Energy storage evaluation platform and equipment for high-proportion new energy power network, and medium

Abstract

The invention provides an energy storage evaluation platform, equipment and medium for a high-proportion new energy power network, and a new energy base inertia characteristic analysis module, which are used for analyzing the inertia characteristic of a wind turbine generator, adding a proportion controller to a fan side MPPT control loop through virtual inertia control, and adjusting the output power of a fan according to the frequency deviation of a power grid. The energy storage characteristic analysis and control module is used for enabling the energy storage system to adopt a two-stage energy storage converter and providing inertia support for the system according to set conditions when the frequency of the energy storage system deviates; the comprehensive benefit evaluation module is used for estimating the total cost of the energy storage system and calculating the wind turbine cost and the photovoltaic cost so as to establish a minimum function model of the total cost of the energy storage system; and the model solving module is used for calculating an optimal configuration combination by adopting a particle swarm algorithm, taking the energy storage configuration economy and the frequency response as decision variables and taking the minimum fitness function as a target. The optimization configuration efficiency of the energy storage system is improved, the cost is reduced, and the power grid stability is improved.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage system design optimization, and in particular relates to an energy storage evaluation platform, equipment and medium for a high-proportion new energy power network. Background Art

[0002] Since the output characteristics of wind power and photovoltaic power are significantly random, intermittent, periodic and volatile during the power generation process, these characteristics have brought unique challenges to the stable operation and dispatch management of the power system. With the large-scale access of new energy, the power output of power generation cannot be controlled on demand, and the accuracy of load prediction on the power side has also dropped significantly, which has led to uncontrollable power generation and user sides.

[0003] For the photovoltaic power generation process, the problem of difficulty in providing inertial response due to lack of rotational inertia has not been effectively solved. Moreover, for large photovoltaic power plants, the power of the site needs to be kept stable. Some power grids require photovoltaic power plants at medium and high voltage levels to have fast frequency response capabilities, which can be enhanced through the combination of photovoltaic and energy storage. However, although load reduction operation can achieve fast frequency response, it will affect the economic efficiency of the photovoltaic system, and it is difficult to match production and demand, affecting the stability of the power system. Summary of the invention

[0004] The present invention provides an energy storage evaluation platform for a high-proportion renewable energy power network. The system reduces conflicts by optimizing control parameters and other means, thereby improving its effectiveness and stability in power grid frequency regulation.

[0005] The platform includes: new energy base inertia characteristics analysis module, energy storage characteristics analysis and control module, comprehensive benefit evaluation module and model solution module; The inertia characteristic analysis module of the new energy base is used to analyze the inertia characteristics of wind turbines. Through virtual inertia control, a proportional controller is added to the MPPT control loop on the wind turbine side to adjust the output power of the wind turbine according to the grid frequency deviation; power reserve control means are used to reserve power for frequency regulation through pitch angle adjustment and load reduction operation; wind turbines are aggregated through an equivalent model to calculate the virtual inertia of the wind farm; The energy storage characteristic analysis and control module is used to enable the energy storage system to adopt a two-stage energy storage converter and provide inertia support for the system according to set conditions when the frequency of the energy storage system shifts; Comprehensive benefit evaluation module, used to estimate the total cost of the energy storage system, and calculate the cost of wind turbines and photovoltaics at the same time to establish a minimum function model for the total cost of the energy storage system; The model solving module is used to adopt the particle swarm algorithm, take the energy storage configuration economy and frequency response as decision variables, and calculate the optimal configuration combination with the minimum fitness function as the goal under the constraints of the comprehensive benefit evaluation module.

[0006] It should be further explained that the energy storage characteristic analysis and control module also realizes constant frequency control based on frequency tracking negative feedback; it also simulates and represents the inertia time constant of energy storage in different time periods according to actual operating data and specification requirements.

[0007] It should be further explained that the methods of achieving constant frequency control by frequency tracking negative feedback include: Set the energy storage inertia time constant to 4s - 12s and calculate the historical power generation data of new energy based on the following formula

[0008] in, Contribute to the overall new energy system. It is the maximum / minimum historical output of the new energy system. is the maximum / minimum value of the energy storage inertia time constant; According to the condition that the inertia requirement meets the maximum frequency change rate, the minimum inertia requirement of the system is calculated based on the following formula:

[0009] in, is the disturbance power, is the maximum frequency allowed by the energy storage system.

[0010] It is further necessary to explain that the minimum function model of the total cost of the energy storage system is established. The total cost of the energy storage system includes: ESS investment cost, ESS maintenance cost, and total operation cost of the new energy base;

[0011] In the formula, is the total cost of ESS; Investment cost for ESS; Maintenance cost for ESS; Total operating cost of the base.

[0012] It is further necessary to explain that

[0013] In the formula, They are the unit power / capacity investment cost of ESS respectively; ESS rated power / rated capacity respectively; is the operating life of the ESS; i is the investment discount factor calculated at an annual interest rate; is the average maintenance cost per unit; The total operating cost of the new energy base is:

[0014] In the formula, Penalty costs for abandoning wind and solar power at bases; To reduce the standby cost of load; To support power costs; are the unit wind power / abandoned solar power penalty coefficients respectively; is the unit standby cost; The unit electricity cost in the northwest region; The predicted output / maximum available output of the wind farm at time t; The predicted output / maximum available output of the photovoltaic power station at time t; is the reserve power of the wind farm; It is the supporting power provided by the weak AC system at time t.

[0015] It should be further explained that the comprehensive benefit evaluation module configures the WT cost, and the WT cost function is as follows:

[0016] In the formula, The cost of WT; is the discount rate; is the WT operation years; is the annual operation and maintenance cost of WT; is the number of WT units; The PV cost function is shown below:

[0017] In the formula, is the PV cost; is the operating life of PV; is the annual operation and maintenance cost of PV; is the number of PV units.

[0018] It should be further explained that the comprehensive benefit evaluation module is also used to provide a constraint setting interface for users to set constraint conditions; the constraints include: power balance constraints, weak AC system support power constraints, new energy output constraints, energy storage operation constraints, and frequency response power and energy constraints.

[0019] It should be further explained that the new energy base inertia characteristic analysis module is used to add a proportional controller to the MPPT control loop on the wind turbine side. When the system frequency fluctuates, the auxiliary power is generated by the proportional controller according to the grid frequency deviation. The calculation method is:

[0020] in, is the differential control coefficient, is the proportional control coefficient, Measure the frequency for the system, is the system reference frequency.

[0021] According to another embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes a module in the energy storage evaluation platform for a high-proportion new energy power network when executing the program.

[0022] According to another embodiment of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the module in the energy storage evaluation platform for a high-proportion new energy power network is implemented.

[0023] It can be seen from the above technical solutions that the present invention has the following advantages: The energy storage evaluation platform for high-proportion new energy power networks provided in this application is aimed at photovoltaics, but has not been able to effectively solve the problem that it has no rotational inertia and is difficult to provide inertial response. The present invention emphasizes the combination of photovoltaics and storage, and the collaborative working mode, so that the platform can better meet the grid frequency response requirements at different voltage levels, reduce the risk of disconnection due to frequency mutation at low voltage levels, improve the rapid frequency response capability at medium and high voltage levels, and enhance the adaptability and stability of photovoltaics in high-proportion new energy bases.

[0024] This application introduces frequency tracking negative feedback to achieve constant frequency control, so that the energy storage device can exert its power regulation capability on the basis of stably tracking the target frequency, fill the instantaneous power gap of the system, and achieve excellent inertial support. At the same time, by integrating virtual inertial control, primary frequency regulation and secondary frequency regulation control functions, an efficient integrated control system is constructed to enhance the flexibility and response speed of the energy storage system in dealing with grid frequency fluctuations, and better maintain its own power output level during the frequency regulation process, reduce dependence on system energy, and significantly improve overall energy efficiency.

[0025] The comprehensive benefit evaluation model established by the present invention comprehensively considers various costs of energy storage, wind turbines and photovoltaics, including investment costs, maintenance costs, total operating costs of new energy bases, and construction costs, operation and maintenance costs of wind turbines and photovoltaics. By constructing an accurate cost function, it provides a more reliable economic decision-making basis for the planning and operation of new energy bases, helping to minimize costs and maximize benefits. Previous optimization configuration models have not fully combined the actual operating characteristics and constraints of new energy bases. The particle swarm algorithm is used to solve the problem, with the economic efficiency of energy storage configuration and frequency response as decision variables, to find the optimal configuration combination, effectively improve the comprehensive benefits of new energy bases, and enhance the power system's ability to absorb and stabilize high-proportion new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0027] Figure 1 A schematic diagram of an energy storage evaluation platform for a high-proportion renewable energy power network; Figure 2 It is the virtual inertia control diagram of the fan; Figure 3 This is the fan power standby control diagram; Figure 4 It is the flow chart of energy storage virtual inertia control; Figure 5 It is the flow chart of PSO algorithm; Figure 6 Schematic diagram of an electronic device. DETAILED DESCRIPTION

[0028] The energy storage evaluation platform for high-proportion renewable energy power networks provided in this application conducts evaluation and analysis from the perspective of inertia characteristics analysis of renewable energy bases, mainly targeting wind turbines, analyzing the virtual inertia control in existing systems affected by frequency dead zones and control delays, as well as conflicts with maximum power point tracking control that lead to reduced control stability and economic benefits, and then reduces conflicts by optimizing control parameters and other means, thereby improving its effectiveness and stability in grid frequency regulation.

[0029] The energy storage evaluation platform for a high-proportion new energy power network provided by this application involves multiple functional modules in the implementation process. In the actual operation process, the platform can provide an operation interface based on the corresponding module for users to operate and use. The user can also manually select to start the corresponding module to execute the energy storage evaluation process. The execution mode of the modules involved in the platform is executed by the background program, and the functions included in the modules involved in this application are mainly executed by software programs.

[0030] The following will describe in detail the purpose and implementation of each module in the energy storage evaluation platform for a high proportion of new energy power networks. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details.

[0031] The phrases such as "one embodiment" or "some embodiments" described in the present application mean that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of the present application. Therefore, the phrases such as "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments" etc. that appear in different places in the present application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] See also Figure 1 Shown is a schematic diagram of an energy storage evaluation platform for a high-proportion new energy power network in a specific embodiment.

[0034] The platform includes: new energy base inertia characteristics analysis module, energy storage characteristics analysis and control module, comprehensive benefit evaluation module and model solving module.

[0035] The inertia characteristic analysis module of the new energy base is used to analyze the inertia characteristics of wind turbines. Through virtual inertia control, a proportional controller is added to the MPPT control loop on the wind turbine side to adjust the output power of the wind turbine according to the grid frequency deviation; power reserve control is used to reserve power for frequency regulation through pitch angle adjustment and load reduction operation; and the virtual inertia of the wind farm is calculated by aggregating wind turbines through an equivalent model.

[0036] When analyzing the inertia characteristics of a wind turbine, this embodiment may collect the operation data of the wind turbine, including speed, power, grid frequency, etc. These data are used to analyze the response characteristics of the wind turbine when the grid frequency changes, that is, the inertia characteristics.

[0037] Then, an inertia model of the wind turbine is established to describe its inertia characteristics. Among them, virtual inertia control is to add a proportional controller to the maximum power point tracking (MPPT) control loop on the wind turbine side.

[0038] In this embodiment, the output power of the wind turbine is adjusted by a proportional controller according to the grid frequency deviation to simulate the inertia response of a traditional generator.

[0039] For power reserve control, this embodiment adjusts the pitch angle of the wind turbine to reduce the wind energy captured by the wind turbine, thereby reserving a portion of power for frequency regulation. The wind turbine operates below its maximum power point to reserve additional power.

[0040] The wind farm virtual inertia calculation of this embodiment can use the equivalent model to aggregate the inertia characteristics of a single wind turbine to the wind farm level. The overall virtual inertia of the wind farm is obtained by calculating the sum of the virtual inertias of all wind turbines in the wind farm.

[0041] The purpose of the new energy base inertia characteristic analysis module is to improve the wind farm's response speed to grid frequency changes and enhance the stability of the grid. Through virtual inertia control, the wind farm can better simulate the inertia characteristics of traditional generators and improve the grid-connected capacity of wind power.

[0042] In some specific embodiments, in the analysis of the inertia characteristics of the wind turbine set, the power standby control module is used to reasonably select the reserved power size and balance the frequency regulation effect with the power generation efficiency loss. The pitch angle adjustment can actively change the pitch angle to reduce the output power of the wind turbine, and the load reduction operation can make the wind turbine operating speed exceed the MPPT operating speed to achieve load reduction. The energy storage characteristic analysis and control module of this embodiment is used to enable the energy storage system to adopt a two-stage energy storage converter, and to provide inertia support for the system according to set conditions when the frequency of the energy storage system shifts.

[0043] Specifically, the energy storage system is configured first, and a two-stage energy storage converter is used to achieve efficient operation of the energy storage system. The conditions for the energy storage system to provide inertia support are set, such as whether the grid frequency deviation is within the preset threshold.

[0044] When the conditions are met, the energy storage system provides inertia support for the grid by adjusting its output power. In this way, the energy storage system can respond faster to grid frequency changes, and through the inertia support strategy, the energy storage system can better participate in the frequency regulation of the grid and improve the reliability of the grid.

[0045] Furthermore, the energy storage characteristic analysis and control module of this embodiment adopts a two-stage PCS control, which is used to control the DC bus voltage to be constant in the DC-DC link, control the system active power and reactive power in the DC-AC link, and control the system current in the inner loop to improve the flexibility and accuracy of energy management.

[0046] In the energy storage characteristic analysis and control module, the two-stage PCS (energy storage converter) control can realize the DC-DC link to control the DC bus voltage to be constant, the DC-AC link to control the system active power and reactive power, and the inner loop to control the system current, so as to improve the flexibility and accuracy of energy management.

[0047] Specifically, the voltage can be sampled and fed back. Optionally, the DC bus voltage value is collected in real time by a voltage sensor. The collected voltage value is compared with a set DC bus voltage reference value to obtain a voltage error signal.

[0048] Combined with the PI controller regulation, the voltage error signal is input into the PI controller. The PI controller calculates and outputs the corresponding control signal according to the size and direction of the error signal to adjust the DC bus voltage. According to the output signal of the PI controller, the corresponding pulse width modulation control signal is generated. The PWM signal is used to control the on and off state of the switching device in the DC-DC converter (such as the DC / DC converter).

[0049] This embodiment changes the duty cycle of the converter by adjusting the on-off state of the switch device in the DC-DC converter. The change of the duty cycle will directly affect the magnitude of the DC bus voltage, thereby achieving accurate control of the DC bus voltage.

[0050] Finally, the closed-loop feedback regulation is implemented, that is, the actual regulated DC bus voltage value is compared with the reference value again. If there is still an error, the above steps are repeated for closed-loop feedback regulation until the voltage error is within the allowable range.

[0051] Optionally, the PI controller output signal is calculated as: u(t) = K_p * e(t) + K_i ∫e(t)dt in, u(t) is the output signal of the PI controller, K_p is the proportionality coefficient, K_i is the integration coefficient.

[0052] This allows for a fast response to voltage errors and elimination of steady-state errors.

[0053] Through the above steps and algorithms, the two-stage PCS control module can realize the DC-DC link control to keep the DC bus voltage constant, thereby improving the flexibility and accuracy of energy management.

[0054] The comprehensive benefit evaluation module of this embodiment is used to estimate the total cost of the energy storage system and simultaneously calculate the costs of wind turbines and photovoltaics to establish a minimum function model for the total cost of the energy storage system.

[0055] The cost estimation of the comprehensive benefit assessment module involves the total cost, including the cost of components such as energy storage equipment, converters, and control systems. The cost of wind turbines and photovoltaics is also calculated to fully evaluate the cost of the new energy power network.

[0056] The specific calculation method is to first establish a cost minimization function model, with the goal of minimizing the total cost of the energy storage system, and then establish a function model. Considering factors such as the cost of wind turbines and photovoltaics, as well as the contribution of the energy storage system to the stability of the power grid, the constraints of the model are determined.

[0057] In this way, by estimating costs and establishing a cost minimization function model, an economic basis is provided for the optimal configuration of the energy storage system. The costs of each component of the energy storage system, such as energy storage equipment, converters, control systems, etc., can also be estimated separately, and the total cost of the energy storage system can be obtained by summing them up. At the same time, the costs of wind turbines and photovoltaics are calculated, including equipment costs, installation costs, operation and maintenance costs, etc.

[0058] In some specific embodiments, the comprehensive benefit evaluation module is also used to estimate the total cost of the energy storage system, including investment cost, maintenance cost and total base operation cost, and calculate the wind turbine (WT) and photovoltaic (PV) costs to establish a minimum total cost function model for the energy storage system. A series of constraints are set, including power balance constraints, weak AC system support power constraints, new energy output constraints, energy storage operation constraints, and frequency response power and energy constraints, to achieve comprehensive restrictions on system operation.

[0059] In the comprehensive benefit evaluation module, the cost estimation module accurately calculates various cost parameters, including ESS, WT and PV related costs and sub-item costs, such as wind and solar power abandonment penalty costs, load reduction standby costs, support power costs, etc., providing an accurate basis for system cost analysis. In the comprehensive benefit evaluation module, the constraint condition module clarifies the meaning of each parameter, such as the wind farm, photovoltaic farm, energy storage and load power-related parameters at each moment, the weak AC system support power range parameters, the new energy station output limit parameters, the energy storage system capacity, state variables and frequency modulation power, and energy-related parameters to ensure that the system operates within a reasonable range.

[0060] The model solving module of this embodiment is used to adopt a particle swarm algorithm, take the energy storage configuration economy and frequency response as decision variables, and calculate the optimal configuration combination with the minimum fitness function as the goal under the constraints of the comprehensive benefit evaluation module.

[0061] Specifically, you can choose an optimization algorithm. Here, the particle swarm algorithm is used as the optimization algorithm to solve the optimal configuration combination of the energy storage system. The economic efficiency and frequency response of the energy storage configuration are used as decision variables, that is, the capacity, power and other parameters of the energy storage system. According to the results of the comprehensive benefit evaluation module, set the constraints of the energy storage system configuration.

[0062] In order to solve the optimal configuration combination, the minimum fitness function can be set as the goal, and the particle swarm algorithm can be used to solve the optimal configuration combination of the energy storage system. In this way, the optimal configuration combination is solved by the particle swarm algorithm to improve the optimization configuration efficiency of the energy storage system. In addition, the particle swarm algorithm is selected as the optimization algorithm, and the economic efficiency and frequency response of the energy storage configuration are used as decision variables. Through algorithm iteration, the position and speed of the particles are continuously updated to find the optimal solution. According to the results of the comprehensive benefit evaluation module, the constraints of the energy storage system configuration are set. The fitness function is constructed with the minimum total cost of the energy storage system as the goal.

[0063] It can be seen that the inertia characteristics analysis module of the new energy base improves the response speed of the wind farm to the changes in the grid frequency. The energy storage characteristics analysis and control module enables the energy storage system to better participate in the frequency regulation of the grid, and combines the particle swarm algorithm to solve the optimal configuration combination, which improves the optimization configuration efficiency of the energy storage system, further reduces costs and improves grid stability.

[0064] Further, as a refinement and expansion of the specific implementation methods of the above-mentioned embodiments, in order to fully illustrate the specific implementation process in this embodiment, the energy storage evaluation platform for a high proportion of new energy power networks is further described below in combination with specific implementation methods. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0065] refer to Figure 2 The wind turbine virtual inertia control diagram, combined with the new energy base inertia characteristic analysis module, adds a proportional controller to the wind turbine side MPPT control loop. When the system frequency fluctuates, the auxiliary power is calculated according to the following formula based on the grid frequency deviation.

[0066] in, is the differential control coefficient, is the proportional control coefficient, Measure the frequency for the system, is the system reference frequency.

[0067] This embodiment actively adjusts the output power of the wind turbine by calculating the additional active power, thereby achieving inertia support for the power grid.

[0068] Optionally, power reserve control can be achieved through pitch angle adjustment and load reduction operation. Pitch angle adjustment actively changes the pitch angle to reduce the output power of the wind turbine. Load reduction operation allows the wind turbine to operate at a speed exceeding the MPPT operating speed to achieve load reduction. Power is reserved for frequency regulation, but this will sacrifice wind energy capture efficiency and reduce power generation efficiency, and the size of the reserved power needs to be selected reasonably.

[0069] This embodiment also uses the equivalent model to aggregate wind turbines. The specific calculation method is:

[0070] in, is the virtual inertia time constant of the wind farm at time t, is the virtual inertia time constant of the wind turbine at time t, is the rated capacity of the wind turbine, is the installed capacity of the wind farm, and the virtual inertia of the wind farm is calculated.

[0071] Considering that PV modules have no rotational inertia at rest, they cannot provide inertial response. For large-scale PV power plants, the power of the site needs to be kept stable. Some power grids require PV power plants at medium and high voltage levels to have fast frequency response capabilities. The inertial support capability can be enhanced through the combination of photovoltaic and energy storage. However, although load reduction operation can achieve fast frequency response, it will affect the economic efficiency of the PV system.

[0072] The energy storage system of this embodiment adopts a two-stage energy storage converter (PCS), including a bidirectional DC / DC converter, a bidirectional PWM converter and an LCL filter circuit.

[0073] Taking the wind storage structure as an example, when the wind output power is insufficient, the bidirectional converter controls the battery discharge to maintain power balance; when the wind power generation unit output power exceeds the required power, the DC / DC circuit controls the battery charging to absorb the difference in power.

[0074] The DC-DC link of this embodiment adopts a voltage and current dual closed loop to control the DC bus voltage to be constant, the DC-AC link is responsible for controlling the system active power and reactive power, and the inner loop controls the system current.

[0075] When the absolute value of the system frequency deviation exceeds 0.05Hz and meets certain conditions, the energy storage power station provides support power according to the following formula

[0076] in Provide support power for energy storage power stations, is the energy storage inertia time constant, is the system frequency variation, is the energy storage rated power, and The maximum value should be greater than 10% , the response time is less than 0.5s.

[0077] refer to Figure 4 Energy storage virtual inertia control flow chart, introducing frequency tracking negative feedback to achieve constant frequency control.

[0078] Under extreme frequency conversion conditions, constant frequency tracking technology enables the energy storage device to stably track and maintain the target frequency, exert its power regulation capability, fill the instantaneous power gap of the system, achieve inertial support, and ensure the stable operation of the power system. At the same time, constant frequency control enables the energy storage system to maintain a high power output level without absorbing energy from the system for charging, optimizes frequency regulation performance, and improves overall energy efficiency. By integrating virtual inertial control, primary frequency regulation, and secondary frequency regulation control functions, an efficient integrated control system is constructed to improve the flexibility and response speed of the energy storage system in responding to grid frequency fluctuations.

[0079] This embodiment takes into account the need to pay attention to the energy storage problem after frequency modulation to ensure that it can safely and smoothly exit the system when it is not needed. According to the energy storage inertia time constant value of 4s - 12s, in actual operation, considering the uncertainty of new energy output, the calculation is performed based on the historical power generation data records of new energy combined with the following formula:

[0080] in, Contribute to the overall new energy system. It is the maximum / minimum historical output of the new energy system. It is the maximum / minimum value of the energy storage inertia time constant.

[0081] Approximately simulate and represent the inertia time constant in different time periods, and choose the appropriate To ensure system stability. To prevent the rate of change from exceeding the limit and triggering the relay protection mechanism to cause system failure, the base inertia requirement must meet the maximum frequency change rate assessment standard. The system minimum inertia requirement is calculated according to the following formula:

[0082] in, is the disturbance power, The maximum frequency allowed by the system.

[0083] In the comprehensive benefit evaluation, in order to seek the optimal configuration of ESS, a minimum function model of the total cost of the energy storage system was established. The base ESS cost includes ESS investment cost, ESS maintenance cost, and the total operating cost of the new energy base.

[0084]

[0085] In the formula, is the total cost of ESS; Investment cost for ESS; Maintenance cost for ESS; Total operating cost of the base.

[0086] Further:

[0087] In the formula, They are the unit power / capacity investment cost of ESS respectively; ESS rated power / rated capacity respectively; is the operating life of the ESS; i is the investment discount factor calculated at an annual interest rate; is the average maintenance cost per unit.

[0088] The total operating cost of the new energy base can be broken down into:

[0089] In the formula, Penalty costs for abandoning wind and solar power at bases; To reduce the standby cost of load; To support power costs; are the unit wind power / abandoned solar power penalty coefficients respectively; is the unit standby cost; The unit electricity cost in the northwest region; The predicted output / maximum available output of the wind farm at time t; The predicted output / maximum available output of the photovoltaic power station at time t; is the reserve power of the wind farm; It is the supporting power provided by the weak AC system at time t.

[0090] The WT cost function is shown below:

[0091] In the formula, The cost of WT; is the discount rate; is the WT operation years; is the annual operation and maintenance cost of WT; is the number of WT units.

[0092] The PV cost function is shown below:

[0093] In the formula, is the PV cost; is the operating life of PV; is the annual operation and maintenance cost of PV; is the number of PV units.

[0094] In terms of constraints, the power balance constraint requires that the charging and discharging power of wind farms, photovoltaic farms, and energy storage be balanced with the load power at each moment; the weak AC system support power constraint limits the weak AC system support power range; the new energy output constraint ensures that the actual output of new energy is reasonable; the energy storage operation constraint ensures the normal operation of energy storage by limiting the energy storage charging and discharging power and state variables; the frequency response power and energy constraint constrains the ESS frequency modulation power and energy, including limiting the frequency modulation power not to exceed the rated power and the frequency modulation energy to be within the rated energy range. These constraints work together to ensure stable system operation and optimal configuration.

[0095] In this embodiment, a particle swarm algorithm is applied to take the economic efficiency and frequency response of energy storage configuration as decision variables, and under the constraints of the comprehensive benefit evaluation module, the optimal configuration combination is calculated with the minimum fitness function as the goal.

[0096] Particle swarm optimization is an optimization algorithm based on swarm intelligence. In PSO, each potential solution is regarded as a "particle" in the search space. Particles explore the solution space based on the current optimal solution and find the global optimal solution by iteratively updating their positions and velocities. The above model is now calculated using the improved particle swarm algorithm, with the energy storage configuration economy and frequency response as decision variables. Under the above constraints, the optimal configuration combination is calculated with the goal of minimizing the fitness function. The algorithm flow chart is as follows: Figure 5 shown.

[0097] The energy storage evaluation platform for high-proportion new energy power networks provided by the present invention optimizes inertia support through a variety of control methods and virtual inertia representation, and improves adaptability with the help of photovoltaic and storage combination. A comprehensive cost estimation model is established based on the comprehensive benefit evaluation and optimization construction and solution module, which comprehensively considers various costs, sets multiple types of constraints, and also uses a particle swarm algorithm to solve the optimal configuration combination. Compared with the prior art, the present invention has made significant progress in inertia characteristic analysis, energy storage function and control, comprehensive benefit evaluation and configuration optimization, and providing guidance for actual projects. It effectively solves the problem of high-proportion new energy grid connection, improves the stability and reliability of the power system, enhances the new energy absorption capacity, and provides a solid guarantee for the efficient operation of the new energy power system.

[0098] like Figure 6 As shown, the present application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored in the memory and executable on the processor 101, wherein the processor 101 implements an energy storage evaluation platform for a high proportion of new energy power networks when executing the program.

[0099] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0100] In the embodiment of the present application, the processor 101 can be implemented by using at least one of an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an implementation can be implemented in a controller. For software implementation, implementations such as processes or functions can be implemented with separate software modules that allow execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.

[0101] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc.

[0102] The memory 102 may be used to store software programs and various data. The memory 102 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0103] The present application also provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the module in the energy storage evaluation platform for a high-proportion new energy power network is implemented.

[0104] The storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0105] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy storage evaluation platform for a high-proportion renewable energy power network, characterized in that: include: New energy base inertia characteristics analysis module, energy storage characteristics analysis and control module, comprehensive benefit evaluation module and model solution module; The inertia characteristic analysis module of the new energy base is used to analyze the inertia characteristics of wind turbines. Through virtual inertia control, a proportional controller is added to the MPPT control loop on the wind turbine side to adjust the output power of the wind turbine according to the grid frequency deviation; power reserve control means are used to reserve power for frequency regulation through pitch angle adjustment and load reduction operation; wind turbines are aggregated through an equivalent model to calculate the virtual inertia of the wind farm; The energy storage characteristic analysis and control module is used to enable the energy storage system to adopt a two-stage energy storage converter and provide inertia support for the system according to set conditions when the frequency of the energy storage system shifts; Comprehensive benefit evaluation module, used to estimate the total cost of the energy storage system, and calculate the cost of wind turbines and photovoltaics at the same time to establish a minimum function model for the total cost of the energy storage system; The model solving module is used to adopt the particle swarm algorithm, take the energy storage configuration economy and frequency response as decision variables, and calculate the optimal configuration combination with the minimum fitness function as the goal under the constraints of the comprehensive benefit evaluation module.

2. The energy storage evaluation platform for high-proportion new energy power networks according to claim 1 is characterized in that: The energy storage characteristic analysis and control module also realizes constant frequency control based on frequency tracking negative feedback; it also simulates and represents the inertia time constant of energy storage in different time periods according to actual operation data and specification requirements.

3. The energy storage evaluation platform for high-proportion new energy power networks according to claim 2 is characterized in that: The ways to achieve constant frequency control by frequency tracking negative feedback include: Set the energy storage inertia time constant to 4s - 12s and calculate the historical power generation data of new energy based on the following formula in, Contribute to the overall new energy system. It is the maximum / minimum historical output of the new energy system. is the maximum / minimum value of the energy storage inertia time constant; According to the condition that the inertia requirement meets the maximum frequency change rate, the minimum inertia requirement of the system is calculated based on the following formula: in, is the disturbance power, is the maximum frequency allowed by the energy storage system.

4. The energy storage evaluation platform for high-proportion new energy power networks according to claim 1 is characterized in that: Establish a minimum function model for the total cost of the energy storage system. The total cost of the energy storage system includes: ESS investment cost, ESS maintenance cost, and total operating cost of the new energy base; In the formula, is the total cost of ESS; Investment cost for ESS; Maintenance cost for ESS; Total operating cost of the base.

5. The energy storage evaluation platform for high-proportion new energy power networks according to claim 4 is characterized in that: In the formula, They are the unit power / capacity investment cost of ESS respectively; ESS rated power / rated capacity respectively; is the operating life of the ESS; i is the investment discount factor calculated at an annual interest rate; is the average maintenance cost per unit; The total operating cost of the new energy base is: In the formula, Penalty costs for abandoning wind and solar power at bases; Reserve costs for load shedding; To support power costs; are the unit wind power / abandoned solar power penalty coefficients respectively; is the unit standby cost; The unit electricity cost in the northwest region; The predicted output / maximum available output of the wind farm at time t; The predicted output / maximum available output of the photovoltaic power station at time t; is the reserve power of the wind farm; It is the supporting power provided by the weak AC system at time t.

6. The energy storage evaluation platform for high-proportion new energy power networks according to claim 4 is characterized in that: The comprehensive benefit evaluation module configures the WT cost, and the WT cost function is as follows: In the formula, The cost of WT; is the discount rate; is the WT operation years; is the annual operation and maintenance cost of WT; is the number of WT units; The PV cost function is shown below: In the formula, is the PV cost; is the operating life of PV; is the annual operation and maintenance cost of PV; is the number of PV units.

7. The energy storage evaluation platform for high-proportion new energy power networks according to claim 1 is characterized in that: The comprehensive benefit evaluation module is also used to provide a constraint setting interface for users to set constraint conditions; the constraints include: power balance constraints, weak AC system support power constraints, new energy output constraints, energy storage operation constraints, and frequency response power and energy constraints.

8. The energy storage evaluation platform for high-proportion new energy power networks according to claim 1 is characterized in that: The inertia characteristic analysis module of the new energy base is used to add a proportional controller to the MPPT control loop on the wind turbine side. When the system frequency fluctuates, auxiliary power is generated by the proportional controller according to the grid frequency deviation. The calculation method is: in, is the differential control coefficient, is the proportional control coefficient, Measure the frequency for the system, is the system reference frequency.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it executes the module in the energy storage evaluation platform for a high-proportion new energy power network as described in any one of claims 1 to 8.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the module in the energy storage evaluation platform for a high-proportion new energy power network as described in any one of claims 1 to 7.

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