Multi-objective optimization scheduling method and system for source network load storage

Abstract

The invention discloses a multi-objective optimization scheduling method and system for source network load storage, and relates to the technical field of power system optimization, and the method comprises the steps: building a first objective function according to the charging power, the discharging power, the electric quantity state, the electric energy conversion power and the charging and discharging sequence through taking the discharging income and the charging cost as a first optimization objective; calculating an accumulated discharge income according to the discharge income of each time period, calculating an energy utilization rate according to the charging amount of each time period and the discharge power of the energy storage unit, judging an overload ratio and an idle ratio of the energy storage system under a source side fluctuation scene established based on meteorological data according to a power grid load index, and establishing a second optimization objective function; constructing a multi-objective optimization model based on the second optimization objective function and the first objective function in the K source side fluctuation scenes; and solving the multi-objective optimization model based on a genetic algorithm to obtain an optimal scheduling strategy, thereby solving the optimal scheduling of the energy storage system in a complex and changeable environment.

Description

Technical Field

[0001] The present application relates to the technical field of power system optimization, and in particular to a multi-objective optimization scheduling method and system for source, grid, load and storage. Background Art

[0002] In modern power systems, with the increasing penetration of renewable energy sources such as wind and solar, the grid faces challenges from high volatility and uncertainty on the power supply side. Furthermore, the grid load is constantly changing. Energy storage systems, as a flexible resource, can effectively smooth out power supply fluctuations and optimize the load curve by charging during periods of low grid load or surplus power generation, and discharging during periods of peak load or insufficient power generation. This improves the stability, economy, and energy efficiency of the power system.

[0003] Patent application publication number CN109861276A discloses a flexible DC grid pumped-storage power station wide-area power generation control system, comprising: an upper-level grid dispatching platform, a sending-end converter station, a regulating converter station, a pumped-storage power station, a receiving-end converter station, and a hydropower unit speed regulator. A wide-area power generation control master station is set up on the upper-level grid dispatching platform, and an execution substation is set up in the pumped-storage power station. The master station is responsible for main data analysis, issuing active power adjustment instructions for the pumped-storage power station, and other functions, and has different control modes; the substation is responsible for receiving active power adjustment instructions issued by the master station for the pumped-storage power station, and issuing actual execution instructions to the speed regulator of each unit based on the real-time operating conditions in the pumped-storage power station.

[0004] However, the effective operation of energy storage systems relies on precise scheduling strategies. A key technical challenge is developing an optimal scheduling strategy that maximizes economic benefits, improves energy efficiency, and maintains grid stability in the complex environment of fluctuating power sources and varying grid loads. Summary of the Invention

[0005] This application aims to solve, at least to some extent, one of the technical problems in the related art. To this end, one purpose of this application is to propose a multi-objective optimization scheduling method and system for source-grid-load-storage, which quantifies and balances multiple important indicators of energy storage system operation and calculates an optimal scheduling strategy that takes into account economic benefits, energy efficiency, and grid adaptability based on source-side volatility.

[0006] One aspect of the present application provides a multi-objective optimization scheduling method for source, grid, load and storage, including:

[0007] Step S100: collecting the energy storage system's power status, power conversion power, grid load indicators, and charging capacity, collecting the energy storage unit's charging power and discharging power, and recording meteorological data;

[0008] Step S200: Taking discharge benefit and charging cost as the first optimization goal, a first objective function is established according to charging power and discharging power, state of charge, electric energy conversion power, and charging and discharging sequence;

[0009] Step S300: Calculate the cumulative discharge benefit based on the discharge benefit in each period, calculate the energy utilization rate based on the charge amount and discharge power of the energy storage unit in each period, determine the overload ratio and idle ratio of the energy storage system under the source-side fluctuation scenario established based on meteorological data based on the grid load index, and establish a second optimization objective function;

[0010] Step S400: constructing a multi-objective optimization model based on the second optimization objective function and the first objective function under K source-side fluctuation scenarios;

[0011] Step S500: Solving the multi-objective optimization model based on the genetic algorithm to obtain the optimal scheduling strategy;

[0012] The specific method for collecting the energy storage system's state of charge, electric energy conversion power, grid load index, and charging capacity, collecting the energy storage unit's charging power and discharging power, and recording meteorological data is as follows:

[0013] Step S110: collecting the power state and charge capacity of the energy storage system and the power conversion power of the power conversion device at a historical time interval of t;

[0014] Step S120: power sensors are arranged in the source-grid-load-storage system to collect historical charging power and discharging power of the energy storage unit, with the collection time interval being t;

[0015] Step S130: deploying a monitoring device at the power grid load monitoring unit to collect historical power grid load indicators at a collection time interval of t;

[0016] Step S140: Record corresponding meteorological data during historical time;

[0017] The specific method of establishing the first objective function is:

[0018] Step S210: Maximize the total revenue of the energy storage system As the first optimization goal, the total benefit of the energy storage system Discharge income for energy storage system Charging costs with energy storage systems difference;

[0019] Step S220: Establishing a first constraint condition for the first objective function, wherein the first constraint condition includes an energy storage unit constraint, an energy storage system state of charge constraint, and a charge and discharge constraint. The energy storage unit constraint is established based on the discharge power and electric energy conversion power of the energy storage unit i. The SOC upper and lower limit constraints are established based on the state of charge of the energy storage system. The charge and discharge constraints are established based on the charging power and discharge power of the energy storage unit, the electric energy conversion power, and the charge and discharge sequence.

[0020] The specific method for establishing energy storage unit constraints based on the discharge power and electric energy conversion power of energy storage unit i is:

[0021] Step S221: The discharge state of the energy storage unit i in the tth period is expressed as , discharge state The value of is 0 or 1. When it is 0, it means that the energy storage unit i is not in the discharge state in the t period. When it is 1, it means that the energy storage unit i is in the discharge state in the t period, and the rated discharge power of the energy storage unit i in the t period is obtained. , the rated discharge power and discharge status The product of is taken as the upper limit of the discharge power of energy storage unit i , obtain the discharge power constraint of energy storage unit i;

[0022] Step S222: Establish the discharge power of energy storage unit i Power conversion with electrical energy The power constraints of electric energy conversion between

[0023] Step S223: The sum of the discharge powers of all energy storage units in the tth period Equal to the total discharge power of the energy storage system in period t , get the total discharge power constraint of the energy storage unit;

[0024] Step S224: Obtaining the energy storage unit constraint from the discharge power constraint of the energy storage unit i, the electric energy conversion power constraint, and the total discharge power constraint of the energy storage unit;

[0025] The specific method for establishing the SOC upper and lower limit constraints according to the state of charge of the energy storage system is:

[0026] Step S225: Obtaining the minimum power state and maximum power state The value of the minimum power state and maximum power state As the energy storage system power state The upper and lower limits of SOC are established;

[0027] The specific method for establishing the charge and discharge constraints based on the charging power and discharging power, electric energy conversion power, and charge and discharge sequence of the energy storage unit is as follows:

[0028] Step S226: Energy storage unit i can only be in the discharge state in the t period , charging status and shutdown status One of the states, and can only be in one of the states, to obtain the uniqueness constraint of the state of charging and discharging of the energy storage unit;

[0029] Step S227: Based on the upper limit of the discharge power of the energy storage unit i The charge and discharge power constraints of energy storage unit i are constructed based on the discharge state and charge state of energy storage unit i;

[0030] Step S228: The sum of the charging powers of all energy storage units in the tth period Equal to the total charging power of the energy storage system in period t , get the total charging power constraint of the energy storage unit;

[0031] Step S229: obtaining a charge and discharge constraint by combining the uniqueness constraint of the energy storage unit charge and discharge state, the charge and discharge power constraint, and the total charge power constraint;

[0032] The specific method of establishing the second optimization objective function is:

[0033] Step S310: Set the future optimization time domain T, divide T into N time periods, and the length of each time period is , calculate the discharge benefit of each period , the cumulative discharge benefit in the future optimization time domain T is obtained by summing the discharge benefits of all time periods;

[0034] Step S320: Based on the charging capacity of the energy storage system in each period Calculate the total charge capacity in the future optimization time domain T , according to the power conversion efficiency And the discharge power of the energy storage unit in each period Calculate the actual discharge amount in the future optimization time domain T , according to the actual discharge capacity Total charge The energy utilization rate is obtained by the ratio ;

[0035] Step S330: Construct K source-side fluctuation scenarios based on the meteorological data of the power grid area, and calculate the cumulative discharge benefits and energy utilization rates corresponding to the K source-side fluctuation scenarios; the cumulative discharge benefits corresponding to the kth source-side fluctuation scenario are , the energy utilization rate corresponding to the k-th source side fluctuation scenario is ;

[0036] Step S340: Set overload status and idle state , compare the grid load threshold and the grid load index, and judge whether each period in the future optimization time domain T is in an overload state Or idle state, set n1 as the number of overload states, set n2 as the number of idle states, if the tth period is overloaded, then the number of overload states n1 is increased by 1, if the tth period is idle, then the number of idle states n2 is increased by 1;

[0037] Step S350: Calculate the overload ratio and idle ratio of the energy storage system under each source-side fluctuation scenario based on the number of overload states, the number of idle states, and the total number of time periods in the future optimization time domain T; the overload ratio and idle ratio of the energy storage system under the kth source-side fluctuation scenario are respectively and ;

[0038] Step S360: constructing a second optimization objective function for each source-side fluctuation scenario based on the energy storage system's overload ratio and idle ratio, cumulative discharge benefit, and energy utilization rate;

[0039] The specific method for constructing the multi-objective optimization model based on the second optimization objective function and the first objective function in K source-side fluctuation scenarios is:

[0040] Step S410: averaging the second optimization objective functions under K source-side fluctuation scenarios to obtain a comprehensive second optimization objective function;

[0041] Step S420: performing a weighted summation of the comprehensive second optimization objective function and the first objective function, and combining the first constraint condition of the first objective function to construct a multi-objective optimization model;

[0042] The specific method of solving the multi-objective optimization model based on the genetic algorithm to obtain the optimal scheduling strategy is:

[0043] Step S510: Set the population size M and the maximum evolutionary number G of the genetic algorithm, and randomly generate M scheduling strategies as the initial population. Each scheduling strategy includes the discharge power and charging power of each energy storage unit in each time period within the future optimization time domain T.

[0044] Step S520: Taking the total revenue of the energy storage system as the fitness function, for each scheduling strategy in the mth population, calculate its fitness function value, and select the scheduling strategy with a fitness function value greater than the fitness threshold as the excellent scheduling strategy to form a new parent population;

[0045] Step S530: Perform crossover and recombination operations on the scheduling strategies in the parent population to generate new child scheduling strategies, perform mutation operations on the child scheduling strategies to obtain a child population, merge the child population with the parent population, calculate the fitness function values of the scheduling strategies in the merged population, and select M scheduling strategies with the largest fitness function values to form a new generation population;

[0046] Step S540: Repeat steps S520 to S530 until the maximum number of evolutionary generations is reached, and the last generation of population is obtained;

[0047] Step S550: Select the scheduling strategy with the highest fitness function value from the last generation population as the optimal scheduling strategy.

[0048] One aspect of the present application provides a multi-objective optimization scheduling system for source, grid, load and storage, including:

[0049] Data acquisition and recording module, used to collect the energy storage system's power status, power conversion power, grid load indicators and charging capacity, collect the energy storage unit's charging power and discharge power, and record meteorological data;

[0050] A first objective function module is used to establish a first objective function based on the charging power and discharging power, the state of charge, the electric energy conversion power, and the charging and discharging sequence, taking the discharge benefit and the charging cost as the first optimization target;

[0051] The second objective function module calculates the cumulative discharge revenue based on the discharge revenue in each period, calculates the energy utilization rate based on the charge amount and discharge power of the energy storage unit in each period, and determines the overload ratio and idle ratio of the energy storage system under the source-side fluctuation scenario based on meteorological data based on the grid load index to establish the second optimization objective function.

[0052] A multi-objective optimization module builds a multi-objective optimization model based on the second optimization objective function and the first objective function under K source-side fluctuation scenarios;

[0053] The scheduling strategy optimization module solves the multi-objective optimization model based on genetic algorithm to obtain the optimal scheduling strategy.

[0054] The multi-objective optimization scheduling method and system for source, grid, load and storage proposed in this application has the following advantages over the existing technology:

[0055] This application uses discharge revenue and charging cost as the primary optimization objectives and establishes a primary objective function that includes energy storage unit constraints, SOC constraints, and charge and discharge constraints, achieving a quantitative expression of the economic benefits of the energy storage system. Introducing physical constraints such as the power characteristics, state of charge limits, and charge and discharge logic of the energy storage units into the optimization model can avoid infeasible solutions and ensure the feasibility of the scheduling plan.

[0056] This application quantitatively describes the long-term economic viability of energy storage systems based on cumulative discharge benefits and introduces energy utilization metrics to evaluate their impact on renewable energy consumption. It also uses meteorological data to provide a scenario-based representation of source-side fluctuations and uses overload and idle ratios to measure the energy storage system's contribution to grid stability. This achieves comprehensive multi-objective modeling, balancing economic viability, energy efficiency, and stability.

[0057] This application combines the second objective function with the first objective function under multiple source-side fluctuation scenarios to form a unified multi-objective optimization model. By adjusting the weights of different objectives, we can flexibly balance economic efficiency, energy efficiency, and stability to achieve the optimal compromise solution that meets actual needs.

[0058] This application uses genetic algorithms to solve multi-objective optimization problems, overcoming the shortcomings of traditional optimization methods that are prone to falling into local optimality, and can obtain high-quality global optimal solutions.

[0059] The multi-objective optimization scheduling method for source, grid, load and storage proposed in this application has effectively solved the problem of energy storage optimization scheduling under the background of source-side fluctuations and load changes. It not only improves the economy and energy utilization efficiency of the energy storage system, but also takes into account the stable operation of the power grid. It has significant technical advantages and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A method flow chart of a multi-objective optimization scheduling method for source, grid, load and storage provided in this application;

[0061] Figure 2 A flow chart of the method for establishing energy storage unit constraints provided in this application;

[0062] Figure 3 Flowchart of the method for establishing charge and discharge constraints provided in this application;

[0063] Figure 4 Flowchart of the counting process for overload state and idle state provided by this application;

[0064] Figure 5 This is a functional module diagram of a multi-objective optimization scheduling system for source, grid, load and storage provided in this application. DETAILED DESCRIPTION

[0065] To better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are merely descriptions of exemplary embodiments of the present application and are not intended to limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0066] In the accompanying drawings, the size, dimensions, and shapes of the elements have been slightly adjusted for ease of illustration. The accompanying drawings are for illustration only and are not drawn strictly to scale. As used herein, the terms "substantially," "approximately," and similar terms are used to indicate approximate values, not degrees, and are intended to illustrate inherent deviations in measurements or calculations that would be recognized by a person of ordinary skill in the art. In addition, in this application, the order in which the steps are described does not necessarily represent the order in which these steps would occur in actual operation, unless otherwise specified or inferred from the context.

[0067] It should also be understood that expressions such as "including", "comprising", "having", "containing" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present application, "may" is used to mean "one or more embodiments of the present application". And, the term "exemplary" is intended to refer to an example or illustration.

[0068] Unless otherwise defined, all terms used herein (including engineering and scientific terms) have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that, unless otherwise specified in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0069] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0070] Example 1

[0071] like Figure 1 As shown, this application provides a multi-objective optimization scheduling method for source, grid, load and storage, including:

[0072] Step S100: collecting the energy storage system's power status, power conversion power, grid load indicators, and charging capacity, collecting the energy storage unit's charging power and discharging power, and recording meteorological data;

[0073] The specific method for collecting the energy storage system's state of charge, electric energy conversion power, grid load index, and charging capacity, collecting the energy storage unit's charging power and discharging power, and recording meteorological data is as follows:

[0074] Step S110: collecting the power state and charge capacity of the energy storage system and the power conversion power of the power conversion device at a historical time interval of t;

[0075] Step S120: power sensors are arranged in the source-grid-load-storage system to collect historical charging power and discharging power of the energy storage unit, with the collection time interval being t;

[0076] Step S130: deploying a monitoring device at the power grid load monitoring unit to collect historical power grid load indicators at a collection time interval of t;

[0077] Step S140: Record corresponding meteorological data during historical time;

[0078] For example, in a modern industrial park, an integrated source-grid-load-storage system is running. The energy storage unit refers to the most basic physical equipment in the integrated source-grid-load-storage system, such as a lithium battery pack. Each energy storage unit is equipped with its own charge and discharge controller, which can independently collect its power, voltage, charge and discharge status. The energy storage system is composed of a combination of multiple energy storage units and is equipped with a controller, converter, and battery energy management system. The energy storage system will be uniformly connected to the power grid or load side; the integrated source-grid-load-storage system is a complete energy system, which includes four parts: source, grid, load, and storage. The source is the power generation end, the grid is the power grid, the load is the load side, and the storage is the energy storage system.

[0079] Step S100 collects key information such as the energy storage system's state of charge, power conversion power, grid load indicators, and charge capacity, as well as the storage unit's charge and discharge power and meteorological data, providing the necessary data support for subsequent optimization and scheduling. State of charge and charge and discharge power data can be used for economic optimization, grid load indicators can be used for stability analysis, and meteorological data can be used for source-side fluctuation modeling. These all form the basis for constructing the optimization objective function and constraints.

[0080] Step S200: Taking discharge benefit and charging cost as the first optimization goal, a first objective function is established according to charging power and discharging power, state of charge, electric energy conversion power, and charging and discharging sequence;

[0081] The specific method of establishing the first objective function is:

[0082] Step S210: Maximize the total revenue of the energy storage system As the first optimization goal, the total benefit of the energy storage system Discharge income for energy storage system Charging costs with energy storage systems difference;

[0083] The calculation formula for the discharge benefit of the energy storage system is: ,in, is the discharge power of energy storage unit i in time period t, is the electricity price in period t, is the length of the t-th period;

[0084] The calculation formula for the charging cost of the energy storage system is: ,in, is the power consumption of energy storage unit i in time period t;

[0085] The functional expression of the total benefit of the energy storage system is: ;

[0086] The total benefit of the maximized energy storage system is expressed as: ;

[0087] Step S220: Establishing a first constraint condition for the first objective function, wherein the first constraint condition includes an energy storage unit constraint, an energy storage system state of charge constraint, and a charge and discharge constraint. The energy storage unit constraint is established based on the discharge power and electric energy conversion power of the energy storage unit i. The SOC upper and lower limit constraints are established based on the state of charge of the energy storage system. The charge and discharge constraints are established based on the charging power and discharge power of the energy storage unit, the electric energy conversion power, and the charge and discharge sequence.

[0088] The constraints above define the physical and operational feasible domain of the energy storage system and storage units, ensuring that the resulting scheduling strategy is practical. The storage unit constraints ensure the power limits of individual units and their relationship to the power conversion equipment. The SOC constraints ensure that the battery is not overcharged or over-discharged. The charge and discharge constraints ensure the unique state of the unit and its power limit at any given time.

[0089] like Figure 2 The flowchart of the method for establishing energy storage unit constraints provided by this application is shown in FIG. The specific method for establishing energy storage unit constraints based on the discharge power and electric energy conversion power of energy storage unit i is as follows:

[0090] Step S221: The discharge state of the energy storage unit i in the tth period is expressed as , discharge state The value of is 0 or 1. When it is 0, it means that the energy storage unit i is not in the discharge state in the t period. When it is 1, it means that the energy storage unit i is in the discharge state in the t period, and the rated discharge power of the energy storage unit i in the t period is obtained. , the rated discharge power and discharge status The product of is taken as the upper limit of the discharge power of energy storage unit i , obtain the discharge power constraint of energy storage unit i;

[0091] The constraint expression of the discharge power constraint of the energy storage unit i is: ;

[0092] Step S222: Establish the discharge power of energy storage unit i Power conversion with electrical energy The power constraints of electric energy conversion between

[0093] The constraint expression of the electric energy conversion power constraint is: ,in, 、 、 is the fitting coefficient between the discharge power and the electric energy conversion power of the energy storage unit i;

[0094] The value of the fitting coefficient is set by those skilled in the art based on experience. In actual engineering, the efficiency curve data can also be obtained from the manufacturer's manual and regression fitting can be performed to obtain the value. 、 、 The fitting coefficient value should reflect the conversion efficiency. The higher the conversion efficiency, The closer it is to 1, and In practical applications, the energy conversion efficiency of lithium battery energy storage system should be ≥94%, and the rated efficiency of converter should be ≥97%. Usually in the range of 0.94~0.97, Usually very small, close to 0, Often close to 0.

[0095] Step S223: The sum of the discharge powers of all energy storage units in the tth period Equal to the total discharge power of the energy storage system in period t , get the total discharge power constraint of the energy storage unit;

[0096] The constraint expression of the total discharge power constraint of the energy storage unit is: ;

[0097] Step S224: Obtaining the energy storage unit constraint from the discharge power constraint of the energy storage unit i, the electric energy conversion power constraint, and the total discharge power constraint of the energy storage unit;

[0098] The specific method for establishing the SOC upper and lower limit constraints according to the state of charge of the energy storage system is:

[0099] Step S225: Obtaining the minimum power state and maximum power state The value of the minimum power state and maximum power state As the energy storage system power state The upper and lower limits of SOC are established;

[0100] The constraint expressions of the SOC upper and lower limit constraints are: ;

[0101] like Figure 3 As shown in FIG, a flow chart of a method for establishing charge and discharge constraints provided by the present application is provided. The specific method for establishing charge and discharge constraints according to the charging power and discharging power of the energy storage unit, the electric energy conversion power, and the charge and discharge sequence is as follows:

[0102] Step S226: Energy storage unit i can only be in the discharge state in the t period , charging status and shutdown status One of the states, and can only be in one of the states, to obtain the uniqueness constraint of the state of charging and discharging of the energy storage unit;

[0103] The constraint expression of the state uniqueness constraint is: , where the discharge state When it is 1 or 0, it means the energy storage unit is in the discharging state or not in the discharging state, and the charging state When it is 1 or 0, it means the energy storage unit is in charging state or not in charging state, or in shutdown state. When it is 1 or 0, it indicates that the energy storage unit is in shutdown state or not in shutdown state respectively;

[0104] Step S227: Based on the upper limit of the discharge power of the energy storage unit i The charge and discharge power constraints of energy storage unit i are constructed based on the discharge state and charge state of energy storage unit i;

[0105] The constraint expression of the charge and discharge power constraint of the energy storage unit i is: ;

[0106] Step S228: The sum of the charging powers of all energy storage units in the tth period Equal to the total charging power of the energy storage system in period t , get the total charging power constraint of the energy storage unit;

[0107] The constraint expression of the total charging power constraint of the energy storage unit is: ;

[0108] Step S229: obtaining a charge and discharge constraint by combining the uniqueness constraint of the energy storage unit charge and discharge state, the charge and discharge power constraint, and the total charge power constraint;

[0109] When optimizing energy storage system scheduling, it's important to consider the unique characteristics of each storage unit and the overall benefits of the system. By setting targeted constraints for each unit and optimizing the overall benefits of the system, we can arrive at an optimal scheduling solution that balances the unique characteristics of each unit with the overall benefits of the system.

[0110] The short-term power coupling optimization scheduling method constructed by the first objective function of the above steps takes into account the short-term time scale, focuses on the real-time optimization operation scheduling of the energy storage system, and maximizes the immediate benefits of the energy storage system under various real-time constraints.

[0111] Step S300: Calculate the cumulative discharge benefit based on the discharge benefit in each period, calculate the energy utilization rate based on the charge amount and discharge power of the energy storage unit in each period, determine the overload ratio and idle ratio of the energy storage system under the source-side fluctuation scenario established based on meteorological data based on the grid load index, and establish a second optimization objective function;

[0112] The specific method of establishing the second optimization objective function is:

[0113] Step S310: Set the future optimization time domain T, divide T into N time periods, and the length of each time period is , calculate the discharge benefit of each period , the cumulative discharge benefit in the future optimization time domain T is obtained by summing the discharge benefits of all time periods;

[0114] The discharge benefit is: ,in, is the discharge benefit in period t, ;

[0115] The cumulative discharge benefit is: ;

[0116] Step S320: Based on the charging capacity of the energy storage system in each period Calculate the total charge capacity in the future optimization time domain T , according to the power conversion efficiency And the discharge power of the energy storage unit in each period Calculate the actual discharge amount in the future optimization time domain T , according to the actual discharge capacity Total charge The energy utilization rate is obtained by the ratio ;

[0117] The calculation formula of the total charge capacity is: ,in, is the charge amount in the tth period;

[0118] The calculation formula for the actual discharge capacity in the future optimization time domain T is: ,in, is the power conversion efficiency;

[0119] The power conversion efficiency is obtained by referring to the equipment parameter table; according to the equipment parameter table provided by the manufacturer, the power conversion efficiency under different working conditions is obtained;

[0120] The energy utilization rate The calculation formula is: ;

[0121] Step S330: Construct K source-side fluctuation scenarios based on the meteorological data of the power grid area, and calculate the cumulative discharge benefits and energy utilization rates corresponding to the K source-side fluctuation scenarios; the cumulative discharge benefits corresponding to the kth source-side fluctuation scenario are , the energy utilization rate corresponding to the k-th source side fluctuation scenario is ;

[0122] This application constructs optimization objectives based on multiple source-side fluctuation scenarios, obtains a more robust solution through weighted averaging, and improves the adaptability and reliability of the scheduling strategy in the face of uncertain factors.

[0123] Step S340: Set overload status and idle state , compare the grid load threshold and the grid load index, and judge whether each period in the future optimization time domain T is in an overload state Or idle state, set n1 as the number of overload states, set n2 as the number of idle states, if the tth period is overloaded, then the number of overload states n1 is increased by 1, if the tth period is idle, then the number of idle states n2 is increased by 1;

[0124] The grid load threshold value is determined empirically by those skilled in the art. It is divided into an overload threshold and a recovery threshold. The overload threshold should be slightly lower than the rated capacity of the device. In practical scenarios, 80% to 90% of the rated capacity of the distribution transformer is often used as the overload threshold. The recovery threshold is generally set at a lower percentage of the rated capacity to ensure charging during extremely low loads. 50% to 60% of the rated capacity of the distribution transformer is often used as the recovery threshold.

[0125] like Figure 4 The figure shows a flow chart of the counting process of the overload state and the idle state provided by the present application. When the grid load index is greater than the overload threshold, it is in the overload state, and when the grid load index is less than or equal to the recovery threshold, it is in the idle state.

[0126] Step S350: Calculate the overload ratio and idle ratio of the energy storage system under each source-side fluctuation scenario based on the number of overload states, the number of idle states, and the total number of time periods in the future optimization time domain T; the overload ratio and idle ratio of the energy storage system under the kth source-side fluctuation scenario are respectively and ;

[0127] The calculation formula of the energy storage system overload ratio is: , where N is the total number of time periods in the future optimization time domain T;

[0128] The calculation formula of the idle ratio is: ;

[0129] Step S360: constructing a second optimization objective function for each source-side fluctuation scenario based on the energy storage system's overload ratio and idle ratio, cumulative discharge benefit, and energy utilization rate;

[0130] The function expression of the second optimization objective function is: ,in, 、 、 、 are the weight coefficients of cumulative discharge benefit, energy utilization rate, energy storage system overload ratio and idle ratio, ;

[0131] The weight coefficients of the cumulative discharge benefit, energy utilization rate, energy storage system overload ratio and idle ratio are set by those skilled in the art according to actual needs;

[0132] Preferably, = = = =0.25;

[0133] The second optimization objective function in the above steps constructs a medium- and long-term equilibrium optimization method with the future optimization time domain T as the scheduling time domain, focusing on the strategic optimization decision-making of the energy storage system. The goal is to maximize the long-term comprehensive benefits of the energy storage system. The two objective functions complement each other on the time scale.

[0134] This application adopts a multi-objective optimization approach. While taking into account the economic benefits of the energy storage system, it takes into account indicators such as the proportion of overload states, the proportion of idle states, and energy utilization rate to build a more comprehensive and balanced scheduling model, which helps to obtain a scheduling strategy with better overall benefits.

[0135] Step S400: constructing a multi-objective optimization model based on the second optimization objective function and the first objective function under K source-side fluctuation scenarios;

[0136] The specific method for constructing the multi-objective optimization model based on the second optimization objective function and the first objective function in K source-side fluctuation scenarios is:

[0137] Step S410: averaging the second optimization objective functions under K source-side fluctuation scenarios to obtain a comprehensive second optimization objective function;

[0138] The calculation formula of the comprehensive second optimization objective function is: ;

[0139] Step S420: performing a weighted summation of the comprehensive second optimization objective function and the first objective function, and combining the first constraint condition of the first objective function to construct a multi-objective optimization model;

[0140] The expression of the multi-objective optimization model is: ,in, and are the weight coefficients of the first objective function and the comprehensive second optimization objective function, ;

[0141] The weight coefficients of the first objective function and the comprehensive second optimization objective function are set by those skilled in the art according to actual needs; preferably, = =0.5;

[0142] This step expands the first objective function of short-term optimal scheduling to a second optimization objective function on a medium- to long-term timescale. This comprehensively considers factors such as discharge revenue, energy utilization, and fluctuations in the operation of the energy storage system. It also introduces source-side fluctuation scenarios to improve the model's adaptability and robustness. Combining the first and second optimization objective functions effectively connects the power plant's short-term operational scheduling with medium- and long-term optimization decisions, forming a scientific scheduling system that balances the power plant's economic and ecological benefits, achieving multi-objective balanced optimization and maximizing the combined benefits of source, grid, load, and storage.

[0143] Step S500: Solving the multi-objective optimization model based on the genetic algorithm to obtain the optimal scheduling strategy;

[0144] The specific method of solving the multi-objective optimization model based on the genetic algorithm to obtain the optimal scheduling strategy is:

[0145] Step S510: Set the population size M and the maximum evolutionary number G of the genetic algorithm, and randomly generate M scheduling strategies as the initial population. Each scheduling strategy includes the discharge power and charging power of each energy storage unit in each time period within the future optimization time domain T.

[0146] Step S520: Taking the total revenue of the energy storage system as the fitness function, for each scheduling strategy in the mth population, calculate its fitness function value, and select the scheduling strategy with a fitness function value greater than the fitness threshold as the excellent scheduling strategy to form a new parent population;

[0147] Step S530: Perform crossover and recombination operations on the scheduling strategies in the parent population to generate new child scheduling strategies, perform mutation operations on the child scheduling strategies to obtain a child population, merge the child population with the parent population, calculate the fitness function values of the scheduling strategies in the merged population, and select M scheduling strategies with the largest fitness function values to form a new generation population;

[0148] Step S540: Repeat steps S520 to S530 until the maximum number of evolutionary generations is reached, and the last generation of population is obtained;

[0149] Step S550: Select the scheduling strategy with the highest fitness function value from the last generation population as the optimal scheduling strategy.

[0150] This application introduces genetic algorithm optimization technology into the scheduling decision-making of the source-grid-load-storage system, achieving an innovative leap from empirical scheduling to intelligent scheduling, and providing new ideas and methods for solving complex hydropower optimization scheduling problems.

[0151] Example 2

[0152] like Figure 5 As shown, a multi-objective optimization scheduling system for source, grid, load and storage provided by this application includes:

[0153] Data acquisition and recording module, used to collect the energy storage system's power status, power conversion power, grid load indicators and charging capacity, collect the energy storage unit's charging power and discharge power, and record meteorological data;

[0154] A first objective function module is used to establish a first objective function based on the charging power and discharging power, the state of charge, the electric energy conversion power, and the charging and discharging sequence, taking the discharge benefit and the charging cost as the first optimization target;

[0155] The second objective function module calculates the cumulative discharge revenue based on the discharge revenue in each period, calculates the energy utilization rate based on the charge amount and discharge power of the energy storage unit in each period, and determines the overload ratio and idle ratio of the energy storage system under the source-side fluctuation scenario based on meteorological data based on the grid load index to establish the second optimization objective function.

[0156] A multi-objective optimization module builds a multi-objective optimization model based on the second optimization objective function and the first objective function under K source-side fluctuation scenarios;

[0157] The scheduling strategy optimization module solves the multi-objective optimization model based on genetic algorithm to obtain the optimal scheduling strategy.

[0158] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0159] The above-described specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A multi-objective optimization scheduling method for source, grid, load and storage, characterized in that: include: Collect the energy storage system's power status, power conversion power, grid load indicators and charging capacity, collect the energy storage unit's charging power and discharging power, and record meteorological data; Taking discharge benefit and charging cost as the first optimization goal, the first objective function is established according to charging power and discharging power, state of charge, electric energy conversion power and charging and discharging sequence; The cumulative discharge benefit is calculated based on the discharge benefit in each period. The energy utilization rate is calculated based on the charge amount and discharge power of the energy storage unit in each period. The overload ratio and idle ratio of the energy storage system under the source-side fluctuation scenario established based on meteorological data are determined based on the grid load index to establish the second optimization objective function. A multi-objective optimization model is constructed based on the second optimization objective function and the first objective function under K source-side fluctuation scenarios; The multi-objective optimization model is solved based on the genetic algorithm to obtain the optimal scheduling strategy.

2. A multi-objective optimization scheduling method for source, grid, load and storage according to claim 1, characterized in that: The specific method for collecting the energy storage system's state of charge, electric energy conversion power, grid load index, and charging capacity, collecting the energy storage unit's charging power and discharging power, and recording meteorological data is as follows: Collect the power state and charge amount of the energy storage system and the power conversion power of the power conversion equipment in historical time, with the collection time interval being t; Power sensors are placed in the source-grid-load-storage system to collect the charging power and discharging power of the energy storage unit over time, with a collection time interval of t; A monitoring device is arranged at the power grid load monitoring unit to collect the power grid load index of the historical time, and the collection time interval is t; During historical time, record the corresponding meteorological data.

3. A multi-objective optimization scheduling method for source, grid, load and storage according to claim 2, characterized in that: The specific method of establishing the first objective function is: Maximize the total benefits of the energy storage system As the first optimization goal, the total benefit of the energy storage system Discharge income for energy storage system Charging costs with energy storage systems difference; Establish a first constraint condition for the first objective function, which includes an energy storage unit constraint, an energy storage system state of charge constraint, and a charge and discharge constraint. The energy storage unit constraint is established based on the discharge power and electric energy conversion power of the energy storage unit i. The SOC upper and lower limit constraints are established based on the state of charge of the energy storage system. The charge and discharge constraints are established based on the charging power and discharge power of the energy storage unit, the electric energy conversion power, and the charge and discharge sequence.

4. A multi-objective optimization scheduling method for source, grid, load and storage according to claim 3, characterized in that: The specific method for establishing energy storage unit constraints based on the discharge power and electric energy conversion power of energy storage unit i is: The discharge state of energy storage unit i in time period t is expressed as , discharge state The value of is 0 or 1. When it is 0, it means that the energy storage unit i is not in the discharge state in the t period. When it is 1, it means that the energy storage unit i is in the discharge state in the t period, and the rated discharge power of the energy storage unit i in the t period is obtained. , the rated discharge power and discharge status The product of is taken as the upper limit of the discharge power of energy storage unit i , obtain the discharge power constraint of energy storage unit i; Establish the discharge power of energy storage unit i Power conversion with electrical energy The power constraints of electric energy conversion between The sum of the discharge power of all energy storage units in time period t Equal to the total discharge power of the energy storage system in period t , get the total discharge power constraint of the energy storage unit; The energy storage unit constraint is obtained by the discharge power constraint of energy storage unit i, the electric energy conversion power constraint and the total discharge power constraint of the energy storage unit.

5. A multi-objective optimization scheduling method for source, grid, load and storage according to claim 4, characterized in that: The specific method for establishing the SOC upper and lower limit constraints according to the state of charge of the energy storage system is: Get the minimum power status and maximum power state The value of the minimum power state and maximum power state As the energy storage system power state The upper and lower limits of SOC are established.

6. A multi-objective optimization scheduling method for source, grid, load and storage according to claim 5, characterized in that: The specific method for establishing the charge and discharge constraints based on the charging power and discharging power, electric energy conversion power, and charge and discharge sequence of the energy storage unit is as follows: Energy storage unit i can only be in the discharge state in the t period , charging status and shutdown status One of the states, and can only be in one of the states, to obtain the uniqueness constraint of the state of charging and discharging of the energy storage unit; According to the upper limit of the discharge power of energy storage unit i The charge and discharge power constraints of energy storage unit i are constructed based on the discharge state and charge state of energy storage unit i; The sum of the charging power of all energy storage units in time period t Equal to the total charging power of the energy storage system in period t , get the total charging power constraint of the energy storage unit; The charge and discharge constraints are obtained by combining the uniqueness constraint of the energy storage unit charge and discharge state, the charge and discharge power constraint, and the total charging power constraint.

7. A multi-objective optimization scheduling method for source, grid, load and storage according to claim 6, characterized in that: The specific method of establishing the second optimization objective function is: Set the future optimization time domain T, divide T into N time periods, and the length of each time period is , calculate the discharge benefit of each period , the cumulative discharge benefit in the future optimization time domain T is obtained by summing the discharge benefits of all time periods; According to the charging amount of the energy storage system in each period Calculate the total charge capacity in the future optimization time domain T , according to the power conversion efficiency And the discharge power of the energy storage unit in each period Calculate the actual discharge amount in the future optimization time domain T , according to the actual discharge capacity Total charge The energy utilization rate is obtained by the ratio ; Construct K source-side fluctuation scenarios based on the meteorological data of the power grid area, and calculate the cumulative discharge benefits and energy utilization rates corresponding to the K source-side fluctuation scenarios; Set overload status and idle state , compare the grid load threshold and the grid load index, and judge whether each period in the future optimization time domain T is in an overload state Or idle state, set n1 as the number of overload states, set n2 as the number of idle states, if the tth period is overloaded, then the number of overload states n1 is increased by 1, if the tth period is idle, then the number of idle states n2 is increased by 1; Based on the number of overload states, the number of idle states, and the total number of time periods in the future optimization time domain T, the overload ratio and idle ratio of the energy storage system under each source-side fluctuation scenario are calculated. According to the overload ratio and idle ratio of the energy storage system, the cumulative discharge benefit and the energy utilization rate, the second optimization objective function is constructed for each source-side fluctuation scenario.

8. A multi-objective optimization scheduling method for source, grid, load and storage according to claim 7, characterized in that: The specific method for constructing the multi-objective optimization model based on the second optimization objective function and the first objective function in K source-side fluctuation scenarios is: The second optimization objective function under K source-side fluctuation scenarios is comprehensively averaged to obtain a comprehensive second optimization objective function; The second optimization objective function and the first objective function are weightedly summed, and the multi-objective optimization model is constructed in combination with the first constraint condition of the first objective function.

9. A multi-objective optimization scheduling method for source, grid, load and storage according to claim 8, characterized in that: The specific method of solving the multi-objective optimization model based on the genetic algorithm to obtain the optimal scheduling strategy is: Step S510: Set the population size M and the maximum evolutionary number G of the genetic algorithm, and randomly generate M scheduling strategies as the initial population. Each scheduling strategy includes the discharge power and charging power of each energy storage unit in each time period within the future optimization time domain T. Step S520: Taking the total revenue of the energy storage system as the fitness function, for each scheduling strategy in the mth population, calculate its fitness function value, and select the scheduling strategy with a fitness function value greater than the fitness threshold as the excellent scheduling strategy to form a new parent population; Step S530: Perform crossover and recombination operations on the scheduling strategies in the parent population to generate new child scheduling strategies, perform mutation operations on the child scheduling strategies to obtain a child population, merge the child population with the parent population, calculate the fitness function values of the scheduling strategies in the merged population, and select M scheduling strategies with the largest fitness function values to form a new generation population; Step S540: Repeat steps S520 to S530 until the maximum number of evolutionary generations is reached, and the last generation of population is obtained; Step S550: Select the scheduling strategy with the highest fitness function value from the last generation population as the optimal scheduling strategy.

10. A multi-objective optimization scheduling system for source, grid, load and storage, which is used to implement a multi-objective optimization scheduling method for source, grid, load and storage according to any one of claims 1 to 9, characterized in that: include: Data acquisition and recording module, used to collect the energy storage system's power status, power conversion power, grid load indicators and charging capacity, collect the energy storage unit's charging power and discharge power, and record meteorological data; A first objective function module is used to establish a first objective function based on the charging power and discharging power, the state of charge, the electric energy conversion power, and the charging and discharging sequence, taking the discharge benefit and the charging cost as the first optimization target; The second objective function module calculates the cumulative discharge revenue based on the discharge revenue in each period, calculates the energy utilization rate based on the charge amount and discharge power of the energy storage unit in each period, and determines the overload ratio and idle ratio of the energy storage system under the source-side fluctuation scenario based on meteorological data based on the grid load index to establish the second optimization objective function. A multi-objective optimization module builds a multi-objective optimization model based on the second optimization objective function and the first objective function under K source-side fluctuation scenarios; The scheduling strategy optimization module solves the multi-objective optimization model based on genetic algorithm to obtain the optimal scheduling strategy.

Images