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

Abstract

The invention relates to the technical field of power systems, and aims to solve the problem that an existing new energy storage system is difficult to realize multi-objective optimization to adapt to new energy power generation and load dynamic change under a complex system architecture. In order to achieve the purpose, the invention provides a new energy storage system control method for self-adaptive power supply mode switching, and the method comprises the steps: collecting operation data in real time; judging whether the system reaches a self-adaptive power supply mode switching triggering condition or not; if yes, inputting the operation data into a preset objective function including three objective functions of energy efficiency maximization, economic cost minimization and system reliability maximization; calculating a Pareto optimal solution set of a preset objective function by adopting an NSGA-I (Non-dominated Sorting Genetic Algorithm-I) algorithm; selecting an optimal control strategy from the Pareto optimal solution set based on a decision model of an ideal point method; and controlling system operation based on the optimal control strategy. The method can consider three targets of energy efficiency maximization, economic cost minimization and system reliability, and is adaptive to new energy power generation and load dynamic change.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a control method for a new energy storage system with adaptive power supply mode switching. Background Art

[0002] As a key way to deal with the depletion of traditional fossil energy and environmental pollution, renewable energy power generation is experiencing rapid development. Among them, photovoltaic power generation devices and wind power generation devices occupy an increasingly important position in the energy supply system due to their inexhaustible and environmentally friendly characteristics. However, the inherent intermittent and volatile characteristics of renewable energy power generation constitute a major obstacle to its large-scale and stable application. Taking photovoltaic power generation as an example, its power generation is closely related to factors such as light intensity, weather conditions, seasonal changes, and day and night alternation. In a clear and cloudless day, when there is sufficient light, the photovoltaic power generation power can reach the peak level; once it encounters bad weather such as cloudy, rainy, snowy days, or at sunrise and sunset, the light intensity drops sharply, and the power generation power will also drop sharply, even approaching zero. Wind power generation is also deeply affected by natural conditions. The size of wind speed, changes in direction, and the stability of wind force will have a decisive impact on the power generation power. In a breeze, wind power generation is limited; when the wind is strong and the wind direction is stable, the power generation is significantly increased; when the wind speed is too high and exceeds the rated wind speed range of the wind turbine, or when the wind direction changes frequently and drastically, in order to ensure the safety of the equipment, the wind turbine may stop running or adjust the power output, causing the power generation to fluctuate greatly or even be interrupted. This unstable power generation characteristic makes it difficult to ensure the continuity, stability and reliability of power supply when relying solely on renewable energy generation to directly supply power to the load, and it cannot meet the stringent requirements of modern society for diversified loads on power quality.

[0003] In order to effectively solve the intermittent and volatile problems of renewable energy generation, energy storage technology has emerged and become an indispensable supporting means. Electrochemical energy storage devices, represented by lithium-ion batteries, have the advantages of relatively high energy density, relatively good charging and discharging efficiency, fast response speed and low self-discharge rate. They can charge and discharge quickly in a short time and flexibly adjust the system power balance; mechanical energy storage devices, such as pumped storage systems, can use different water level differences to achieve large-scale energy storage and release, and flywheel energy storage relies on high-speed rotating flywheels to store kinetic energy. They have the characteristics of large energy storage capacity, long service life, and relatively mature technology. They perform well in responding to long-term, large-capacity energy storage and regulation needs; electromagnetic energy storage devices, such as supercapacitors, have extremely high power density and can release or absorb a large amount of power in an instant; hydrogen energy storage devices achieve energy storage and conversion through water electrolysis, hydrogen storage, and hydrogen fuel cell power generation. They have unique advantages such as large energy storage capacity, long storage cycle, and cross-seasonal storage. As a clean energy carrier, hydrogen has broad application prospects in the future energy system. These unique energy storage devices are organically combined with new energy power generation equipment to build a new energy storage system. When new energy power generation is sufficient, excess electricity can be stored in the energy storage device, and when power generation is insufficient, the stored energy can be released to make up for the power gap, thereby significantly improving the comprehensive utilization efficiency of energy and enhancing the stability and reliability of the power supply system.

[0004] Nevertheless, it is not easy to achieve efficient, economical and reliable operation in a new energy storage system that includes multiple energy storage types, and it faces many technical challenges. In the actual operation process, it is difficult for the system to achieve the optimization of multiple conflicting goals. For example, from the perspective of energy efficiency, improving energy efficiency means reducing the loss of energy during conversion, storage and transmission, and giving full play to the efficiency of new energy power generation and energy storage devices. This requires precise optimization of the charging and discharging strategies of each energy storage device, the working mode of the power conversion device, and the energy management logic of the system. However, the pursuit of energy efficiency improvement may often be accompanied by an increase in economic costs. For example, the use of higher-performance energy storage devices or more advanced power conversion equipment may significantly increase their purchase costs, installation costs, and maintenance costs. From the perspective of economic costs, reducing economic costs requires that in the process of system planning, equipment selection and operation management, factors such as the investment cost, operation and maintenance cost of energy storage devices, and the energy procurement and sales costs during the interaction with the power grid should be fully considered. However, excessive attention to economic costs may lead to insufficient performance and reduced reliability of energy storage devices, or neglect of energy efficiency in the energy management process, resulting in energy waste, which in turn affects the overall reliability of the system. In terms of system reliability, it is necessary to ensure that the system can stably and reliably supply power to the load under various working conditions, especially in the face of extreme fluctuations in renewable energy generation, energy storage device failures, and sudden changes in loads. Redundant energy storage capacity, reliable power conversion and control systems, and a complete fault detection and response mechanism are required. However, this will undoubtedly increase the construction and operating costs of the system, and in some cases may reduce energy efficiency due to over-configuration.

[0005] In addition, the application scenarios of new energy storage systems are extremely extensive and complex, covering multiple fields such as peak shaving and valley filling of power systems, power guarantee in industrial production processes, uninterrupted power supply of commercial facilities, and electricity consumption in residents' lives. The load demand characteristics in different application scenarios vary greatly. Industrial loads may have the characteristics of high power, high load fluctuations, and extremely high requirements for power supply reliability; commercial loads show the characteristics of concentrated power consumption time, power demand changes with business hours, and are more sensitive to power quality; although the individual power of residential loads is small, the total amount is huge and the power consumption behavior has obvious periodicity and randomness. At the same time, various parameters in the system operation process, including new energy power generation power, load power demand, state of charge of energy storage devices, operating state parameters such as voltage and current, and environmental factors (such as light intensity, wind speed, temperature, etc.) are in real-time dynamic changes. This requires the system to be able to adaptively adjust the power supply mode and control strategy according to these complex and changeable working conditions to achieve multi-objective optimization balance. However, traditional control methods are often based on fixed rules or simple models, which are difficult to cope with such complex system dynamic characteristics and multi-objective optimization requirements.

[0006] In view of this, the art needs a new energy storage system control method with adaptive power supply mode switching to solve the above problems. Summary of the invention

[0007] In order to solve the above technical problems, that is, to solve the problem that the existing new energy storage system is difficult to achieve multi-objective optimization under a complex system architecture to adapt to the dynamic changes of new energy power generation and load.

[0008] The present invention provides a control method for a new energy storage system with adaptive power supply mode switching, wherein the new energy storage system comprises 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, wherein the central control unit is respectively 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 for communication, wherein 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 respective adaptive power conversion devices, wherein the common bus connects the new energy power generation equipment composed of the photovoltaic power generation device and the wind power generation device with various loads;

[0009] The control method comprises:

[0010] Collecting the operating data of 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 in real time;

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

[0012] If so, the operation data collected in real time is input into a preset objective function, wherein the preset objective function includes an energy efficiency maximization objective function, an economic cost minimization objective function and a system reliability maximization objective function, and 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] Using NSGA-II algorithm to calculate the Pareto optimal solution set of the preset objective function;

[0014] A decision model based on an ideal point method selects an 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 certain 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 solution and negative ideal solution of the energy efficiency maximization target, economic cost minimization target and system reliability maximization target respectively;

[0018] The Euclidean distance formula is used to calculate the distance between each Pareto optimal solution and the ideal solution in the Pareto optimal solution set. and the distance to the negative ideal solution

[0019]

[0020] Among them, k = 1, 2, and 3 correspond to the three goals of maximizing energy efficiency, minimizing economic costs, and maximizing system reliability, respectively. is the value of the j-th Pareto optimal solution on the k-th objective, is the value of the ideal solution on the kth objective, is the value of the negative ideal solution on the kth objective;

[0021] The relative closeness C of each Pareto optimal solution is calculated by the following formula: j :

[0022]

[0023] Among them, C j is the closeness between the jth Pareto optimal solution and the ideal solution;

[0024] Select 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 certain 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 a plurality of energy storage units, and the energy efficiency maximization objective function is:

[0026]

[0027] Among them, η system is the energy efficiency of the system, T is the total time period, P load,t is the load power at time t, Δt is the time interval, P storagedischarged,i,t is the discharge power of the i-th energy storage unit at time t, P storagecharged,i,t is the charging power of the i-th energy storage unit at time t, P renewable,tis the power generation capacity of the new energy power generation equipment at time t, and n is the number of all energy storage units.

[0028] In certain preferred embodiments, the economic cost minimization objective function is:

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

[0030] Among them, C total is the total economic cost, C investment is the equipment investment cost, C operation is the operation and maintenance cost, C energy The cost of purchasing energy.

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

[0032]

[0033] Among them, EID is the system reliability index, m is the number of types of power outages, and p k is the probability of the kth power outage, d k is the duration of the kth power outage.

[0034] In certain 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 a plurality of energy storage units, the bus is a DC bus, and the power balance constraint condition is:

[0035]

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

[0037] In certain 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 a plurality of energy storage units, the bus is an AC bus, and the power balance constraint condition is:

[0038]

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

[0040] In certain 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 a plurality of energy storage units;

[0041] The energy storage device state constraint condition is:

[0042]

[0043] Among them, SOC i,t is the actual state of charge of the i-th energy storage unit at time t, SOC min,i is the minimum allowable state of charge of the i-th energy storage unit, SOC max,i is the maximum allowable state of charge of the i-th energy storage unit, is the charging power of the i-th energy storage unit at time t, is the discharge power of the i-th energy storage unit at time t, is the minimum allowable charging and discharging power of the i-th energy storage unit, is the maximum allowable charging and discharging power of the i-th energy storage unit;

[0044] The energy conversion and transmission constraints are:

[0045]

[0046] Among them, η PCS,i,t is the actual conversion efficiency of the i-th energy storage unit at time t, is the minimum conversion efficiency of the i-th energy storage unit, is the maximum conversion efficiency of the i-th energy storage unit, P PCS,i,t is the actual transmission power of the i-th energy storage unit at time t, is the minimum power capacity of the i-th energy storage unit, is the maximum power capacity of the i-th energy storage unit, V bus,t is the bus voltage value at time t, is the minimum allowable voltage value of the busbar, is the maximum allowable voltage value of the bus;

[0047] The constraints for renewable energy generation are:

[0048]

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

[0050] The load demand constraint condition is:

[0051]

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

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

[0054] Initialize parameters and randomly generate the initial population;

[0055] Perform non-dominated sorting;

[0056] Calculate the objective function value range and individual crowding degree;

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

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

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

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

[0061] It can be seen from the above that the new energy storage system control method for adaptive power supply mode switching provided by the present invention has the following beneficial technical effects:

[0062] The present invention introduces preset objective functions of maximizing energy efficiency, minimizing economic costs and maximizing system reliability, and combines a comprehensive constraint system including power balance constraints, energy storage device state constraints, energy conversion and transmission constraints, new energy power generation constraints and load demand constraints, and uses the NSGA-II algorithm to calculate the Pareto optimal solution set, and then selects the optimal control strategy based on the decision model of the ideal point method. This multi-objective optimization method effectively solves the problem that traditional control methods are difficult to achieve a good balance between energy efficiency, economic costs and system reliability. In actual operation, the charging and discharging strategies of each energy storage device, the working mode of the power conversion device and the interaction mode with the power grid can be dynamically adjusted according to the real-time state of the system, so that the conversion, storage and transmission process of energy in the entire system is more efficient, which not only reduces energy waste, but also reduces the operating cost of the system, while greatly improving the reliability of the system in responding to various working conditions, ensuring continuous, stable and high-quality power supply to various loads. Since the operating data of 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 are collected in real time, and the trigger conditions for adaptive power supply mode switching are judged based on these data, once the trigger conditions are met, the multi-objective optimization calculation process is quickly started and the power supply mode is switched. This adaptive mechanism driven by real-time data enables the system to accurately respond to the dynamic changes of new energy power generation and loads. Whether facing rapid changes in natural conditions such as light intensity and wind speed, or frequent fluctuations in load demand in industrial production, commercial operations and residents' lives, the power supply mode can be smoothly switched, effectively improving the flexibility and adaptability of the entire new energy storage system, so that it can always maintain the best operating state in complex and changeable actual application scenarios. Through the synergy of the above-mentioned multi-objective optimization and adaptive power supply mode switching functions, the overall performance of the new energy storage system has been comprehensively improved. In terms of energy utilization, the improvement of energy efficiency means that more new energy can be effectively utilized, reducing dependence on traditional fossil energy 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 technology; in terms of enhancing system reliability, it reduces the risk and duration of power outages and reduces economic losses caused by power failures. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0064] Figure 1 It is a flow chart of the control method of the new energy storage system with adaptive power supply mode switching of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0066] Based on the problem pointed out in the background technology that the existing new energy storage system is difficult to achieve multi-objective optimization to adapt to the dynamic changes of new energy generation and load under a complex system architecture. The present invention provides a new energy storage system control method with adaptive power supply mode switching, which aims to take into account the three goals of maximizing energy efficiency, minimizing economic costs and system reliability, and adaptively adapt to the dynamic changes of new energy generation and load.

[0067] The new energy storage system with adaptive power supply mode switching 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 respectively 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 for communication. The electrochemical energy storage device, the mechanical energy storage device, the electromagnetic energy storage device and the hydrogen energy storage device are connected to the 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 data collection, analysis, control instruction generation of the entire system and communication interaction with external systems (such as power grid monitoring system, meteorological data platform, etc.). The electrochemical energy storage device, mechanical energy storage device, electromagnetic energy storage device and hydrogen energy storage device each include multiple energy storage units. For example, the 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 (such as sodium-sulfur battery energy storage units, flow battery energy storage units, etc.). The mechanical energy storage device includes one or more pumped energy storage units, and one or more flywheel energy storage units. The electromagnetic energy storage device includes one or more supercapacitor energy storage units, and one or more superconducting energy storage units. The hydrogen energy storage device includes multiple hydrogen energy storage units. The busbar can be a DC busbar or an AC busbar. Various types of loads can include industrial loads, commercial loads and residential loads, or only one or two of the above loads, depending on the application location of the new energy energy storage system of the present invention. The new energy energy storage system of the present invention can be a completely independent power supply system, or it can be a local power supply system or an auxiliary power supply system as a power grid.

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

[0069] Real-time collection of operating data of 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 meets the triggering conditions for switching to an adaptive power supply mode;

[0071] If not, maintain the current operation mode and return to the step of collecting the operation data of 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 in real time;

[0072] If so, the real-time collected operation data is input into the preset objective function, wherein the preset objective function includes the energy efficiency maximization objective function, the economic cost minimization objective function and the system reliability maximization objective function, and the constraints of the preset objective function include the power balance constraint condition, the energy storage device state constraint condition, the energy conversion and transmission constraint condition, the new energy power generation constraint condition and the load demand constraint condition;

[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 of controlling the operation of the new energy storage system based on the optimal control strategy and returning the real-time collection of the operation data of 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.

[0076] It should be noted that the operation data of 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 that the central control unit needs to collect can be determined according to the preset objective function. The construction of the preset objective function can be constructed based on historical data. When the real-time collected operation data is input into the preset objective function, the Pareto optimal solution set of the preset objective function can be calculated by the NSGA-II algorithm, and the decision model based on the ideal point method can be obtained from the NSGA-I 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 the new energy storage system and the load are both dynamically changing, the Pareto optimal solution set and the optimal control strategy corresponding to different time nodes are different. When the new energy storage system reaches the trigger condition for adaptive power supply mode switching (for example, the change value of the power generation power 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 the regulation is completed, the steps of real-time collection of operating data of 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 are continued. The control method of the present invention is suitable for cyclic regulation of new energy storage systems. When regulating, the central control unit generates accurate control instructions 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 the possible reactive power compensation device, etc.; for each energy storage unit, the control instructions may include charging and discharging power setting values, PCS working mode switching instructions, etc. For example, in the energy storage power supply mode, according to the selected power supply distribution mode of each energy storage unit, the central control unit sends a specific discharge power instruction to the PCS of the electrochemical energy storage unit, and sends a corresponding control signal to the mechanical energy storage unit to collaboratively provide the power required by the load; for new energy power generation equipment, in the independent power supply or hybrid power supply mode, the central control unit can adjust the maximum power tracking point of the photovoltaic array or the pitch angle of the wind turbine and other parameters according to the system power balance requirements and the operating status of the power generation equipment to optimize the power output of new energy power generation; in addition, if the system is connected to the power grid, the central control unit can also control the working state of the PCS connected to the power grid according to the power grid interaction strategy (such as purchasing electricity from the power grid for storage during low-price periods or selling electricity to the power grid during high-price periods) to achieve energy interaction with the power grid.

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

[0078]

[0079] Among them, η system is the energy efficiency of the system, T is the total time period, P load,t is the load power at time t, Δt is the time interval, is the discharge power of the i-th energy storage unit at time t (positive when discharging), is the charging power of the i-th energy storage unit at time t (positive when charging), P renewable,t is the power generation of the new energy power generation equipment at time t, n is the number of all 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, is the effective charging power of the i-th energy storage unit at time t, is the effective discharge power of the i-th energy storage unit at time t. The above effective power is substituted into the energy efficiency formula for correction calculation to more accurately reflect the real energy utilization efficiency of the system.

[0082] Preferably, the economic cost minimization objective function is:

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

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

[0085]

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

[0087]

[0088] Where n is the number of all energy storage units, T is the total time period, C OM,i,t is the operation and maintenance cost coefficient per unit power of the i-th energy storage unit at time t, is the actual power used (charging or discharging power) of the i-th energy storage unit at time t, Δt is the time interval, C OM,i,t It can comprehensively consider factors such as the regular maintenance costs of energy storage units, parts replacement costs, and labor costs, and dynamically adjust as the energy storage units age or usage intensity changes;

[0089]

[0090] Where T is the total time period, P grid,t is the power of the system interacting with the grid at time t (buying power from the grid is positive, selling power to the grid is negative), Δt is the time interval, C grid,t is the grid electricity price at time t;

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

[0092]

[0093] Among them, EID is the system reliability index, m is the number of types of power outages, and p k is the probability of the kth power outage, d k is the duration of the kth power outage.

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

[0095]

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

[0097]

[0098] Among them, m w is the number of lines, j w is the line number, is the line j at time t w The current in For line j w resistance.

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

[0100]

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

[0102]

[0103] Among them, m w is the number of lines, j w is the line number, is the line j at time t w The current in For line j w Resistance;

[0104] It should be noted that if the new energy storage system of the present invention is an independent power supply 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, which means they are not included in the calculation.

[0105] Preferably, the energy storage device state constraint condition is:

[0106]

[0107] Among them, SOC i,t is the actual state of charge of the i-th energy storage unit at time t, SOC min,iis the minimum allowable state of charge of the i-th energy storage unit, SOC max,i is the maximum allowable state of charge of the i-th energy storage unit, is the charging power of the i-th energy storage unit at time t, is the discharge power of the i-th energy storage unit at time t, is the minimum allowable charging and discharging power of the i-th energy storage unit, is the maximum allowable charge and discharge power of the i-th energy storage unit; in the above, SOC i,t The calculation of 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 is the initial state of charge of the i-th energy storage unit, k is the time index, Δk is the time step, is the charging power of the i-th energy storage unit at time k, is the discharge power of the i-th energy storage unit at time k, E rated,i is the rated capacity of the i-th energy storage unit, η charge,i is the charging efficiency of the i-th energy storage unit, η discharge,i is the discharge efficiency of the i-th energy storage unit;

[0110] The energy conversion and transmission constraints are:

[0111]

[0112] Among them, η PCS,i,t is the actual conversion efficiency of the i-th energy storage unit at time t, is the minimum conversion efficiency of the i-th energy storage unit, is the maximum conversion efficiency of the i-th energy storage unit, P PCS,i,t is the actual transmission power of the i-th energy storage unit at time t, is the minimum power capacity of the i-th energy storage unit, is the maximum power capacity of the i-th energy storage unit, V bus,t is the bus voltage value at time t, is the minimum allowable voltage value of the busbar, is the maximum allowable voltage value of the bus;

[0113] The constraints for renewable energy generation are:

[0114]

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

[0116] The load demand constraints are:

[0117]

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

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

[0120] initialization:

[0121] Parameter setting: determine the population size N; set the maximum number of iterations G max ; Define the crossover probability p c , the value range is usually between 0.6-0.9, for example, set to 0.8; determine the mutation probability p m , generally between 0.01-0.1, for example, set to 0.05.

[0122] Population generation: Randomly generate the initial population p0, and each individual in the population represents a control strategy combination. For this new energy storage system, the gene coding of the individual may include the charging and discharging power allocation ratio of each energy storage unit (for example, n numerical values ​​are used to represent the power allocation ratio of n energy storage units, and the sum is 1) and the working mode selection of PCS (using binary coding to represent different working modes). Calculate the objective function value of each individual in the initial population p0, that is, the energy efficiency η system 、Economic cost C total And the system reliability index EID. According to the objective function and constraints defined above, the calculation is performed through the collected real-time system data (new energy generation power, load power demand, energy storage unit status, etc.).

[0123] Non-dominated sorting:

[0124] Compare the advantages and disadvantages of individuals: For any two individuals i in the population p0 g and j g , and compare their performance on the three objective functions.g Not inferior to individual j in all objective functions g , and is better than individual j in at least one objective function g , then individual i g Dominant individual j g , traverse all pairs of individuals in the population and determine the dominance relationship of each individual.

[0125] Divide non-dominated classes: Divide individuals that are not dominated by any other individuals into non-dominated class F1. These individuals are the best in the current population and they have achieved a good balance between the three objectives. Remove individuals in F1 from the population, and then continue to look for non-dominated individuals among the remaining individuals and divide them into non-dominated class F2. Repeat the above process until the entire population is divided into multiple non-dominated classes. l n is the 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 the minimum value F min For example, for energy efficiency, Where i traverses 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 g The ranking of individuals in terms of energy efficiency is Then individual j(2≤j≤m g -1) Crowding in terms of energy efficiency for: Boundary individuals (j = 1 and j = m g ) is set to be infinite. The total crowding degree of individual i is That is, the sum of the crowding degrees of individuals on the three objective functions.

[0129] Select an action:

[0130] Tournament selection: Tournament selection is performed from all non-dominated levels. First, two non-dominated levels F are randomly selected. i and F j (i≠j). Randomly select two individuals a and b from the two selected non-dominated ranks. 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 F i F whose rank number is smaller than that of bj If individual a and b are at the same non-dominated level, their crowding degrees are compared and the individual with the larger crowding degree is selected to enter the next generation population. Repeat the above process several times until N individuals are selected to form the next generation population p1.

[0131] Crossover Operation:

[0132] Select crossover individuals: Randomly select N×p from population p1 c Perform a crossover operation on the individual as the parent individual.

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

[0134] Mutation Operation

[0135] Select mutation individuals: Randomly select N×p from the temporary offspring population C n Individuals are considered as variant individuals.

[0136] Perform mutation operation: For each mutant individual, perform mutation operation on its gene. For example, for the gene representing the charging and discharging power distribution ratio of the energy storage unit, randomly select an energy storage unit and change its power distribution ratio within a certain range (such as ±0.1); for the gene representing the PCS working mode selection, invert the corresponding binary bit. Check the constraints of the mutated individual. If all constraints are met, replace the original mutant individual in the temporary offspring population C; otherwise, repeat the mutation operation until a mutant individual that meets the constraints is generated.

[0137] Update population:

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

[0139] Non-dominated sorting and crowding calculation: Perform non-dominated sorting on the new population R and divide it into multiple non-dominated levels F′1, F′2, ... F l'. Calculate the crowding degree of individuals in each non-dominated level. Select the next generation population: select individuals to enter the next generation population p2 in order of non-dominated levels from low to high, until the population size reaches N. If the number of individuals in a non-dominated level exceeds the number of remaining individuals during the selection process, sort the individuals in the level according to the crowding degree, and select individuals with larger crowding degree to enter the next generation population.

[0140] Iteration and termination:

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

[0142] Termination condition judgment: After each round of iteration, determine whether the termination condition is met. If the current number of iterations reaches the maximum number of iterations G max , the algorithm terminates and outputs the Pareto optimal solution set in 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 above NSGA-II algorithm specifically including:

[0144] Determine the ideal solution and negative ideal solution of the energy efficiency maximization target, economic cost minimization target and system reliability maximization target respectively;

[0145] Specifically, for the energy efficiency target, the ideal solution is in is the energy efficiency value of the jth solution in the Pareto optimal solution set, and the negative ideal solution is That is, the larger the value of the energy efficiency target, the better.

[0146] For the economic cost target, the ideal solution is in is the economic cost value of the jth solution in the Pareto optimal solution set, and the negative ideal solution is That is, the smaller the value of the economic cost target, the better.

[0147] For the system reliability target, the ideal solution is EID * =min{EID j}, where EID j is the system reliability value of the jth solution in the Pareto optimal solution set, and the negative ideal solution is EID - =max{EID j}, that is, the smaller the value of the system reliability target, the better, that is, the shorter the power supply interruption time, the better.

[0148] The Euclidean distance formula is used to calculate the distance between each Pareto optimal solution and the ideal solution in the Pareto optimal solution set. and the distance to the negative ideal solution

[0149]

[0150] Among them, k = 1, 2, and 3 correspond to the three goals of maximizing energy efficiency, minimizing economic costs, and maximizing system reliability, respectively. is the value of the j-th Pareto optimal solution on the k-th objective, is the value of the ideal solution on the kth objective, is the value of the negative ideal solution on the kth objective;

[0151] The relative closeness C of each Pareto optimal solution is calculated by the following formula: j :

[0152]

[0153] Among them, C j is the closeness between the jth Pareto optimal solution and the ideal solution;

[0154] Select 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 (for example, when the power generated by renewable energy is sufficient to meet the load demand and the energy storage unit is in an appropriate state), energy storage power supply (for example, the power generation by renewable energy is insufficient and the energy storage unit can meet the load demand) or a hybrid power supply mode (for example, the power generation by renewable energy partially meets the load demand and the energy storage unit supplements the insufficient part), as well as the specific power supply distribution method of each energy storage unit in the case of energy storage power supply and hybrid power supply (for example, the charging and discharging power ratio of different energy storage units is determined based on the characteristics, state and cost-effectiveness of each energy storage unit), so as to switch the power supply mode adaptively.

[0156] It should be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0157] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

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

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

[0160] One or more embodiments of the present invention are intended to cover all such replacements, modifications and variations that fall within the broad scope of the present invention. Therefore, any omissions, modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of the present invention should be included in 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, wherein the central control unit is respectively 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 for communication, and 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, and the common bus connects the new energy power generation equipment composed of the photovoltaic power generation device and the wind power generation device with various loads; The control method comprises: Collecting the operating data of 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 in real time; Determine whether the new energy storage system meets the adaptive power supply mode switching trigger condition; If so, the operation data collected in real time is input into a preset objective function, wherein the preset objective function includes an energy efficiency maximization objective function, an economic cost minimization objective function and a system reliability maximization objective function, and 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 NSGA-II algorithm is used to calculate the Pareto optimal solution set of the preset objective function; A decision model based on an ideal point method selects an 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.

2. The control method of the new energy storage system with adaptive power supply mode switching according to claim 1 is characterized in that: 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 includes: Determine the ideal solution and negative ideal solution of the energy efficiency maximization target, economic cost minimization target and system reliability maximization target respectively; The Euclidean distance formula is used to calculate the distance between each Pareto optimal solution and the ideal solution in the Pareto optimal solution set. and the distance to the negative ideal solution Among them, k = 1, 2, and 3 correspond to the three goals of maximizing energy efficiency, minimizing economic costs, and maximizing system reliability, respectively. is the value of the j-th Pareto optimal solution on the k-th objective, is the value of the ideal solution on the kth objective, is the value of the negative ideal solution on the kth objective; The relative closeness C of each Pareto optimal solution is calculated by the following formula: j : Among them, C j is the closeness between the jth Pareto optimal solution and the ideal solution; Select 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.

3. The control method of the new energy storage system with adaptive power supply mode switching according to claim 1 is 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 a plurality of energy storage units, and the energy efficiency maximization objective function is: Among them, η system is the energy efficiency of the system, T is the total time period, P load,t is the load power at time t, Δt is the time interval, is the discharge power of the i-th energy storage unit at time t, is the charging power of the i-th energy storage unit at time t, P renewable,t is the power generation capacity of the new energy power generation equipment at time t, and n is the number of all energy storage units.

4. The control method of the new energy storage system with adaptive power supply mode switching according to claim 1 is characterized in that: The economic cost minimization objective function is: C total =C investment +C operation +C energy Among them, C total is the total economic cost, C investment is the equipment investment cost, C operation is the operation and maintenance cost, C energy The cost of purchasing energy.

5. The control method of the new energy storage system with adaptive power supply mode switching according to claim 1 is characterized in that: The system reliability maximization objective function is: Among them, EID is the system reliability index, m is the number of types of power outages, and p k is the probability of the kth power outage, d k is the duration of the kth power outage.

6. The control method of the new energy storage system with adaptive power supply mode switching according to claim 1 is 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 a plurality of energy storage units, the bus is a DC bus, and the power balance constraint condition is: Among them, P renewable,t is the power generation capacity of the new energy power generation equipment at time t, n is the number of all energy storage units, is the discharge power of the i-th energy storage unit at time t, is the charging power of the i-th energy storage unit at time t, P load,t is the load power at time t, P loss,t is the power loss of the system on the DC bus and related connecting lines at time t.

7. The control method of the new energy storage system with adaptive power supply mode switching according to claim 1 is 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 a plurality of energy storage units, the bus is an AC bus, and the power balance constraint condition is: Among them, P renewable,t is the power generation capacity of the new energy power generation equipment at time t, n is the number of all energy storage units, is the discharge power of the i-th energy storage unit at time t, is the charging power of the i-th energy storage unit at time t, P load,t is the load power at time t, P loss,t is the power loss of the system on the AC bus and related connecting lines at time t, P grid,t is the power of the system interacting with the grid at time t, Q renewable,t is the reactive power generated by the new energy power generation equipment at time t, is the reactive power of the i-th energy storage unit when it discharges at time t, is the reactive power of the i-th energy storage unit when charging at time t, Q load,t is the reactive power of the load at time t, Q loss,t is the reactive power loss of the system on the AC bus and related connecting lines at time t, Q grid,t is the reactive power interacting between the system and the grid at time t.

8. The control method of the new energy storage system with adaptive power supply mode switching according to claim 1 is 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 a plurality of energy storage units; The energy storage device state constraint condition is: Among them, SOC i,t is the actual state of charge of the i-th energy storage unit at time t, SOC min,i is the minimum allowable state of charge of the i-th energy storage unit, SOC max,i is the maximum allowable state of charge of the i-th energy storage unit, is the charging power of the i-th energy storage unit at time t, is the discharge power of the i-th energy storage unit at time t, is the minimum allowable charging and discharging power of the i-th energy storage unit, is the maximum allowable charging and discharging power of the i-th energy storage unit; The energy conversion and transmission constraints are: Among them, η PCS,i,t is the actual conversion efficiency of the i-th energy storage unit at time t, is the minimum conversion efficiency of the i-th energy storage unit, is the maximum conversion efficiency of the i-th energy storage unit, P PCS,i,t is the actual transmission power of the i-th energy storage unit at time t, is the minimum power capacity of the i-th energy storage unit, is the maximum power capacity of the i-th energy storage unit, V bus,t is the bus voltage value at time t, is the minimum allowable voltage value of the busbar, is the maximum allowable voltage value of the bus; The constraints for renewable energy generation are: Among them, P renewable,t is the power generation capacity of the new energy power generation equipment at time t, is the predicted value of the power generation of the new energy power generation equipment at time t, δ t is the error range of the power generation of the new energy power generation equipment at time t; The load demand constraint condition is: Among them, P load,t is the load power at time t, is the minimum required value of load active power at time t, is the maximum demand value of the load active power at time t, Q load,t is the reactive power of the load at time t, is the minimum required value of load reactive power at time t, is the maximum required value of load reactive power at time t.

9. The control method of the new energy storage system with adaptive power supply mode switching according to claim 1 is characterized in that: The method of using the NSGA-II algorithm to calculate the Pareto optimal solution set of the preset objective function specifically includes: Initialize parameters and randomly generate the initial population; Perform non-dominated sorting; Calculate the objective function value range and individual crowding degree; Perform selection operations, crossover operations, mutation operations, and population update operations; Repeat the steps of non-dominated sorting, crowding calculation, selection operation, crossover operation, mutation operation, and population update operation until the maximum number of iterations is reached; Output the Pareto optimal solution set in the last generation of the population.

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

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