CN115600757A - Coordination optimization method and system for offshore wind power sharing energy storage participation spot market trading - Google Patents

Abstract

The invention discloses a coordination optimization method and a system for offshore wind power sharing energy storage to participate in spot market trading, wherein the coordination optimization method comprises the following steps: acquiring parameters of an offshore wind power cluster and self-distribution energy storage; generating offshore wind power output information by using a scene method; establishing a profit model of the offshore wind farm participating in spot market transaction based on the parameters and the wind power output information; establishing an optimized operation model and constraint conditions for participating in spot market trading of the offshore wind power cluster based on the shared energy storage based on the profit model with the maximum yield of the combined operation of the offshore wind power and the shared energy storage as an objective function; and carrying out optimization solution on the optimized operation model of the offshore wind power cluster participating in spot market trading to obtain the optimal charge and discharge power of the shared energy storage. The invention integrates the offshore wind power plants to form an alliance, and jointly dispatches in a shared energy storage mode, thereby improving the income of the whole offshore wind power cluster.

Description

Coordination and optimization method and system for offshore wind power shared energy storage to participate in spot market transactions

technical field

The invention belongs to the technical field of offshore wind power, and in particular relates to a coordination and optimization method, system, equipment and medium for sharing energy storage of offshore wind power to participate in spot market transactions.

Background technique

In recent years, with the increasingly serious energy crisis and environmental problems, renewable energy such as wind power has attracted more and more attention. Compared with onshore wind power, offshore wind power has the characteristics of being closer to the power load center, offshore wind turbines do not occupy land resources, output fluctuations are smaller, and offshore wind turbines are more efficient. The large-scale application of offshore wind power can effectively deal with energy crisis and environmental problems.

Aiming at the problem of large-scale offshore wind power consumption, the energy storage system is one of the effective ways to solve this problem. It stores electric energy during the peak stage of offshore wind power output and releases electric energy during the low output stage to obtain more electric energy revenue. However, the investment cost of the current energy storage system is still relatively high, especially for large-scale energy storage systems. The method of only relying on the energy storage system to participate in electric energy transactions to improve economic benefits has a long recovery period for the cost of the energy storage system, and the utilization rate of the energy storage system is low. . The energy storage system has become a high-quality frequency modulation resource because it can effectively participate in frequency modulation auxiliary services due to its fast adjustment and other characteristics. Through appropriate energy management strategies and control strategies for energy storage power stations, offshore wind power and energy storage power stations can jointly participate in peak-shaving and frequency-regulating auxiliary services.

Contents of the invention

In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions. Based on the concept of cooperation, the method of the invention shares the energy storage of the offshore wind farms in an alliance, and jointly participates in the dispatching of the power grid, which can not only effectively improve the consumption level of the offshore wind power, but also improve the economic benefits of the offshore wind power.

In order to achieve the above object, the present invention adopts the following technical solutions to achieve:

A method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions, including:

Obtain various parameters of offshore wind power and self-configured energy storage; use the scenario method to generate wind power output information;

Based on the various parameters and the wind power output information, an income model for offshore wind farms participating in spot market transactions is established; with the maximum income from the combined operation of offshore wind power and shared energy storage as the objective function, a shared energy storage based model is constructed based on the income model. Optimal operation model and constraints for offshore wind power clusters to participate in spot market transactions;

The optimized operation model is optimized and solved based on the constraint conditions, and the target charge and discharge power of the shared energy storage is obtained.

As a further improvement of the present invention, the various parameters include: rated power of the wind farm, energy storage capacity, maximum charging and discharging power of the energy storage, SOC interval of the energy storage, construction cost and maximum charging and discharging times.

As a further improvement of the present invention, the generation of wind power output information using the scenario method includes:

Predict the wind speed information according to the autoregressive moving average model;

The wind speed prediction errors are sampled using Latin hypercube stratified sampling, assuming that each sample collected has an equal probability;

Use backward reduction technology to reduce the sample set of prediction error scenarios, and merge similar scenarios to generate offshore wind power output scenarios;

According to the relationship between wind speed and wind power, the wind power limited output scenario of the offshore wind power output scenario and the probability of the corresponding scenario are given;

The wind power limited output scenario and the probability of the corresponding scenario are used as wind power output information.

As a further improvement of the present invention, the establishment of a revenue model for offshore wind farms participating in spot market transactions, the revenue model includes a day-ahead market revenue model, a real-time market revenue model, and an energy storage cycle life cost model, specifically:

The day-ahead market return model is:

The real-time market return model is:

In the formula: T is the number of Δt segments included in the alliance cycle, S is the total number of offshore wind power output scenarios, γ _s is the probability of scenario s, W _A,t is the total income of alliance A participating in the spot market transaction at time period t, E _{i , t} is the total revenue of offshore wind farm i in period t, specifically expressed as follows:

为海上风电场在日前市场上的收益，

为海上风电场在实时市场上的收益,

为日前出清价格，

为海上风电场在日前市场的投标值，

为实际发电功率，λ⁺、λ^-分别为正惩罚系数、负惩罚系数；In the formula:

is the return of the offshore wind farm in the day-ahead market,

For the earnings of offshore wind farms in the real-time market,

is the day-ahead clearing price,

is the bidding value of the offshore wind farm in the day-ahead market,

is the actual generated power, λ ⁺ , λ ^- are positive penalty coefficient and negative penalty coefficient respectively;

The cycle life cost model of energy storage is:

为储能在第t个时段的储能等效循环次数。In the formula, C _cycle is the cycle life cost of energy storage, N is the construction cost of energy storage,

is the number of energy storage equivalent cycles of energy storage in the tth period.

As a further improvement of the present invention, the objective function is to maximize the joint operating income of offshore wind power and shared energy storage, and the objective function is:

Max(E _A -C _cycle )

In the formula, C _cycle is the energy storage cycle life cost, and E _A is the day-ahead market revenue model;

The shared energy storage provides charging and discharging power to the offshore wind farm according to the power generation error state of the offshore wind farm;

Statistical error status of offshore wind farm power generation:

ΔP _t =P _real -P _d

In the formula, ΔP _t is the power generation error of the offshore wind farm at time t, P _real is the actual power generation, and P _d is the power bid in the day-ahead market.

As a further improvement of the present invention, the constraints include:

Wind power bidding power constraints:

为风电场在日前市场的投标功率，Pt,max为风电场额定功率；In the formula,

is the bidding power of the wind farm in the day-ahead market, and Pt,max is the rated power of the wind farm;

Positive and negative deviation assessment price constraints:

0<λ ⁺ <1

λ ^- >1

In the formula, λ ⁺ , λ ^- are the positive deviation assessment price coefficient and the negative deviation assessment price technology of the wind power supplier's current bidding output and actual output, respectively;

Power balance constraints:

In the formula, P _i,t is the charging and discharging power provided by the shared energy storage to new energy power station i during t period, and P _d,i,t is the charging and discharging demand power of new energy power station i during t period;

Energy storage charge and discharge power constraints:

-P _max ≤P _i,t ≤P _max

P _max ＝min{P _c ,P _m,i,t }

P _m,i,t ＝(S _SOC,i,i-1 -S _SOC,min )C _i η _dis /Δt

In the formula, P _max is the maximum charging and discharging power of the energy storage, P _c is the rated power of the energy storage, P _m,i,t is the corresponding average power when the available electricity of the new energy power station i in the t period is fully released in the t period , S _SOC,min is the lower limit value of the self-provided energy storage state of the new energy power station, C _i is the rated capacity of the energy storage, and η _dis is the charging and discharging efficiency;

Energy storage capacity constraints:

S _SOC,min ≤S _soc,i,t ≤S _SOC,max

In the formula, S _SOC,min and S _SOC,max are the lower limit value and upper limit value of the energy storage state of charge respectively;

Energy storage charge and discharge state constraints:

In the formula, β _ch and β _dis are the charging and discharging state variables of the energy storage system, respectively, 0 represents charging, and 1 represents discharging.

As a further improvement of the present invention, the optimal operation model of the shared energy storage-based offshore wind power cluster participating in the spot market transaction is optimized and solved to obtain the target charging and discharging power of the shared energy storage, including:

Summarize the power generation information and energy storage status of each offshore wind farm;

Randomly generate charging and discharging power schemes for N groups of shared energy storage to provide to each offshore wind farm during period t;

After the iterative optimization of the optimized operation model with a preset number of iterations, the final target charge and discharge power scheme provided by the shared energy storage to each offshore wind farm in the period t is obtained.

A coordination and optimization system for offshore wind power shared energy storage to participate in spot market transactions, including:

The acquisition module is used to obtain various parameters of offshore wind power and self-configured energy storage; use the scenario method to generate wind power output information;

The modeling module is used to establish a revenue model for offshore wind farms participating in spot market transactions based on the various parameters and the wind power output information; taking the maximum profit of joint operation of offshore wind power and shared energy storage as the objective function, based on the revenue model Build an optimized operation model and constraints for offshore wind power clusters participating in spot market transactions based on shared energy storage;

A solving module is configured to optimize and solve the optimal operation model based on constraint conditions to obtain the target charging and discharging power of the shared energy storage.

An electronic device, comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program, the offshore wind power shared energy storage participating spot Steps of a market transaction coordination optimization method.

A computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for the coordination and optimization of offshore wind power shared energy storage participating in spot market transactions are realized.

Compared with the prior art, the present invention has the following beneficial effects:

The coordination and optimization method for offshore wind power shared energy storage participating in spot market transactions of the present invention aims at maximizing the benefits of offshore wind power and shared energy storage systems, and constructs an optimized operation model for offshore wind power cluster alliances participating in shared energy storage to participate in the spot market; the generated model is provided to Optimal charge and discharge power schemes for each offshore wind farm. The invention integrates offshore wind farms to form an alliance, and jointly dispatches in the form of shared energy storage, thereby increasing the income of the entire offshore wind farm cluster. Based on the idea of cooperation, this method shares the energy storage of offshore wind farms in an alliance and participates in power grid dispatching, which can not only effectively improve the consumption level of offshore wind power, but also improve the economic benefits of offshore wind power.

Description of drawings

Fig. 1 is a flow chart of a method for coordinating and optimizing offshore wind power shared energy storage participating in spot market transactions provided by the present invention;

Figure 2 shows the topological structure diagram of offshore wind farms and shared evil energy;

Figure 3 shows the optimal charging and discharging power process for the offshore wind farm provided by the chaotic quantum genetic algorithm to optimize and solve the shared energy storage;

Fig. 4 is a coordination and optimization system for sharing energy storage of offshore wind power to participate in spot market transactions according to the present invention;

Fig. 5 is a schematic diagram of an electronic device of the present invention.

detailed description

In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed Those steps or elements may instead include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

The invention provides a coordination and optimization method for offshore wind power shared energy storage to participate in spot market transactions. Based on the idea of cooperation, this method shares the energy storage of offshore wind farms in an alliance and participates in power grid dispatching, which can not only effectively improve the consumption level of offshore wind power, but also improve the economic benefits of offshore wind power. The specific plan is as follows:

A method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions, comprising the following steps:

(1) Select an offshore wind farm cluster in a coastal area, and obtain various parameters of the offshore wind farm and self-provisioned energy storage;

(2) Using the scene method to generate wind power output information;

(3) Establish the income model for offshore wind farms participating in spot market transactions, including the day-ahead market income model, real-time market income model, and energy storage cycle life cost model;

(4) Taking the maximum income from the joint operation of offshore wind power and shared energy storage as the objective function, construct an optimal operation model for offshore wind farm clusters participating in spot market transactions based on shared energy storage;

(5) Establish model constraints, including energy storage charge and discharge state constraints, shared energy storage power balance constraints, capacity constraints, and energy storage charge constraints;

(6) Based on the established optimal operation model of offshore wind power shared energy storage participating in spot market transactions, the chaotic quantum genetic algorithm is used to optimize the solution, and the charging and discharging power of the shared energy storage target is obtained.

The method of the present invention will be described in detail below in conjunction with specific examples.

As shown in Figure 1, it is a flowchart of a method for coordinating and optimizing offshore wind power shared energy storage participating in spot market transactions provided by the present invention. The method for coordinating and optimizing offshore wind power shared energy storage participating in spot market transactions in the present invention includes:

Select a number of offshore wind farms with self-provided energy storage to form an alliance to obtain the predicted output value of offshore wind turbines and various parameters of self-provided energy storage;

With the goal of maximizing the benefits of offshore wind power and shared energy storage systems, build an optimized operation model for offshore wind power cluster alliances participating in the spot market in which shared energy storage participates;

Among them, the present invention considers the cycle life cost of energy storage, and establishes the cost model of energy storage cycle life; the offshore wind power cluster reports daily power generation shortage, power generation surplus, and self-provided energy storage to the shared energy storage platform, and uses offshore wind power and shared energy storage The maximum operating income can be combined as the objective function to generate the optimal charging and discharging power scheme provided to each offshore wind farm. The invention integrates offshore wind farms to form an alliance, and jointly dispatches in the form of shared energy storage, thereby increasing the income of the entire offshore wind farm cluster.

Each step is explained as follows:

Step 1. Select an offshore wind farm cluster in a coastal area, and obtain various parameters of the offshore wind farm and self-configured energy storage. The parameters include: rated power of the wind farm, energy storage capacity, maximum charge and discharge power of energy storage, and Energy SOC range, construction cost, maximum charge and discharge times.

Step 2. Generate offshore wind power output scenarios, and use the scenario method to generate wind power output information; the scenario method is specifically expressed as:

Predict the wind speed information according to the autoregressive moving average model (ARMA);

The wind speed prediction errors are sampled using Latin hypercube stratified sampling, assuming that each sample collected has an equal probability;

Use backward reduction technology to reduce the sample set of prediction error scenarios, and merge similar scenarios to generate offshore wind power output scenarios;

According to the relationship between wind speed and wind power, the limited wind power output scenario of the offshore wind power output scenario and the probability of the corresponding scenario are given.

Step 3. Establish the income model for offshore wind farms participating in spot market transactions, including the day-ahead market income model, real-time market income model, and energy storage cycle life cost model, specifically expressed as:

The day-ahead market return model is:

The real-time market return model is:

In the formula: T is the number of Δt segments included in the alliance cycle, S is the total number of offshore wind power output scenarios, γ _s is the probability of scenario s, W _A,t is the total income of alliance A participating in the spot market transaction at time period t, E _{i , t} is the total revenue of offshore wind farm i in period t, specifically expressed as follows:

为海上风电场在日前市场上的收益，

为海上风电场在实时市场上的收益,

为日前出清价格，

为海上风电场在日前市场的投标值，

为实际发电功率，λ⁺、λ^-分别为正惩罚系数、负惩罚系数；In the formula:

is the return of the offshore wind farm in the day-ahead market,

For the earnings of offshore wind farms in the real-time market,

is the day-ahead clearing price,

is the bidding value of the offshore wind farm in the day-ahead market,

is the actual generated power, λ ⁺ , λ ^- are positive penalty coefficient and negative penalty coefficient respectively;

The cycle life cost model of energy storage is:

为储能在第t个时段的储能等效循环次数；In the formula, C _cycle is the cycle life cost of energy storage, N is the construction cost of energy storage,

is the number of energy storage equivalent cycles of energy storage in the tth period;

Step 4. Taking the maximum income from the joint operation of offshore wind power and shared energy storage as the objective function, construct an optimal operation model for offshore wind farm clusters participating in shared energy storage to participate in spot market transactions, specifically expressed as:

The objective function is:

Max(E _A -C _cycle )

The shared energy storage provides charging and discharging power to the offshore wind farm according to the power generation error state of the offshore wind farm, specifically expressed as:

The status of power generation errors of the alliance's offshore wind farms:

ΔP _t =P _real -P _d

In the formula, ΔP _t is the power generation error of the offshore wind farm at time t, P _real is the actual power generation, and P _d is the power bid in the day-ahead market;

As shown in Figure 2, it is a topological diagram of an offshore wind farm and a shared energy storage platform.

State 1, ΔP>>0, excess power generation. At this time, the shared energy storage platform provides charging power to the offshore wind farm. When the excess power generated exceeds the power supplied to the external shared energy storage, the remaining power is used for self-provisioned energy storage. Charging; when the power supplied to the external shared energy storage exceeds the excess power generated, the self-provisioned energy storage is consumed to supply the external energy storage demand;

State 2, ΔP<0, power generation shortage, at this time the shared energy storage platform provides discharge power to the offshore wind farm,

When the self-provisioned energy storage can fully make up for the shortfall in power generation, shared energy storage provides charging power for the power station; when self-provisioned energy storage cannot fully make up for the shortfall in power generation, shared energy storage provides discharge power for the power station;

Step 5. Establish model constraints, including energy storage charge and discharge state constraints, shared energy storage power balance constraints, capacity constraints, and energy storage charge constraints;

Wind power bidding power constraints:

为风电场在日前市场的投标功率，Pt,max为风电场额定功率。In the formula,

is the bidding power of the wind farm in the day-ahead market, and Pt,max is the rated power of the wind farm.

Positive and negative deviation assessment price constraints:

The electricity price for positive and negative deviation electricity assessment refers to the day-ahead clearing price setting, and meets the constraints that the negative deviation assessment price is greater than the day-ahead market clearing price, and the positive deviation assessment price is less than the day-ahead market clearing price. The specific expression is:

0<λ ⁺ <1

λ ^- >1

In the formula, λ ⁺ , λ ^- are the positive deviation assessment price coefficient and the negative deviation assessment price technology of the wind power supplier's current bidding output and actual output, respectively;

Power balance constraints:

In the formula, P _i,t is the charging and discharging power provided by the shared energy storage to new energy power station i during t period, and P _d,i,t is the charging and discharging demand power of new energy power station i during t period;

Energy storage charge and discharge power constraints:

-P _max ≤P _i,t ≤P _max

P _max ＝min{P _c ,P _m,i,t }

P _m,i,t ＝(S _SOC,i,i-1 -S _SOC,min )C _i η _dis /Δt

In the formula, P _max is the maximum charging and discharging power of the energy storage, P _c is the rated power of the energy storage, P _m,i,t is the corresponding average power when the available electricity of the new energy power station i in the t period is fully released in the t period , S _SOC,min is the lower limit value of the self-provided energy storage state of the new energy power station, C _i is the rated capacity of the energy storage, and η _dis is the charging and discharging efficiency;

Energy storage capacity constraints:

S _SOC,min ≤S _soc,i,t ≤S _SOC,max

In the formula, S _SOC,min and S _SOC,max are the lower limit value and upper limit value of the energy storage state of charge respectively;

Energy storage charge and discharge state constraints:

In the formula, β _ch and β _dis are the charging and discharging state variables of the energy storage system, respectively, 0 represents charging, and 1 represents discharging;

Step 6. Based on the established offshore wind power shared energy storage participating in the spot market optimization operation model, the chaotic quantum genetic algorithm is used to optimize and solve, and the optimal charging and discharging power of the shared energy storage is obtained, as shown in Figure 3, which is the chaotic quantum genetic algorithm Optimize and solve the optimal charging and discharging power process provided by the shared energy storage to the offshore wind farm.

As shown in Figure 3, the specific expression is:

Summarize the power generation information and energy storage status of each offshore wind farm;

Randomly generate charging and discharging power schemes for N groups of shared energy storage to provide to each offshore wind farm during period t;

After the iterative optimization of the maximum number of iterations, the final charging and discharging power scheme provided by the shared energy storage to each offshore wind farm in the period t is obtained through optimization.

Among them, the iterative optimization after the maximum number of iterations (the maximum number of iterations is preset) includes:

Initialize the population and calculate the alliance income of the initial population;

Compare the fitness of the current population to find the global optimal solution;

Use the rotating quantum gate to update the population individual;

The maximum number of iterations is reached, and the optimal charging and discharging power of the shared energy storage target is obtained.

If the maximum number of iterations is not reached, return to the step of initializing the population and re-calculate iteratively.

As shown in Figure 4, the present invention also provides a coordination and optimization system for offshore wind power shared energy storage to participate in spot market transactions, including:

The acquisition module is used to obtain various parameters of offshore wind power and self-configured energy storage; use the scenario method to generate wind power output information;

The modeling module is used to establish a revenue model for offshore wind farms participating in spot market transactions based on the various parameters and the wind power output information; taking the maximum profit of joint operation of offshore wind power and shared energy storage as the objective function, based on the revenue model Build an optimized operation model and constraints for offshore wind power clusters participating in spot market transactions based on shared energy storage;

A solving module is configured to optimize and solve the optimal operation model based on constraint conditions to obtain the target charging and discharging power of the shared energy storage.

As shown in FIG. 5, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, the computer program is implemented. The steps of the method for coordinating and optimizing offshore wind power shared energy storage participating in spot market transactions.

The method for coordinating and optimizing offshore wind power shared energy storage participating in spot market transactions includes the following steps:

Obtain various parameters of the offshore wind farms of the offshore wind farm cluster and self-configured energy storage; use the scenario method to generate wind power output information;

Based on various parameters and wind power output information, a profit model for offshore wind farms participating in spot market transactions is established; with the maximum profit of joint operation of offshore wind power and shared energy storage as the objective function, an offshore wind farm cluster participating in shared energy storage is constructed to participate in spot market transactions The optimization operation model, and the establishment of revenue model constraints;

Based on the constraint conditions of the revenue model, the optimal operation model of the offshore wind farm cluster participating in the spot market transaction is optimized and solved, and the optimal charging and discharging power of the shared energy storage is obtained.

The present invention further provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the coordination and optimization method of the offshore wind power shared energy storage participating in spot market transactions is realized. step.

The method for coordinating and optimizing offshore wind power shared energy storage participating in spot market transactions includes the following steps:

Obtain various parameters of the offshore wind farms of the offshore wind farm cluster and self-configured energy storage; use the scenario method to generate wind power output information;

Based on various parameters and wind power output information, a profit model for offshore wind farms participating in spot market transactions is established; with the maximum profit of joint operation of offshore wind power and shared energy storage as the objective function, an offshore wind farm cluster participating in shared energy storage is constructed to participate in spot market transactions The optimization operation model, and the establishment of revenue model constraints;

Based on the constraint conditions of the revenue model, the optimal operation model of the offshore wind farm cluster participating in the spot market transaction is optimized and solved, and the optimal charging and discharging power of the shared energy storage is obtained.

Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the present invention can still be Any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention shall fall within the protection scope of the claims of the present invention.

Claims (10)

1. A method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions, characterized in that it includes: Obtain various parameters of offshore wind power and self-configured energy storage; use the scenario method to generate wind power output information; Based on the various parameters and the wind power output information, an income model for offshore wind farms participating in spot market transactions is established; with the maximum income from the combined operation of offshore wind power and shared energy storage as the objective function, a shared energy storage based model is constructed based on the income model. Optimal operation model and constraints for offshore wind power clusters to participate in spot market transactions; The optimized operation model is optimized and solved based on the constraint conditions, and the target charge and discharge power of the shared energy storage is obtained. 2. The method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions according to claim 1, wherein the various parameters include: rated power of wind farm, energy storage capacity, maximum charging and discharging power of energy storage, storage capacity Energy SOC range, construction cost and maximum charge and discharge times. 3. The method for coordinating and optimizing offshore wind power shared energy storage to participate in spot market transactions according to claim 1, wherein the generation of wind power output information using the scenario method includes: Predict the wind speed information according to the autoregressive moving average model; The wind speed prediction errors are sampled using Latin hypercube stratified sampling, assuming that each sample collected has an equal probability; Use backward reduction technology to reduce the sample set of prediction error scenarios, and merge similar scenarios to generate offshore wind power output scenarios; According to the relationship between wind speed and wind power, the wind power limited output scenario of the offshore wind power output scenario and the probability of the corresponding scenario are given; The wind power limited output scenario and the probability of the corresponding scenario are used as wind power output information. 4. The method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions according to claim 1, wherein the establishment of a revenue model for offshore wind farms participating in spot market transactions, the revenue model includes a day-ahead market revenue model, The real-time market revenue model and energy storage cycle life cost model, specifically: The day-ahead market return model is:

The real-time market return model is:

In the formula: T is the number of Δt segments included in the alliance cycle, S is the total number of offshore wind power output scenarios, γ _s is the probability of scenario s, W _A,t is the total income of alliance A participating in the spot market transaction at time period t, E _{i , t} is the total revenue of offshore wind farm i in period t, specifically expressed as follows:

In the formula:

is the return of the offshore wind farm in the day-ahead market,

For the earnings of offshore wind farms in the real-time market,

is the day-ahead clearing price,

is the bidding value of the offshore wind farm in the day-ahead market,

is the actual generated power, λ ⁺ , λ ^- are positive penalty coefficient and negative penalty coefficient respectively; The cycle life cost model of energy storage is:

In the formula, C _cycle is the cycle life cost of energy storage, N is the construction cost of energy storage,

is the number of energy storage equivalent cycles of energy storage in the tth period. 5. The method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions according to claim 1, wherein the objective function is to maximize the joint operating income of offshore wind power and shared energy storage, and the objective function is: Max(E _A -C _cycle ) In the formula, C _cycle is the energy storage cycle life cost, and E _A is the day-ahead market revenue model; The shared energy storage provides charging and discharging power to the offshore wind farm according to the power generation error state of the offshore wind farm; Statistical error status of offshore wind farm power generation: ΔP _t =P _real -P _d In the formula, ΔP _t is the power generation error of the offshore wind farm at time t, P _real is the actual power generation, and P _d is the power bid in the day-ahead market. 6. The method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions according to claim 1, wherein the constraints include: Wind power bidding power constraints:

In the formula,

is the bidding power of the wind farm in the day-ahead market, and Pt,max is the rated power of the wind farm; Positive and negative deviation assessment price constraints: 0<λ ⁺ <1 λ ^- >1 In the formula, λ ⁺ , λ ^- are the positive deviation assessment price coefficient and the negative deviation assessment price technology of the wind power supplier's current bidding output and actual output, respectively; Power balance constraints:

In the formula, P _i,t is the charging and discharging power provided by the shared energy storage to new energy power station i during t period, and P _d,i,t is the charging and discharging demand power of new energy power station i during t period; Energy storage charge and discharge power constraints: -P _max ≤P _i,t ≤P _max P _max ＝min{P _c ,P _m,i,t } P _m,i,t ＝(S _SOC,i,i-1 -S _SOC,mon )C _i η _dis /Δt In the formula, P _max is the maximum charging and discharging power of the energy storage, P _c is the rated power of the energy storage, P _m,i,t is the corresponding average power when the available electricity of the new energy power station i in the t period is fully released in the t period , S _SOC,min is the lower limit value of the self-provided energy storage state of the new energy power station, C _i is the rated capacity of the energy storage, and η _dis is the charging and discharging efficiency; Energy storage capacity constraints: S _SOC,min ≤S _soc,i,t ≤S _SOC,max In the formula, S _SOC,min and S _SOC,max are the lower limit value and upper limit value of the energy storage state of charge respectively; Energy storage charge and discharge state constraints:

In the formula, β _ch and β _dis are the charging and discharging state variables of the energy storage system, respectively, 0 represents charging, and 1 represents discharging. 7. The method for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions according to claim 1, characterized in that the optimal operation model of the shared energy storage-based offshore wind power cluster participating in spot market transactions is optimized and solved , to get the target charging and discharging power of the shared energy storage, including: Summarize the power generation information and energy storage status of each offshore wind farm; Randomly generate charging and discharging power schemes for N groups of shared energy storage to provide to each offshore wind farm during period t; After the iterative optimization of the optimized operation model with a preset number of iterations, the final target charge and discharge power scheme provided by the shared energy storage to each offshore wind farm in the period t is obtained. 8. A system for coordination and optimization of offshore wind power shared energy storage participating in spot market transactions, characterized in that it includes: The acquisition module is used to obtain various parameters of offshore wind power and self-configured energy storage; use the scenario method to generate wind power output information; The modeling module is used to establish a revenue model for offshore wind farms participating in spot market transactions based on the various parameters and the wind power output information; taking the maximum profit of joint operation of offshore wind power and shared energy storage as the objective function, based on the revenue model Build an optimized operation model and constraints for offshore wind power clusters participating in spot market transactions based on shared energy storage; A solving module is configured to optimize and solve the optimal operation model based on constraint conditions to obtain the target charging and discharging power of the shared energy storage. 9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program, any one of claims 1-7 is realized. The steps of the coordinated optimization method for offshore wind power shared energy storage participating in spot market transactions described in the item. 10. A computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the offshore wind power shared energy storage according to any one of claims 1-7 can be implemented to participate in the spot market Steps of a transaction coordination optimization method.

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