A new energy side centralized shared energy storage capacity planning method and system

Abstract

The application discloses a new energy side centralized shared energy storage capacity planning method and system, relates to the technical field of power system optimization planning, and comprises the following steps: based on a Copula function, determining a wind-solar combined output typical scene set according to a historical wind power output data set and a historical photovoltaic output data set; determining a shared energy storage constraint condition set based on a new energy shared energy storage system model, including a power balance constraint, a new energy station power balance constraint, a shared energy storage and new energy station charge-discharge power balance constraint, a new energy station shared energy storage charge-discharge demand power constraint, a thermal power unit constraint, a frequency modulation constraint, a shared energy storage state of charge constraint and a shared energy storage charge-discharge power constraint; taking the maximum value of the new energy shared energy storage system as a target, constructing a shared energy storage capacity planning model, and solving to obtain a shared energy storage capacity optimal planning result. The application improves the power resource utilization rate.

Description

Technical Field

[0001] This invention relates to the field of power system optimization planning technology, and in particular to a method and system for planning centralized shared energy storage capacity on the new energy side. Background Technology

[0002] With the rapid development of the new energy industry, power generation costs are continuously decreasing, and the installed capacity of new energy in the power system is constantly increasing. However, compared with traditional power generation, new energy power generation is characterized by strong randomness and significant intermittency, making accurate prediction impossible. Large-scale grid connection of new energy can have a significant impact on the safe and stable operation of the power system. Energy storage can realize the spatial and temporal transfer of energy and enhance the flexibility of the system, but its high cost and low utilization rate limit its large-scale application.

[0003] Constructing shared energy storage in areas with concentrated renewable energy power plants allows them to serve multiple renewable energy power plants and generate revenue through capacity leasing services. Conversely, renewable energy power plants can lease a certain percentage of their energy storage capacity to gain priority grid connection and consumption qualifications, reducing their own investment and operation and maintenance costs for building energy storage power stations. This also lowers the cost of assessing deviations in power generation plans and increases revenue from grid-connected electricity sales. Both parties benefit from this mutually beneficial and win-win cooperation. As market players and technical support systems mature, the joint clearing of electricity and ancillary services will become a trend. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for planning centralized shared energy storage capacity on the new energy side, so as to improve the utilization rate of power resources.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for planning centralized shared energy storage capacity on the new energy side, comprising:

[0007] A model of a new energy shared energy storage system is constructed; the new energy shared energy storage system model includes new energy power stations, shared energy storage power stations, and thermal power units; the new energy power stations include wind farms and photovoltaic power stations;

[0008] Based on the new energy shared energy storage system model, the historical wind power output dataset of the wind farm and the historical photovoltaic power output dataset of the photovoltaic power station are determined within any new energy power station.

[0009] Based on the Copula function, a set of typical scenarios for combined wind and solar power output is determined according to the historical wind power output dataset and the historical solar power output dataset; each scenario in the set of typical scenarios for combined wind and solar power output includes wind power output data and solar power output data;

[0010] Based on the aforementioned new energy shared energy storage system model, a set of shared energy storage constraints is determined. The set of shared energy storage constraints includes power balance constraints, new energy power plant power balance constraints, charging and discharging power balance constraints between shared energy storage and new energy power plants, charging and discharging power constraints of new energy power plants for shared energy storage, thermal power unit constraints, frequency regulation constraints, shared energy storage state of charge constraints, and shared energy storage charging and discharging power constraints.

[0011] Based on the set of shared energy storage constraints and the set of typical scenarios for wind and solar combined power output, a shared energy storage capacity planning model is constructed with the goal of maximizing the value of the new energy shared energy storage system.

[0012] The shared energy storage capacity planning model is solved to obtain the optimal planning result for the shared energy storage capacity.

[0013] Optionally, based on the Copula function, and according to the historical wind power output dataset and the historical photovoltaic power output dataset, a set of typical scenarios for combined wind and solar power output is determined, specifically including:

[0014] Nonparametric estimation is performed on the historical wind power output dataset to obtain the wind power probability density function; nonparametric estimation is performed on the historical photovoltaic power output dataset to obtain the photovoltaic probability density function.

[0015] Based on the preset copula function, the wind power probability density function and the photovoltaic probability density function are used to determine the wind and solar power combined output probability density function.

[0016] Monte Carlo random sampling is performed on the probability density function of the combined wind and solar power output, and the inverse function is used to obtain the set of combined wind and solar power output curves.

[0017] Based on K-means clustering, a set of typical scenarios for combined wind and solar power generation is determined according to the set of combined wind and solar power output curves.

[0018] To achieve the above objectives, the present invention also provides the following technical solutions:

[0019] A centralized shared energy storage capacity planning system for new energy sources includes:

[0020] The model building module is used to construct a model of a new energy shared energy storage system; the new energy shared energy storage system model includes new energy power stations, shared energy storage power stations, and thermal power units; the new energy power stations include wind farms and photovoltaic power stations;

[0021] The historical data acquisition module is used to determine the historical wind power output dataset of the wind farm and the historical photovoltaic power output dataset of the photovoltaic power station within any new energy power station, based on the new energy shared energy storage system model.

[0022] The typical scenario determination module is used to determine a set of typical wind and solar power combined output scenarios based on the Copula function and the historical wind power output dataset and the historical photovoltaic power output dataset; each scenario in the set of typical wind and solar power combined output scenarios includes wind power output data and photovoltaic power output data;

[0023] The constraint construction module is used to determine the set of shared energy storage constraints based on the new energy shared energy storage system model. The set of shared energy storage constraints includes power balance constraints, power balance constraints of new energy power plants, power balance constraints between shared energy storage and new energy power plants, power demand constraints of new energy power plants for charging and discharging shared energy storage, thermal power unit constraints, frequency regulation constraints, state of charge constraints of shared energy storage, and power constraints for charging and discharging shared energy storage.

[0024] The capacity planning model construction module is used to construct a shared energy storage capacity planning model based on the set of shared energy storage constraints and the set of typical wind and solar combined power output scenarios, with the goal of maximizing the value of the new energy shared energy storage system.

[0025] The planning result determination module is used to solve the shared energy storage capacity planning model to obtain the optimal planning result for the shared energy storage capacity.

[0026] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0027] This invention discloses a centralized shared energy storage capacity planning method and system for new energy sources. For scenarios involving large-scale wind and solar power with uncertain output, it considers a centralized energy storage sharing model. Based on historical wind power output datasets from wind farms and historical photovoltaic power output datasets from photovoltaic power plants within new energy power plants, a copula function is used to determine a set of typical scenarios for combined wind and solar power output. Considering the interaction mode between new energy power plants and shared energy storage, based on the constructed new energy shared energy storage system model, a set of shared energy storage constraints is determined, including power balance constraints, new energy power plant power balance constraints, charging and discharging power balance constraints between shared energy storage and new energy power plants, power demand constraints from new energy power plants for charging and discharging shared energy storage, thermal power unit constraints, frequency regulation constraints, shared energy storage state of charge constraints, and shared energy storage charging and discharging power constraints. Then, considering the system level, based on the set of constraints for shared energy storage and the set of typical scenarios for wind and solar combined power generation, a shared energy storage capacity planning model is constructed with the goal of maximizing the value of the new energy shared energy storage system. The shared energy storage capacity planning model is solved, and based on the generated typical scenarios, the optimal planning result for shared energy storage capacity is given, which both conforms to the overall interests of the power system and ensures reasonable returns for shared energy storage investors. In summary, this invention considers the uncertainty and correlation of wind and solar power output within a certain area, aims to maximize the system value including new energy power plants, shared energy storage, and conventional units, and achieves multi-purpose use of a single power plant under the condition of independent operation. This allows operation to guide planning, improves resource utilization, and enhances the project's economic efficiency, ensuring that the overall system conforms to the overall interests of the system while guaranteeing reasonable returns for shared energy storage investors. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating the centralized shared energy storage capacity planning method for the new energy side of the present invention.

[0030] Figure 2 This is a flowchart illustrating the shared energy storage capacity planning method in a specific embodiment of the present invention.

[0031] Figure 3 This is a schematic diagram of the structure of the centralized shared energy storage capacity planning system for the new energy side of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] In the electricity market, energy storage power stations "report capacity but not price," and are prioritized for exit under the conditions of ensuring the safe and stable operation of the power grid and the absorption of new energy. In the frequency regulation market, energy storage power stations must compete for prices alongside generator units. Under the condition of independent operation, the remaining capacity after shared energy storage leasing can be profited from through spot price differences or frequency regulation services, realizing multiple uses for one station, improving the utilization rate of energy storage resources, and reducing the cost recovery period.

[0034] In constructing a shared energy storage planning model for the new energy sector at the system level, the optimal operating strategy for shared energy storage is formulated by considering the leasing service revenue obtained by shared energy storage in mitigating the day-ahead planned fluctuations of new energy power plants, the electricity market revenue obtained by utilizing the peak-valley price difference in the spot market, and the ancillary service market revenue obtained by participating in system frequency regulation. The new energy power plants determine their leasing demand for shared energy storage by considering grid connection revenue and the assessment penalties under the "two detailed rules." Conventional generating units and shared energy storage determine their power participation in the frequency regulation market through bidding. These three entities influence each other and engage in cooperative game theory. A planning model is established with the goal of maximizing system value, taking into account the correlation and uncertainty of wind and solar power output, and considering a series of system operation constraints such as energy storage and thermal power, to determine the planning results for centralized shared energy storage on the new energy side.

[0035] Based on this, the present invention provides a method and system for planning centralized shared energy storage capacity on the new energy side. Considering the uncertain scenario of large-scale wind and solar power output, it provides theoretical guidance for planning shared energy storage capacity on the new energy side and for investors' behavioral decisions from the perspective of system value.

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] Example 1

[0038] like Figure 1 As shown, this invention provides a method for planning centralized shared energy storage capacity on the new energy side, including:

[0039] Step 100: Construct a new energy shared energy storage system model; the new energy shared energy storage system model includes new energy power stations, shared energy storage power stations and thermal power units; the new energy power stations include wind farms and photovoltaic power stations.

[0040] Step 200: Based on the new energy shared energy storage system model, determine the historical wind power output dataset of the wind farm and the historical photovoltaic power output dataset of the photovoltaic power station within any new energy power station.

[0041] Step 300: Based on the Copula function, determine a set of typical scenarios for combined wind and solar power output according to the historical wind power output dataset and the historical photovoltaic power output dataset; each scenario in the set of typical scenarios for combined wind and solar power output includes wind power output data and photovoltaic power output data.

[0042] Step 300 specifically includes:

[0043] (1) Perform nonparametric estimation on the historical wind power output dataset to obtain the wind power probability density function; perform nonparametric estimation on the historical photovoltaic power output dataset to obtain the photovoltaic probability density function.

[0044] (2) Based on the preset copula function, the wind power probability density function and the photovoltaic probability density function are used to determine the wind and solar power combined output probability density function; wherein, the optimal copula function is selected by the Euclidean distance discrimination method.

[0045] Specifically, kernel density estimation is performed based on existing sample data, and the estimated value of the probability density function at point x is obtained. The calculation formula is:

[0046]

[0047] Where n is the number of samples; h is the window width; K(·) is the kernel function, and X i Let be the value of the i-th sample extracted.

[0048] (3) The probability density function of the combined wind and solar power output is randomly sampled by Monte Carlo and inverted using the inverse function to obtain a set of combined wind and solar power output curves; in a specific example, 300 wind and solar power output curves are generated by Monte Carlo sampling.

[0049] (4) Based on K-means clustering, determine the set of typical scenarios for wind and solar power combined output according to the set of wind and solar power combined output curves; specifically, multiple typical scenarios and the corresponding probability of each typical scenario can be obtained.

[0050] The correlation model for typical scenarios of combined wind and solar power is as follows:

[0051]

[0052] in, (i = 1, 2, ..., n) is the marginal distribution function of a single variable, C(·) is the Copula join function; F(x1, x2, ..., xn Let be the joint distribution function of n variables.

[0053] Step 400: Based on the new energy shared energy storage system model, determine the set of shared energy storage constraints; the set of shared energy storage constraints includes power balance constraints, new energy power plant power balance constraints, charging and discharging power balance constraints between shared energy storage and new energy power plants, charging and discharging power constraints of new energy power plants to shared energy storage, thermal power unit constraints, frequency regulation constraints, shared energy storage state of charge constraints, and shared energy storage charging and discharging power constraints.

[0054] The functional formula for the power balance constraint is:

[0055]

[0056] in, To contribute to the real-time planning of new energy power stations during time period t. and These represent the total charging power and total discharging power of the shared energy storage power station during time period t, respectively. P represents the output of thermal power unit m during time period t; load,t For the load of the new energy shared energy storage system model during time period t, N gen N represents the number of thermal power units. sta This refers to the number of new energy power stations.

[0057] The functional formula for the power balance constraint of the new energy power station is:

[0058]

[0059] in, The negative imbalance power of renewable energy power station i during time period t. and These represent the demand and supply of shared energy storage for renewable energy power station i during time period t. The positive imbalance power of renewable energy power station i during time period t; To contribute to the day-ahead plan of the new energy power station during the t-period.

[0060] The functional formula for the power balance constraint between shared energy storage and new energy power stations is as follows:

[0061]

[0062] in, and These refer to the charging and discharging power provided by shared energy storage power stations to reduce penalties for new energy power stations.

[0063] The functional formula for the power constraint of the shared energy storage charging and discharging demand of the new energy power station is as follows:

[0064]

[0065]

[0066]

[0067] Among them, P max For the maximum charging and discharging power of the shared energy storage power station, and It is a binary 0-1 variable, representing the demand status of new energy power station i for shared energy storage charging and the demand status of shared energy storage discharging during time period t.

[0068] The functional formula for the constraints of the thermal power unit is:

[0069]

[0070] in, and These represent the lower and upper limits of thermal power output, respectively; P gen,t The output of the thermal power unit during time period t.

[0071] The functional formula for the frequency modulation constraint is:

[0072]

[0073]

[0074] in, The frequency regulation capacity of the shared energy storage power station during time period t within a day. To share the frequency regulation capacity ratio of energy storage power stations; The frequency regulation power of thermal power units during time period t within a day. This is the ratio of system frequency regulation capacity requirement.

[0075] The functional formula for the shared energy storage state of charge constraint is:

[0076]

[0077] E(0) = E(T).

[0078] E min ≤E(t)≤E max .

[0079] Where E(t) is the capacity of the shared energy storage power station during time period t, E(t-1) is the capacity of the shared energy storage power station during time period t-1, Δt is the time difference between time period t and time period t-1, E(0) is the capacity of the shared energy storage power station during the initial time period, and E(T) is the capacity of the shared energy storage power station during time period T; η ch and ηdis These are the charging efficiency and discharging efficiency of the shared energy storage power station, respectively; E min and E max These represent the lower and upper limits of the energy storage battery capacity, respectively.

[0080] The functional formula for the shared energy storage charging and discharging power constraint is:

[0081]

[0082]

[0083]

[0084] in, and The discharge and charging power of shared energy storage power stations participating in the electricity market; and These are the upward and downward frequency regulation powers of energy storage, respectively. and These are binary 0-1 variables. When either value is 1, it indicates that the shared energy storage power station is performing a charging operation or a discharging operation during time period t, respectively. The power used by the shared energy storage power station to track the new energy output plan and the power used to participate in the electricity market and frequency regulation ancillary service market cannot exceed the upper limit of shared energy storage output; at the same time, the shared energy storage power station can only be in a charging state or a discharging state.

[0085] Step 500: Based on the set of shared energy storage constraints and the set of typical wind-solar combined power output scenarios, a shared energy storage capacity planning model is constructed with the goal of maximizing the value of the new energy shared energy storage system. The objective function of the shared energy storage capacity planning model includes three parts: the net revenue of the new energy power station, the net revenue of the shared energy storage power station, and the revenue of the thermal power unit. The formula for the objective function is as follows:

[0086] maxF = F1 + F2 + F3.

[0087] F1 = S PW -S rent 1-C pun .

[0088] F2 = S arbi +S auxi +S rent 2-C inv -C om .

[0089] F3 = S gen -C coal .

[0090] Where F represents the total value of the new energy shared energy storage system, F1 represents the net revenue of the new energy power station, and S PW To generate electricity market revenue in typical scenarios of wind and solar power integration, S rent 1 represents the capacity leasing fee for new energy power stations, C pun The penalty fee for deviation from planned power output in a typical scenario of wind and solar power co-generation; F2 is the net revenue of the shared energy storage power station, S arbi To reduce peak and valley losses, S auxi For frequency modulation revenue, S rent 2 represents the capacity leasing revenue of shared energy storage power stations, C inv For construction costs, C om For operation and maintenance costs; F3 is the revenue of thermal power units, C coal For the operating costs of thermal power units, S gen For revenue from frequency modulation and internet access.

[0091] (1) Net revenue of new energy power stations.

[0092] New energy power plants coordinate operation through leasing and sharing energy storage, reducing the discrepancy between day-ahead and real-time bidding capacities. Considering the coupling of day-ahead and real-time markets, the formula for calculating the electricity market revenue in a typical scenario of wind and solar combined power output is as follows:

[0093]

[0094] in, For the current electricity market revenue, To balance market expectations of returns.

[0095]

[0096] Where D represents the number of annual operating days of the energy storage power station; N sce The number of scenes in the typical scene set for joint efforts in wind and light, ω s Let s be the probability of scenario s occurring. The clearing factor for the market's electricity consumption during the t-hour period is given. To contribute to the day-ahead plan of new energy power stations in scenario s during the t-period.

[0097]

[0098]

[0099] in, and These represent the positive and negative unbalanced power during time period t, respectively, for scenario s; and These are the settlement prices for positive and negative imbalanced electricity in the balancing market, respectively. and These are the penalty coefficients for positive unbalanced electricity with a value less than 1 and the penalty coefficients for negative unbalanced electricity with a value greater than 1, respectively.

[0100] Since capacity leasing revenues cancel each other out in the objective function, they will not be discussed further.

[0101] The formula for calculating the deviation penalty cost under the typical scenario of wind and solar combined power output is as follows:

[0102]

[0103] Where, δ pun This refers to the unit penalty coefficient for new energy power stations. Provide real-time planned power output for new energy power station i in scenario s during time period t; n max and n min These are the upper and lower limits, respectively, for the permissible power fluctuations of new energy sources.

[0104] (2) Net revenue from shared energy storage power stations.

[0105] Shared energy storage participates in the electricity market as an independent market player. The formula for calculating the peak-shaving and valley-filling revenue generated through this process is as follows:

[0106]

[0107] Among them, D arbi The number of days a shared energy storage power station participates in the electricity market throughout the year; λ price The electricity price at the generation side node; and The discharge and charging power of energy storage power stations participating in the electricity market; and These represent the state variables for discharging and charging of energy storage participating in the electricity market, respectively. hour, vice versa.

[0108] Frequency regulation revenue comprises two parts: capacity revenue and mileage revenue. The formula for calculating the average annual revenue of a frequency regulation service at an energy storage power station, i.e., frequency regulation revenue, is as follows:

[0109]

[0110] Among them, D auxi The number of days a shared energy storage power station participates in the frequency regulation ancillary services market throughout the year; λ represents the frequency regulation capacity of the energy storage power station during time period t within a day; cap To clear the electricity price for frequency regulation capacity, λ mil The clearing price is for frequency regulation mileage; π is the frequency regulation mileage capacity ratio. This refers to the historical frequency regulation performance coefficient of the shared energy storage power station.

[0111] The formula for calculating the construction cost is as follows:

[0112]

[0113] Where r is the discount rate; y is the service life of the energy storage system; α and β are the cost per unit energy capacity and the cost per unit power capacity, respectively; E max and P max These refer to the energy capacity and power capacity of the independent shared energy storage power station, respectively.

[0114] The formula for calculating the maintenance cost is as follows:

[0115]

[0116] Where D represents the number of annual operating days of the energy storage power station; δ om This is the energy storage charging and discharging unit coefficient; T is the number of scheduling periods in a 24-hour day, which is taken as 24 in this specific example. and These are binary 0-1 variables. When their values ​​are 1, it indicates that the shared energy storage power station performs a charging operation and a discharging operation during time period t, respectively. hour, vice versa; and These represent the total charging power and total discharging power of the shared energy storage power station during time period t, respectively.

[0117] (3) Revenue from thermal power units.

[0118] The formula for calculating the operating cost of the thermal power unit is as follows:

[0119]

[0120]

[0121] Among them, D gen This represents the number of operating days per year for thermal power units. Let a be a quadratic function of the cost of thermal power units, where a m b m c m The preset coefficients, u represents the output of thermal power unit m during time period t. m,t Let u be the start-up and shutdown state of thermal power unit m during time period t, which is a 0-1 variable; m,t-1 This refers to the start-up and shutdown status of thermal power unit m during the time period t-1; C m,t This refers to the start-up and shutdown costs of thermal power unit m during time period t.

[0122] The formula for calculating the revenue from frequency modulation and internet access is as follows:

[0123]

[0124] In the formula, the first part represents frequency modulation revenue, and the second part represents internet access revenue. The frequency regulation power of thermal power unit m during time period t; p represents the number of days in a year that the thermal power unit regulates its frequency; grid The benchmark on-grid tariff for coal-fired power generation is a preset value; p freq The subsidy for frequency regulation units of thermal power plants is a preset value. This refers to the comprehensive frequency regulation performance indicators of thermal power plants.

[0125] Step 600: Solve the shared energy storage capacity planning model to obtain the optimal planning result for the shared energy storage capacity.

[0126] like Figure 2 As shown, in a specific instance, the planning process includes:

[0127] Step 1, Relevant Scene Generation. 1) Collect n days of actual historical wind and solar power data and perform kernel density estimation; 2) Select a suitable copula function, model the correlation between wind and solar power output based on the copula function, and inverse the copula function to obtain the wind and solar power combined output curve; 3) Use Monte Carlo sampling to generate 300 wind and solar power combined output curves, and use K-means clustering to obtain typical scenes and corresponding probabilities.

[0128] Step 2, Shared Energy Storage Operation Mechanism. Conventional generating units are thermal power units, which compete with shared energy storage power stations in bidding. A capacity leasing and capacity leasing revenue relationship exists between shared energy storage power stations and renewable energy power plants. The remaining capacity of shared energy storage power stations is fed into the frequency regulation market, which then transfers its frequency regulation revenue to the shared energy storage power stations. The remaining capacity of shared energy storage power stations is also fed into the electricity market, which transfers its peak-valley price difference revenue to the shared energy storage power stations.

[0129] Step 3, Shared Energy Storage Planning Model. Based on the combined wind and solar power output curves considering randomness and spatial correlation obtained in the above steps, the subsequent models use the data foundation to establish a shared energy storage capacity planning model on the renewable energy side, aiming to maximize system value (i.e., maximize system net revenue). This model comprehensively considers the interaction mechanism between renewable energy power plants and shared energy storage, as well as the dispatch strategy for shared energy storage participation in the electricity market, to formulate the optimal operating scheme for shared energy storage. Finally, a shared energy storage capacity planning model on the renewable energy side is established with the goal of maximizing system value.

[0130] This invention targets scenarios with large-scale, uncertain wind and solar power output on the new energy side. It considers diversified business models for shared energy storage, incorporating shared leasing, spot price differences, and frequency regulation revenue into the optimized operation strategy of shared energy storage. This enables one-stop multi-purpose use, improves operational guidance and planning, increases the utilization rate of shared resources, reduces cost recovery cycle, provides ideas for exploring business models for shared energy storage in the future, and provides theoretical guidance for capital investors to build shared energy storage.

[0131] Finally, it should be noted that the objective function in the implementation steps of this invention can be flexibly selected and customized according to the actual system planning requirements, and the constraints can be added or removed according to the actual system parameters and scenario conditions, which has strong scalability. Therefore, the above implementation steps are only used to illustrate and not limit the technical methods of this invention. Any modifications or partial substitutions that do not depart from the spirit and scope of this invention should be covered by the claims of this invention.

[0132] Example 2

[0133] like Figure 3 As shown, in order to implement the technical solution in Embodiment 1 and achieve the corresponding functions and technical effects, this embodiment also provides a centralized shared energy storage capacity planning system for the new energy side, including:

[0134] The model building module 101 is used to build a new energy shared energy storage system model; the new energy shared energy storage system model includes new energy power stations, shared energy storage power stations and thermal power units; the new energy power stations include wind farms and photovoltaic power stations.

[0135] The historical data acquisition module 201 is used to determine the historical wind power output dataset of the wind farm and the historical photovoltaic power output dataset of the photovoltaic power station within any new energy power station, based on the new energy shared energy storage system model.

[0136] The typical scenario determination module 301 is used to determine a set of typical wind and solar power combined output scenarios based on the Copula function, according to the historical wind power output dataset and the historical photovoltaic power output dataset; each scenario in the set of typical wind and solar power combined output scenarios includes wind power output data and photovoltaic power output data.

[0137] The constraint construction module 401 is used to determine the set of shared energy storage constraints based on the new energy shared energy storage system model. The set of shared energy storage constraints includes power balance constraints, power balance constraints of new energy power plants, power balance constraints between shared energy storage and new energy power plants, power constraints on the charging and discharging demand of new energy power plants for shared energy storage, thermal power unit constraints, frequency regulation constraints, state of charge constraints of shared energy storage, and power constraints on the charging and discharging of shared energy storage.

[0138] The capacity planning model construction module 501 is used to construct a shared energy storage capacity planning model based on the set of shared energy storage constraints and the set of typical wind and solar power output scenarios, with the goal of maximizing the value of the new energy shared energy storage system.

[0139] The planning result determination module 601 is used to solve the shared energy storage capacity planning model to obtain the optimal planning result for the shared energy storage capacity.

[0140] Example 3

[0141] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to execute the centralized shared energy storage capacity planning method for the new energy side as described in Embodiment 1. Optionally, the aforementioned electronic device may be a server.

[0142] In addition, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the centralized shared energy storage capacity planning method for the new energy side of Embodiment 1.

[0143] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0144] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for planning centralized shared energy storage capacity on the new energy side, characterized in that, The methods include: A model of a shared energy storage system for new energy sources is constructed. This model includes new energy power plants, shared energy storage power stations, and thermal power units. The new energy power plants include wind farms and photovoltaic power stations. The shared energy storage power stations are used for shared energy storage. Based on the new energy shared energy storage system model, the historical wind power output dataset of the wind farm and the historical photovoltaic power output dataset of the photovoltaic power station are determined within any new energy power station. Based on the Copula function, a set of typical scenarios for combined wind and solar power output is determined according to the historical wind power output dataset and the historical solar power output dataset; each scenario in the set of typical scenarios for combined wind and solar power output includes wind power output data and solar power output data; Based on the aforementioned new energy shared energy storage system model, a set of shared energy storage constraints is determined. The set of shared energy storage constraints includes power balance constraints, new energy power plant power balance constraints, charging and discharging power balance constraints between shared energy storage and new energy power plants, charging and discharging power constraints of new energy power plants for shared energy storage, thermal power unit constraints, frequency regulation constraints, shared energy storage state of charge constraints, and shared energy storage charging and discharging power constraints. Based on the set of shared energy storage constraints and the set of typical scenarios for wind and solar combined power output, a shared energy storage capacity planning model is constructed with the goal of maximizing the value of the new energy shared energy storage system. Solve the shared energy storage capacity planning model to obtain the optimal planning result for the shared energy storage capacity; The objective function of the shared energy storage capacity planning model is: ； ； ； ； in, F The value of new energy shared energy storage systems, The net revenue of new energy power plants. To generate electricity market revenue in typical scenarios of wind and solar power integration. 1 represents the capacity leasing fee for new energy power stations. Penalty fees for deviations from planned power output in typical scenarios of wind and solar power integration; To share the net revenue of the energy storage power station, To reduce peak and valley losses, For frequency modulation revenue, 2 refers to the revenue from capacity leasing of shared energy storage power stations. For construction costs, For operation and maintenance costs; For the benefit of thermal power units, For the operating costs of thermal power units, For revenue from frequency modulation and internet access; The formula for calculating the peak shaving and valley filling benefits is as follows: ； in, The number of days a shared energy storage power station participates in the electricity market throughout the year; The electricity price at the generation side node; and The discharge and charging power of shared energy storage power stations participating in the electricity market; and These represent the state variables of a shared energy storage power station participating in the electricity market for discharge and charging, respectively. The formula for calculating the frequency modulation revenue is as follows: ； in, The number of days a shared energy storage power station participates in the frequency regulation ancillary services market throughout the year; For a shared energy storage power station within one day t Frequency modulation capacity during a given time period; To clear electricity prices for frequency regulation capacity, Clearing out electricity prices for frequency regulation mileage; For frequency modulation mileage capacity ratio, The historical frequency regulation performance coefficient of the shared energy storage power station; The formula for calculating the construction cost is as follows: ； in, The discount rate; The service life of the shared energy storage system; and These are the unit capacity cost and the unit power cost, respectively. and These refer to the capacity and power of the shared energy storage power station, respectively. The formula for calculating the maintenance cost is as follows: ； in, D This indicates the number of days a shared energy storage power station operates per year; This is the unit coefficient for energy storage charging and discharging; T The number of scheduling periods in a 24-hour day; and These are binary 0-1 variables. When their values ​​are 1, it indicates that the shared energy storage power station performs charging and discharging operations during time period t, respectively. and These represent the total charging power and total discharging power of the shared energy storage power station during time period t, respectively.

2. The method for planning centralized shared energy storage capacity on the new energy side according to claim 1, characterized in that, Based on the Copula function, and according to the historical wind power output dataset and the historical photovoltaic power output dataset, a set of typical scenarios for combined wind and solar power output is determined, specifically including: Nonparametric estimation is performed on the historical wind power output dataset to obtain the wind power probability density function; nonparametric estimation is performed on the historical photovoltaic power output dataset to obtain the photovoltaic probability density function. Based on the preset copula function, the wind power probability density function and the photovoltaic probability density function are used to determine the wind and solar power combined output probability density function. Monte Carlo random sampling is performed on the probability density function of the combined wind and solar power output, and the inverse function is used to obtain the set of combined wind and solar power output curves. Based on K-means clustering, a set of typical scenarios for combined wind and solar power generation is determined according to the set of combined wind and solar power output curves.

3. The method for planning centralized shared energy storage capacity on the new energy side according to claim 1, characterized in that, The functional formula for the power balance constraint is: ； in, For new energy power stations i exist t Real-time planned output for each time period and For shared energy storage power stations t Total charging power and total discharging power during the period For thermal power units m exist t Efforts during a specific time period; For the new energy shared energy storage system model t Time-of-use load, This refers to the number of thermal power units. The number of new energy power stations; The functional formula for the power balance constraint of the new energy power station is: ； in, For new energy power stations i exist t Negative unbalanced power during the period and They are respectively new energy power stations i exist t Demand and supply of shared energy storage during different time periods For new energy power stations i exist t Positive and unbalanced power during different time periods; For new energy power stations i exist t The planned output for the current period; The functional formula for the power balance constraint between shared energy storage and new energy power stations is as follows: ； in, and These refer to the charging service power and discharging service power of shared energy storage power stations in order to reduce the assessment penalties for new energy power stations. The functional formula for the power constraint of the shared energy storage charging and discharging demand of the new energy power station is as follows: ； ； ； in, and Binary 0-1 variables, representing new energy power stations i exist t The demand status of shared energy storage charging and the demand status of shared energy storage discharging during different time periods; The functional formula for the constraints of the thermal power unit is: ； in, and These are the lower and upper limits of thermal power output, respectively. For thermal power units t During specific time periods; The functional formula for the frequency modulation constraint is: ； ； in, For a shared energy storage power station within one day t Frequency modulation capacity during the time period To share the frequency regulation capacity ratio of energy storage power stations; For thermal power units within one day t Frequency modulation power during the time period This is the ratio of system frequency regulation capacity requirement; The functional formula for the shared energy storage state of charge constraint is: ； ； ； in, E ( t (for shared energy storage power stations) t Time slot capacity, E ( t- 1) For shared energy storage power stations t- Capacity for one time period for t Time period and t- Time difference in a given period E (0) represents the capacity of the shared energy storage power station during the initial period. E ( T (for shared energy storage power stations) T Capacity of a time period; and These are the charging efficiency and discharging efficiency of the shared energy storage power station, respectively. This represents the lower limit of the capacity of a shared energy storage power station. The functional formula for the shared energy storage charging and discharging power constraint is: ； ； ； in, and The discharge and charging power of shared energy storage power stations participating in the electricity market; and These are the upward and downward frequency regulation powers of the shared energy storage power station, respectively. and These are binary 0-1 variables. When their values ​​are 1, it indicates that the shared energy storage power station is performing a charging operation and a discharging operation during time period t, respectively.

4. The method for planning centralized shared energy storage capacity on the new energy side according to claim 1, characterized in that, The formula for calculating the electricity market revenue under the typical scenario of combined wind and solar power output is as follows: ； in, For the current electricity market revenue, To balance market expectations of returns; ； in, D To share the annual operating days of the energy storage power station; The number of scenes in the typical scene set for joint efforts in wind and light industries. For the scene s The probability of occurrence; For the current electricity market t Time-based clearing energy factor For new energy power stations i In the scene s of t The planned output for the current period; For the number of new energy power stations, T The number of scheduling periods in a 24-hour day; ； ； in, and Scenes s of t Positive and negative unbalanced power during different time periods; and These are the settlement prices for positive and negative imbalanced electricity in the balanced electricity market, respectively. and These are the penalty coefficients for positive unbalanced electricity with a value less than 1 and the penalty coefficients for negative unbalanced electricity with a value greater than 1, respectively. The formula for calculating the deviation penalty cost under the typical scenario of wind and solar combined power output is as follows: ； in, This refers to the unit penalty coefficient for new energy power stations. For new energy power stations i In the scene s of t Real-time planned output for each time period; and These represent the upper and lower limits of the permissible power fluctuations for new energy power plants.

5. The method for planning centralized shared energy storage capacity on the new energy side according to claim 1, characterized in that, The formula for calculating the operating cost of the thermal power unit is as follows: ； ； in, This represents the number of operating days per year for thermal power units. T This refers to the number of scheduling periods in a 24-hour day. This refers to the number of thermal power units. Let be a quadratic function of the cost of thermal power units, where , , The preset coefficients, For thermal power units m exist t Efforts during a specific time period; For thermal power units m exist t The start / stop status of a time period is a 0-1 variable; For thermal power units m exist t- The start / stop status during a specific time period; For thermal power units m exist t Start-up and shutdown fees during specific time periods; The formula for calculating the revenue from frequency modulation and internet access is as follows: ； in, For thermal power units m exist t Frequency modulation power during specific time periods; This represents the number of days a thermal power unit adjusts its frequency in a year. The benchmark on-grid tariff for coal-fired power generation is a preset value. The subsidy for frequency regulation units of thermal power plants is a preset value. This refers to the comprehensive frequency regulation performance indicators of thermal power plants.

6. A centralized shared energy storage capacity planning system for new energy sources, characterized in that, The system employing the centralized shared energy storage capacity planning method for new energy sources according to any one of claims 1-5 includes: The model building module is used to construct a model of a new energy shared energy storage system; the new energy shared energy storage system model includes new energy power stations, shared energy storage power stations, and thermal power units; the new energy power stations include wind farms and photovoltaic power stations; The historical data acquisition module is used to determine the historical wind power output dataset of the wind farm and the historical photovoltaic power output dataset of the photovoltaic power station within any new energy power station, based on the new energy shared energy storage system model. The typical scenario determination module is used to determine a set of typical wind and solar power combined output scenarios based on the Copula function and the historical wind power output dataset and the historical photovoltaic power output dataset; each scenario in the set of typical wind and solar power combined output scenarios includes wind power output data and photovoltaic power output data; The constraint construction module is used to determine the set of shared energy storage constraints based on the new energy shared energy storage system model. The set of shared energy storage constraints includes power balance constraints, power balance constraints of new energy power plants, power balance constraints between shared energy storage and new energy power plants, power demand constraints of new energy power plants for charging and discharging shared energy storage, thermal power unit constraints, frequency regulation constraints, state of charge constraints of shared energy storage, and power constraints for charging and discharging shared energy storage. The capacity planning model construction module is used to construct a shared energy storage capacity planning model based on the set of shared energy storage constraints and the set of typical wind and solar combined power output scenarios, with the goal of maximizing the value of the new energy shared energy storage system. The planning result determination module is used to solve the shared energy storage capacity planning model to obtain the optimal planning result for the shared energy storage capacity.

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