Offshore wind power energy storage microgrid capacity optimization configuration method based on genetic algorithm

Abstract

An offshore wind power energy storage microgrid capacity optimization configuration method based on a genetic algorithm is characterized in that on the basis of analyzing and establishing a mathematical model of charge and discharge states of a storage battery, a super capacitor and a hybrid energy storage system, energy storage capacity optimization configuration analysis is carried out based on the genetic algorithm, and an offshore wind power resource curve data model is combined. And finally, obtaining the capacity optimization configuration of the offshore wind power energy storage microgrid. According to the configuration method, the management efficiency of managers is improved and the management cost is reduced. The method is high in expansibility, high in predictability and stable in performance, and a solution is provided for realizing scientific, stable and efficient capacity optimization configuration of the offshore wind power energy storage microgrid.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power energy storage microgrid capacity optimization, and in particular to an offshore wind power energy storage microgrid capacity optimization configuration method based on a genetic algorithm. Background Art

[0002] With the development of urbanization, the supply of traditional fossil energy such as coal, oil and natural gas has become increasingly tight. Compared with the proportion of traditional coal resources in the energy consumption structure in my country, the proportion of renewable energy consumption is relatively low. The development of clean energy and sustainable development energy in the future is the focus of global attention. Some renewable clean energy such as wind energy, solar energy, tidal energy, and ocean energy have attracted people's attention and great concern. Among them, wind energy has many advantages such as continuous supply, high availability, clean and pollution-free. It has now been unanimously recognized by the world and will become the fastest-growing power generation method in the future development of sustainable energy.

[0003] Among various forms of wind power generation, offshore wind power has the advantages of wide coverage, good resource endowment, and low transmission cost, so it has huge development potential. What is expected to continue to exceed expectations is the installed capacity of offshore wind power. Compared with onshore wind power generation, offshore wind power generation shares all the benefits of onshore power plants and has further advantages. It has greater development space and superiority.

[0004] However, due to the influence of natural conditions such as the marine environment and wind speed on offshore wind power generation, the power volatility increases, which in turn affects the stability and reliability of the offshore wind power generation system, which will directly limit the large-scale application of wind power generation. The output of hydropower and thermal power is much more stable than offshore wind power, which also brings a series of threats to the safety and reliability of the power system operation. The impact of the volatility of wind power generation on the safe operation of the power grid is positively correlated with the number of wind turbines connected to the grid, which to a certain extent also endangers the operation and dispatch of the power grid. When the scale of the wind turbines connected to the grid is large, the output power based on wind power generation will be affected by its own volatility, and the result is difficult to control. Therefore, the optimal configuration of the capacity of offshore wind power storage microgrid is a prerequisite for ensuring the safe operation of the power grid, and it can provide technical support for the dispatchers of the power grid in ensuring the real-time balance of electric energy. To solve the above problems, a method for optimizing the capacity of offshore wind power storage microgrid based on genetic algorithm is proposed. Summary of the invention

[0005] In view of the above technical problems, the present invention provides a method for optimizing the capacity of an offshore wind power energy storage microgrid based on a genetic algorithm, which is characterized by comprising the following steps:

[0006] S1: Establish mathematical models of the charging and discharging states of batteries, supercapacitors and hybrid energy storage systems and establish offshore wind power output curves;

[0007] S2: Analysis of optimal configuration of energy storage capacity based on genetic algorithm; including the calculation process of load power shortage rate, life cycle cost, optimization constraints and genetic algorithm process;

[0008] S3: Run the genetic algorithm in Matlab to obtain the optimal solution of the objective function and analyze the results;

[0009] The method for establishing the mathematical model of the charging and discharging state of the battery, supercapacitor and hybrid energy storage system and establishing the offshore wind power output curve in step S1 is as follows:

[0010] a) Establishment of the battery model: The rated voltage of each battery is Ub (V), the rated capacity is Cb (A·h), the battery pack consists of m batteries, and the total energy storage E (b MW h) is:

[0011] E b =m·C b ·U b / 10 6 ,

[0012] When the battery is discharged and the maximum depth of discharge is DOD, the minimum remaining energy storage Eb min (nMW·h) of the battery pack is:

[0013] E bmin =m·C b ·U b (1-DOD) / 10 6 ,

[0014] The battery is discharged at a time rate of C10, and the rated output power of the battery pack is:

[0015] P b =m·C b ·U b / 10 7 ;

[0016] b) Establishment of supercapacitor model: The terminal voltage of each supercapacitor is Uc, the capacitance is Cc, the supercapacitor group consists of n supercapacitors, and the total energy storage is:

[0017]

[0018] The operating voltage range of the supercapacitor is recorded as Uc min ~ Uc max, and the maximum energy storage capacity of the supercapacitor group is:

[0019]

[0020] The minimum values ​​are:

[0021]

[0022] The maximum value of the supercapacitor operating current is Ic max, and the maximum value of the supercapacitor group output power is:

[0023] P c max =n·U c max I c max / 10 6 ;

[0024] c) Establishment of charging and discharging state model of hybrid energy storage system: The wind power output power Pw and load power P1 in the system have great fluctuations, resulting in the energy storage capacity of the energy storage part changing with time. The unbalanced power is recorded as:

[0025] ΔP=P w (kt)·η c -P l (kt),

[0026] Where: ηc is the power conversion rate of the inverter in the wind power system,

[0027] ΔP>0, the energy storage device is in a charging state, and the storage capacity of the energy storage device is:

[0028]

[0029] ΔP<0, the energy storage device is in a discharging state, and the storage capacity of the energy storage device is:

[0030] E b (kt) = E b [(k-1)t]-P b (kt)·t·η bd

[0031] E c (kt) = E c [(k-1)t]-P c (kt)·t·η cd ,

[0032] Where: Eb(kt), Ec(kt) are the energy storage of the battery group and the supercapacitor group at time kt; Eb[(k-1)t], Ec[(k-1)t] are the energy storage of the battery group and the supercapacitor group at time (k-1)t; Pb(kt), Pc(kt) are the charging and discharging powers of the battery group and the supercapacitor group at time kt; ηbc, ηcc are the charging efficiencies of the battery group and the supercapacitor group; ηbd, ηcd are the discharging efficiencies of the battery group and the supercapacitor group;

[0033] d) Obtaining 24-hour curve data model of offshore wind resources;

[0034] Among them, the optimization configuration analysis method of energy storage capacity based on genetic algorithm in S2 is as follows:

[0035] a) The operating index of the wind power generation system is the load power shortage rate, which is the ratio of the load power shortage Elps to the total load demand El, that is:

[0036]

[0037] When the wind power meets the load demand, the load power shortage Elps = 0, and the energy storage device is charged; when the wind power is insufficient, the energy storage device discharges to supplement the power shortage, that is:

[0038] Elps(kt)=[P 1 (kt)-P w (kt)·η c ]·t;

[0039] b) The life cycle cost of equipment is the total cost of the equipment from planning, design, manufacturing, installation, use, maintenance and disposal, including purchase cost, ownership cost and disposal cost, namely:

[0040] LCC=C iv +C ow +C dc ,

[0041] Where: LCC is the life cycle cost; Civ is the purchase cost of the equipment; Cow is the ownership cost of the equipment; Cdc is the disposal cost of the equipment;

[0042] The target model for energy storage system optimization is the sum of the four major costs: purchase cost, operation cost, maintenance cost and processing cost, namely:

[0043] minC=C iv +C oc +C mc +C dc ,

[0044] Where: Civ is the purchase cost; Coc is the operating cost; Cmc is the maintenance cost; Cdc is the processing cost;

[0045] c) Based on the operating characteristics of the independent wind power system and the management strategy of the energy storage system, the constraints are established as follows:

[0046]

[0047] Where: m is the number of batteries in the energy storage system; n is the number of supercapacitors in the energy storage system; α is the proportion of the basic part in the unbalanced power; β is the proportion of the important load in the rated load;

[0048] d) Genetic algorithm is a highly parallel, random search and adaptive optimization method that has evolved from the evolutionary laws of the biological world. The process steps of genetic algorithm are as follows:

[0049] 1) Randomly generate N initial individuals to form the initial population;

[0050] 2) Calculate the fitness function value of each individual;

[0051] 3) Based on the fitness function value, a new generation of population is generated through selection, crossover and mutation;

[0052] 4) Determine whether the population stops meeting the conditions. If not, return to step 2. If so, proceed to the next step.

[0053] 5) Select the best individual from the contemporary group as the optimal solution to the optimization problem;

[0054] Among them, S3 runs the genetic algorithm in Matlab to obtain the optimal solution of the objective function and analyzes the results. The specific method is as follows:

[0055] Input typical energy storage system parameters to obtain some typical offshore wind farm parameters;

[0056] Run the genetic algorithm code in Matlab;

[0057] The power output balance and output curve are obtained, and the approximate optimal solution for capacity configuration is calculated.

[0058] Beneficial effects of the present invention:

[0059] The present invention discloses a method for optimizing the configuration of offshore wind power energy storage microgrid capacity based on genetic algorithm. It is based on the analysis and establishment of a mathematical model of the charging and discharging state of batteries, supercapacitors and hybrid energy storage systems, and is based on the optimization configuration analysis of energy storage capacity based on genetic algorithm, combined with the offshore wind resource curve data model, and finally obtains the optimal configuration of offshore wind power energy storage microgrid capacity. This configuration method improves the management efficiency of management personnel and reduces management costs. This patent has strong scalability, high predictability and stable performance, and proposes a solution for achieving scientific, stable and efficient optimal configuration of offshore wind power energy storage microgrid capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a structure diagram of an offshore microgrid system of an offshore wind power energy storage microgrid capacity optimization configuration method based on a genetic algorithm according to the present invention;

[0061] Figure 2 It is the genetic algorithm flow chart of the present invention;

[0062] Figure 3 This is the electric balance output diagram of the offshore wind power microgrid of the present invention;

[0063] Figure 4 This is an output curve diagram of the offshore wind power microgrid of the present invention;

[0064] Figure 5 This is a diagram of the optimization configuration results of the present invention. DETAILED DESCRIPTION

[0065] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0067] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0068] Example 1

[0069] like Figure 1 and Figure 2 As shown, a method for optimizing the configuration of offshore wind power storage microgrid capacity based on genetic algorithm is characterized by comprising the following steps:

[0070] Step 1: Establish a mathematical model of the charging and discharging state of batteries, supercapacitors and hybrid energy storage systems and establish an offshore wind power output curve;

[0071] Step 2: Optimal configuration analysis of energy storage capacity based on genetic algorithm; this includes the calculation process of load power shortage rate, life cycle cost, optimization constraints and genetic algorithm process;

[0072] Step 3: Run the genetic algorithm in Matlab to obtain the optimal solution of the objective function and analyze the results;

[0073] like Figure 2 and Figure 3 As shown, in step 1, the mathematical model of the charging and discharging state of the battery, supercapacitor and hybrid energy storage system is established and the method of establishing the offshore wind power output curve is as follows:

[0074] a) Establishment of the battery model: The rated voltage of each battery is Ub (V), and the rated capacity is Cb (A·h). Assuming that the battery pack consists of m batteries, the total energy storage E (b MW h) is:

[0075] E b =m·C b ·U b / 10 6 ,

[0076] When the battery is discharged, assuming that the maximum depth of discharge is DOD, the minimum remaining energy storage Ebmin (n MW·h) of the battery pack is:

[0077] E bmin =m·C b ·U b (1-DOD) / 10 6 ,

[0078] The battery is usually discharged at a time rate of C10, so the rated output power of the battery pack is:

[0079] P b =m·C b ·U b / 10 7 ,

[0080] b) Establishment of supercapacitor model: The terminal voltage of each supercapacitor is Uc, and the capacitance value is Cc. Assuming that the supercapacitor group consists of n supercapacitors, the total energy storage is:

[0081]

[0082] In actual situations, the operating voltage of a supercapacitor has a range, recorded as Uc min ~ Uc max, then the maximum energy storage of the supercapacitor group is:

[0083]

[0084] The minimum values ​​are:

[0085]

[0086] Assuming that the maximum value of the supercapacitor operating current is Ic max, the maximum value of the supercapacitor group output power can be expressed as:

[0087] P c max =n·U c max I c max / 10 6 ,

[0088] c) Establishment of charging and discharging state model of hybrid energy storage system: The wind power output power Pw and load power P1 in the system have great fluctuations, resulting in the storage capacity of the energy storage part changing with time. The unbalanced power is recorded as:

[0089] ΔP=P w (kt)·η c -P l (kt),

[0090] Where: ηc is the power conversion rate of the inverter in the wind power system;

[0091] When ΔP>0, the energy storage device is in a charging state, and the storage energy of the energy storage device can be expressed as:

[0092]

[0093] When ΔP<0, the energy storage device is in a discharging state, and the storage energy of the energy storage device can be expressed as:

[0094]

[0095] Where: Eb(kt), Ec(kt) are the energy storage of the battery group and the supercapacitor group at time kt; Eb[(k-1)t], Ec[(k-1)t] are the energy storage of the battery group and the supercapacitor group at time (k-1)t; Pb(kt), Pc(kt) are the charging and discharging powers of the battery group and the supercapacitor group at time kt; ηbc, ηcc are the charging efficiencies of the battery group and the supercapacitor group; ηbd, ηcd are the discharging efficiencies of the battery group and the supercapacitor group;

[0096] d) Obtaining 24-hour curve data model of offshore wind resources;

[0097] like Figure 2 and Figure 4 As shown, the optimization configuration analysis method of energy storage capacity based on genetic algorithm in step 2 is as follows:

[0098] a) The operating index of the wind power generation system is the load power shortage rate, which is the ratio of the load power shortage Elps to the total load demand El. That is:

[0099]

[0100] When the wind power meets the load demand (ΔP>0), the load power shortage Elps=0, and the energy storage device is charged. When the wind power is insufficient (ΔP<0), the energy storage device discharges to supplement the power shortage of the power supply, that is:

[0101] Elps(kt)=[P 1 (kt)-P w (kt)·η c ]·t,

[0102] b) The life cycle cost (LCC) of the equipment is the total cost of the equipment from planning, design, manufacturing, installation, use, maintenance and disposal, including purchase cost, ownership cost and disposal cost, namely:

[0103] LCC=C iv +C ow +C dc ,

[0104] In the formula: LCC is the life cycle cost; Civ is the purchase cost of the equipment; Cow is the cost of owning the equipment; Cdc is the disposal cost of the equipment (residual value and scrapping cost);

[0105] The target model for energy storage system optimization is the sum of the four major costs: purchase cost, operation cost, maintenance cost and processing cost, namely:

[0106] minC=C iv +C oc +C mc +C dc ,

[0107] Where: Civ is the purchase cost; Coc is the operating cost (including experiment, installation, loss, labor cost, etc.); Cmc is the maintenance cost (including maintenance cost before and after failure); Cdc is the disposal cost (including scrapping cost and residual value cost);

[0108] c) Considering the operating characteristics of the independent wind power system and the management strategy of the energy storage system, the constraints are established as follows:

[0109]

[0110] In the formula: m is the number of batteries in the energy storage system; n is the number of supercapacitors in the energy storage system; α is the proportion of the basic part in the unbalanced power; β is the proportion of the important load in the rated load; d) Genetic algorithm is a highly parallel, random search and adaptive optimization method evolved from the evolutionary laws of the biological world. It has the advantages of good robust performance and convenient use for solving discrete and complex nonlinear problems; the process steps of genetic algorithm are as follows:

[0111] 1) Randomly generate N initial individuals to form the initial population;

[0112] 2) Calculate the fitness function value of each individual;

[0113] 3) Based on the fitness function value, a new generation of population is generated through selection, crossover and mutation;

[0114] 4) Determine whether the population stops if the conditions are not met, return to step 2), if so, proceed to the next step;

[0115] 5) Select the best individual from the contemporary group as the optimal solution to the optimization problem;

[0116] like Figure 5 As shown, in step 3, the genetic algorithm is run in Matlab to obtain the optimal solution of the objective function and analyze the results. The specific method is as follows:

[0117] Input typical energy storage system parameters to obtain some typical offshore wind farm parameters;

[0118] Run the genetic algorithm code in Matlab;

[0119] The power output balance and output curve are obtained, and the approximate optimal solution for capacity configuration is calculated.

[0120] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. The various components mentioned in the present invention are common technologies in the existing field. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for optimizing the configuration of offshore wind power storage microgrid capacity based on genetic algorithm, characterized by , including the following steps: S1: Establish mathematical models of the charging and discharging states of batteries, supercapacitors and hybrid energy storage systems and establish offshore wind power output curves; S2: Analysis of optimal configuration of energy storage capacity based on genetic algorithm; including the calculation process of load power shortage rate, life cycle cost, optimization constraints and genetic algorithm process; S3: Run the genetic algorithm in Matlab to obtain the optimal solution of the objective function and analyze the results.

2. According to the method for optimizing the configuration of offshore wind power energy storage microgrid capacity based on genetic algorithm in claim 1, the method for establishing the mathematical model of the charging and discharging state of the battery, supercapacitor and hybrid energy storage system and establishing the offshore wind power output curve in step S1 is as follows: a) Establishment of the battery model: The rated voltage of each battery is Ub (V), the rated capacity is Cb (A·h), the battery pack consists of m batteries, and the total energy storage E (b MW h) is: E b =m·C b ·U b / 10 6 , When the battery is discharged and the maximum depth of discharge is DOD, the minimum remaining energy storage capacity of the battery pack Eb min (n MW·h) is: E bmin =m·C b ·YOU b ·(1-DOD) / 10 6 , The battery is discharged at a time rate of C10, and the rated output power of the battery pack is: P b =m·C b ·U b / 10 7 ; b) Establishment of supercapacitor model: The terminal voltage of each supercapacitor is Uc, the capacitance is Cc, the supercapacitor group consists of n supercapacitors, and the total energy storage is: The operating voltage range of the supercapacitor is recorded as Uc min ~ Uc max, and the maximum energy storage capacity of the supercapacitor group is: The minimum values ​​are: The maximum value of the supercapacitor operating current is Ic max, and the maximum value of the supercapacitor group output power is: P cmax =n·U cmax ·I cmax / 10 6 ; c) Establishment of charging and discharging state model of hybrid energy storage system: The wind power output power Pw and load power P1 in the system have great fluctuations, resulting in the energy storage capacity of the energy storage part changing with time. The unbalanced power is recorded as: ΔP=P w (noun)·η c -P l (noun) Where: ηc is the power conversion rate of the inverter in the wind power system, ΔP>0, the energy storage device is in a charging state, and the storage capacity of the energy storage device is: ΔP<0, the energy storage device is in a discharging state, and the storage capacity of the energy storage device is: As follows: Where: Eb(kt), Ec(kt) is the energy storage of the battery group and the supercapacitor group at time kt; Eb[(k-1)t], Ec[(k-1)t] is the energy storage of the battery group and the supercapacitor group at time (k-1)t; Pb(kt), Pc(kt) is the charging and discharging power of the battery group and the supercapacitor group at time kt; ηbc, ηcc is the charging efficiency of the battery group and the supercapacitor group; ηbd, ηcd is the discharging efficiency of the battery group and the supercapacitor group; d) Obtain the 24-hour curve data model of offshore wind resources.

3. A method for optimizing the configuration of offshore wind power energy storage microgrid capacity based on genetic algorithm according to claim 1, wherein: The analysis method for optimizing energy storage capacity configuration based on genetic algorithm in S2 is: a) The operating index of the wind power generation system is the load power shortage rate, which is the ratio of the load power shortage Elps to the total load demand El, that is: When the wind power meets the load demand, the load power shortage Elps = 0, and the energy storage device is charged; when the wind power is insufficient, the energy storage device discharges to supplement the power shortage of the power source, that is: Breath(kt)=[P1(kt)-P w (kt)·η c ]·t; b) The life cycle cost of equipment is the total cost of the equipment from planning, design, manufacturing, installation, use, maintenance and disposal, including purchase cost, ownership cost and disposal cost, namely: LCC=C iv +C ow +C dc , Where: LCC is the life cycle cost; Civ is the purchase cost of the equipment; Cow is the ownership cost of the equipment; Cdc is the disposal cost of the equipment; The target model for energy storage system optimization is the sum of the four major costs: purchase cost, operation cost, maintenance cost and processing cost, namely: minC=C iv +C oc +C mc +C dc , Where: Civ is the purchase cost; Coc is the operating cost; Cmc is the maintenance cost; Cdc is the processing cost; c) Based on the operating characteristics of the independent wind power system and the management strategy of the energy storage system, the constraints are established as follows: Where: m is the number of batteries in the energy storage system; n is the number of supercapacitors in the energy storage system; α is the proportion of the basic part in the unbalanced power; β is the proportion of important load in rated load; d) Genetic algorithm is a highly parallel, random search and adaptive optimization method that has evolved from the evolutionary laws of the biological world. The process steps of genetic algorithm are as follows: 1) Randomly generate N initial individuals to form the initial population; 2) Calculate the fitness function value of each individual; 3) Based on the fitness function value, a new generation of population is generated through selection, crossover and mutation; 4) Determine whether the population stops meeting the conditions. If not, return to step 2. If so, proceed to the next step. 5) Select the best individual from the contemporary group as the optimal solution to the optimization problem.

4. A method for optimizing the configuration of offshore wind power energy storage microgrid capacity based on genetic algorithm according to claim 1, wherein: S3 runs the genetic algorithm in Matlab to obtain the optimal solution of the objective function and analyzes the results. The specific method is as follows: Input typical energy storage system parameters to obtain some typical offshore wind farm parameters; Run the genetic algorithm code in Matlab; The power output balance and output curve are obtained, and the approximate optimal solution for capacity configuration is calculated.

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