Energy storage capacity optimal configuration method and system based on multi-objective particle swarm optimization algorithm

Abstract

The invention relates to the technical field of energy storage auxiliary peak regulation, and discloses an energy storage capacity optimal configuration method and system based on a multi-target particle swarm optimization algorithm, and the method comprises the steps: obtaining the performance parameters and state variables of various energy sources in a power system, and constructing various power generation and energy storage mathematical models; setting constraint conditions of the power system according to the mathematical model, and constructing an energy storage multi-target optimization scheduling model including operation cost, environmental protection cost and peak regulation effectiveness based on the constraint conditions; and solving an optimal solution of the energy storage multi-objective optimization scheduling model by using a multi-objective particle swarm optimization algorithm, and obtaining an energy storage peak regulation configuration strategy according to the optimal solution. According to the method, the three targets of operation cost, environmental protection cost and peak regulation effectiveness are comprehensively considered for optimal scheduling, and on the basis of the stability of energy storage auxiliary peak regulation, economic benefits and environmental protection benefits are improved, the stability and reliability of a power system are enhanced, and the quality of power supply is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage-assisted peak regulation, and in particular to a method and system for optimizing energy storage capacity configuration based on a multi-objective particle swarm optimization algorithm. Background Art

[0002] As the scale of renewable energy power generation increases, the peak-to-valley difference of the power system continues to increase, and the peak-to-valley difference of conventional units is increasingly unable to meet the peak-to-peak demand of the system. Therefore, studying the method of peak-to-peak shortage is of great significance to the safe and stable operation of the power system containing renewable energy power generation.

[0003] In terms of the optimal configuration and coordinated operation of energy storage systems, current research and practice have not yet formed a comprehensive consideration. Current methods generally only consider cost-based economics, such as the large-capacity liquid flow battery assisted new energy peak-shaving capacity demand configuration model and method with application number CN118539487A. This public patent only considers economic costs as the objective function of the model, so the peak-shaving scheduling obtained by solving the model is not comprehensive. Summary of the invention

[0004] In view of the problem that the optimization configuration model of the energy storage system in the prior art does not take into account all factors, the present invention provides a method and system for optimizing the configuration of energy storage capacity based on a multi-objective particle swarm optimization algorithm, which can comprehensively consider the operating cost, environmental cost and peak-shaving effectiveness, and ensure the reliability of the system peak-shaving. The specific technical solution is as follows:

[0005] A method for optimizing energy storage capacity configuration based on a multi-objective particle swarm optimization algorithm, comprising:

[0006] Obtain the performance parameters and state variables of various energy sources in the power system, and construct various mathematical models of power generation and energy storage;

[0007] Setting constraints of the power system according to the mathematical model, and constructing a multi-objective optimization scheduling model of energy storage including operation cost, environmental protection cost and peak load effectiveness based on the constraints;

[0008] The multi-objective particle swarm optimization algorithm is used to obtain the optimal solution of the energy storage multi-objective optimization scheduling model, and the energy storage peak load configuration strategy is obtained based on the optimal solution.

[0009] Preferably, the mathematical models for constructing various power generation and energy storage include a wind power generation model, a wind power generation model and an energy storage battery model;

[0010] Wind power generation model:

[0011]

[0012] Where PWT , v are the actual output power and actual wind speed of the corresponding wind turbine respectively; P n 、v n and are the rated output power and rated wind speed of the wind turbine respectively; v ci and v co are the cut-in and cut-out wind speeds, respectively;

[0013] Photovoltaic power generation model:

[0014]

[0015] Where P PV is the output power of photovoltaic cells; G n is the spectral irradiance; T c is the actual solar cell temperature; T r is the reference solar cell temperature; P n is the solar peak power; λ is the temperature condition coefficient;

[0016] Energy storage battery model:

[0017]

[0018] Where P ch (t), P dch (t) are the charge and discharge amounts of the battery at time t; ξ, η sb are the battery self-discharge and charge-discharge conversion rate respectively; E (t) is the battery capacity at time t; P sb is the rated capacity of the battery; SOC (t) is the state of charge of the battery at time t.

[0019] Preferably, the constraints of the power system set according to the mathematical model include system power balance constraints, upper and lower limit constraints on energy storage system output, multi-period energy coupling constraints on energy storage system, charge state constraints on energy storage system, thermal power unit ramp rate constraints, upper and lower limit constraints on thermal power unit output, wind power and photovoltaic output constraints, daily wind abandonment constraints, probabilistic reliability constraints and mean reliability constraints.

[0020] Preferably, the energy storage multi-objective optimization scheduling model based on the constraint conditions including operation cost, environmental protection cost and peak load effectiveness comprises:

[0021] Based on the constraints, objective functions of operating cost, environmental cost and peak-shaving effectiveness are constructed, and a multi-objective optimization scheduling model is obtained according to the objective functions of operating cost, environmental cost and peak-shaving effectiveness.

[0022] Preferably, the objective function of the operating cost is as follows:

[0023] C p =C p +C wind +C lack +C ess -R peak

[0024] In the formula, C p Operating cost; C t is the power generation cost; C ess Invest in the operation and maintenance costs of the energy storage system; C wind Penalty cost for wind curtailment; C lack Penalty cost for insufficient peak load regulation; R peak For peak load-shaving economic benefits.

[0025] Preferably, the objective function of the environmental cost is as follows:

[0026]

[0027] In the formula, C e environmental cost; T is the total number of time periods; K is the total number of pollutant emission types; N is the total number of different electricity production methods; G j is the unit cost of treating the jth pollutant; H ij is the emission coefficient of the jth pollutant under the i-th electricity production mode; P i,t is the output power of the i-th electricity production mode in time period t.

[0028] Preferably, the objective function of the peak shaving effectiveness is as follows:

[0029]

[0030] In the formula, σ 2 is the net load variance; represents the average value of all net load values; n is the total number of time points; t is the sequence number of the time point; x n Indicates the net load value at the nth time point.

[0031] Preferably, the method of obtaining the optimal solution of the energy storage multi-objective optimization scheduling model by using a multi-objective particle swarm optimization algorithm, and obtaining the energy storage peak load configuration strategy according to the optimal solution includes:

[0032] The initial particle swarm is generated by using chaotic mapping, and the particle swarm is updated and iterated in combination with the constraints.

[0033] In each generation of iteration, the fitness value of the particle is evaluated, and the fitness value is determined by a multi-objective optimization scheduling model of energy storage that includes economic and environmental costs as well as peak-shaving effectiveness.

[0034] By evaluating the fitness value of particles in each generation of iteration, the individual optimal solution and the global optimal solution are updated, and when the preset iteration termination condition is reached, the final optimization scheduling result is output;

[0035] The optimal capacity configuration strategy of the energy storage system is obtained according to the optimization scheduling result.

[0036] A system for optimizing energy storage capacity configuration based on a multi-objective particle swarm optimization algorithm, applied to the aforementioned method for optimizing energy storage capacity configuration based on a multi-objective particle swarm optimization algorithm, comprises:

[0037] Parameter variable acquisition unit, used to obtain performance parameters and state variables of various energy sources in the power system and build various mathematical models of power generation and energy storage;

[0038] A model building unit, used to set constraints of the power system according to the mathematical model, and build an energy storage multi-objective optimization scheduling model based on the constraints;

[0039] The configuration strategy generation unit is used to use the multi-objective particle swarm algorithm to obtain the optimal solution of the energy storage multi-objective optimization scheduling model, and obtain the energy storage peak load configuration strategy based on the optimal solution.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention provides an energy storage capacity optimization configuration method based on a multi-objective particle swarm optimization algorithm, which sets reasonable constraints, comprehensively considers the three objectives of operating cost, environmental protection cost and peak-shaving effectiveness for optimization scheduling, and obtains the optimal solution of the energy storage multi-objective optimization scheduling model through the multi-objective particle swarm optimization algorithm. On the basis of the stability of energy storage-assisted peak-shaving, it not only improves the economic and environmental benefits, but also enhances the stability and reliability of the power system, ensuring the quality of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0043] Figure 1 The present invention is a flow chart of a method for optimizing energy storage capacity configuration based on a multi-objective particle swarm optimization algorithm.

[0044] Figure 2 It is a schematic diagram of comprehensive evaluation indicators of an energy storage capacity optimization configuration method based on a multi-objective particle swarm optimization algorithm of the present invention.

[0045] Figure 3 The present invention is a flow chart of another embodiment of a method for optimizing energy storage capacity configuration based on a multi-objective particle swarm optimization algorithm.

[0046] Figure 4 This is a schematic diagram of an energy storage capacity optimization configuration system based on a multi-objective particle swarm optimization algorithm of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0049] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0050] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0051] The following embodiments are described by taking a multi-energy type power system consisting of wind power generation, photovoltaic power generation and dynamic power generation as an example.

[0052] See the following examples Figures 1 to 4 .

[0053] The present application provides a method for optimizing energy storage capacity based on a multi-objective particle swarm optimization algorithm, including:

[0054] Step S01, obtaining performance parameters and state variables of various energy sources in the power system, and constructing various mathematical models of power generation and energy storage;

[0055] By obtaining the performance parameters of wind energy, solar energy, and traditional fossil energy in the power system, including output power, wind speed, spectral irradiance, temperature, etc. and obtaining the state variables of the energy storage battery, such as energy storage capacity, charge and discharge rate, remaining power, etc., mathematical models of wind power generation, photovoltaic power generation, and energy storage batteries are constructed based on the performance parameters and state variables.

[0056] Wind power generation model:

[0057]

[0058] Where P WT and v are the actual output power and actual wind speed of the corresponding wind turbine respectively; P n 、v n and are the rated output power and rated wind speed of the wind turbine respectively; v ci and v co are the cut-in and cut-out wind speeds, respectively;

[0059] Photovoltaic power generation model:

[0060]

[0061] Where P PV is the output power of the photovoltaic cell; G n is the spectral irradiance, set the spectral irradiance G n 1000W / m 2 ;;T c is the actual solar cell temperature; T r As the reference solar cell temperature, the solar cell temperature is set to 25°C; P n is the solar peak power; λ is the temperature condition coefficient;

[0062] Energy storage battery model:

[0063]

[0064] Where P ch (t), P dch (t) are the charge and discharge amounts of the battery at time t; ξ, η sb are the battery self-discharge and charge-discharge conversion rate respectively; E (t) is the battery capacity at time t; P sb is the rated capacity of the battery; SOC (t) is the state of charge of the battery at time t.

[0065] By obtaining the performance parameters and state variables of various energy sources in the power system, various mathematical models of power generation and energy storage are constructed, providing quantitative tools. The mathematical models of power generation and energy storage accurately reflect the power generation characteristics of different energy sources and the operating laws of energy storage systems.

[0066] Step S02: setting constraints of the power system according to the mathematical model, and constructing a multi-objective optimization scheduling model of energy storage including operating cost, environmental protection cost and peak load effectiveness based on the constraints;

[0067] According to the mathematical model, constraints of the power system are set, including system power balance constraints, upper and lower limit constraints of energy storage system output, multi-period energy coupling constraints of energy storage system, charge state constraints of energy storage system, climbing rate constraints of thermal power units, upper and lower limit constraints of thermal power unit output, wind power and photovoltaic output constraints, daily wind abandonment constraints, probabilistic reliability constraints and mean reliability constraints.

[0068] By constructing the objective function of operating cost, the objective function of environmental cost and the objective function of peak-shaving effectiveness based on the constraints, a multi-objective optimization scheduling model is obtained according to the objective function of operating cost, the objective function of environmental cost and the objective function of peak-shaving effectiveness.

[0069] The objective function of the operating cost is as follows:

[0070] C p =C p +C wind +C lack +C ess -R peak

[0071] In the formula, C p Operating cost; C t is the power generation cost; C ess Invest in the operation and maintenance costs of the energy storage system; C wind Penalty cost for wind curtailment; C lack Penalty cost for insufficient peak load regulation; R peak For peak load-shaving economic benefits.

[0072] The objective function of the environmental cost is as follows:

[0073]

[0074] In the formula, C e environmental cost; T is the total number of time periods; K is the total number of pollutant emission types; N is the total number of different electricity production methods; G j is the unit cost of treating the jth pollutant; H ij is the emission coefficient of the jth pollutant under the i-th electricity production mode; P i,tis the output power of the i-th electricity production mode in time period t.

[0075] The objective function of the peak load effectiveness is as follows:

[0076]

[0077] In the formula, σ 2 is the net load variance; represents the average value of all net load values; n is the total number of time points; t is the sequence number of the time point; x n Indicates the net load value at the nth time point.

[0078] According to the characteristics of wind power generation model, photovoltaic power generation model and energy storage battery model, the wind power and photovoltaic output constraints, energy storage system charge state constraints, etc. can be determined. The multi-objective optimization scheduling model ensures that the energy storage capacity optimization configuration method achieves a balance between economy, environmental protection and stability while meeting the actual needs of the power system.

[0079] Step S03: Use a multi-objective particle swarm optimization algorithm to find the optimal solution of the energy storage multi-objective optimization scheduling model, and obtain an energy storage peak-shaving configuration strategy based on the optimal solution.

[0080] Specifically, the method of using a multi-objective particle swarm optimization algorithm to obtain the optimal solution of the energy storage multi-objective optimization scheduling model, and obtaining the energy storage peak load configuration strategy according to the optimal solution includes:

[0081] Step S31, using chaotic mapping to generate an initial particle swarm, and combining the constraint conditions to perform an iterative update process of the particle swarm,

[0082] The calculation formula for generating the initial particle swarm by chaotic mapping is as follows:

[0083] x n+1 = r·x n ·(1-x n )

[0084] In the formula, r is the control parameter, x n is the current state;

[0085] The combination of chaotic mapping and MOPSO can effectively improve the global search capability of the algorithm, increase the diversity of the population, and help find a better optimal solution set.

[0086] Step S32: During each iteration of the generation, the fitness value of the particle is evaluated. The fitness value is determined by a multi-objective optimization scheduling model for energy storage that includes economic efficiency (operating cost), environmental cost, and peak load effectiveness.

[0087] The formulas of the multi-objective optimization model and particle swarm update process are as follows:

[0088] The speed update process formula is as follows:

[0089]

[0090] In the formula, v i is the velocity of the particle; x i is the position of the particle; c1 and c2 are acceleration coefficients, and w is also called the learning factor, which is used to adjust the degree to which the particle moves toward the individual optimum and the global optimum. 1d and r 2d are two independent random numbers in dimension d, usually in the range [0,1]; is the individual optimal position of particle i in dimension d in generation t; is the global optimal position of the entire population in the tth generation on dimension d; is the current position of particle i in dimension d at generation t.

[0091] The position update formula is as follows:

[0092]

[0093] In the formula, is the value of the position of particle i in the t+1th generation in dimension d; is the value of the position of particle i in the tth generation in dimension d.

[0094] Step S33: by evaluating the fitness value of the particles in each generation of iteration, updating the individual optimal solution and the global optimal solution, and when the preset iteration termination condition is reached, outputting the final optimization scheduling result;

[0095] Set the termination condition as the maximum number of iterations and output the final optimization scheduling result.

[0096] Step S34: obtaining an optimal capacity configuration strategy for the energy storage system according to the optimization scheduling result.

[0097] By solving the multi-objective particle swarm optimization algorithm, a set of energy storage capacity configuration schemes that meet multiple optimization objectives are obtained. According to the peak load configuration strategy formulated according to the optimal solution, the effective utilization of energy storage equipment and the stable operation of the power grid are realized in the power system.

[0098] The present invention provides an energy storage capacity optimization configuration method based on a multi-objective particle swarm optimization algorithm, builds an accurate mathematical model, sets reasonable constraints, and comprehensively considers the three objectives of operating cost, environmental protection cost and peak-shaving effectiveness for optimization scheduling. The optimal solution of the energy storage multi-objective optimization scheduling model obtained by the multi-objective particle swarm optimization algorithm realizes the stability of energy storage-assisted peak-shaving, which not only improves the economic and environmental benefits, but also enhances the stability and reliability of the power system and ensures the quality of power supply.

[0099] Specifically, the constraints include the following:

[0100] (1) System power balance constraints:

[0101]

[0102] In the formula, is the actual output of the i-th thermal power unit at the t-th time on the d-th day; is the actual output of wind power at time t on day d; is the peak-shaving discharge power of the energy storage system at time t on day d; P is the peak-shaving charging power of the energy storage system at time t on day d; load,d,i is the system load at time t on day d; u is the display value of the system's insufficient peak load regulation at time t on day d; l,d,i is the display value of the system's insufficient peak load regulation at time t on day d; P lock,d,i It is the peak load shortage at the tth moment on the dth day.

[0103] (2) Upper and lower limits of energy storage system output:

[0104] Using 0-1 variables to constrain the upper and lower limits of the energy storage system's output can ensure that the energy storage can be charged and discharged between 0 and maximum power without simultaneous charging and discharging.

[0105] (3) Multi-period energy coupling constraints of energy storage systems:

[0106]

[0107] In the formula, E ess,d,t is the amount of energy stored at the tth moment on the dth day; E ess,d,t-1 is the amount of energy stored at time t-1 on day d; Charging efficiency for energy storage systems; is the peak-shaving charging power of the energy storage system at time t on day d; is the peak-shaving discharge power of the energy storage system at time t on day d; is the discharge efficiency of the energy storage system.

[0108] (4) Energy storage system charge state constraints:

[0109] The state of charge should be no less than the minimum state of charge and less than or equal to the maximum state of charge.

[0110] (5) Thermal power unit ramp rate constraints:

[0111] The ramp rate constraint refers to the maximum output that the unit can increase or decrease within a unit time scale. The increase in the unit's output per unit time scale is called the ramp rate, and vice versa.

[0112] (6) Upper and lower limits of thermal power unit output:

[0113] The output of the unit must be greater than or equal to its minimum allowable output and less than or equal to its maximum allowable output.

[0114] (7) Wind power and photovoltaic output constraints, and daily wind abandonment constraints:

[0115] Wind power system output P WT,t Less than or equal to the maximum output; photovoltaic system output Less than or equal to the maximum output.

[0116] (8) System daily abandoned air volume constraints:

[0117] In order to ensure efficient consumption of wind power, daily wind abandonment constraints are introduced to ensure that the system's wind abandonment volume is within a certain range within a day.

[0118] (9) Probabilistic reliability constraints:

[0119] The probability constraint of insufficient peak load regulation of the system is that the probability of insufficient peak load regulation of the system within a year is less than the set parameter K. wind,p The system wind abandonment probability constraint, the system wind abandonment probability within a year is less than the set parameter K l,p .

[0120] (10) Mean reliability constraint:

[0121] The constraint is the system's daily average peak-shaving shortage constraint. The system's average daily peak-shaving shortage in a year is less than the set parameter K. l,m The constraint is the system daily wind abandonment constraint. The system’s average daily wind abandonment in a year is less than the set parameter K. w,m .

[0122] Specifically, the objective function of operating costs includes the objective functions of power generation costs, energy storage system investment and operation and maintenance costs, wind curtailment penalty costs, insufficient peak load penalty costs, and peak load economic benefits. The details are as follows:

[0123] The cost of electricity generation is calculated as follows:

[0124]

[0125] In the formula, C t represents the cost of power generation; a i represents the quadratic coefficient of the power generation cost function of thermal power unit i; b i represents the coefficient of the first-order term of the power generation cost function of thermal power unit i; ci represents the constant term coefficient of the power generation cost function of thermal power unit i; P t,d,t,i represents the output of thermal power unit i at the tth period on the dth day; u t,d,t,i represents the state of thermal power unit i at the tth period on the dth day, u t,d,t,i =1 means power on, u t,d,t,i =0 means shutdown; N G is the total number of thermal power units; D is the total number of days; T is the total number of time periods;

[0126] The calculation of energy storage system investment and operation and maintenance costs is as follows:

[0127]

[0128] In the formula, C ess is the investment and operation cost of the energy storage system; r ess is the time value; ess is the service life of the energy storage system; ess is the operation and maintenance cost ratio coefficient; C Pess is the investment cost per unit power; P ess The power of the energy storage system;

[0129] The calculation of wind curtailment penalty cost is as follows:

[0130]

[0131] In the formula, is the maximum active power output of wind power; P wind,d,t is the actual output of the wind turbine at the tth moment on the dth day; C wind Penalty cost for wind curtailment; C w is the penalty cost per unit wind abandonment; D is the total number of days; T is the total number of time periods;

[0132] The calculation of the penalty cost for insufficient peak load is as follows:

[0133]

[0134] In the formula, u l,d,t is the variable of the system's insufficient peak load regulation at the tth moment on the dth day; P lock,d,t is the available peak load shortage at time t on day d; C l C is the penalty cost for insufficient peak load regulation; lack is the penalty cost for insufficient peak load; D is the total number of days; T is the total number of time periods;

[0135] The calculation of energy storage peak load regulation benefits is as follows:

[0136]

[0137] In the formula, Rpeak is the peak load regulation income of energy storage; C d The electricity price of the power grid during peak hours; C is the discharge power of the energy storage system during the peak period of electricity consumption at the tth period on the dth day; c The electricity price of the power grid during the off-peak period; is the discharge power of the energy storage system during the off-peak period of electricity consumption at the tth period on the dth day; η d and η c are the discharge efficiencies during peak and off-peak hours, respectively; D is the total number of days; T is the total number of time periods;

[0138] Establish an objective function with operating cost as the target, and calculate it as follows

[0139] C p =C wind +C lack +C ess -R peak

[0140] Specifically, the objective function of environmental cost is to minimize environmental cost, and the specific analysis is as follows:

[0141] The environmental cost mainly considers the emission treatment cost of CO2, SO2 and NOX of the unit. Since PV and WT are clean energy (PV is photovoltaic power generation, WT is wind power generation), no polluting gas will be generated during operation, so the environmental cost of PV and WT is not considered. The objective function is to minimize the environmental cost, and its expression is:

[0142]

[0143] Where Ce is the environmental cost; K is the total number of pollutant emission types (including CO2, SO2 and NOX); G j is the unit cost of treating the jth pollutant, $ / kg; H ij Output under different electricity production modes; P i,t The emission coefficient of the jth pollutant emitted when the electricity is generated, g / (kW·h).

[0144] Specifically, the objective function of peak load effectiveness is calculated using net load variance, as follows:

[0145] Net load variance is an important indicator to measure the degree of net load fluctuation in the power system. It reflects the fluctuation of net load within a certain period of time. The calculation formula of net load variance usually involves statistical analysis of net load values ​​at a series of time points. Specifically, net load variance can be calculated by the following steps:

[0146]

[0147] In the formula, σ 2 is the net load variance; represents the average value of all net load values; x n Indicates the net load value at the nth time point;

[0148] The net load fluctuation is determined by calculating the average of the square of the difference between the net load value at each time point and the net load average value. The larger the variance, the greater the volatility of the net load.

[0149] The embodiment of the present application further provides an energy storage capacity optimization configuration system based on a multi-objective particle swarm optimization algorithm, which is applied to the aforementioned energy storage capacity optimization configuration method based on a multi-objective particle swarm optimization algorithm, including:

[0150] Parameter variable acquisition unit, used to obtain performance parameters and state variables of various energy sources in the power system and build various mathematical models of power generation and energy storage;

[0151] A model building unit, used to set constraints of the power system according to the mathematical model, and build an energy storage multi-objective optimization scheduling model based on the constraints;

[0152] The configuration strategy generation unit is used to use the multi-objective particle swarm algorithm to obtain the optimal solution of the energy storage multi-objective optimization scheduling model, and obtain the energy storage peak load configuration strategy based on the optimal solution.

[0153] The functions of each unit of the energy storage capacity optimization configuration system based on a multi-objective particle swarm optimization algorithm in this embodiment are the same as the explanation of the embodiment of the energy storage capacity optimization configuration method based on a multi-objective particle swarm optimization algorithm and have the same technical effects, and will not be repeated here.

[0154] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0155] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0156] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for optimizing energy storage capacity based on a multi-objective particle swarm optimization algorithm, characterized in that: include: Obtain the performance parameters and state variables of various energy sources in the power system, and construct various mathematical models of power generation and energy storage; Setting constraints of the power system according to the mathematical model, and constructing a multi-objective optimization scheduling model of energy storage including operation cost, environmental protection cost and peak load effectiveness based on the constraints; The multi-objective particle swarm optimization algorithm is used to obtain the optimal solution of the energy storage multi-objective optimization scheduling model, and the energy storage peak-shaving configuration strategy is obtained based on the optimal solution.

2. The energy storage capacity optimization configuration method based on multi-objective particle swarm optimization algorithm according to claim 1 is characterized in that: The mathematical models for constructing various power generation and energy storage include wind power generation model, wind power generation model and energy storage battery model; Wind power generation model: Where P WT , v are the actual output power and actual wind speed of the corresponding wind turbine respectively; P n 、v n and are the rated output power and rated wind speed of the wind turbine respectively; v ci and v co are the cut-in and cut-out wind speeds, respectively; Photovoltaic power generation model: Where P PV is the output power of photovoltaic cells; G n is the spectral irradiance; T c is the actual solar cell temperature; T r is the reference solar cell temperature; P n is the solar peak power; λ is the temperature condition coefficient; Energy storage battery model: Where P ch (t), P dch (t) are the charge and discharge amounts of the battery at time t; ξ, η sb are the battery self-discharge and charge-discharge conversion rate E (t) P is the battery capacity at time t sb is the rated capacity of the battery; SOC (t) is the state of charge of the battery at time t.

3. The energy storage capacity optimization configuration method based on multi-objective particle swarm optimization algorithm according to claim 2 is characterized in that: The constraints of the power system set according to the mathematical model include system power balance constraints, upper and lower limit constraints on energy storage system output, multi-period energy coupling constraints on energy storage system, charge state constraints on energy storage system, climbing rate constraints on thermal power units, upper and lower limit constraints on thermal power unit output, wind power and photovoltaic output constraints, daily wind abandonment constraints, probabilistic reliability constraints and mean reliability constraints.

4. The energy storage capacity optimization configuration method based on a multi-objective particle swarm optimization algorithm according to claim 3 is characterized in that: The energy storage multi-objective optimization scheduling model based on the constraint conditions including operation cost, environmental protection cost and peak load effectiveness includes: Based on the constraints, objective functions of operating cost, environmental cost and peak-shaving effectiveness are constructed, and a multi-objective optimization scheduling model is obtained according to the objective functions of operating cost, environmental cost and peak-shaving effectiveness.

5. The energy storage capacity optimization configuration method based on multi-objective particle swarm optimization algorithm according to claim 4 is characterized in that: The objective function of the operating cost is as follows: C p =C p +C wind +C lack +C ess -R peak In the formula, C p is the operating cost; C t is the power generation cost; C ess Invest in the operation and maintenance costs of the energy storage system; C wind Penalty cost for wind curtailment; C lack Penalty cost for insufficient peak load regulation; R peak For peak load-shaving economic benefits.

6. The energy storage capacity optimization configuration method based on a multi-objective particle swarm optimization algorithm according to claim 4 is characterized in that: The objective function of the environmental cost is as follows: In the formula, C e is the environmental cost; T is the total number of time periods; K is the total number of pollutant emission types; N is the total number of different electricity production methods; G j is the unit cost of treating the jth pollutant; H ij is the emission coefficient of the jth pollutant under the i-th electricity production mode; P i，t is the output power of the i-th electricity production mode in time period t.

7. The energy storage capacity optimization configuration method based on multi-objective particle swarm optimization algorithm according to claim 4 is characterized in that: The objective function of the peak load effectiveness is as follows: In the formula, σ 2 is the net load variance; L is the average value of all net load values; n is the total number of time points; t is the sequence number of the time point; x n Indicates the net load value at the nth time point.

8. The energy storage capacity optimization configuration method based on multi-objective particle swarm optimization algorithm according to claim 4 is characterized in that: The method of using a multi-objective particle swarm optimization algorithm to obtain the optimal solution of the energy storage multi-objective optimization scheduling model and obtaining the energy storage peak load configuration strategy according to the optimal solution includes: The initial particle swarm is generated by using chaotic mapping, and the particle swarm is updated and iterated in combination with the constraints. In each iteration, the fitness value of the particle is evaluated; the fitness value is confirmed by the energy storage multi-objective optimization scheduling model obtained by operating cost, environmental cost and peak load effectiveness; By evaluating the fitness value of particles in each generation of iteration, the individual optimal solution and the global optimal solution are updated, and when the preset iteration termination condition is reached, the final optimization scheduling result is output; The optimal capacity configuration strategy of the energy storage system is obtained according to the optimization scheduling result.

9. A system for optimizing energy storage capacity based on a multi-objective particle swarm optimization algorithm, characterized in that: A method for optimizing energy storage capacity configuration based on a multi-objective particle swarm optimization algorithm as described in any one of claims 1 to 8, comprising: Parameter variable acquisition unit, used to obtain the performance parameters and state variables of various energy sources in the power system and build various mathematical models of power generation and energy storage; A model building unit, used to set constraints of the power system according to the mathematical model, and build an energy storage multi-objective optimization scheduling model based on the constraints; The configuration strategy generation unit is used to use the multi-objective particle swarm algorithm to obtain the optimal solution of the energy storage multi-objective optimization scheduling model, and obtain the energy storage peak load configuration strategy based on the optimal solution.

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