New energy storage system control method for adaptive power supply mode switching

Abstract

The present application relates to the technical field of power system, and aims to solve the problem that the existing new energy storage system is difficult to realize multi-objective optimization under complex system architecture to adapt to new energy generation and load dynamic change. To this end, the present application provides a new energy storage system control method for adaptive power supply mode switching, comprising: collecting real-time operation data; judging whether the system reaches the adaptive power supply mode switching trigger condition; if yes, inputting the operation data into a preset target function including three objective functions of energy efficiency maximization, economic cost minimization and system reliability maximization; using NSGA-II algorithm to calculate the Pareto optimal solution set of the preset target function; selecting the optimal control strategy from the Pareto optimal solution set based on the ideal point method decision model; and controlling the system operation based on the optimal control strategy. The present application can consider the three objectives of energy efficiency maximization, economic cost minimization and system reliability, and adapt to new energy generation and load dynamic change.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a new energy storage system control method for adaptive power supply mode switching. BACKGROUND

[0002] New energy generation is experiencing rapid development as a key approach to addressing the exhaustion of traditional fossil energy and environmental pollution. Among them, photovoltaic power generation devices and wind power generation devices are playing an increasingly important role in the energy supply system due to their inexhaustible, inexhaustible and environmentally friendly characteristics. However, the intermittent and fluctuating nature of new energy generation from birth constitutes a major obstacle to its large-scale stable application. Taking photovoltaic power generation as an example, its power generation power is closely related to factors such as light intensity, weather conditions, seasonal changes and day and night alternation. In the sunny day, when the light is sufficient, the photovoltaic power generation power can reach the peak level; once it encounters bad weather such as overcast, rainy, snowy weather, or at sunrise and sunset, the light intensity decreases sharply, and the power generation power also decreases sharply, even close to zero. Wind power generation is also deeply influenced by natural conditions, and the size, direction of wind speed and stability of wind power will have a decisive influence on power generation. In a light wind environment, the wind power generation power is limited; when the wind is strong and the wind direction is stable, the power generation power is significantly improved; and when the wind speed is too large beyond the rated wind speed range of the wind turbine, or the wind direction changes frequently and dramatically, the wind turbine may stop running or adjust the power output to ensure the safety of the equipment, resulting in a large fluctuation or even interruption of power generation. This unstable power generation characteristic makes it difficult to ensure the continuity, stability and reliability of power supply when relying solely on new energy generation to directly power the load, and it is difficult to meet the stringent requirements of modern society for power quality.

[0003] To effectively solve the intermittency and volatility problem of new energy power generation, energy storage technology emerges as the times require and becomes an indispensable supporting means. Electrochemical energy storage devices, represented by lithium-ion batteries, have the advantages of relatively high energy density, considerable charging and discharging efficiency, fast response speed and low self-discharge rate, and can quickly charge and discharge in a short time, flexibly adjusting the system power balance. Mechanical energy storage devices, such as pumped storage systems, can store and release large amounts of energy by utilizing different water head differences, and flywheel energy storage stores kinetic energy by high-speed rotating flywheels. They have the characteristics of large energy storage capacity, long service life and relatively mature technology, and perform well in dealing with long-term and large-capacity energy storage and regulation requirements. Electromagnetic energy storage devices, such as supercapacitors, have very high power density and can release or absorb a large amount of power in an instant. Hydrogen energy storage devices store and convert energy through the processes of hydrogen production by water electrolysis, hydrogen storage and hydrogen fuel cell power generation, and have the unique advantages of large energy storage capacity, long storage period and cross-seasonal storage. Moreover, hydrogen gas as a clean energy carrier has broad application prospects in the future energy system. The combination of these energy storage devices with different characteristics and new energy power generation equipment can build a new energy storage system, which can store excess energy in energy storage devices when new energy power generation is abundant, and release stored energy to make up for power gaps when power generation is insufficient, thereby significantly improving the comprehensive utilization efficiency of energy and enhancing the stability and reliability of the power supply system.

[0004] Nevertheless, achieving efficient, economical, and reliable operation in new energy storage systems encompassing various energy storage types is no easy task, facing numerous technical challenges. In actual operation, it is difficult for the system to optimize multiple conflicting objectives simultaneously. For example, from an energy efficiency perspective, improving energy efficiency means reducing energy losses during conversion, storage, and transmission, and fully leveraging the effectiveness of new energy generation and storage devices. This requires precise optimization of the charging and discharging strategies of each energy storage device, the operating modes of power conversion devices, and the system's energy management logic. However, pursuing improved energy efficiency often comes with increased economic costs. For instance, using higher-performance energy storage devices or more advanced power conversion equipment can significantly increase purchase, installation, and maintenance costs. From an economic cost perspective, reducing economic costs requires fully considering factors such as investment costs, operation and maintenance costs, and energy procurement and sales costs during grid interaction during system planning, equipment selection, and operation management. However, excessive focus on economic costs may lead to insufficient performance and reduced reliability of energy storage devices, or neglect of energy efficiency during energy management, resulting in energy waste and ultimately affecting the overall reliability of the system. In terms of system reliability, ensuring the system can stably and reliably supply power to the load under various operating conditions is crucial. This is especially important when facing extreme fluctuations in renewable energy generation, energy storage device failures, and sudden load changes. This requires redundant energy storage capacity, a reliable power conversion and control system, and a robust fault detection and response mechanism. However, this undoubtedly increases the system's construction and operating costs, and in some cases, over-configuration may reduce energy efficiency.

[0005] Furthermore, the application scenarios of new energy storage systems are extremely broad and complex, covering multiple fields such as peak shaving and valley filling in power systems, power supply security in industrial production processes, uninterrupted power supply for commercial facilities, and residential electricity consumption. The load demand characteristics vary greatly under different application scenarios. Industrial loads may be characterized by high power, high load fluctuations, and extremely high requirements for power supply reliability; commercial loads exhibit concentrated electricity consumption time, power demand that varies with business hours, and high sensitivity to power quality; while residential loads, although individual power is relatively small, have a large total volume and their electricity consumption behavior shows obvious periodicity and randomness. At the same time, various parameters during system operation, including new energy generation power, load power demand, state of charge of energy storage devices, voltage and current, and other operating status parameters, as well as environmental factors (such as light intensity, wind speed, and temperature), are all dynamically changing in real time. This requires the system to adaptively adjust the power supply mode and control strategy based on this complex and ever-changing operating condition information to achieve multi-objective optimal balance. However, traditional control methods are often based on fixed rules or simple models, making it difficult to cope with such complex system dynamic characteristics and multi-objective optimization requirements.

[0006] Therefore, there is a need in this field for a new energy storage system control method with adaptive power supply mode switching to solve the above problems. Summary of the Invention

[0007] To address the aforementioned technical challenges, namely the difficulty of existing new energy storage systems in achieving multi-objective optimization to adapt to dynamic changes in new energy generation and load under complex system architectures.

[0008] This invention provides a control method for a new energy storage system with adaptive power supply mode switching. The new energy storage system includes a central control unit, a photovoltaic power generation device, a wind power generation device, an electrochemical energy storage device, a mechanical energy storage device, an electromagnetic energy storage device, and a hydrogen energy storage device. The central control unit is communicatively connected to the photovoltaic power generation device, the wind power generation device, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device. The electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device are connected to a common bus through their respective adapted power conversion devices. The common bus connects the new energy power generation equipment composed of the photovoltaic power generation device and the wind power generation device to various loads.

[0009] The control method includes:

[0010] Real-time acquisition of operating data from the photovoltaic power generation device, the wind power generation device, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device;

[0011] Determine whether the new energy storage system has reached the adaptive power supply mode switching trigger condition;

[0012] If so, the real-time collected operating data will be input into the preset objective function, which includes an energy efficiency maximization objective function, an economic cost minimization objective function, and a system reliability maximization objective function. The constraints of the preset objective function include power balance constraints, energy storage device state constraints, energy conversion and transmission constraints, new energy power generation constraints, and load demand constraints.

[0013] The Pareto optimal solution set of the preset objective function is calculated using the NSGA-II algorithm;

[0014] The decision model based on the ideal point method selects the optimal control strategy from the Pareto optimal solution set obtained by the NSGA-II algorithm.

[0015] The operation of the new energy storage system is controlled based on the optimal control strategy.

[0016] In some preferred embodiments, the decision model based on the ideal point method selects the optimal control strategy from the Pareto optimal solution set obtained by the NSGA-II algorithm, specifically including:

[0017] Determine the ideal and negative ideal solutions for the objectives of maximizing energy efficiency, minimizing economic cost, and maximizing system reliability, respectively;

[0018] The distance between each Pareto optimal solution and the ideal solution in the Pareto optimal solution set is calculated using the Euclidean distance formula. The distance between the sum and the negative ideal solution

[0019]

[0020] Where k = 1, 2, 3 correspond to the three objectives of maximizing energy efficiency, minimizing economic cost, and maximizing system reliability, respectively. Let j be the value of the Pareto optimal solution for the k-th objective. Let the ideal solution be the value at the k-th objective. The value of the negative ideal solution at the k-th objective;

[0021] The relative proximity C of each Pareto optimal solution is calculated using the following formula. j :

[0022]

[0023] Among them, C j The degree of closeness between the j-th Pareto optimal solution and the ideal solution;

[0024] Choose C from all Pareto optimal solutions. j The Pareto optimal solution with the largest value is used as the operating parameter of the optimal control strategy.

[0025] In some preferred embodiments, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device each include multiple energy storage units, and the objective function for maximizing energy efficiency is:

[0026]

[0027] Where, η system Let P be the system's energy efficiency, T be the total time period, and P be the energy efficiency. load,t Let P be the load power at time t, Δt be the time interval, and P be the load power at time t. storagedischarged,i,t Let P be the discharge power of the i-th energy storage unit at time t. storagecharged,i,t Let P be the charging power of the i-th energy storage unit at time t. renewable,tLet t be the power generation capacity of the new energy power generation equipment, and n be the total number of energy storage units.

[0028] In some preferred embodiments, the objective function for minimizing economic costs is:

[0029] C total =C investment +C operation +C energy

[0030] Among them, C total For the total economic cost, C investment For equipment investment costs, C operation For operation and maintenance costs, C energy For energy procurement costs.

[0031] In some preferred embodiments, the objective function for maximizing system reliability is:

[0032]

[0033] Where EID is the system reliability index, m is the number of types of power outages, and p k Let d be the probability of the k-th power outage scenario. k The duration of the k-th power outage condition.

[0034] In some preferred embodiments, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device each include multiple energy storage units, the bus is a DC bus, and the power balance constraint condition is:

[0035]

[0036] Among them, P renewable,t Let t be the power generation capacity of the new energy power generation equipment, and n be the total number of energy storage units. Let be the discharge power of the i-th energy storage unit at time t. Let P be the charging power of the i-th energy storage unit at time t. load,t Let P be the load power at time t. loss,t Let t be the power loss of the system on the DC bus and related connecting lines at time t.

[0037] In some preferred embodiments, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device each include multiple energy storage units, the bus is an AC bus, and the power balance constraint condition is:

[0038]

[0039] Among them, P renewable,t Let t be the power generation capacity of the new energy power generation equipment, and n be the total number of energy storage units. Let be the discharge power of the i-th energy storage unit at time t. Let P be the charging power of the i-th energy storage unit at time t. load,t Let P be the load power at time t. loss,t Let P be the power loss of the system at time t on the AC bus and related connecting lines. grid,t Let Q be the power exchanged between the system and the grid at time t. renewable,t Let t be the reactive power generated by the new energy power generation equipment. Let be the reactive power of the i-th energy storage unit when it discharges at time t. Let Q be the reactive power when the i-th energy storage unit is charging at time t. load,t Let Q be the reactive power of the load at time t. loss,t Let Q be the reactive power loss of the system at time t on the AC bus and related connecting lines. grid,t Let t be the reactive power of the system interacting with the power grid at time t.

[0040] In some preferred embodiments, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device each include multiple energy storage units;

[0041] The state constraints of the energy storage device are as follows:

[0042]

[0043] Among them, SOC i,t Let SOC be the actual state of charge of the i-th energy storage unit at time t. min,i For the i-th energy storage unit, SOC is the minimum allowable state of charge. max,i The maximum permissible state of charge for the i-th energy storage unit is... Let be the charging power of the i-th energy storage unit at time t. Let be the discharge power of the i-th energy storage unit at time t. Let be the minimum allowable charge / discharge power of the i-th energy storage unit. The maximum allowable charge and discharge power of the i-th energy storage unit;

[0044] The energy conversion and transmission constraints are as follows:

[0045]

[0046] Where, η PCS,i,t Let be the actual conversion efficiency of the i-th energy storage unit at time t. Let be the minimum conversion efficiency of the i-th energy storage unit. P represents the maximum conversion efficiency of the i-th energy storage unit. PCS,i,t Let be the actual transmission power of the i-th energy storage unit at time t. Let be the minimum power capacity of the i-th energy storage unit. V represents the maximum power capacity of the i-th energy storage unit. bus,t Let be the bus voltage value at time t. This is the minimum allowable voltage value for the busbar. This is the maximum allowable voltage value for the busbar;

[0047] The constraints for the new energy power generation are as follows:

[0048]

[0049] Among them, P renewable,t Let t be the power generation capacity of the new energy power generation equipment. Let δ be the predicted power generation of the new energy power generation equipment at time t. t Let t be the error range of the power generation capacity of the new energy power generation equipment;

[0050] The load requirement constraint is as follows:

[0051]

[0052] Among them, P load,t Let be the load power at time t. Let be the minimum required active power of the load at time t. Let Q be the maximum active power demand of the load at time t. load,t Let be the reactive power of the load at time t. Let be the minimum reactive power demand of the load at time t. Let t be the maximum reactive power demand of the load at time t.

[0053] In some preferred embodiments, the step of using the NSGA-II algorithm to calculate the Pareto optimal solution set of the preset objective function specifically includes:

[0054] Perform parameter initialization settings and randomly generate an initial population;

[0055] Perform a non-dominated sort;

[0056] Calculate the range of objective function values ​​and individual crowding.

[0057] Perform selection, crossover, mutation, and population update operations;

[0058] Repeat the steps of non-dominated sorting, crowding calculation, selection operation, crossover operation, mutation operation, and population update until the maximum number of iterations is reached;

[0059] Output the Pareto optimal solution set for the last generation of the population.

[0060] In some preferred embodiments, the adaptive power supply mode switching trigger condition is that the change value of the power generation of the new energy power generation equipment reaches a first power threshold or the total power demand of the various loads reaches a second power threshold.

[0061] As can be seen from the above, the adaptive power supply mode switching control method for new energy storage systems provided by this invention has the following beneficial technical effects:

[0062] This invention introduces a pre-defined objective function that maximizes energy efficiency, minimizes economic cost, and maximizes system reliability. It combines this with a comprehensive constraint system that includes power balance constraints, energy storage device state constraints, energy conversion and transmission constraints, new energy generation constraints, and load demand constraints. The NSGA-II algorithm is used to calculate the Pareto optimal solution set, and then an optimal control strategy is selected based on an ideal point method decision model. This multi-objective optimization approach effectively solves the problem of traditional control methods struggling to achieve a good balance between energy efficiency, economic cost, and system reliability. In actual operation, the charging and discharging strategies of each energy storage device, the operating mode of the power conversion device, and the interaction mode with the power grid can be dynamically adjusted according to the real-time status of the system. This makes the energy conversion, storage, and transmission processes in the entire system more efficient, reducing energy waste and lowering system operating costs. Simultaneously, it greatly improves the system's reliability in responding to various operating conditions, ensuring a continuous, stable, and high-quality power supply to various loads. By collecting real-time operational data from photovoltaic, wind, electrochemical, mechanical, electromagnetic, and hydrogen energy storage devices, and using this data to determine the trigger conditions for adaptive power supply mode switching, the system rapidly initiates a multi-objective optimization calculation process and switches power supply modes once the trigger conditions are met. This real-time data-driven adaptive mechanism enables the system to accurately respond to dynamic changes in new energy generation and load. Whether facing rapid changes in natural conditions such as sunlight intensity and wind speed, or frequent fluctuations in load demand from industrial production, commercial operations, and residential life, the system can smoothly switch power supply modes, effectively improving the flexibility and adaptability of the entire new energy storage system and ensuring it maintains optimal operating conditions in complex and ever-changing real-world application scenarios. Through the synergistic effect of the aforementioned multi-objective optimization and adaptive power supply mode switching functions, the overall performance of the new energy storage system is comprehensively improved. In terms of energy utilization, improved energy efficiency means that more new energy sources can be effectively utilized, reducing dependence on traditional fossil fuels and promoting the green transformation of the energy structure. In terms of economic cost control, it reduces the investment, operation and maintenance costs of the system, improves the economic benefits and market competitiveness of the system, and is conducive to the large-scale promotion and application of new energy storage technologies. In terms of enhanced system reliability, it reduces the risk and duration of power outages and reduces economic losses caused by power failures. Attached Figure Description

[0063] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings:

[0064] Figure 1 This is a flowchart of the adaptive power supply mode switching control method for a new energy storage system according to the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] As pointed out in the background, existing new energy storage systems struggle to achieve multi-objective optimization to adapt to dynamic changes in new energy generation and load under complex system architectures. This invention provides a control method for a new energy storage system with adaptive power supply mode switching, aiming to balance the three objectives of maximizing energy efficiency, minimizing economic costs, and ensuring system reliability, while adapting to dynamic changes in new energy generation and load.

[0067] The adaptive power supply mode switching new energy storage system of the present invention includes: a central control unit, a photovoltaic power generation device, a wind power generation device, an electrochemical energy storage device, a mechanical energy storage device, an electromagnetic energy storage device, and a hydrogen energy storage device. The central control unit is communicatively connected to the photovoltaic power generation device, the wind power generation device, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device, respectively. The electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device are connected to a common bus through their respective adapted power conversion devices. The common bus connects the new energy power generation equipment composed of the photovoltaic power generation device and the wind power generation device to various loads. In the above, the central control unit (CCU) is responsible for the data acquisition, analysis, control command generation, and communication interaction with external systems (such as power grid monitoring systems, meteorological data platforms, etc.) of the entire system. Electrochemical energy storage devices, mechanical energy storage devices, electromagnetic energy storage devices, and hydrogen energy storage devices each include multiple energy storage units. For example, an electrochemical energy storage device includes one or more lithium-ion battery energy storage units, one or more lead-acid battery energy storage units, and one or more other types of electrochemical energy storage units (e.g., sodium-sulfur battery energy storage units, flow battery energy storage units, etc.). A mechanical energy storage device includes one or more pumped hydro storage units and one or more flywheel energy storage units. An electromagnetic energy storage device includes one or more supercapacitor energy storage units and one or more superconducting energy storage units. A hydrogen energy storage device includes multiple hydrogen energy storage units. The bus can be a DC bus or an AC bus. Various loads can include industrial loads, commercial loads, and residential loads, or only one or two of the above-mentioned loads, depending on the application location of the new energy storage system of this invention. The new energy storage system of this invention can be a completely independent power supply system, or it can be a local power supply system or an auxiliary power supply system of the power grid.

[0068] like Figure 1 As shown, the new energy storage system control method of the present invention includes:

[0069] Real-time acquisition of operational data from photovoltaic power generation devices, wind power generation devices, electrochemical energy storage devices, mechanical energy storage devices, electromagnetic energy storage devices, and hydrogen energy storage devices;

[0070] Determine whether the new energy storage system has met the adaptive power supply mode switching trigger condition;

[0071] If not, maintain the current operating mode and return to the steps of real-time collection of operating data for photovoltaic power generation devices, wind power generation devices, electrochemical energy storage devices, mechanical energy storage devices, electromagnetic energy storage devices, and hydrogen energy storage devices;

[0072] If so, the real-time collected operating data will be input into the preset objective function. The preset objective function includes the objective function for maximizing energy efficiency, the objective function for minimizing economic cost, and the objective function for maximizing system reliability. The constraints of the preset objective function include power balance constraints, energy storage device state constraints, energy conversion and transmission constraints, new energy power generation constraints, and load demand constraints.

[0073] The NSGA-II algorithm is used to calculate the Pareto optimal solution set of the preset objective function;

[0074] The decision model based on the ideal point method selects the optimal control strategy from the Pareto optimal solution set obtained by the NSGA-II algorithm;

[0075] The steps involve controlling the operation of a new energy storage system based on an optimal control strategy and returning real-time operational data collected from photovoltaic power generation devices, wind power generation devices, electrochemical energy storage devices, mechanical energy storage devices, electromagnetic energy storage devices, and hydrogen energy storage devices.

[0076] It should be noted that the operational data that the central control unit needs to collect from photovoltaic power generation devices, wind power generation devices, electrochemical energy storage devices, mechanical energy storage devices, electromagnetic energy storage devices, and hydrogen energy storage devices can be determined based on a preset objective function. The preset objective function can be constructed based on historical data. After the real-time collected operational data is input into the preset objective function, the Pareto optimal solution set of the preset objective function can be calculated using the NSGA-II algorithm, and the decision model based on the ideal point method can be used to obtain the optimal solution set from the NSGA-II algorithm. The optimal control strategy is selected from the Pareto optimal solution set obtained by the I algorithm to control the operation of the new energy storage system. Since both the new energy storage system and the load are dynamically changing, the Pareto optimal solution set and optimal control strategy differ for different time points. When the new energy storage system reaches the adaptive power supply mode switching trigger condition (e.g., the change in power generation of the new energy power generation equipment reaches the first power threshold or the total power demand of various loads reaches the second power threshold), the central control unit needs to regulate the entire system. After regulation, it continues to execute the step of real-time acquisition of operating data from photovoltaic power generation devices, wind power generation devices, electrochemical energy storage devices, mechanical energy storage devices, electromagnetic energy storage devices, and hydrogen energy storage devices. The control method of this invention is suitable for cyclic regulation of the new energy storage system. During regulation, the central control unit generates precise control commands according to the optimal control strategy and sends them to the power conversion interface (PCS) of each energy storage unit, the controller of the new energy power generation equipment, and any reactive power compensation devices that may exist. For each energy storage unit, the control commands may include charging and discharging power setpoints, PCS operating mode switching commands, etc. For example, in energy storage power supply mode, according to the selected power distribution method of each energy storage unit, the central control unit sends a specific discharge power command to the PCS of the electrochemical energy storage unit, and at the same time sends corresponding control signals to the mechanical energy storage unit to coordinate the provision of the power required by the load. For new energy power generation equipment, in independent power supply or hybrid power supply mode, the central control unit can adjust parameters such as the maximum power tracking point of the photovoltaic array or the pitch angle of the wind turbine according to the system power balance requirements and the operating status of the power generation equipment to optimize the output of new energy power generation. In addition, if the system is connected to the grid, the central control unit can also control the working status of the PCS connected to the grid according to the grid interaction strategy (such as purchasing electricity from the grid for storage during low-price periods or selling electricity to the grid during high-price periods) to realize energy interaction with the grid.

[0077] Preferably, the objective function for maximizing energy efficiency is:

[0078]

[0079] Where, η system Let P be the system's energy efficiency, T be the total time period, and P be the energy efficiency. load,t Let be the load power at time t, and Δt be the time interval. Let be the discharge power of the i-th energy storage unit at time t (positive during discharge). Let P be the charging power of the i-th energy storage unit at time t (positive during charging). renewable,t Let be the power generation capacity of the new energy power generation equipment at time t, and n be the total number of energy storage units; in the above, the charging efficiency η of different energy storage units can be considered. charge,i and discharge efficiency η discharge,i :

[0080]

[0081] in, Let be the effective charging power of the i-th energy storage unit at time t. Let t be the effective discharge power of the i-th energy storage unit. Substitute the above effective power into the energy efficiency formula for correction calculation to more accurately reflect the true energy utilization efficiency of the system.

[0082] Preferably, the objective function for minimizing economic costs is:

[0083] C total =C investment +C operation +C energy

[0084] Among them, C total For the total economic cost, C investment For equipment investment costs, C operation For operation and maintenance costs, C energy For energy procurement costs; in the above, C total C investment and C operation The following formulas can be used to calculate them respectively:

[0085]

[0086] Where n is the total number of energy storage units, C unit,i Let E be the investment cost per unit capacity of the i-th energy storage unit. rated,i Let be the rated capacity of the i-th energy storage unit. Furthermore, based on the above, for auxiliary equipment such as power conversion systems (PCS), the investment cost can be calculated separately according to their matching relationship with the capacity and power of the energy storage units and then added to C. investment middle;

[0087]

[0088] Where n is the total number of energy storage units, T is the total time period, and C is the total number of energy storage units. OM,i,t Let be the operation and maintenance cost coefficient per unit power of the i-th energy storage unit at time t. Let C be the actual power (charging or discharging power) used by the i-th energy storage unit at time t, where Δt is the time interval. OM,i,t The cost of regular maintenance of energy storage units, replacement of parts, and labor costs can be comprehensively considered and dynamically adjusted as the energy storage units age or their usage intensity changes.

[0089]

[0090] Where T is the total time period, P grid,t Let C be the power exchanged between the system and the grid at time t (positive for purchasing power from the grid, negative for selling power to the grid), Δt be the time interval, and C be the total power exchanged between the system and the grid. grid,t Let be the grid electricity price at time t;

[0091] Preferably, the objective function for maximizing system reliability is:

[0092]

[0093] Where EID is the system reliability index, m is the number of types of power outages, and p k Let d be the probability of the k-th power outage scenario. k The duration of the k-th power outage condition.

[0094] In some preferred embodiments, if the bus is a DC bus, the power balance constraint condition is:

[0095]

[0096] Among them, P renewable,t Let t be the power generation capacity of the new energy power generation equipment, and n be the total number of energy storage units. Let be the discharge power of the i-th energy storage unit at time t. Let P be the charging power of the i-th energy storage unit at time t. load,t Let P be the load power at time t. loss,t Let P be the power loss of the system at time t on the DC bus and related connecting lines; in the above, P loss,t The calculation depends on factors such as line resistance and current magnitude.

[0097]

[0098] Where, m w j represents the number of lines. w For line numbering, For time t, line j w The current in For line j w The resistance.

[0099] In some preferred embodiments, if the bus is an AC bus, the power balance constraint condition is:

[0100]

[0101] Among them, P renewable,t Let t be the power generation capacity of the new energy power generation equipment, and n be the total number of energy storage units. Let be the discharge power of the i-th energy storage unit at time t. Let P be the charging power of the i-th energy storage unit at time t. load,t Let P be the load power at time t. loss,t Let P be the power loss of the system at time t on the AC bus and related connecting lines. grid,t Let Q be the power exchanged between the system and the grid at time t. renewable,t Let t be the reactive power generated by the new energy power generation equipment. Let be the reactive power of the i-th energy storage unit when it discharges at time t. Let Q be the reactive power when the i-th energy storage unit is charging at time t. load,t Let Q be the reactive power of the load at time t. loss,t Let Q be the reactive power loss of the system at time t on the AC bus and related connecting lines. grid,t Let P be the reactive power interacting between the system and the grid at time t; in the above, P loss,t The calculation depends on factors such as line resistance and current magnitude.

[0102]

[0103] Where, m w j represents the number of lines. w For line numbering, For time t, line j w The current in For line j w The resistance;

[0104] It should be noted that if the new energy storage system of this invention is an independently powered system that does not interact with the power grid, then P in the above power balance constraint formula... grid,t and Q grid,t All are zero, meaning they are not included in the calculation.

[0105] Preferably, the state constraints of the energy storage device are as follows:

[0106]

[0107] Among them, SOC i,t Let SOC be the actual state of charge of the i-th energy storage unit at time t. min,iFor the i-th energy storage unit, SOC is the minimum allowable state of charge. max,i The maximum permissible state of charge for the i-th energy storage unit is... Let be the charging power of the i-th energy storage unit at time t. Let be the discharge power of the i-th energy storage unit at time t. Let be the minimum allowable charge / discharge power of the i-th energy storage unit. The maximum allowable charge / discharge power of the i-th energy storage unit; in the above, SOC i,t The calculation is related to the initial charge and charge / discharge history of the energy storage unit, and can be calculated using the following formula:

[0108]

[0109] Among them, SOC i,0 Let k be the initial state of charge of the i-th energy storage unit, k be the time index, and Δk be the time step. Let be the charging power of the i-th energy storage unit at time k. Let E be the discharge power of the i-th energy storage unit at time k. rated,i Let η be the rated capacity of the i-th energy storage unit. charge,i Let η be the charging efficiency of the i-th energy storage unit. discharge,i Let be the discharge efficiency of the i-th energy storage unit;

[0110] The constraints for energy conversion and transmission are:

[0111]

[0112] Where, η PCS,i,t Let be the actual conversion efficiency of the i-th energy storage unit at time t. Let be the minimum conversion efficiency of the i-th energy storage unit. P represents the maximum conversion efficiency of the i-th energy storage unit. PCS,i,t Let be the actual transmission power of the i-th energy storage unit at time t. Let be the minimum power capacity of the i-th energy storage unit. V represents the maximum power capacity of the i-th energy storage unit. bus,t Let be the bus voltage value at time t. This is the minimum allowable voltage value for the busbar. This is the maximum allowable voltage value for the busbar;

[0113] The constraints for new energy power generation are:

[0114]

[0115] Among them, P renewable,t Let t be the power generation capacity of the new energy power generation equipment. Let δ be the predicted power generation of the new energy power generation equipment at time t. t Let t be the error range of the power generation capacity of the new energy power generation equipment;

[0116] The load requirement constraints are as follows:

[0117]

[0118] Among them, P load,t Let be the load power at time t. Let P be the minimum required active power of the load at time t. loadmax,t Let Q be the maximum active power demand of the load at time t. load,t Let be the reactive power of the load at time t. Let be the minimum reactive power demand of the load at time t. Let t be the maximum reactive power demand of the load at time t.

[0119] Preferably, the calculation of the Pareto optimal solution set of the preset objective function using the NSGA-II algorithm specifically includes:

[0120] initialization:

[0121] Parameter settings: Determine the population size N; Set the maximum number of iterations G max Define the crossover probability p c The value typically ranges from 0.6 to 0.9, for example, set to 0.8; determine the mutation probability p. m It is generally between 0.01 and 0.1, for example, set to 0.05.

[0122] Population Generation: An initial population p0 is randomly generated, where each individual represents a combination of control strategies. For this new energy storage system, the genetic code of an individual may include the charging and discharging power allocation ratio of each energy storage unit (e.g., using n values ​​to represent the power allocation ratio of n energy storage units, with a sum of 1) and the PCS operating mode selection (using binary encoding to represent different operating modes), etc. The objective function value, i.e., the energy efficiency η, is calculated for each individual in the initial population p0. system Economic cost C total And the system reliability index EID. Based on the objective function and constraints defined above, calculations are performed using real-time system data (new energy power generation, load power demand, energy storage unit status, etc.).

[0123] Non-dominated sorting:

[0124] Comparing individual merits: For any two individuals i in population p0 g and j g Compare their performance on the three objective functions. If individual ig Not inferior to individual j on all objective functions g And outperforms individual j on at least one objective function. g Then individual i is called g Dominant individual j g Iterate through all pairs of individuals in the population to determine the dominance relationship for each individual.

[0125] Delineating Non-Dominant Ranks: Individuals not dominated by any other individual are assigned to the non-dominated rank F1. These individuals are optimal in the current population, achieving a good balance among the three objectives. Individuals from F1 are removed from the population, and then non-dominated individuals are searched for among the remaining individuals and assigned to the non-dominated rank F2. This process is repeated until the entire population is divided into multiple non-dominated ranks. l n Number of levels.

[0126] Crowding calculation:

[0127] Calculate the range of objective function values: For each objective function (energy efficiency, economic cost, system reliability), calculate its maximum value F in the current population. max and minimum value F min For example, regarding energy efficiency, Where i iterates through all individuals in the current population.

[0128] Calculate individual crowding: For each individual i in the non-dominated level F1, calculate its crowding on each objective function. Taking energy efficiency as an example, suppose there are m people in the population. g The ranking of individuals in terms of energy efficiency is as follows: Then individual j (2≤j≤m) g -1) Congestion in terms of energy efficiency for: Boundary individuals (j=1 and j=m) g The crowding degree of individual i is set to infinity. The total crowding degree of individual i is... That is, the sum of the crowding of an individual across the three objective functions.

[0129] Select operation:

[0130] Tournament Selection: Tournament selection is performed from all non-dominant ranks. First, two non-dominant ranks F are randomly selected. i and F j (i≠j). From the two selected non-dominated ranks, randomly select two individuals, a and b. Compare the non-dominated ranks of individuals a and b: if the non-dominated rank of individual a is lower than that of individual b (i.e., the rank of a is in the F...), then... i The grade number is less than the F in which b is located.j If individuals are in the same non-dominated rank (e.g., rank number), then individual a is selected to enter the next generation population. If individuals a and b are in the same non-dominated rank, their crowding is compared, and the individual with the higher crowding is selected to enter the next generation population. This process is repeated multiple times until N individuals are selected to form the next generation population p1.

[0131] Cross operation:

[0132] Selecting crossover individuals: Randomly select N×p individuals from population p1. c Crossover operations are performed on individuals that are treated as parent individuals.

[0133] Perform crossover: For each pair of parent individuals p1 and p2, crossover is performed using an appropriate method (such as single-point crossover, multi-point crossover, or uniform crossover). Taking single-point crossover as an example, a crossover point is randomly selected, and the genes of parent individuals p1 and p2 after the crossover point are exchanged to generate two offspring individuals c1 and c2. Constraints are checked on offspring individuals c1 and c2. If all constraints are met (power balance constraints, energy storage unit state constraints, energy conversion and transmission constraints, new energy generation constraints, and load demand constraints), they are retained in the temporary offspring population C; otherwise, these two offspring individuals are discarded, and the crossover operation is repeated until offspring individuals that meet the constraints are generated.

[0134] Mutation operation

[0135] Selecting mutant individuals: Randomly select N×p individuals from the temporary offspring population C. n Each individual is considered a variant.

[0136] Perform mutation operations: For each mutated individual, perform mutation operations on its genes. For example, for the gene representing the power allocation ratio of the energy storage unit's charge and discharge, randomly select an energy storage unit and change its power allocation ratio within a certain range (e.g., ±0.1); for the gene representing the PCS operating mode selection, invert its corresponding binary bits. Check the constraints on the mutated individuals. If all constraints are met, replace the original mutated individual in the temporary offspring population C; otherwise, repeat the mutation operation until a mutated individual that meets the constraints is generated.

[0137] Population Update:

[0138] Merging populations: The temporary offspring population C is merged with the parent population p1 to form a new population R = P1∪C.

[0139] Non-dominated ranking and crowding calculation: The new population R is ranked non-dominated and divided into multiple non-dominated levels F′1, F′2...F lCalculate the crowding level of individuals in each non-dominated level. Select the next generation population: Select individuals into the next generation population p2 in ascending order of non-dominated level, until the population size reaches N. If, during the selection process, the number of individuals in a non-dominated level exceeds the remaining required number of individuals, sort the individuals in that level according to their crowding level and select the individuals with higher crowding levels to enter the next generation population.

[0140] Iteration and Termination:

[0141] Iterative operation: Using population p2 as the new parent population, repeat the above steps of non-dominated sorting, crowding calculation, selection, crossover, mutation, and population update to carry out the next round of iteration.

[0142] Termination condition check: After each iteration, check if the termination condition is met. If the current iteration count reaches the maximum iteration count G. max If the condition is met, the algorithm terminates and outputs the Pareto optimal solution set of the last generation of the population.

[0143] Preferably, the decision model based on the ideal point method selects the optimal control strategy from the Pareto optimal solution set obtained by the NSGA-II algorithm, specifically including:

[0144] Determine the ideal and negative ideal solutions for the objectives of maximizing energy efficiency, minimizing economic cost, and maximizing system reliability, respectively;

[0145] Specifically, for the energy efficiency target, the ideal solution is: in Let be the energy efficiency value of the j-th solution in the Pareto optimal solution set, and let the negative ideal solution be... In other words, the higher the energy efficiency target value, the better.

[0146] For the economic cost objective, the ideal solution is: in Let be the economic cost of the j-th solution in the Pareto optimal solution set, and let the negative ideal solution be... In other words, the smaller the economic cost target value, the better.

[0147] For the system reliability objective, the ideal solution is EID. * =min{EID j}, where EID j Let EID be the system reliability value of the j-th solution in the Pareto optimal solution set, and let EID be the negative ideal solution. - =max{EID j The smaller the system reliability target value, the better; that is, the shorter the power outage time, the better.

[0148] The distance between each Pareto optimal solution and the ideal solution in the Pareto optimal solution set is calculated using the Euclidean distance formula. The distance between the sum and the negative ideal solution

[0149]

[0150] Where k = 1, 2, 3 correspond to the three objectives of maximizing energy efficiency, minimizing economic cost, and maximizing system reliability, respectively. Let j be the value of the Pareto optimal solution for the k-th objective. Let the ideal solution be the value at the k-th objective. The value of the negative ideal solution at the k-th objective;

[0151] The relative proximity C of each Pareto optimal solution is calculated using the following formula. j :

[0152]

[0153] Among them, C j The degree of closeness between the j-th Pareto optimal solution and the ideal solution;

[0154] Choose C from all Pareto optimal solutions. j The Pareto optimal solution with the largest value is used as the operating parameter of the optimal control strategy.

[0155] The operating parameters of the above-mentioned optimal control strategy will determine whether the system adopts independent power supply (e.g., when the power generation of new energy is sufficient to meet the load demand and the energy storage unit is in a suitable state), energy storage power supply (e.g., when the power generation of new energy is insufficient but the energy storage unit can meet the load demand), or a hybrid power supply mode (e.g., when the power generation of new energy partially meets the load demand and the energy storage unit supplements the insufficient part), and in the case of energy storage power supply and hybrid power supply, the specific power supply allocation method of each energy storage unit (e.g., determining the charging and discharging power ratio of different energy storage units based on the characteristics, state and cost-effectiveness of each energy storage unit), thereby adaptively switching the power supply mode.

[0156] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0157] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0158] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the invention as described above, which are not provided in detail for the sake of brevity.

[0159] Although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description.

[0160] One or more embodiments of the present invention are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the invention. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of the present invention.

Claims

1. A control method for a new energy storage system with adaptive power supply mode switching, characterized in that, The new energy storage system includes a central control unit, a photovoltaic power generation device, a wind power generation device, an electrochemical energy storage device, a mechanical energy storage device, an electromagnetic energy storage device, and a hydrogen energy storage device. The central control unit is communicatively connected to the photovoltaic power generation device, the wind power generation device, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device, respectively. The electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device are connected to a common bus through their respective adapted power conversion devices. The common bus connects the new energy power generation equipment composed of the photovoltaic power generation device and the wind power generation device to various loads. The control method includes: Real-time acquisition of operating data from the photovoltaic power generation device, the wind power generation device, the electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device; Determine whether the new energy storage system has reached the adaptive power supply mode switching trigger condition; If so, the real-time collected operating data will be input into the preset objective function, which includes an energy efficiency maximization objective function, an economic cost minimization objective function, and a system reliability maximization objective function. The constraints of the preset objective function include power balance constraints, energy storage device state constraints, energy conversion and transmission constraints, new energy power generation constraints, and load demand constraints. The Pareto optimal solution set of the preset objective function is calculated using the NSGA-II algorithm; The decision model based on the ideal point method selects the optimal control strategy from the Pareto optimal solution set obtained by the NSGA-II algorithm; The operation of the new energy storage system is controlled based on the optimal control strategy described above. Specifically, the decision model based on the ideal point method selects the optimal control strategy from the Pareto optimal solution set obtained by the NSGA-II algorithm, including: Determine the ideal and negative ideal solutions for the objectives of maximizing energy efficiency, minimizing economic cost, and maximizing system reliability, respectively; The distance between each Pareto optimal solution and the ideal solution in the Pareto optimal solution set is calculated using the Euclidean distance formula. The distance between the sum and the negative ideal solution : in, These correspond to the three objectives of maximizing energy efficiency, minimizing economic costs, and maximizing system reliability, respectively. For the first The Pareto optimal solution is at the th . The value on each target, For the ideal solution in the first... The value on each target, For the negative ideal solution in the th case Values ​​on each target; The relative proximity of each Pareto optimal solution is calculated using the following formula. : in, For the first The degree of closeness between each Pareto optimal solution and the ideal solution; Select from all Pareto optimal solutions The Pareto optimal solution with the largest value is used as the operating parameter of the optimal control strategy.

2. The control method for a new energy storage system with adaptive power supply mode switching according to claim 1, characterized in that, The electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device each include multiple energy storage units, and the objective function for maximizing energy efficiency is: in, For the system's energy efficiency, For the total time period, for Load power at any given time For time intervals, for Time of the first The discharge power of each energy storage unit for Time of the first The charging power of each energy storage unit for The power generation capacity of new energy power generation equipment at all times. This represents the total number of energy storage units.

3. The control method for a new energy storage system with adaptive power supply mode switching according to claim 1, characterized in that, The objective function for minimizing economic costs is: in, The total economic cost, For equipment investment costs, For operation and maintenance costs, For energy procurement costs.

4. The control method for a new energy storage system with adaptive power supply mode switching according to claim 1, characterized in that, The objective function for maximizing system reliability is: in, As a system reliability indicator, The number of types of power outages. For the first The probability of various power outage scenarios occurring. For the first The duration of each power outage.

5. The control method for a new energy storage system with adaptive power supply mode switching according to claim 1, characterized in that, The electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device each include multiple energy storage units. The bus is a DC bus, and the power balance constraint condition is: in, for The power generation capacity of new energy power generation equipment at all times. The total number of all energy storage units. for Time of the first The discharge power of each energy storage unit for Time of the first The charging power of each energy storage unit for Load power at any given time for Power loss of the time system on the DC bus and related connecting lines.

6. The control method for a new energy storage system with adaptive power supply mode switching according to claim 1, characterized in that, The electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device each include multiple energy storage units. The busbar is an AC busbar, and the power balance constraint condition is: in, for The power generation capacity of new energy power generation equipment at all times. The total number of all energy storage units. for Time of the first The discharge power of each energy storage unit for Time of the first The charging power of each energy storage unit for Load power at any given time for Power loss of the timekeeping system on the AC bus and related connecting lines. for The power of the time system interacting with the power grid, for The reactive power generated by the new energy power generation equipment at all times. for Time of the first The reactive power of each energy storage unit during discharge. for Time of the first The reactive power of each energy storage unit during charging for The reactive power of the load at any given time. for The reactive power loss of the time-of-flight system on the AC bus and related connecting lines. for Reactive power of the system interacting with the power grid at any time.

7. The control method for a new energy storage system with adaptive power supply mode switching according to claim 1, characterized in that, The electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device, and the hydrogen energy storage device each include multiple energy storage units; The state constraints of the energy storage device are as follows: in, for Time of the first The actual state of charge of each energy storage unit For the first The minimum permissible state of charge of each energy storage unit. For the first The maximum permissible state of charge of each energy storage unit for Time of the first The charging power of each energy storage unit for Time of the first The discharge power of each energy storage unit For the first The minimum allowable charge and discharge power of each energy storage unit. For the first The maximum allowable charge and discharge power of each energy storage unit; The energy conversion and transmission constraints are as follows: in, for Time of the first The actual conversion efficiency of each energy storage unit For the first Minimum conversion efficiency of an energy storage unit For the first The maximum conversion efficiency of each energy storage unit for Time of the first The actual transmission power of each energy storage unit For the first Minimum power capacity of each energy storage unit For the first The maximum power capacity of each energy storage unit for Bus voltage value at any time This is the minimum allowable voltage value for the busbar. This is the maximum allowable voltage value for the busbar; The constraints for the new energy power generation are as follows: in, for The power generation capacity of new energy power generation equipment at all times. for The predicted power generation capacity of new energy power generation equipment at any given time. for The error range of power generation capacity of new energy power generation equipment at any given time; The load requirement constraint is as follows: in, for Load power at any given time for The minimum active power requirement of the load at any given time. for The maximum active power demand of the load at any given time. for The reactive power of the load at any given time. for The minimum reactive power requirement of the load at any given time. for The maximum reactive power demand of the load at any given time.

8. The control method for a new energy storage system with adaptive power supply mode switching according to claim 1, characterized in that, The specific steps of calculating the Pareto optimal solution set of the preset objective function using the NSGA-II algorithm include: Perform parameter initialization settings and randomly generate an initial population; Perform a non-dominated sort; Calculate the range of objective function values ​​and individual crowding. Perform selection, crossover, mutation, and population update operations; Repeat the steps of non-dominated sorting, crowding calculation, selection operation, crossover operation, mutation operation, and population update until the maximum number of iterations is reached; Output the Pareto optimal solution set for the last generation of the population.

9. The control method for adaptive power supply mode switching of a new energy storage system according to any one of claims 1 to 8, characterized in that, The adaptive power supply mode switching is triggered when the change in the power generation of the new energy power generation equipment reaches the first power threshold or the total power demand of the various loads reaches the second power threshold.

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