Cooperative control evaluation method and system for new energy station and shared energy storage

Abstract

The invention belongs to the technical field of power systems, and particularly relates to a cooperative control evaluation method and system for a new energy station and shared energy storage, and the method comprises the steps: obtaining the dynamic operation parameters of the new energy station; based on the obtained dynamic operation parameters, considering peak and frequency regulation requirements of the power grid, and constructing a comprehensive evaluation index system of the new energy station and the shared energy storage; according to the constructed comprehensive evaluation index system, constructing a multi-time-scale collaborative optimization model of the new energy station and the energy storage system; based on the constructed multi-time-scale collaborative optimization model, performing collaborative multi-target optimization operation scheduling of the new energy station and the shared energy storage by taking the maximum total benefit of peak regulation and frequency modulation of the power grid as a target; and according to the obtained cooperative multi-objective optimization operation scheduling result, considering multi-station control adjustment margin fusion perception, and completing cooperative control evaluation of the new energy station and the shared energy storage.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and specifically relates to a collaborative control evaluation method and system for a new energy station and shared energy storage. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the high proportion of new energy with volatility and intermittency connected, on the one hand, it will lead to insufficient rotational inertia and rotating reserve to maintain system stability, and the risk of system frequency instability will increase; on the other hand, it will increase the uncertainty of dispatching and operation and the pressure of system peak and frequency regulation, affecting the consumption of new energy and the efficient and optimized operation of the power system. The safe and stable operation and optimized dispatch of power grids with a high proportion of new energy connected have become important challenges facing the new power system.

[0004] The new energy storage system based on electrochemistry has the ability to adjust quickly and flexibly, and can provide emergency control, peak load regulation, frequency regulation, voltage regulation and other services for the power grid. It is an important component and key supporting technology of the new power system. Vigorously developing energy storage technology to improve the level of new energy consumption and system safety and stability has become a research hotspot. However, the positioning of energy storage configured in new energy stations is unclear, the application mode is relatively single, the utilization rate of energy storage is not high, it cannot participate in the market independently, and there is a lack of profit model, which affects the enthusiasm for new energy storage. In order to take into account the needs of new energy and power grids and improve the utilization rate of energy storage at the same time, the "shared energy storage" model continues to emerge.

[0005] At present, the configuration scheme and operation mode of new energy sites and shared energy storage are unclear. The existing site control mechanism is difficult to adapt to the needs of the coordinated operation of multiple entities of new energy and shared energy storage, and cannot give full play to the active support role of large-scale energy storage for the safe, stable and efficient operation of new power systems. It is mainly manifested in: First, there is a lack of shared energy storage configuration schemes that take into account the economic performance and grid friendliness of energy storage operation, and the sustainable shared energy storage operation mode adapted to different application scenarios is unclear. It is impossible to scientifically guide the long-term planning of energy storage and lack effective incentives for energy storage construction; second, the frequency response coupling interaction mechanism of multiple new energy sites and multiple shared energy storage modes is complex. The traditional localized and centralized energy storage peak-shaving and valley-filling control strategy leads to low energy storage utilization and is difficult to meet the peak-shaving and frequency regulation needs of the power grid under the high proportion of new energy access; third, there is a lack of effective closed-loop monitoring and evaluation of the grid friendliness and active support capabilities of the coordinated shared energy storage of multiple new energy sources, resulting in unclear effects and capabilities of the shared energy storage coordinated operation mode, limiting the potential for coordinated control. Summary of the invention

[0006] To solve the above problems, the present invention proposes a collaborative control evaluation method and system for new energy stations and shared energy storage, with the goal of improving the frequency stability of the power grid and increasing the space for new energy consumption, improving the stable operation level of the new energy power grid, and improving the collaborative regulation mechanism between new energy stations and shared energy storage, so as to realize the diversified interaction between new energy stations and shared energy storage.

[0007] According to some embodiments, a first solution of the present invention provides a collaborative control evaluation method for a new energy station and shared energy storage, which adopts the following technical solution:

[0008] A collaborative control evaluation method for a new energy station and shared energy storage, comprising:

[0009] Obtain dynamic operating parameters of new energy stations;

[0010] Based on the dynamic operating parameters obtained, a comprehensive evaluation index system for new energy stations and shared energy storage is constructed;

[0011] According to the constructed comprehensive evaluation index system, a multi-time scale collaborative optimization model of new energy stations and energy storage systems is constructed;

[0012] Based on the constructed multi-time scale collaborative optimization model, the collaborative multi-objective optimization operation and scheduling of new energy stations and shared energy storage is carried out with the goal of maximizing the total benefit of grid peak and frequency regulation;

[0013] Based on the collaborative multi-objective optimization operation and scheduling results obtained, the collaborative control evaluation of new energy stations and shared energy storage is completed.

[0014] As a further technical limitation, the specific process of the collaborative multi-objective optimization operation and scheduling of new energy sites and shared energy storage with the goal of maximizing the total benefit of grid peak-shaving and frequency regulation is as follows: considering the different operating characteristics of regional loads, power sources and multiple types of shared energy storage, calculating the regional grid peak-shaving and frequency regulation power requirements under different scenarios and working conditions; taking the maximization of new energy site benefits as the optimization goal, and taking the power generation power and output adjustment characteristics of new energy sites as constraints, determining the optimal output plan for new energy sites to participate in grid peak-shaving and frequency regulation; taking into account the peak-shaving, frequency regulation benefits and the purchase cost of shared energy storage with different service modes, and taking the maximization of new energy site benefits as the goal, collaborative optimization operation and regulation of new energy and independent shared energy storage and co-built shared energy storage is carried out; collaborative multi-objective optimization operation and scheduling of new energy and independent shared energy storage and co-built shared energy storage with the goal of maximizing the overall benefits of grid peak-shaving and frequency regulation is carried out.

[0015] As a further technical limitation, the specific process of constructing a comprehensive evaluation index system for new energy stations and shared energy storage is as follows: analyzing the output and load fluctuation morphological characteristics of new energy stations, obtaining typical scenarios of different depths of peak and frequency regulation of regional power grids through a multi-time scale morphological clustering algorithm, considering the layout, capacity and regional power grid topology, and operating characteristics of new energy stations, taking the configuration capacity and access points of shared energy storage as decision variables, and constructing from the power grid side a two-layer comprehensive optimization configuration of shared energy storage that takes new energy station operating costs and shared energy storage investment, operating costs and market profits as goals, taking into account the active support of new energy and operational economy and integrating multiple scenarios to complete the peak and frequency regulation needs of the power grid.

[0016] Furthermore, based on the obtained two-layer comprehensive optimization configuration of shared energy storage, combined with the energy conversion characteristics of new energy and shared energy storage, the supporting power required by the new energy stations under frequency disturbances of different durations and sizes is calculated. Combined with the power demand of new energy stations participating in frequency regulation, the rapid frequency response characteristics of independent shared energy storage and equipped shared energy storage under different operating modes are obtained, and a comprehensive evaluation index system is obtained.

[0017] As a further technical limitation, the specific process of the coordinated control evaluation of the new energy station and shared energy storage is: real-time perception of the frequency regulation margin of the new energy station and the shared energy storage, supporting the frequency stability support and safe dispatching operation of the power grid with a high proportion of new energy to effectively participate, and completing the coordinated control evaluation of the new energy station and the shared energy storage; wherein, the real-time perceived frequency regulation margin of the new energy station and the shared energy storage is the coordinated control strategy and real-time operating status of the new energy station and the shared energy storage.

[0018] As a further technical limitation, the dynamic operating parameters of the new energy station obtained include at least wind power frequency regulation parameters, intermittent nature of new energy, active power of the new energy station, power quality and low voltage ride-through capability.

[0019] According to some embodiments, the second solution of the present invention provides a collaborative control and evaluation system for a new energy station and shared energy storage, which adopts the following technical solutions:

[0020] A collaborative control and evaluation system for new energy stations and shared energy storage, comprising:

[0021] An acquisition module, configured to acquire dynamic operation parameters of a new energy station;

[0022] A construction module is configured to construct a comprehensive evaluation index system of new energy stations and shared energy storage based on the acquired dynamic operation parameters; and to construct a multi-time scale collaborative optimization model of new energy stations and energy storage systems according to the constructed comprehensive evaluation index system;

[0023] A dispatching module is configured to perform a coordinated multi-objective optimization operation dispatch of new energy stations and shared energy storage based on the constructed multi-time scale collaborative optimization model with the goal of maximizing the total benefit of power grid peak and frequency regulation;

[0024] The evaluation module is configured to complete the collaborative control evaluation of the new energy station and the shared energy storage according to the obtained collaborative multi-objective optimization operation scheduling results.

[0025] As a further technical limitation, the specific process of the collaborative multi-objective optimization operation and scheduling of new energy sites and shared energy storage with the goal of maximizing the total benefit of grid peak-shaving and frequency regulation is as follows: considering the different operating characteristics of regional loads, power sources and multiple types of shared energy storage, calculating the regional grid peak-shaving and frequency regulation power requirements under different scenarios and working conditions; taking the maximization of new energy site benefits as the optimization goal, and taking the power generation power and output adjustment characteristics of new energy sites as constraints, determining the optimal output plan for new energy sites to participate in grid peak-shaving and frequency regulation; taking into account the peak-shaving, frequency regulation benefits and the purchase cost of shared energy storage with different service modes, and taking the maximization of new energy site benefits as the goal, collaborative optimization operation and regulation of new energy and independent shared energy storage and co-built shared energy storage is carried out; collaborative multi-objective optimization operation and scheduling of new energy and independent shared energy storage and co-built shared energy storage with the goal of maximizing the overall benefits of grid peak-shaving and frequency regulation is carried out.

[0026] As a further technical limitation, the specific process of constructing a comprehensive evaluation index system for new energy stations and shared energy storage is as follows: analyzing the output and load fluctuation morphological characteristics of new energy stations, obtaining typical scenarios of different depths of peak and frequency regulation of regional power grids through a multi-time scale morphological clustering algorithm, considering the layout, capacity and regional power grid topology, and operating characteristics of new energy stations, taking the configuration capacity and access points of shared energy storage as decision variables, and constructing from the power grid side a two-layer comprehensive optimization configuration of shared energy storage that takes new energy station operating costs and shared energy storage investment, operating costs and market profits as goals, taking into account the active support of new energy and operational economy and integrating multiple scenarios to complete the peak and frequency regulation needs of the power grid.

[0027] Furthermore, based on the obtained two-layer comprehensive optimization configuration of shared energy storage, combined with the energy conversion characteristics of new energy and shared energy storage, the supporting power required by the new energy stations under frequency disturbances of different durations and sizes is calculated. Combined with the power demand of new energy stations participating in frequency regulation, the rapid frequency response characteristics of independent shared energy storage and equipped shared energy storage under different operating modes are obtained, and a comprehensive evaluation index system is obtained.

[0028] As a further technical limitation, the specific process of the coordinated control evaluation of the new energy station and shared energy storage is: real-time perception of the frequency regulation margin of the new energy station and the shared energy storage, supporting the frequency stability support and safe dispatching operation of the power grid with a high proportion of new energy to effectively participate, and completing the coordinated control evaluation of the new energy station and the shared energy storage; wherein, the real-time perceived frequency regulation margin of the new energy station and the shared energy storage is the coordinated control strategy and real-time operating status of the new energy station and the shared energy storage.

[0029] As a further technical limitation, the dynamic operating parameters of the new energy station obtained include at least wind power frequency regulation parameters, intermittent nature of new energy, active power of the new energy station, power quality and low voltage ride-through capability.

[0030] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0031] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in the collaborative control evaluation method for a new energy station and shared energy storage as described in the first solution of the present invention.

[0032] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0033] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the collaborative control evaluation method of a new energy station and shared energy storage as described in the first scheme of the present invention are implemented.

[0034] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0035] A computer program product includes software code, wherein the program in the software code executes the steps in the collaborative control evaluation method of new energy stations and shared energy storage as described in the first embodiment of the present invention.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention aims to enhance the frequency stability of the power grid and increase the space for absorbing new energy, improve the stable operation level of the new energy power grid, improve the coordinated regulation mechanism between new energy stations and shared energy storage, and realize the diversified interaction between new energy stations and shared energy storage.

[0038] The present invention provides technical means for the planning and construction of shared energy storage, commercial operation and coordinated dispatch with new energy stations. Most of the current energy storage planning schemes are imperfect and do not take into account the actual needs of new energy consumption, grid access conditions, etc. There is an urgent need to fill the key technical gaps in the comprehensive optimization configuration of shared energy storage that takes into account the active support of new energy and the integration of multiple scenarios of economic operation; by establishing a shared energy storage pricing mechanism and revenue model that takes into account the interests of multiple parties, a profit model for shared energy storage to participate in multiple markets is formed.

[0039] The present invention can improve the regional power grid's ability to absorb new energy, enhance the system's regulatory support capabilities, and provide guidance for the development of the new energy industry and energy storage technology. It can scientifically configure shared energy storage under the background of a high proportion of new energy, achieve efficient operation and active control of new energy sites and shared energy storage, give full play to the advantages of shared energy storage technology, and provide solutions to the problems faced by the development of future high-proportion new energy regions. It can be widely promoted to onshore wind power / photovoltaic power station clusters and large-scale offshore wind power clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0041] Figure 1 This is a schematic diagram of the division of collaborative working areas at different time scales in the first embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of energy storage peak regulation in Embodiment 1 of the present invention;

[0043] Figure 3 The synchronous generator power angle principle and converter equivalent circuit diagram in the first embodiment of the present invention;

[0044] Figure 4 This is a diagram of the virtual inertia control structure in the first embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of an example curve of primary frequency modulation in the first embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of energy storage frequency modulation in Embodiment 1 of the present invention;

[0047] Figure 7 Schematic diagram of active power waveform during active power control of a wind farm in Embodiment 1 of the present invention;

[0048] Figure 8 This is a wiring diagram of a fast frequency response field test in Embodiment 1 of the present invention;

[0049] Fig. 9 It is a schematic diagram of indicators for calculating various control times, overshoots, and steady-state deviations of inertia response and primary frequency modulation in the first embodiment of the present invention;

[0050] Fig.10 This is a framework diagram of the collaborative control evaluation method of the new energy station and shared energy storage in the first embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0052] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0053] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0054] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0055] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0056] Embodiment 1

[0057] Embodiment 1 of the present invention introduces a collaborative control evaluation method for a new energy station and shared energy storage.

[0058] This embodiment takes into account the active support of new energy and the comprehensive optimization configuration of shared energy storage that integrates multiple scenarios of economic operation. The strong volatility and randomness of a high proportion of new energy make it difficult to control the operation of the power system. It is urgent to tap into rapid peak-shaving and frequency-regulation resources to support the optimized and stable operation of the power grid. The rapid adjustment capability of energy storage has become an important means to enhance the flexibility of the power system to cope with source-load fluctuations, but conventional supporting energy storage projects often only serve a single new energy site, resulting in a waste of resources. The introduction of shared energy storage eliminates the defects of high initial investment and low utilization brought about by the "exclusive" model of traditional energy storage, but there is currently no mature research on the mechanism for shared energy storage to coordinate with new energy to support peak-shaving and frequency regulation of the power grid and participate in the market operation and benefit. Therefore, when configuring shared energy storage, how to give full play to its rapid response capabilities, take into account the active support of new energy for peak-shaving and frequency regulation of the power grid, reasonably share the configuration costs, and how to participate in the market to benefit are the key points and difficulties of this embodiment.

[0059] This embodiment proposes a multi-time scale collaborative rapid frequency response control and dispatch control method for new energy stations and shared energy storage. Specifically, shared energy storage has multiple forms such as independent energy storage and equipped energy storage. In the equipped energy storage, it can be operated in different ways such as partial capacity independence and partial capacity joint. There are complex characteristics of multiple subjects and multiple access points, and operation control and management are difficult. Making full use of the regulation capacity of shared energy storage to realize intelligent collaborative control and optimized operation of shared energy storage system and new energy is an effective means to solve the balancing problem of power systems with a high proportion of new energy. At present, the joint operation of shared energy storage and new energy is mainly aimed at smoothing fluctuations and tracking planned output of local stations, and has failed to give full play to the improvement of shared energy storage on the flexibility, safety and new energy consumption of the power system.

[0060] The participation of shared energy storage in the rapid frequency response of new energy sources must first be based on the access location and method, analyze the unit output characteristics, combine the timing coordination logic and response mechanism, and consider the cost-benefit new energy and shared energy storage collaborative control strategy, and consider the collaborative control implementation scheme under different communication methods. Therefore, how to achieve the collaborative optimization of shared energy storage and new energy on different time scales and solve the absorption and stability problems brought about by large-scale new energy access is the key point and difficulty of this embodiment.

[0061] This embodiment considers the real-time perception technology of frequency regulation margin coordinated by new energy stations and energy storage systems. In order to achieve the coordinated and efficient operation of new energy stations and shared energy storage, it is necessary to comprehensively consider the system coordination control strategy and the real-time operation status of the equipment, and perform real-time perception of the frequency regulation margin of new energy stations and shared energy storage, so as to support the frequency stability support and safe dispatching operation of high-proportion new energy to effectively participate in the power grid. There are many wind turbines and photovoltaic units in new energy stations and they are widely distributed. Their active and reactive outputs are highly dependent on the external environmental factors with strong uncertainty and the operation status of highly controllable energy converters; in addition, the comprehensive frequency regulation performance and active adjustable margin of new energy stations and shared energy storage are seriously coupled with factors such as energy storage system configuration parameters, system dispatching plan and equipment control mode. Therefore, how to comprehensively consider the random intermittent characteristics of natural environmental resources such as wind speed and light and the coupled operation mechanism of new energy units and energy storage systems, and realize data-driven real-time perception of active adjustable margin of new energy and shared energy storage and prediction and evaluation of active support capacity of power grid is one of the key points and difficulties of this embodiment.

[0062] (1) Shared energy storage demand assessment in typical scenarios

[0063] like Figure 1 As shown, when energy storage devices are used in different scenarios, they work on different time scales, but in essence they all play the role of flexible regulation and peak-shaving.

[0064] Based on the specific application conditions and value to the power system, shared energy storage application scenarios can be divided into five categories: (1) ensuring system safety under faults or abnormal operation, (2) ensuring power transmission and distribution functions, (3) providing auxiliary services such as frequency regulation, (4) shifting peak loads and filling valley loads, and (5) improving the level of new energy utilization, as shown in 1.

[0065] Table 1 Typical application scenarios of energy storage in power systems

[0066]

[0067]

[0068] 1) Frequency regulation and energy storage requirements

[0069] In order to ensure the real-time balance between power supply and consumption in the power system, power generation resources and loads need to correct their own output according to the power gap or surplus of the system to maintain the stability of the system frequency. With the current trend of large-scale grid connection of new energy and the continuous development of the power market, the imbalance between power generation output and load caused by prediction errors and equipment failures will become more significant, and the traditional frequency regulation method based on the automatic generation control (AGC) system to adjust the output of generators will face huge challenges. Energy storage equipment has fast startup and response speed and flexible adjustment, which can effectively track the random imbalance of load and new energy output. Charging when the system has a surplus of electricity and discharging when there is a gap. As a high-quality flexibility resource, participating in the rapid frequency regulation of the system is an important application scenario for energy storage to play its value.

[0070] Through the relevant data of the AGC system or the ancillary service market, the historical frequency modulation power demand at any time within a certain time range can be obtained. The frequency modulation power demand of the entire system at time i is denoted as r(i). Since the historical frequency modulation data are usually data points sampled at equal time intervals, r(i) can be expressed in discrete form: r(i) = R i ,i=1,2,3,K,T; where T is the total number of time periods; R i is the frequency modulation demand in the i-th time period, i.e., the magnitude of the system power imbalance. Since both positive and negative frequency modulation signals exist at the same time, the value of r(i) can be positive or negative. The data period of the fast frequency modulation signal is generally in the second level.

[0071] For the energy storage frequency modulation power demand, the size of the frequency modulation signal of the system at a certain moment is first expressed as a random variable R, and the probability distribution of the random variable R can be obtained according to the absolute value of the historical data r(i). Mathematically, the methods of inferring probability distribution through historical sampling data include parameter estimation and non-parametric estimation. Due to the strong randomness of the frequency modulation signal, its distribution presents multi-peak and asymmetric characteristics, so it is difficult to use the existing probability distribution types for parameter estimation and fitting. Commonly used non-parametric estimation methods include histogram density estimation, kernel density function method, etc. This embodiment is illustrated by histogram density estimation.

[0072] Divide the distribution interval of |r(i)|, that is, [0, max(|r(i)|)], into N small intervals. Let the number of |r(i)| values ​​falling in the nth small interval be kn, and the interval length be L. Assuming that the random variable R is uniformly distributed in each small segment, the probability density distribution function of R can be obtained Where r∈((n-1)L,nL]; integrating the above formula, we can get the cumulative probability distribution function of R

[0073] Due to the high investment cost of energy storage at this stage, the probability of demand peaks is usually low, and it is not economical to use energy storage to meet all frequency regulation needs. In order to consider the economy of the total demand, the expected probability of energy storage meeting the frequency regulation power is artificially set, denoted as α, then the total energy storage frequency regulation power demand at this time is for Among them, f cdf -1 (.) is the inverse function of the cumulative probability distribution.

[0074] To evaluate the energy storage capacity, it is necessary to first integrate the historical frequency regulation data to obtain the frequency regulation energy storage energy demand Q(i) of the entire system at time i: Among them, Δt is the data period of the frequency modulation signal; Q0 is the initial energy of the energy storage device. After obtaining the historical energy frequency modulation data, the total demand for energy storage frequency modulation energy capacity is obtained using the above histogram method.

[0075] 2) Smoothing out the fluctuating energy storage demand of new energy

[0076] The high penetration of renewable energy leads to strong volatility in power generation output, which increases the system's demand for flexible resources that can smooth out the fluctuations of renewable energy. On the one hand, power system dispatch or power market day-ahead trading usually requires power generation resources to maintain a stable and certain output for a period of time. Large-scale fluctuations in photovoltaic or wind power output in a short period of time will bring great difficulties to system operation; on the other hand, the fluctuations of renewable energy will inevitably require traditional power sources to adjust their output accordingly to compensate, which will generate a large amount of unit ramping costs. In extreme cases, the ramping rate of traditional units cannot track the fluctuations of renewable energy, which will greatly limit the access to renewable energy. Note that unlike the demand for frequency regulation, smoothing the fluctuations of renewable energy is aimed at coping with the volatility of new energy units, while frequency regulation is to compensate for the power mismatch caused by the uncertainty in system operation.

[0077] If the output data of wind power or photovoltaic power in the whole network is known as P(i), the difference △P(i) between the output of renewable energy at each time point and the average value of P(i) in the dispatching period (such as 1h) can be calculated, that is, in, is the average output of renewable energy in time period i; that is, the power demand data of the whole network to smooth the fluctuation of renewable energy is obtained, and its cycle is generally at the minute level.

[0078] However, there are many new energy power generation companies in the system, and it is often difficult to obtain complete historical output data of new energy. Due to the inherent randomness of new energy power generation, the volatility generated by multiple power plants can offset each other, and a complementary effect can be formed within the regional space, resulting in a reduction in the overall volatility after the output of multiple power plants is added together. This is called the smoothing effect of the new energy power plant cluster. Therefore, directly using the ratio of the installed capacity of a known power plant to the installed capacity of the entire network to obtain the fluctuation of new energy in the entire network according to this ratio, the energy storage demand result obtained in this way will be too conservative.

[0079] Considering spatial correlation, a method is proposed to infer the fluctuation of the whole network through the output fluctuation of some new energy power plants. Assuming that there are M new energy power plants in the whole network, the output fluctuation data of m of them are known, and the total installed capacity of the whole network is I times the installed capacity of the power plants with known data.

[0080] The fluctuation of m power plants is denoted as a random variable δm. First, its standard deviation σ(δm) is calculated. The volatility of data can be characterized by the standard deviation. Therefore, to obtain the fluctuation of the entire network, we only need to infer the relationship between the overall standard deviation and σ(δ m ) between the two. The fluctuation of the whole network δ M It can be expressed as

[0081] Assume that the random variable σ(δ m 1 )…σ(δ m I ) has a standard deviation of σ(δ m ), then the standard deviation σ(δM) is: Among them, ρ is the average value of the correlation coefficient between the fluctuating random variables, which can be calculated by the distance between the new energy power stations or approximated by the average correlation of the known data of m power plants; by calculating the ratio of σ(δM) to σ(δm), the fluctuation of the whole network can be calculated in multiples. Further, according to the above-mentioned method of fitting the cumulative probability distribution, the total demand for power and energy capacity of energy storage to smooth the fluctuation of renewable energy can be obtained.

[0082] 3) Peak-shaving energy storage demand

[0083] Due to the daily load fluctuations, the system needs to start and stop the peak load units to meet the load demand during the peak and valley periods. Figure 2 As shown, energy storage equipment can store low-cost electricity during low-load periods and generate electricity during peak periods, reducing system operating costs and saving investment in peak-shaving units, generating considerable economic benefits.

[0084] Unlike the energy storage requirements for frequency regulation and smoothing fluctuations in renewable energy, there is no clear peak-shaving requirement in system operation. The value of energy storage in peak-shaving needs to be reflected through grid operation simulation. On this basis, the benefits and costs generated are comprehensively considered, and the installed capacity requirements of energy storage are determined based on this.

[0085] (2) Business model of shared energy storage

[0086] Based on the concept of shared energy storage, energy storage operators can purchase or build energy storage power stations for leasing, and trade the energy storage resources with multiple stakeholders. Based on the concept of shared energy storage, the advantage of this model is that it can clarify the status of the energy storage subject, and can gather and uniformly allocate the idle energy storage resources of various social entities, so that the energy storage resources can be fully utilized. From the perspective of shared energy storage projects that have been carried out or are being carried out, energy storage capacity leasing + peak and frequency regulation auxiliary services are a more feasible profit model under the existing policies and mechanisms.

[0087] 1) Peak shaving and valley filling

[0088] In some provinces where electricity spot is being piloted, profits are realized through participation in the electricity spot market. When electricity consumption is low and electricity prices are low, the energy storage system is equivalent to the load, and the power grid charges it. When electricity consumption is high and electricity prices are high, the energy storage is equivalent to the power source, discharging the load, and the energy storage system earns profits through the peak-valley electricity price difference.

[0089] 2) Peak load shaving services

[0090] At present, the ways in which energy storage participates in peak load regulation are roughly as follows:

[0091] (1) Deep peak load regulation in conjunction with thermal power units, which is the most common method;

[0092] (2) When the power grid calls for peak load regulation, the energy storage receives dispatch instructions to charge and obtains peak load regulation compensation. For example, the peak load regulation compensation standard for energy storage in a certain province is 0.5 yuan / kWh, and the annual utilization hours are not less than 540 hours; the peak load regulation service compensation price for the energy storage pilot project in a certain province in 2022 and 2023 is 0.8 yuan / kWh, and the annual call times are not less than 300 times;

[0093] (3) Peak load bidding transactions: Energy storage completes quotation and clearing through the trading platform according to market demand;

[0094] (4) Carry out bilateral transactions with wind farms and solar power stations, negotiate and determine transaction prices, transaction periods, etc., which will be confirmed after safety verification by the power dispatching agency;

[0095] (5) For start-stop peak load regulation, energy storage is not included in the quotation. The basic charging capacity and compensation coefficient are determined by the relevant departments.

[0096] 3) Frequency modulation auxiliary services

[0097] During the operation of the power system, AGC mainly controls the grid frequency and the power of the interconnection lines by adjusting the active output of the frequency-modulating power source in the grid in real time, solving the problem of random active imbalance in the regional grid on a short time scale of seconds or minutes. This puts forward high requirements on the performance of the AGC power supply, such as fast adjustment rate, high adjustment accuracy, and frequent power conversion adjustment. When the AGC command is issued, the energy storage auxiliary frequency regulation system responds quickly and releases the unit to help the unit quickly meet the requirements of the AGC command. Compared with gas-fired units, battery energy storage has greater advantages in ramping speed and regulation accuracy, so it is generally used to provide frequency regulation services with higher returns. With the continuous growth of demand for ancillary services, the average clearing prices of various ancillary services have shown a clear upward trend in recent years, and the revenue of energy storage projects is expected to increase accordingly.

[0098] 4) Energy storage capacity leasing

[0099] Provide energy storage leasing services for new energy power stations and obtain leasing income. This method is currently the core source of income for most independent shared energy storage power stations. The basic principle is to transfer the energy storage that was originally compulsorily built by new energy owners to the shared energy storage power stations built by social capital. New energy owners are exempted from the one-time capital expenditure of building energy storage power stations, while shared energy storage investors recover their investment by charging capacity leasing fees to surrounding new energy power stations. Domestically, it is generally between 250-350 yuan / kW / year. For a 100MW shared energy storage power station, the capacity leasing fee can reach 25-35 million yuan / year. For example, a region's commitment to energy storage leasing can be regarded as a new energy storage quota, and the estimated rental level is 359 yuan / kW.

[0100] The market environment provides greater development space for shared energy storage. For social capital, the market can help discover the value of energy storage and lower the threshold for market participation. The market environment helps energy storage to achieve a virtuous cycle based on the concept of sharing. Shared energy storage can be interconnected, coordinated and controlled through different levels of power devices, and provide power auxiliary services for new energy power stations and power grids within a certain area. During the shared energy storage transaction, when the output of the new energy power stations participating in the transaction is limited, the dispatching agency can store the original abandoned wind and photovoltaic power in the shared energy storage system, and release the power during the peak power consumption or the low output of new energy. The transaction power can be calculated according to the released power of the energy storage power station, and the new energy and energy storage power stations can share the transaction power revenue in a way that the new energy and energy storage power stations share the transaction power revenue, so as to achieve a win-win situation for the new energy station and the energy storage power station. Through energy storage sharing, the investment cost of energy storage can be greatly reduced, the utilization rate of existing energy storage equipment can be improved, the economic effect of energy storage devices can be maximized, the large-scale grid connection and consumption of renewable energy can be promoted, and strong support can be provided for the regional power grid.

[0101] (3) Coordinated operation control technology between new energy stations and shared energy storage

[0102] 1) Virtual synchronous machine control

[0103] Virtual synchronous machine control is a control strategy that simulates the operation mode of conventional synchronous generators. It controls the active power of the controlled equipment by simulating the rotor motion equation and power angle relationship of the synchronous generator, and controls the reactive power of the controlled equipment by simulating the reactive-voltage droop equation. The power angle principle of conventional synchronous generators and the equivalent circuit of converters are shown in Figure 1. Figure 3 As shown, a conventional synchronous generator controls the active and reactive power of the generator by controlling the phase and amplitude of the internal voltage E0.

[0104] The virtual synchronous generator (VSG) embeds the mathematical model of the synchronous generator into the control of the power electronic converter, and uses the mathematical model of the synchronous generator to generate the current and voltage instructions of the converter, thereby controlling the output current and voltage. The virtual inertia, damping coefficient, excitation inductance and other control parameters can be designed as needed to give it the characteristics of a synchronous generator.

[0105] 2) Virtual inertia control

[0106] Inertia represents the system's ability to prevent sudden frequency changes. The inertia characteristics of conventional synchronous units enable them to suppress frequency oscillations caused by load oscillations by utilizing the kinetic energy stored in the rotor. Energy storage and new energy are introduced into the power system through converters, making it impossible for the station output to be directly coupled with the system frequency. As the penetration rate of new energy continues to increase, the impact on the frequency stability of the power grid is also increasing. Virtual inertia control uses frequency as the input signal. In the active control part of the power generation unit, additional frequency differentiation is used to make the unit output power correlated with the change in grid frequency to simulate the inertia response process. The power output signal of virtual inertia control can be expressed as

[0107] like Figure 4 As shown in the figure, virtual inertia control is to control the inertia of the power generation unit by introducing the frequency change rate. Virtual inertia control takes the frequency change rate as the target. The inertia response is obtained through the differential link. If the power system frequency fluctuates, the electromagnetic power of the power generation unit is quickly adjusted to supplement the active power shortage, so that new energy and energy storage can provide inertia support to the system like synchronous units.

[0108] 3) Primary frequency modulation

[0109] Once the frequency of the power grid deviates from the rated value, the control system of the unit in the power grid will automatically control the increase or decrease of the unit's active power, limit the change of the power grid frequency, and keep the power grid frequency stable. The power grid is a huge inertial system. According to the rotor motion equation, when the power grid has a shortage of active power, the generator rotor accelerates and the power grid frequency increases, and vice versa. Therefore, the primary frequency regulation function is one of the means to dynamically ensure the balance of the active power of the power grid. Figure 5 As shown in Figure 1, when the grid frequency increases, the primary frequency regulation function requires the unit to reduce the grid-connected active power, otherwise, the unit increases the grid-connected active power; where P N is the rated power of the station; P0 is the actual operating power of the station.

[0110] 4) Active power dispatch and control model

[0111] 1. Objective Function

[0112] The online economic dispatch of energy storage systems is a multi-objective decision-making problem, and the optimization objectives include system economy, safety, and fairness in the dispatch of new energy power stations.

[0113] a. Economic indicators

[0114] The system operation economic index is expressed as the sum of the coal consumption costs of conventional units: in, is a 0-1 variable indicating the start and stop status of the unit, which is determined by the unit combination on the previous day; P it is the output of conventional unit i in period t; N T is the number of time periods in the forward-looking time window. In this embodiment, it is taken as the time window for ultra-short-term prediction, that is, N T =16; NG is the number of conventional units.

[0115] b. Safety indicators

[0116] The system safety index is expressed as the sum of the system's abandoned wind power and lost load power: Among them, P curt，wt D is the wind power abandoned by the energy storage field w in period t; curt，jt is the load loss power of load node j in period t; N w is the number of energy storage electric fields; N J is the number of load nodes; Δt is the time period length, which is 15 minutes in this embodiment.

[0117] c. Fairness Index

[0118] The fairness of the scheduling of the energy storage field is expressed by the penalty power of the energy storage power station whose power prediction error of the day-ahead energy storage power station does not meet the grid connection standard requirements of the energy storage power station. The formula is: Among them,: wtis a 0-1 variable representing the type of energy storage field. If the energy storage field w is a controlled energy storage field, that is, W∈W C0 , otherwise δ wt = 0. To adopt different energy storage electric field energy storage system scheduling strategies, only the selection method of the controlled energy storage electric field and the penalty power P need to be modified. punish，wt The calculation method of .

[0119] 2. Constraints

[0120] The constraints of the classic dynamic economic dispatch model include: system operation constraints, conventional unit operation constraints, new energy power station constraints, energy storage system operation constraints, and coordination constraints at different time levels of the rolling planning model (such as the coordination constraints between the rolling power generation plan and the day-ahead plan).

[0121] a. System operation constraints

[0122] System operation constraints include: power balance constraints, load loss constraints, and network security constraints.

[0123]

[0124] 0≤D curt，jt ≤D jt

[0125] Among them, P wt is the ultra-short-term predicted energy storage power station power of the energy storage field w in time period t; P ch，wt and P d，wt D is the charging and discharging power of the energy storage system of the energy storage field w; curt，jt is the abandoned wind power at load node j; D jt is the load demand of node j; G(·) represents the line power flow calculation formula, and this embodiment adopts the calculation method based on DC power flow sensitivity; is the maximum transmission capacity of line l.

[0126] b. Constraints on conventional thermal power units

[0127] The operating constraints of conventional thermal power units include: unit output constraints and unit ramp rate constraints, namely

[0128] in, is a 0-1 variable indicating the start of the unit. When the unit is shut down in period t-1 and started in period t, is a 0-1 variable indicating the shutdown of the unit. When the unit is started in period t-1 and shut down in period t, r ui and r di They are the maximum upward and downward climbing rates of the unit respectively; and is the maximum and minimum output of unit i; it is the state variable that represents the startup or shutdown of the unit and It is usually determined by the day-ahead plan.

[0129] c. Energy storage field constraint (wind power abandonment constraint), i.e. 0≤P curt，wt ≤P wt .

[0130] d. Energy storage system constraints

[0131] The operation constraints of the energy storage system include the charge and discharge mode constraint, the charge and discharge power correction constraint, the charge and discharge power constraint, and the energy storage system capacity constraint, namely: Where: μ ch，wt and μ d，wt To represent the energy storage in real-time planning

[0132]

[0133] 0-1 variable of the charge and discharge pattern of system w in period t, Δμ ch，wt and Δμ d，wt The energy storage system adjusts the power mode relative to the day-ahead plan; ΔP ch，wt and ΔP d，wt is the charge and discharge power adjusted relative to the day-ahead power generation plan; and is the day-ahead charging and discharging plan of the energy storage system w in time period t; E wt is the remaining power of the energy storage system w in time period t; E cap，w is the rated capacity; and are the minimum and maximum allowable state of charge; S w，min and S w，max is the charging power and discharging power of the energy storage system.

[0134] 5) Principle of energy storage participating in frequency regulation

[0135] When the power system frequency changes due to changes in load, generator sets, etc., the active power change ΔP of the power grid is required G1 It satisfies a certain characteristic relationship with the frequency change Δf, that is, the change in power can compensate for the change in frequency. The change in the active power of the power grid can be achieved by changing the output of the generator, or by using the charging and discharging of the energy storage system to adjust the power of the power grid. When the system frequency drops, the energy storage system discharges, releasing electrical energy into the power grid, causing the active power of the power grid to increase; when the system frequency rises, the energy storage system charges, absorbs electrical energy from the power grid, causing the active power of the power grid to decrease. This is the primary frequency regulation of the energy storage system. When the active power change of the energy storage system ΔP G2When it is issued by the grid dispatch center, it belongs to the secondary frequency regulation of the energy storage system. Figure 6 As shown in the figure, the station-level AGC system is responsible for sending the frequency modulation power to the energy storage EMS master station (the frequency modulation power can be the primary frequency modulation power ΔP generated by the AGC itself G1 , or it can be the secondary frequency modulation power ΔP sent by the provincial regulator G2 ), the energy storage EMS master station distributes the frequency modulation power to each energy storage EMS substation according to the frequency modulation power and the status information of each energy storage EMS substation, and each energy storage EMS substation performs charging and discharging according to the power instruction.

[0136] Assume that the frequency regulation capability of a wind farm is the same as that of a thermal power plant of the same capacity, and that the frequency regulation capability is entirely provided by energy storage batteries. The following takes a 200MW wind farm as an example to calculate the energy storage capacity configuration that meets the frequency regulation requirements:

[0137] Primary frequency modulation parameters: load variation range ±10%, frequency modulation stabilization time 40s.

[0138] Secondary frequency modulation parameters: The maximum power change per minute is 3% of the rated power, and the frequency modulation time is 3 minutes.

[0139] Assume the required battery capacity is Q. The battery energy storage system has special operating requirements, avoiding deep charging and deep discharging, and ensuring the reliability of frequency modulation. Therefore, the capacity calculation method that the battery energy storage system should be equipped with is: Q = 2P·T + SOC 下限值 +SOC 上限值 ; Where P is the battery charge and discharge power; T is the frequency modulation time, which is 40S for the first frequency modulation and 3min for the second frequency modulation; SOC 下限值 is the discharge cut-off SOC value, which is 0.1Q; SOC 上限值 is the charging cut-off SOC value, which is 0.1Q.

[0140] For primary frequency regulation, the battery charging and discharging power is 10% of the rated power, 20MW. The capacity required for primary frequency regulation is So Q1=0.556MWh.

[0141] For the secondary frequency regulation, after 3 minutes of frequency regulation, the output power is 200*3%*3=18MW. Since the power gradually rises to 18MW during the frequency regulation time, the capacity calculation adopts the average value of 9MW. Then the capacity required for the secondary frequency regulation is: So Q2=1.125MWh.

[0142] If the energy storage system is used to replace the 200MW wind farm with primary and secondary frequency regulation capabilities for frequency regulation, the power is the maximum power required for primary and secondary frequency regulation, 20MW; the capacity is selected by assuming that the secondary system performs primary frequency regulation and then puts into secondary frequency regulation, then the capacity of the energy storage system is the sum of the primary and secondary frequency regulation requirements, that is, Q s =Q1+Q2=1.681MWh.

[0143] In summary, since the energy storage system has a fast power response capability and can achieve positive and negative bidirectional power regulation, through reasonable control of the energy storage system, the wind farm can have the frequency regulation characteristics of a conventional thermal power plant.

[0144] 6) Cooperative control principle

[0145] Based on the principle of self-organization and utilizing the inherent nonlinear characteristics of the controlled system, it has good asymptotic stability. The basic idea of ​​cooperative control theory is to construct a manifold in the state space of the controlled object according to the required dynamic performance indicators and control objectives, and use the manifold to solve the control law of the system. The manifold not only contains the system control performance indicator information, but also contains the specific relationship between the system state variables.

[0146] Assume the following nonlinear system Where x is the state variable, d is the control variable, and t is time. The first step in the design of a collaborative controller is to construct a macro variable, which is a specific function of the state variables ψ = ψ(N). Usually, a linear combination of the state variables can be simply selected. The control objective is to make the system converge to the manifold ψ = 0 within a finite time. The macro variable ψ can also be defined as a multidimensional function vector according to the dimension of the control variable d. Once the manifold is determined, it is equivalent to adding new constraints in the state space, thereby reducing the order of the original system.

[0147] After constructing the macro variables, the control law can be solved according to the dynamic characteristics of the manifold expectation. The dynamic characteristics equation of the manifold expectation is Among them, T is a design parameter, which determines the speed at which the system state variables converge to the manifold ψ = 0. Under the premise of ensuring the stability of the system, the smaller T is, the shorter the time it takes for the state variables to reach the manifold, that is, the faster the system responds and the better the dynamic performance. The macro variable is a function of the state variable. Taking the derivative of the macro variable, we can get After obtaining the control quantity d, the system can approach and remain on the manifold ψ=0 at a certain rate from any initial state. If the stability of the system after the introduction of the control quantity can be guaranteed at the same time, the system will inevitably converge to the equilibrium point along the manifold. The key to cooperative control is to construct the manifold ψ=0 and use the constraints contained in the manifold to obtain the control law.

[0148] New energy power station grid connection detection and evaluation technology

[0149] According to the grid connection guidelines, new energy stations should complete grid connection operation characteristics testing within 6 months after all power generation units are grid-connected and commissioned. The testing content includes the active / reactive power control capability, power quality, low voltage ride-through capability, and voltage / frequency adaptability of the new energy power station. Based on the test reports of the power generation units and power stations, the grid connection performance of the new energy power station can be evaluated. Figure 7 The schematic diagram of active power waveform during active power control is shown, and the relevant performance parameters of active power setting value control of new energy stations are as follows:

[0150] The allowable range of active power steady-state value during setpoint control is The active power setting value controls the overshoot, i.e., σ = |P3-P2|; the active power setting value controls the response time, i.e., t p，reg =t p，1 -t p，0 .

[0151] The on-site test of the rapid frequency response capability of the new energy station is to test the station's active support capability of the grid frequency by simulating the grid frequency change. Using the frequency simulation equipment, the frequency acquisition unit of the new energy power station rapid frequency control system inputs the simulated grid frequency change to test the frequency modulation characteristics of the new energy power station. The test principle is as follows: Figure 8 As shown, the test content includes inertia response and primary frequency regulation capability test. The inertia response is tested under power-limited and non-power-limited conditions respectively, and the primary frequency regulation test includes primary frequency regulation dead zone test, frequency step disturbance test, simulated grid frequency disturbance test, anti-disturbance performance verification and automatic generation control (AGC) coordination test. According to the test results, the rise time, active power change and power control deviation of the inertia response of the new energy station are calculated, and the response lag time, rise time, adjustment time, active power control deviation and integral point qualification rate of the primary frequency regulation characteristics are also determined to determine whether the coordination with AGC meets the relevant requirements.

[0152] Taking the wind farm as an example, the calculation of relevant indicators is shown in the following formula.

[0153] The actual integrated power of the wind farm primary frequency regulation is Among them, Q S is the actual integrated power of the primary frequency regulation of the wind farm (high frequency less power generation or low frequency more power generation is positive, high frequency more power generation or low frequency less power generation is negative), in kilowatt-hour (kW·h); t0 is the starting time when the system frequency changes beyond the dead zone of the primary frequency regulation of the wind farm, in seconds (s); t1 is the time when the system frequency returns to the dead zone of the primary frequency regulation of the wind farm, in seconds (s); P s(t) is the actual active power output of the wind farm at time t within the time of the first frequency modulation response action of the wind farm (if the time exceeds 60s, it will be calculated as 60s), in kilowatts (kW); P0 is the actual active power output of the wind farm at time t0, in kilowatts (kW).

[0154] The primary frequency modulation response action time is the effective disturbance time of the primary frequency modulation, which is the minimum duration from the frequency change exceeding the primary frequency modulation dead zone to the frequency returning to the primary frequency modulation dead zone.

[0155] The theoretical integral power of primary frequency regulation of wind farm is Among them, Q E is the theoretical integral power of the primary frequency regulation of the wind farm (always positive), in kilowatt-hours (kW·h); Δf(t) is the deviation between the frequency at time t and the frequency dead zone, in Hertz (Hz); ΔP E (t) is the adjustment amount corresponding to the theoretical output of the wind farm at time t within the time of the wind farm's frequency regulation response action (if the time exceeds 60s, it will be calculated as 60s), in kilowatts (kW); f N is the rated frequency of the system, in Hertz (Hz); P N is the rated active output of the wind farm, in kilowatts (kW); K c K is the primary frequency regulation rate of the wind farm; p It is the limit of the primary frequency modulation output of the wind farm.

[0156] The qualified rate of wind farm primary frequency regulation integral electricity is Among them, Q ％ It is the qualified rate of the primary frequency regulation integrated electricity of the wind farm, which refers to the percentage of the actual integrated electricity of the primary frequency regulation response of the wind farm to the theoretical integrated electricity within the primary frequency regulation response action time (if the time exceeds 60s, it will be calculated as 60s).

[0157] Inertia response and various control time, overshoot, and steady-state deviation calculation indicators of primary frequency modulation are as follows: Fig. 9 shown.

[0158] Energy storage system detection and evaluation technology

[0159] At present, the detection and evaluation of energy storage systems include energy storage battery status monitoring and energy storage power station grid connection detection. Energy storage battery status monitoring mainly focuses on battery health status and life, and grid connection detection focuses on the grid connection characteristics of the energy storage system, including normal operation performance testing and anti-interference characteristics testing such as grid adaptability and low voltage ride-through capability. The normal operation performance includes energy storage power station capacity, rated power charging and discharging time, rated power charging and discharging response time and conversion time, energy efficiency characteristics, self-discharge rate, dynamic response detection and power quality detection.

[0160] Battery status parameters include state of charge (SOC) and state of health (SOH). SOC is the ratio of the current available power of the battery to the current available capacity, and SOH is the ratio of the current available capacity of the battery to the rated capacity when it leaves the factory.

[0161] According to the new battery SOC-Uoc relationship curve, the remaining available capacity Ct of the battery under the current battery performance state is estimated as C t_avail =C0(1-SOC0(U OC (0)) Where C0 is the rated capacity of a new battery.

[0162] According to the definition of SOH, the SOH of the battery at this time is According to the definition of SOC, taking into account the current aging of the battery, the available power Ct of the battery at this time is C t =C0SOC t (U OC (t))-C0(1-SOH(t)); then the battery SOC(t) is

[0163] like Fig.10 The collaborative control evaluation method of the new energy station and shared energy storage shown in the figure specifically includes:

[0164] (1) Research on online monitoring technology for the operation status of new energy stations and shared energy storage

[0165] Based on the demand for measurement data in collaborative support capability evaluation, the amount of data to be collected, the collection cycle, the data collection location, and the communication methods supported by the data source are analyzed. Based on the size, density, and frequency of transient and steady-state data, the communication access requirements of data platforms at all levels are determined, and the distributed data hierarchical collection scheme for the dynamic evaluation of regional "new energy stations + shared energy storage" collaborative control and active support capabilities is determined. Based on the requirements of the dynamic evaluation of active support capabilities, the distributed collection data synchronization method and data synchronization accuracy that are convenient for engineering applications are determined to achieve the synchronization of distributed collection data based on edge computing.

[0166] In order to further avoid the distortion of index calculation and evaluation results caused by abnormal data collection, the fusion processing method of multi-source heterogeneous data is studied for the data collection equipment caused by the disturbance of the on-site operating environment. The discrete data is processed by conversion, filling, interpolation and other processing methods, and finally the synchronous processing and efficient fusion of the data collected by the distributed new energy stations and shared energy storage power stations are realized. Based on the online evaluation data requirements of the collaborative support capabilities and control response of new energy stations and shared energy storage, a time series database with high throughput and strong stability is used to collect transient data and steady-state data to meet the requirements of data reading and writing performance. In view of the high density and large amount of transient data collection, the optimization method of data transmission and storage is studied, and it is planned to use a structured database or a file database for data storage.

[0167] (2) Research on comprehensive evaluation technology of collaborative support capabilities of new energy stations and shared energy storage

[0168] First, considering the operating characteristics of power grids with a high proportion of new energy, a frequency and voltage stability analysis model for new energy grid-connected power is established. The interaction mechanism of new energy and energy storage actively participating in system frequency and voltage regulation under regional power grid is simulated and analyzed, and the evaluation requirements for the time scale, control performance, and regulation capability of the active support capability of new energy and energy storage systems are sorted out.

[0169] Secondly, based on the analysis of the coordinated control strategy and model simulation analysis of new energy stations and energy storage, the key factors affecting the frequency / voltage support capacity and control response process of new energy stations and shared energy storage are studied through parameter sensitivity analysis and other methods. Furthermore, considering different evaluation time scales, the key technical indicators of frequency / voltage support capacity are studied from the two dimensions of regulation capability and control response performance.

[0170] Finally, in view of the time scale and key factors involved in the calculation of key technical indicators, the dynamic evaluation technology of frequency / voltage collaborative support capability based on time series data is studied, and the capability level of active frequency regulation, reactive voltage regulation, and dispatch response (AGC, AVC) within the relevant time scale is evaluated online; from the performance perspective, based on transient and steady-state operation data and in accordance with relevant standards, the online calculation method of frequency / voltage support performance related indicators of new energy stations and shared energy storage is studied, including active support comprehensive control performance and single station / energy storage control response performance indicators, such as response time, control accuracy, etc.; combined with capacity calculation and performance indicator calculation, the research forms a comprehensive evaluation method and indicator system for active support performance of new energy station source-energy storage power station collaboration, including collaborative support capabilities such as active support of a single power station, comprehensive active support of new energy-energy storage, shared energy storage contribution rate, and dispatch response capability.

[0171] (3) Research on real-time perception technology of new energy stations and shared energy storage control regulation margin

[0172] According to the on-site time series measurement data, key influencing factors and the need for coordinated control between stations, the prediction time scale is determined, and a method for estimating the active frequency / voltage support capability (active / reactive adjustable capability) of new energy stations based on machine learning is studied to achieve real-time perception of the control adjustment margin of new energy stations based on historical-predicted data. Based on the monitoring data of energy storage power stations, the key features of the station frequency support capability are extracted, and the active power and support time estimation method based on feature extraction and data mining is studied to provide a data basis for the optimal scheduling of new energy stations and shared energy storage to support grid frequency and voltage regulation.

[0173] Based on the multi-station and multi-scale collaborative support capabilities of new energy and shared energy storage power stations, the intrinsic connection between the collaborative support capabilities of new energy and energy storage power stations and the control and regulation margin is established. Combined with factors such as the physical location of the station, the collection line, the capacity of the collection point, and the operation mode of the power grid, a control and regulation margin perception method for the multi-station integration of new energy and energy storage power stations based on dynamic estimation and prediction of collaborative support capabilities is studied to achieve accurate perception and effective prediction of the collaborative control and regulation margin of new energy and energy storage power stations.

[0174] This embodiment aims to improve the stability of grid frequency and increase the space for new energy consumption, improve the stable operation level of the new energy power grid, improve the coordinated regulation mechanism between new energy sites and shared energy storage, and realize the diversified interaction between new energy sites and shared energy storage.

[0175] This embodiment provides technical means for the planning and construction of shared energy storage, commercial operation and coordinated dispatch with new energy sites. Most of the current energy storage planning schemes are imperfect and do not take into account the actual needs of new energy consumption and grid access conditions. It is urgent to fill the key technical gaps in the comprehensive optimization configuration of shared energy storage that takes into account the active support of new energy and the integration of multiple scenarios of economic operation; by establishing a shared energy storage pricing mechanism and revenue model that takes into account the interests of multiple parties, a profit model for shared energy storage to participate in multiple markets is formed.

[0176] This embodiment can improve the regional power grid's ability to absorb new energy, enhance the system's regulatory support capabilities, and provide guidance for the development of new energy industries and energy storage technologies. It can scientifically configure shared energy storage under the background of a high proportion of new energy, achieve efficient operation and active control of new energy sites and shared energy storage, give full play to the advantages of shared energy storage technology, and provide solutions to the problems faced by the development of regions with a high proportion of new energy in the future. It can be widely promoted to onshore wind power / photovoltaic power station clusters and large-scale offshore wind power clusters.

[0177] Embodiment 2

[0178] Embodiment 2 of the present invention introduces a collaborative control and evaluation system for a new energy station and shared energy storage.

[0179] A collaborative control and evaluation system for new energy stations and shared energy storage, comprising:

[0180] An acquisition module, configured to acquire dynamic operation parameters of a new energy station;

[0181] A construction module is configured to construct a comprehensive evaluation index system of new energy stations and shared energy storage based on the acquired dynamic operation parameters; and to construct a multi-time scale collaborative optimization model of new energy stations and energy storage systems according to the constructed comprehensive evaluation index system;

[0182] A dispatching module is configured to perform a coordinated multi-objective optimization operation dispatch of new energy stations and shared energy storage based on the constructed multi-time scale collaborative optimization model with the goal of maximizing the total benefit of power grid peak and frequency regulation;

[0183] The evaluation module is configured to complete the collaborative control evaluation of the new energy station and the shared energy storage according to the obtained collaborative multi-objective optimization operation scheduling results.

[0184] The detailed steps are the same as the collaborative control evaluation method of the new energy station and shared energy storage provided in Example 1, and will not be repeated here.

[0185] Embodiment 3

[0186] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0187] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in the collaborative control evaluation method for a new energy station and shared energy storage as described in Embodiment 1 of the present invention.

[0188] The detailed steps are the same as the collaborative control evaluation method of the new energy station and shared energy storage provided in Example 1, and will not be repeated here.

[0189] Embodiment 4

[0190] A fourth embodiment of the present invention provides an electronic device.

[0191] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the collaborative control evaluation method of a new energy station and shared energy storage as described in the first embodiment of the present invention are implemented.

[0192] The detailed steps are the same as the collaborative control evaluation method of the new energy station and shared energy storage provided in Example 1, and will not be repeated here.

[0193] Embodiment 5

[0194] Embodiment 5 of the present invention provides a computer program product.

[0195] A computer program product includes software code, wherein the program in the software code executes the steps in the collaborative control evaluation method for a new energy station and shared energy storage as described in the first embodiment of the present invention.

[0196] The detailed steps are the same as the collaborative control evaluation method of the new energy station and shared energy storage provided in Example 1, and will not be repeated here.

[0197] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A collaborative control evaluation method for new energy stations and shared energy storage, characterized in that: include: Obtain dynamic operating parameters of new energy stations; Based on the dynamic operating parameters obtained, a comprehensive evaluation index system for new energy stations and shared energy storage is constructed; According to the constructed comprehensive evaluation index system, a multi-time scale collaborative optimization model of new energy stations and energy storage systems is constructed; Based on the constructed multi-time scale collaborative optimization model, the collaborative multi-objective optimization operation and scheduling of new energy stations and shared energy storage is carried out with the goal of maximizing the total benefit of grid peak and frequency regulation; Based on the collaborative multi-objective optimization operation and scheduling results obtained, the collaborative control evaluation of new energy stations and shared energy storage is completed.

2. A collaborative control evaluation method for a new energy station and shared energy storage as described in claim 1, characterized in that: The specific process of the collaborative multi-objective optimization operation and scheduling of new energy stations and shared energy storage with the goal of maximizing the total benefit of grid peak-shaving and frequency regulation is as follows: considering the different operating characteristics of regional loads, power sources and multiple types of shared energy storage, calculating the regional grid peak-shaving and frequency regulation power requirements under different scenarios and working conditions; taking the maximum benefit of new energy stations as the optimization goal, and taking the power generation power and output adjustment characteristics of new energy stations as constraints, determining the optimal output plan for new energy stations to participate in grid peak-shaving and frequency regulation; taking into account the benefits of peak-shaving and frequency regulation and the purchase cost of shared energy storage with different service modes, and taking the maximum benefit of new energy stations as the goal, collaborative optimization operation and regulation of new energy and independent shared energy storage and co-built shared energy storage are carried out; collaborative multi-objective optimization operation and scheduling of new energy and independent shared energy storage and co-built shared energy storage with the goal of maximizing the overall benefit of grid peak-shaving and frequency regulation; The obtained dynamic operation parameters of the new energy station include at least wind power frequency regulation parameters, intermittency of new energy, active power of the new energy station, power quality and low voltage ride-through capability.

3. A collaborative control evaluation method for a new energy station and shared energy storage as claimed in claim 1, characterized in that: The specific process of constructing the comprehensive evaluation index system of new energy stations and shared energy storage is as follows: analyzing the output and load fluctuation morphological characteristics of new energy stations, obtaining typical scenarios of different depths of peak and frequency regulation of regional power grids through a multi-time scale morphological clustering algorithm, considering the layout and capacity of new energy stations and the topological structure and operation characteristics of the regional power grid, taking the configuration capacity and access points of shared energy storage as decision variables, constructing from the power grid side the operation cost of new energy stations and the investment, operation cost and market profit of shared energy storage as the goal, taking into account the active support of new energy and the integration of multiple scenarios of operation economy to complete the two-layer comprehensive optimization configuration of shared energy storage considering the peak and frequency regulation needs of the power grid; according to the obtained two-layer comprehensive optimization configuration of shared energy storage, combined with the energy conversion characteristics of new energy and shared energy storage, calculate the support power required to be provided by new energy stations under frequency disturbances of different durations and sizes, and combine the power demand of new energy stations participating in frequency regulation to obtain the rapid frequency response characteristics of independent shared energy storage and equipped shared energy storage under different operation modes, and obtain a comprehensive evaluation index system.

4. A collaborative control evaluation method for a new energy station and shared energy storage as claimed in claim 1, characterized in that: The specific process of the collaborative control evaluation of the new energy station and shared energy storage is: real-time perception of the frequency regulation margin of the new energy station and shared energy storage, supporting the frequency stability support and safe dispatching operation of the power grid with a high proportion of new energy to effectively participate, and completing the collaborative control evaluation of the new energy station and shared energy storage; wherein, the real-time perceived frequency regulation margin of the new energy station and shared energy storage is the coordinated control strategy and real-time operation status of the new energy station and shared energy storage.

5. A collaborative control and evaluation system for new energy stations and shared energy storage, characterized in that: include: An acquisition module, configured to acquire dynamic operation parameters of a new energy station; A construction module is configured to construct a comprehensive evaluation index system of new energy stations and shared energy storage based on the acquired dynamic operation parameters; and to construct a multi-time scale collaborative optimization model of new energy stations and energy storage systems according to the constructed comprehensive evaluation index system; A dispatching module is configured to perform a coordinated multi-objective optimization operation dispatch of new energy stations and shared energy storage based on the constructed multi-time scale collaborative optimization model with the goal of maximizing the total benefit of power grid peak and frequency regulation; The evaluation module is configured to complete the collaborative control evaluation of the new energy station and the shared energy storage according to the obtained collaborative multi-objective optimization operation scheduling results.

6. A collaborative control and evaluation system for a new energy station and shared energy storage as described in claim 5, characterized in that: The specific process of the collaborative multi-objective optimization operation and scheduling of new energy stations and shared energy storage with the goal of maximizing the total benefit of grid peak-shaving and frequency regulation is as follows: considering the different operating characteristics of regional loads, power sources and multiple types of shared energy storage, calculating the regional grid peak-shaving and frequency regulation power requirements under different scenarios and working conditions; taking the maximum benefit of new energy stations as the optimization goal, and taking the power generation power and output adjustment characteristics of new energy stations as constraints, determining the optimal output plan for new energy stations to participate in grid peak-shaving and frequency regulation; taking into account the benefits of peak-shaving and frequency regulation and the purchase cost of shared energy storage with different service modes, and taking the maximum benefit of new energy stations as the goal, collaborative optimization operation and regulation of new energy and independent shared energy storage and co-built shared energy storage are carried out; collaborative multi-objective optimization operation and scheduling of new energy and independent shared energy storage and co-built shared energy storage with the goal of maximizing the overall benefit of grid peak-shaving and frequency regulation; The obtained dynamic operation parameters of the new energy station include at least wind power frequency regulation parameters, intermittency of new energy, active power of the new energy station, power quality and low voltage ride-through capability.

7. A collaborative control and evaluation system for a new energy station and shared energy storage as described in claim 5, characterized in that: The specific process of constructing the comprehensive evaluation index system of new energy stations and shared energy storage is as follows: analyzing the output and load fluctuation morphological characteristics of new energy stations, obtaining typical scenarios of different depths of peak and frequency regulation of regional power grids through a multi-time scale morphological clustering algorithm, considering the layout and capacity of new energy stations and the topological structure and operation characteristics of the regional power grid, taking the configuration capacity and access points of shared energy storage as decision variables, constructing from the power grid side the operation cost of new energy stations and the investment, operation cost and market profit of shared energy storage as the goal, taking into account the active support of new energy and the integration of multiple scenarios of operation economy to complete the two-layer comprehensive optimization configuration of shared energy storage considering the peak and frequency regulation needs of the power grid; according to the obtained two-layer comprehensive optimization configuration of shared energy storage, combined with the energy conversion characteristics of new energy and shared energy storage, calculate the support power required to be provided by new energy stations under frequency disturbances of different durations and sizes, and combine the power demand of new energy stations participating in frequency regulation to obtain the rapid frequency response characteristics of independent shared energy storage and equipped shared energy storage under different operation modes, and obtain a comprehensive evaluation index system.

8. A collaborative control and evaluation system for a new energy station and shared energy storage as described in claim 5, characterized in that: The specific process of the collaborative control evaluation of the new energy station and shared energy storage is: real-time perception of the frequency regulation margin of the new energy station and shared energy storage, supporting the frequency stability support and safe dispatching operation of the power grid with a high proportion of new energy to effectively participate, and completing the collaborative control evaluation of the new energy station and shared energy storage; wherein, the real-time perceived frequency regulation margin of the new energy station and shared energy storage is the coordinated control strategy and real-time operation status of the new energy station and shared energy storage.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the collaborative control evaluation method of a new energy station and shared energy storage are implemented as described in any one of claims 1 to 4.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the collaborative control evaluation method of the new energy station and shared energy storage as described in any one of claims 1-4 are implemented.

11. A computer program product comprising software code, characterized in that The program in the software code executes the steps of the collaborative control evaluation method for new energy stations and shared energy storage as described in any one of claims 1-4.

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