine the rate of return on investment of the project plan by combining market operating environment data when the optimization results that meet the optimization objectives are obtained.

[0071] The iterative configuration module 305 is used to execute a solution loop starting with adjusting the scale parameters when the rate of return on investment does not meet the preset threshold requirement, until the rate of return on investment that meets the preset threshold requirement is obtained, and then the optimization result obtained from the current loop is used as the configuration result of the project planning.

[0072] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0073] Although the operations are described in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0074] The methods and systems of this application can be implemented using standard programming techniques, utilizing rule-based logic or other logic to implement various method steps. It should also be noted that the terms "system" and "module" as used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0075] Any step, operation, or procedure described herein may be performed or implemented using one or more hardware or software modules, alone or in combination with other devices. In one implementation, the software module is implemented using a computer program product comprising a computer-readable medium containing computer program code, which is executable by a computer processor to perform any or all of the described steps, operations, or procedures.

[0076] The foregoing description of implementations of this application has been provided for illustrative and descriptive purposes. The foregoing description is not exhaustive and is not intended to limit this application to the exact forms disclosed. Various modifications and variations may exist in accordance with the foregoing teachings, or may arise from practice of this application. These embodiments were chosen and described to illustrate the principles of this application and its practical application, enabling those skilled in the art to utilize this application in various implementations and modifications to suit the specific purpose of the concept.

[0077] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0078] It can be further understood that, unless otherwise specified, "connection" includes both direct connections where no other components exist between the two parties and indirect connections where other components exist between them.

[0079] It is further understood that although the operations are described in a specific order in the accompanying drawings in the embodiments of this application, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all the operations shown to be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0080] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the field of this application that are not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0081] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

[0082] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing the allocation of wind, solar, and energy storage considering the spot market and capacity pricing, characterized in that, include: The technical and economic parameters of each element in the project plan are obtained, as well as the market operation environment data of the project plan; wherein, each element includes wind power, photovoltaic and energy storage, and the scale parameter of the energy storage is obtained based on a preset; Based on the aforementioned technical and economic parameters and the aforementioned market operating environment data, a total factor optimization model is constructed and solved; wherein, the total factor optimization model takes minimizing the total cost of the project planning as the optimization objective and includes the operational constraints of each element in the project planning; In response to the optimization results that satisfy the optimization objective, and in conjunction with the market operating environment data, the planned rate of return on investment for the project is determined; In response to the fact that the rate of return on investment does not meet the preset threshold requirement, a solution loop is executed starting with adjusting the scale parameter until the rate of return on investment that meets the preset threshold requirement is obtained. Then, the optimization result obtained from the current loop is used as the configuration result of the project planning.

2. The method according to claim 1, characterized in that, The economic optimization objective of the total factor optimization model is to minimize the total cost, and the calculation formula is as follows: ； in, l This indicates the number of elements involved in the project plan, including wind power, solar power, and energy storage. This represents the fixed operating costs of each factor. This represents the variable operating cost per unit of each factor. This indicates the actual output of each element. This indicates the initial investment in each factor.

3. The method according to claim 2, characterized in that, The operational constraints of the total factor optimization model include wind power output constraints, photovoltaic power output constraints, and energy storage constraints. The wind power output constraints include: 0 p w [t] p ideal w [t]; in, p w [t] represents the actual power generated by wind power. p ideal w [t] represents the ideal output of wind power generation; The photovoltaic output constraints include: 0 p p [t] p ideal p [t]; in, p p [t] represents the actual power of photovoltaic power generation. p ideal p [t] represents the ideal output of photovoltaic power generation; The energy storage constraints consist of upper and lower limits for charging and discharging power and upper and lower limits for stored energy capacity. The upper and lower limits of the charging and discharging power constraints include: p ch b ch [t] *p ch + ; p dc [t] b dc [t] *p dc + ; b ch [t]+b dc [t]<=1; in, p ch [t] represents the energy storage power station in t Charging power at any time p dc [t] represents the energy storage power station in t Discharge power at any given time p ch + This refers to the upper limit of the energy storage charging power of an energy storage power station. p dc + b is the upper limit of the energy storage discharge power of the energy storage power station. ch [t] represents the energy storage system in t The charging status at any time, b dc [t] represents the energy storage system in t Discharge state at time b ch [t] and b dc Each of [t] can take the value 0 or 1; The upper and lower limits of the energy storage capacity constraints include: E [t]= E [t0]+ ; E [t] ； in, E [t] represents the energy storage power station in t Battery level at any moment For energy storage charging efficiency, For the discharge efficiency of energy storage, This is the upper limit of the energy storage capacity. This is the lower limit for the amount of energy stored.

4. The method according to claim 2, characterized in that, The electricity market operating environment data includes peak load periods, spot price curves, market trading rules, and capacity pricing. The variable operating costs of each element are calculated based on the market trading rules, the spot price curve, the capacity electricity price, and the operation and maintenance costs.

5. The method according to claim 4, characterized in that, The operational constraints of the total factor optimization model also include confidence output constraints, which are determined based on the peak load period and are expressed as follows: P out ， l A； ； in, P out Indicates the wind-solar-storage cluster at any time l The total output power, Let A represent the minimum output level, A be the set of peak load periods, and count(A) represent the number of elements in set A. Indicates the confidence level.

6. The method according to claim 4, characterized in that, The spot price curve was obtained in the following way: Obtain historical electricity price data, regional electricity load, and meteorological factors for the project plan; The hyperparameters of the Long Short-Term Memory network model are optimized using a Bayesian optimization method; wherein the hyperparameters include the number of hidden layer neurons, the learning rate, and the batch size. Using a long short-term memory network model, time-series analysis and prediction are performed on the historical electricity price data, the regional electricity load, and the meteorological factors to obtain the spot price curve.

7. The method according to claim 6, characterized in that, The calculation formula for the Bayesian optimization method includes: ； ； in, Hyperparameters The probability distribution of the performance function, This represents the hyperparameters in the Long Short-Term Memory (LSTM) network model. , Let these represent the mean function and the variance function, respectively. Expressing expectations, This represents the expected promotion function. This represents the current optimal hyperparameter configuration. This indicates taking the maximum value of the set; The calculation formula for the Long Short-Term Memory (LSTM) network model includes: ； ； ； ； ； ； in, This represents the predicted spot price curve. i , f , o , c These represent the input gate, forget gate, output gate, and memory unit in a long short-term memory network model. W , U and b Let represent the input weight matrix, hidden state matrix, and bias term for each of the above steps, and t represent the time corresponding to the parameters.

8. The method according to claim 1 or 4, characterized in that, The market trading rules are expressed as follows: ； in, This represents the total annual electricity market revenue, with EN indicating the total electricity output of the project throughout the year. This represents the project's average power output per hour. , These represent medium- to long-term prices and spot prices, respectively.

9. The method according to claim 8, characterized in that, The investment return rate of the project is determined by combining the market operating environment data, using the following formula: ； Where IRR represents the rate of return on investment, and s represents the number of periods. This represents the net cash flow in period s; in, = - ; in, This represents the total annual electricity market revenue, and 'l' represents the number of elements involved in the project planning. This represents the initial investment cost of the i-th element. This represents the total initial investment cost of all elements in the project plan.

10. A wind-solar-storage optimized allocation system considering spot market and capacity pricing, characterized in that, include: The parameter acquisition module is used to acquire the technical and economic parameters of each element in the project plan, as well as the market operation environment data of the project plan; wherein, each element includes wind power, photovoltaic and energy storage, and the scale parameter of the energy storage is obtained based on a preset; An optimization model construction module is used to construct a full-factor optimization model based on the technical and economic parameters and the market operating environment data; wherein, the full-factor optimization model takes minimizing the total cost of the project planning as the optimization objective and includes the operational constraints of each element in the project planning; The model solving module is used to solve the total factor optimization model to obtain optimization results that satisfy the optimization objective; The economic evaluation module is used to determine the rate of return on investment of the project plan by combining the market operating environment data when the optimization result that satisfies the optimization objective is obtained. The iterative configuration module is used to execute a solution loop starting with adjusting the scale parameter when the rate of return on investment does not meet the preset threshold requirement, until the rate of return on investment that meets the preset threshold requirement is obtained, and then use the optimization result obtained from the current loop as the configuration result of the project planning.

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