Optimal planning and selection of electrical systems for offshore wind farms considering the N+ principle

Abstract

The present invention relates to an offshore wind farm electrical system planning and selection optimization method considering the N+ principle, comprising the following steps: 1) constructing a double-layer optimization model for offshore wind farm electrical equipment selection based on the N+ principle; 2) according to the acquired initial data of the offshore wind farm, based on the constructed double-sided optimization model, using the chaotic butterfly algorithm and the enumeration method to perform game optimization on the electrical system selection, and obtaining alternative planning schemes for planning and selection; 3) comprehensively evaluating the alternative planning schemes for planning and selection, and obtaining a better planning and selection scheme. Compared with the prior art, the present invention has the advantages of reducing the initial investment cost of the electrical system and helping to achieve full parity of offshore wind power.

Description

Technical Field

[0001] The present invention relates to the field of planning and optimization of offshore wind farm electrical systems, and in particular to a method for optimizing the selection of submarine cables and offshore booster stations for offshore wind farm electrical systems taking into account the N+ principle. Background Art

[0002] It is a general trend to accelerate the construction of a new power system with new energy as the main body and develop green and low-carbon energy. Offshore wind power will become a new growth pole for green power supply. From the current development of global offshore wind power, offshore wind power development shows a trend of deep sea and large scale, and the investment in electrical systems is constantly increasing. How to "reduce costs, reduce subsidies and even eliminate subsidies" is the key to the sustainable development of offshore wind power. Therefore, it is necessary to further optimize and study the electrical system planning.

[0003] The electrical system has a large number of submarine cables and electrical equipment, and the investment in the electrical system accounts for about 30% of the total investment in the entire wind farm. The planning and optimization of the electrical system has been one of the research hotspots of offshore wind farms in recent years. G.Tan et al. considered the location and number of offshore substations in "Research on primary frequency regulation of wind turbine based on new nonlinear droop control" and conducted a comprehensive evaluation of the investment cost and reliability of different electrical network structures. Syed Hamza Hasan Kazmi et al. used a transformer design based on dynamic thermal setting in "Cost optimized dynamic design of offshore windfarm transformers with reliability and contingency considerations" to significantly reduce the size of offshore transformers without compromising operational reliability, thus achieving economic optimization. Fu Yang et al. considered the electromagnetic environmental constraints of the electrical system in "Game optimization of the topology of the collector network of a large offshore wind farm considering electromagnetic environmental constraints", established an optimization model combining environmental, economic and reliability, and solved the problem of topological structure planning of the collector system under environmental constraints at two levels, effectively reducing the range of magnetic disturbance radiation of the wind farm. S.Tao et al. constructed a two-layer multi-objective model in "Joint Optimization of Wind Turbine Micrositing and Cabling in an Offshore Wind Farm" to realize the configuration of wind turbines in offshore wind farms while optimizing the topology of the power collection system. Wang L et al. proposed coupled random fork tree coding in "An Integration Optimization Method for Power Collection Systems of Offshore Wind Farms", which realized the coupled coding of substation location, topological wiring, and cable cross-section for the first time, providing a basis for the integrated design of the power collection system. Yuan Yue et al. converted the uncertainty planning model of the power transmission system into their respective deterministic equivalent classes in "Flexible Planning of Transmission Networks with Large Wind Farms Based on Multi-Scenario Probability", and then used traditional methods to establish their equivalent deterministic planning models, thereby avoiding the establishment of complex power grid planning models and reducing the difficulty of modeling and solving.

[0004] In the above studies, in order to meet the reliability requirements, there are clear redundancy levels (N-1, N-2) when selecting the electrical system of offshore wind farms. However, offshore wind power has large volatility and the clustering effect of wind farms will reduce the maximum power output of wind farms. According to statistics, the annual average output power of wind farms is about 30%-40% of the installed capacity. The existing electrical system selection method will cause offshore electrical equipment to operate in a low-load state for a long time, increase the design redundancy of the transmission system, and cause great waste in the construction, investment and operation of offshore electrical systems. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide an offshore wind farm electrical system planning and selection optimization method considering the N+ principle.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for planning and optimizing the electrical system of an offshore wind farm considering the N+ principle, the method comprising the following steps:

[0008] 1) Construct a two-layer optimization model for the selection of electrical equipment for offshore wind farms based on the N+ principle;

[0009] 2) According to the initial data of the offshore wind farm, based on the constructed bilateral optimization model, the chaotic butterfly algorithm and enumeration method are used to optimize the selection of the electrical system, and the alternative planning schemes for planning and selection are obtained;

[0010] 3) Comprehensively evaluate the alternative planning and selection schemes to obtain the best planning and selection scheme.

[0011] The outer model of the two-layer optimization model is used to obtain the optimal value of the net present value of the electrical system of the offshore wind farm, optimize the selection of submarine cables and booster stations, and provide parameter input for the inner model. The outer model aims to maximize the net present value, and its expression is:

[0012] maxΔNPV=ΔC I +(ΔC o +ΔC qi )×P V.sum

[0013] i Hc.max+ ≤K Hc I H.sfc.o

[0014]

[0015] S H ≥S H.min

[0016] |ΔU H |≤|ΔU H.max |

[0017]

[0018] Among them, ΔNPV is the net present value benefit, ΔC I is the difference in initial investment cost of the electrical system, ΔC O is the difference in operating losses of the electrical system, ΔC qi is the wind abandonment loss caused by N+ redundancy, P V.sum is the annual investment cost present value and the discount factor, I Hc.max+ To consider the maximum continuous load current flowing through the N+ redundant high-voltage submarine cable, K Hc is the overall correction factor for the long-term allowable current carrying capacity of the high-voltage submarine cable, I Hc.max is the long-term current carrying capacity of the high-voltage submarine cable, P wt,N+ is the upper limit of wind turbine output to be optimized considering N+ redundancy, sub is the set of wind turbine numbers in the substation, U H,rate is the rated voltage of the transmission system, S H is the cross section of the selected medium voltage submarine cable, S H.min ΔU is the minimum cross-section of the medium voltage submarine cable that meets the short-circuit thermal stability standard. H is the voltage drop of medium voltage submarine cable, ΔU H.max is the maximum voltage drop allowed for medium voltage submarine cables, P Tsum is the capacity of the selected offshore substation.

[0019] The difference in initial investment cost of the electrical system ΔC I The expression is:

[0020] ΔC I =∑(ΔC M.cab +ΔC inst )+ΔC H.cab +ΔC Sub

[0021] Where, ΔC M.cab , ΔC inst are the difference in operating loss and laying cost of medium voltage submarine cables, ΔC H.cab is the difference in high-voltage submarine cable operation loss, ΔC sub Investment and construction cost of offshore booster station

[0022] The difference in electrical system operating losses ΔC O The expression is:

[0023] ΔC O =ΔC H.cab +ΔC T

[0024] ΔC H.cab =c[(I u 2 R u -I u+ 2 R u+ )t H

[0025]

[0026] Where, ΔC H.cab , ΔC T are the difference in operating losses between the high-voltage submarine cable and the transformer, c is the on-grid price of offshore wind power, I u ,I u+ are the RMS current through the high-voltage submarine cable considering N-1 redundancy and N+ redundancy, R u , R u+ Considering the resistance value of N-1 redundant high-voltage submarine cable and the resistance value of N+ redundant high-voltage submarine cable, t H is the total running time, Z T,base is the initial substation operation loss, P T,base is the initial substation capacity, P T,+ is the substation capacity under the N+ principle, and e is the scaling index.

[0027] The wind curtailment loss ΔC qi Wind energy resources lost due to wind turbine output limitation caused by transmission limitation of electrical equipment C qi1 And considering the loss caused by the reduction of wind power transmission capacity due to the decrease in redundancy level when some offshore substations or high-voltage submarine cables of the wind farm fail, C qi2 Composition, then:

[0028] C qi =C qi1 +C qi2

[0029]

[0030]

[0031] Where T is the running time, P wt,N+ To consider the upper limit of the fan output to be optimized with N+ redundancy, P wt is the output power of the wind turbine at the corresponding wind speed v, t Tht is the downtime due to failure, λ is the failure simultaneity coefficient, which refers to the possibility of failure of high-voltage submarine cable or transformer, P sub,N+ The substation capacity considering N+ redundancy.

[0032] The output power P of the fan wt The expression is:

[0033]

[0034]

[0035] Among them, P R is the rated power of a single fan, P R+ is the upper limit of the fan output to be optimized considering N+ redundancy, and P R+ ≥P R,min , P R,min P R+ As the lower limit of the optimization variable, v is the instantaneous wind speed at the wind turbine installation site, V ci , V co are the fan cut-in and cut-out speeds, V r+ is the wind speed corresponding to the upper limit of the fan output under N+ redundancy. The binomial parameters A, B, and C are the parameters of the fan power characteristic curve, which are determined only by the fan's own design parameters.

[0036] The wind speed v satisfies the Weibull distribution, and its probability density function is:

[0037]

[0038] Among them, v is the wind speed, c and k are the quantitative parameters of the average wind speed and the shape parameters of the wind speed variation range, and they are estimated by maximum likelihood estimation.

[0039] The inner model of the two-layer optimization model takes the lowest topological cost of the power collection system as the objective function, and its expression is:

[0040]

[0041] i M.sfc.max+ ≤K M.sfc I M.sfc.o

[0042]

[0043] S M ≥S M.min

[0044] |ΔU M |≤|ΔU M.max |

[0045] Among them, C M.cab is the topological cost of the power collection system, ΔU M is the voltage drop of medium voltage submarine cable, ΔU M.max is the maximum voltage drop allowed for medium voltage submarine cables, IM.sfc.max+ To consider the maximum continuous load current flowing through the cth section of the submarine cable of the fth series feeder carried by the sth booster station of N+ redundancy, K M.sfc is the overall correction factor for the long-term allowable current carrying capacity of medium-voltage submarine cables, I M.sfc.o is the long-term current carrying capacity of the medium voltage submarine cable, S M is the cross section of the medium voltage submarine cable, S M.min S is the minimum cross-section of the medium voltage submarine cable SFC that meets the short-circuit thermal stability standard. sfc is the set of wind turbine numbers wt carried by this section of submarine cable, ΔU M.max is the maximum voltage drop allowed for medium voltage submarine cables, U M,rate is the collector system voltage.

[0046] In the step 2), the initial data of the offshore wind farm includes the coordinates of the offshore wind turbines, the coordinates of the onshore booster station, the submarine cable, the transformer data and the historical wind speed data.

[0047] In the step 3), the mixed strategy Nash equilibrium principle is used to conduct a comprehensive evaluation of the alternative planning schemes.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] The present invention quantifies the cost and wind abandonment decision under N+ redundancy, evaluates the possible wind abandonment losses under the N+ principle, seeks the optimal solution between the cost savings and wind abandonment losses under the N+ principle, realizes the optimization of offshore wind farm gas system planning and selection, effectively reduces the initial investment cost of the electrical system, and provides technical support for achieving full parity and sustainable development of offshore wind power. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is the optimization flow chart of the “N+” selection game based on the chaotic butterfly optimization algorithm.

[0051] Figure 2 This is the optimization flow chart of the “N+” selection game based on enumeration method.

[0052] Figure 3 A flow chart for comprehensive evaluation of planning options.

[0053] Figure 4 Schematic diagram of the locations of wind turbines in an offshore wind farm and onshore grid connection points in an embodiment.

[0054] Figure 5 It is the historical wind speed distribution data of the wind farm in the embodiment.

[0055] Figure 6 This is the optimization result of the “N+” selection game of the submarine cable of the power collection system based on the chaotic butterfly optimization algorithm.

[0056] Figure 7 This is the optimization result of the “N+” selection game for the collection system submarine cable based on the enumeration method.

[0057] Figure 8 This is a schematic diagram of the selection results of submarine cables for the power collection system based on the N-1 principle.

[0058] Fig. 9 This is a schematic diagram of submarine cable models and offshore substation capacity.

[0059] Fig.10 The following are cost structure diagrams for three planning options. DETAILED DESCRIPTION

[0060] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] The present invention provides an offshore wind farm electrical system planning and selection optimization method considering the N+ principle. The method first considers the initial investment cost of the electrical system, the operating loss and the wind abandonment loss caused by "N+", and establishes a relevant mathematical model to quantify the cost and wind abandonment risk under "N+" redundancy; then a planning scheme considering N+ redundancy is proposed, the output upper limit of the wind farm wind turbine is used as the optimization parameter, and a suitable algorithm is selected for iterative optimization; finally, the planning scheme is comprehensively evaluated based on the mixed strategy Nash equilibrium principle, and the optimal planning scheme is output.

[0062] The present invention specifically comprises the following steps:

[0063] 1) Read the initial data of the offshore wind farm, including the coordinates of the offshore wind turbines, the coordinates of the onshore booster station, the submarine cable, the transformer data and the historical wind speed data;

[0064] 2) Based on the initial data, the electrical system selection game optimization based on the chaotic butterfly algorithm and the electrical system selection game optimization method based on the enumeration method are respectively adopted. The upper limit of wind turbine output is used as the optimization parameter, and a double-layer optimization model is constructed with the outer layer taking the maximum net present value benefit as the goal and the inner layer taking the minimum investment cost of the collection system as the goal. The electrical system submarine cable and offshore booster station are selected and optimized. Among them, the outer model is mainly to obtain the optimal value of the net present value benefit of the offshore wind farm electrical system, and the overall optimization of the submarine cable and booster station selection, and also provides parameter input for the inner model; the inner optimization model mainly takes into account the changes in the optimal value of the collection system that may be caused by the different wind turbine output upper limits of the outer model as parameter input, takes the collection system topology cost as the objective function, and iterates based on the intelligent algorithm to find the optimal solution;

[0065] The expressions of the outer optimization model and constraints are as follows:

[0066] maxΔNPV=ΔC I +(ΔC o +Cqi )×P V.sum

[0067] i Hc.max+ ≤K Hc I H.sfc.o

[0068]

[0069] S H ≥S H.min

[0070] |ΔU H |≤|ΔU H.max |

[0071]

[0072] The expressions of the inner optimization model and constraints are as follows:

[0073]

[0074] i M.sfc.max+ ≤K M.sfc I M.sfc.o

[0075]

[0076] S M ≥S M.min

[0077] |ΔU M |≤|ΔU M.max |

[0078] Where ΔNPV is the net present value of benefits, ΔC I is the difference in initial investment cost of the electrical system, ΔC O is the difference in operating losses of the electrical system, ΔC qi is the wind curtailment loss caused by “N+” redundancy, P V.sum is the present value of annual investment cost and the discount factor; I Hc.max+ To consider the maximum continuous load current flowing through the N+ redundant high-voltage submarine cable, K Hc is the overall correction factor for the long-term allowable current carrying capacity of the high-voltage submarine cable, I Hc.max is the long-term current carrying capacity of the high-voltage submarine cable; P wt,N+ is the upper limit of wind turbine output to be optimized considering N+ redundancy, sub is the set of wind turbine numbers in the substation, U H,rate is the rated voltage of the transmission system; S H.min ΔU is the minimum cross-section allowed for medium voltage submarine cable SFC to meet the short-circuit thermal stability standard; H.maxThe maximum voltage drop allowed for high-voltage submarine cables; I M.sfc.max+ To consider the maximum continuous load current flowing through the cth section of the submarine cable of the fth series feeder carried by the sth booster station of N+ redundancy, K M.sfc is the overall correction factor for the long-term allowable current carrying capacity of medium-voltage submarine cables, I M.sfc.max is the long-term current carrying capacity of the medium voltage submarine cable, S sfc S is the number set of wind turbines carried by the submarine cable; M.min ΔU is the minimum cross-section allowed for medium voltage submarine cable SFC to meet the short-circuit thermal stability standard; M.max It is the maximum voltage drop allowed for medium voltage submarine cables.

[0079] 3) Comprehensively evaluate the above two planning schemes based on N+ redundancy and the N-1 redundancy planning scheme as a control scheme, and output the optimization results.

[0080] A detailed description is given below.

[0081] 1. The relevant mathematical model established by the present invention is as follows:

[0082] 1.1 Single wind turbine output power model considering “N+” redundancy

[0083]

[0084] V r+ Depends on P R+ , let P wt (v) = P R+ We can solve for V r+ , the expression is as follows:

[0085]

[0086] Where P R is the rated power of a single fan, P R+ Considering the upper limit of wind turbine output with “N+” redundancy and serving as the optimization variable in the electrical system selection game (P R+ ≥P R,min , P R,min P R+ as the lower limit of the optimization variable), v is the instantaneous wind speed at the wind turbine installation location, V ci 、V co are the fan cut-in and cut-out speeds, V r+ is the wind speed corresponding to the upper limit of the fan output under "N+" redundancy. The binomial parameters A, B, and C are the parameters of the fan power characteristic curve, which are determined only by the fan's own design parameters.

[0087] 1.2 Wind speed model

[0088] The most commonly used wind resource distribution statistics is the Weibull distribution, and the probability density function is as follows:

[0089]

[0090] In the formula, v is the wind speed, m / s; c is the quantitative parameter of the average wind speed; k is the shape parameter of the wind speed variation range. k and c can be estimated from the data through maximum likelihood estimation, then:

[0091]

[0092]

[0093] In the formula, v i and n are the i-th observed wind speed and the total number of wind speed data, respectively.

[0094] 1.3 Initial investment cost benefit.

[0095] The initial investment includes the investment in medium and high voltage submarine cables and the material and construction costs of the offshore substation, which are:

[0096]

[0097] In the formula: Δ represents the difference between the cost in the initial state and the cost after optimization by the "N+" principle. The Δ in the following formulas all abide by the above principles. ΔC M.cab , ΔC inst are the difference in operating loss and laying cost of medium voltage submarine cables, ΔC H.cab is the difference in high-voltage submarine cable operation loss, ΔC sub It is the investment and construction cost of the offshore substation.

[0098] 1.4 Loss and gain of electrical system operation

[0099] ΔC O =ΔC H.cab +ΔC T

[0100] ΔC H.cab =c[(I u 2 -I u+ 2 )R u t H

[0101]

[0102] Where: ΔC H.cab , ΔC T are the difference in operating losses between the high-voltage submarine cable and the transformer; c is the on-grid price of offshore wind power, I u ,Iu+ are the RMS current through the high-voltage submarine cable considering N-1 redundancy and N+ redundancy, R u is the resistance value of the high voltage submarine cable, t H is the total running time; Z T,base is the initial substation operation loss, P T,base is the initial substation capacity, P T,+ is the substation capacity under the N+ principle, and e is the scaling index, which is generally 0.8.

[0103] 1.5 Wind curtailment loss model

[0104] The wind curtailment loss is mainly composed of two parts. One part is the loss of wind energy resources C due to the limited output of wind turbines caused by the transmission limitation of electrical equipment. qi1 The other part is the loss caused by the reduction of wind power transmission capacity due to the decrease of redundancy level when some offshore substations or high-voltage submarine cables of wind farms fail. qi2 .

[0105] C qi =C qi1 +C qi2

[0106]

[0107]

[0108] Where: T is the running time, P wt,N+ To consider the upper limit of fan output for N+ redundancy, P wt is the output power of the fan at the corresponding wind speed; t Tht is the downtime due to failure, λ is the failure simultaneity coefficient, which refers to the possibility of failure of high-voltage submarine cable or transformer, sub is the set of wind turbine numbers of the substation, P sub,N+ The substation capacity considering N+ redundancy.

[0109] 2. The present invention constructs two selection game optimization methods

[0110] 2.1 "N+" selection game optimization based on chaotic butterfly algorithm (hereinafter referred to as "N+" redundancy 1), the flowchart is detailed in Figure 1 .

[0111] 1) Cluster and partition offshore wind farms based on the FCM clustering algorithm and determine the location of offshore substations;

[0112] 2) Optimize the optimal topology of the power collection system under different wind turbine output limits based on the parthenogenetic algorithm;

[0113] 3) Based on the chaotic butterfly optimization algorithm, the output upper limit of wind turbines in the wind farm zone is used as the optimization parameter, and the maximum net present value benefit is used as the objective function to optimize the selection of offshore electrical equipment.

[0114] 2.2 "N+" selection game optimization based on enumeration method (hereinafter referred to as "N+" redundancy 2), the flowchart is detailed in Figure 2 .

[0115] 1) Clustering and zoning of offshore wind farms and optimization of offshore booster station site selection based on the FCM clustering algorithm;

[0116] 2) Given the initial optimization range and step size, the topology of the power collection system is solved based on the partheno-genetic algorithm, and the submarine cable selection, booster station capacity and transmission system selection are optimized based on the given wind turbine output upper limit;

[0117] 3) Output the planning scheme. To improve the speed of the next iterative optimization, narrow the optimization parameter traversal range according to the results, output the results and feed them back to step 1 to start the next iterative optimization.

[0118] 3. Comprehensive evaluation process of electrical system planning scheme, the specific process is as follows Figure 3 As shown:

[0119] 1) Comprehensively consider various factors and determine the evaluation indicators of the planning scheme;

[0120] 2) The weight of each evaluation index is optimized based on the combined weight method of the analytic hierarchy process-CRITIC weight method, and the influence weight of each evaluation index guides the determination of the payment function;

[0121] The present invention uses the analytic hierarchy process-CRITIC weight method to optimize the weight allocation of n evaluation indicators in m game schemes, and combines and optimizes the subjective and objective weights through the multiplication synthesis method.

[0122] The subjective weights of the planning scheme evaluation indicators are determined by the analytic hierarchy process (AHP). By comparing the relative importance of the evaluation indicators pairwise, the judgment matrix A is obtained:

[0123]

[0124] Take the result of multiplying each row of the matrix to the power of n:

[0125]

[0126] The obtained weights are normalized and tested for consistency, and the subjective weights of each evaluation index are obtained:

[0127]

[0128] The objective weights of the planning scheme evaluation indicators are determined by the CRITIC weight method. In order to eliminate the impact of different dimensions on the evaluation results, each evaluation indicator is dimensionless.

[0129] The variability and conflict of indicators are expressed by standard deviation and correlation coefficient respectively:

[0130]

[0131] The amount of information contained in the i-th indicator:

[0132] C i =S i ×R i

[0133] The weight of the i-th indicator is expressed as:

[0134]

[0135] The subjective and objective weights (α i , β i ) to perform combined optimization and obtain the comprehensive weight W i :

[0136]

[0137] 3) Comprehensively evaluate the planning schemes based on the mixed strategy Nash equilibrium theory and output the optimal planning scheme.

[0138] In the scheme evaluation analysis, the planning scheme constitutes the strategy set Z of the game model, and the set of players H in the model is composed of evaluation indicators. Combined with the calculation results of the evaluation indicator weights, the influence intensity W between the constituent elements is considered. i , whose payment function is u(W i ), payment function combined A optimization model for electrical system planning schemes is constructed, and the mathematical expression is G={H,Z,U}.

[0139] The model is converted into a linear model for calculation.

[0140]

[0141] Where: y i represents any mixed strategy based on the effective strategy set Zi of alternative i; a ij is the coefficient.

[0142] Example

[0143] In this case, an offshore wind farm with a total capacity of 366MW is taken as an example to optimize the design of its electrical system submarine cable and electrical equipment selection. The wind farm has 56 6MW wind turbines. The distribution of wind turbines and the location of onshore substations are detailed in Figure 5 The voltage level of the medium-voltage submarine cable is 35kV, the voltage level of the high-voltage submarine cable is 220kV, the failure rate of the submarine cable is 0.03 times / (km·a); the repair time of the submarine cable is 1000 hours; the failure rate of the main transformer of the offshore substation is 0.01, the maintenance cost is about 800,000 yuan, the maintenance time is 200 hours, and the service life of the offshore wind farm is 20 years.

[0144] There are two electrical system planning schemes based on the N+ principle, and the electrical system planning scheme based on the N-1 principle is used as a reference scheme. The specific results of the optimized topological structure of the collection system, the offshore substation and the submarine cable selection of the transmission system of each scheme are as follows: Figure 4 , 5 , 6, 7 as shown:

[0145] The selection results of high-voltage submarine cables and booster stations in different offshore wind farm planning schemes are shown in Table 1, and the cost structure is shown in Fig.10 .

[0146] Table 1 Electrical system selection results under different planning schemes

[0147]

[0148] In order to compare and analyze the economic efficiency of different solutions, the electrical system optimization results are summarized as shown in Table 2:

[0149] Table 2 Electrical system costs of different planning schemes (10,000 yuan)

[0150]

[0151] In order to ensure the owner's requirements for economic and operational reliability when commissioning, and to achieve economic and environmental coordination by considering the long-term sustainability of the marine life environment, in line with the concept of sustainable development of offshore wind power, the comprehensive evaluation of the planning scheme needs to consider multiple influencing factors, mainly the economic feasibility of the investment and the possible risk of wind abandonment under the N+ principle, as well as the reliability of the electrical system and the possible impact on the offshore environment after the planning scheme is put into operation. The economic feasibility of the electrical system mainly considers the full life cycle cost, and the risk of wind abandonment is composed of the wind power abandoned under the "N+" principle. The reliability evaluation of the electrical system uses the sequential Monte Carlo simulation method to calculate the average annual output of the offshore wind farm. The environmental friendliness mainly considers the impact of electromagnetic radiation generated by offshore electrical equipment and submarine cable operation.

[21] .

[0152] The present invention uses the analytic hierarchy process-CRITIC weight method to optimize the weight allocation of n evaluation indicators in m planning schemes. The subjective and objective weights are combined and optimized by the multiplication synthesis method. The three candidate schemes are comprehensively evaluated based on the mixed strategy Nash equilibrium principle. The specific process is as follows Figure 3 As shown:

[0153] The strategy set for game analysis is the above three alternatives. The main influencing factors of the collector system topology planning relative to the influence weights of the alternatives constitute the payment function U, which serves as the quantitative analysis entry for game evaluation. The calculation results of the evaluation index weights are shown in Table 3:

[0154] Table 3 Evaluation index weight calculation results

[0155] Economical reliability Radiation range Wind curtailment risk 0.25 0.34 0.15 0.26

[0156] According to the calculation results in the above table, the values ​​of each payment function are as follows: the payment function value of the analysis system is 100, the sum of the payment function values ​​corresponding to the economic factor N1 is 25, and based on the principle of minimum economic cost, the payment function of each alternative plan is negatively correlated with the economic cost; the sum of the payment function values ​​corresponding to the electromagnetic environment factor N2 is 15, and based on the principle of minimum radiation range, the payment function of each plan is negatively correlated with the radiation range; the sum of the payment function values ​​corresponding to the seabed resource occupancy factor N3 is 20, and based on the principle of minimum seabed resource occupancy, the payment function of each plan is negatively correlated with the total length of the submarine cable; the sum of the payment function values ​​corresponding to the reliability factor N4 is 34, and based on the principle of maximum reliability, the payment function of each plan is positively correlated with the average annual available capacity.

[0157] In summary, the comprehensive evaluation results are shown in the following table:

[0158] Table 4 Comprehensive evaluation results

[0159] N+ Redundancy 1 N+ Redundancy 2 N-1 Redundancy Evaluation results 0.1919 3.484e-11 0.5807e-11

[0160] Among them, the fourth option with a higher probability is the best option, and the second option is the best option. From the above results, we can know that:

[0161] The electrical system planning scheme based on "N+" redundancy is highly economical. Among them, the N+ redundancy 1 scheme has the best economic efficiency, and the N+ redundancy 2 scheme has relatively high reliability.

Claims

1. A method for planning and optimizing the electrical system of an offshore wind farm considering the N+ principle, characterized in that: The method comprises the following steps: 1) Construct a two-layer optimization model for the selection of electrical equipment for offshore wind farms based on the N+ principle; 2) According to the initial data of offshore wind farms, based on the constructed two-layer optimization model, the chaotic butterfly algorithm and enumeration method are used to optimize the selection of electrical systems, and the alternative planning schemes for planning and selection are obtained; The outer model of the double-layer optimization model is mainly used to obtain the optimal value of the net present value of the electrical system of the offshore wind farm, to optimize the selection of submarine cables and booster stations, and to provide parameter input for the inner model. The inner optimization model of the double-layer optimization model mainly considers the changes in the optimal value of the collection system caused by the upper limits of wind turbine output in the outer model as parameter input, takes the topological cost of the collection system as the objective function, and iterates the optimization based on the intelligent algorithm. The expressions of the outer optimization model and constraints are as follows: maxΔNPV=ΔC I +(ΔC o +C qi )×P V.sum s.t.I Hc.max+ ≤K Hc I H.sfc.o S H ≥S H.min |ΔU H |≤|ΔU H.max | The expressions of the inner optimization model and constraints are as follows: s.t.I M.sfc.max+ ≤K M.sfc I M.sfc.o S M ≥S M.min |ΔU M |≤|ΔU M.max | Where ΔNPV is the net present value of benefits, ΔC I is the difference in initial investment cost of the electrical system, ΔC O is the difference in operating losses of the electrical system, ΔC qi is the wind curtailment loss caused by "N+" redundancy, P V.sum is the present value of annual investment cost and the discount factor; I Hc.max+ To consider the maximum continuous load current flowing through the N+ redundant high-voltage submarine cable, K Hc is the overall correction factor for the long-term allowable current carrying capacity of the high-voltage submarine cable, I Hc.max is the long-term current carrying capacity of the high-voltage submarine cable; P wt,N+ is the upper limit of wind turbine output to be optimized considering N+ redundancy, sub is the set of wind turbine numbers in the substation, U H,rate is the rated voltage of the transmission system; S H.min ΔU is the minimum cross-section allowed for medium voltage submarine cable SFC to meet the short-circuit thermal stability standard; H.max The maximum voltage drop allowed for high-voltage submarine cables; I M.sfc.max+ To consider the maximum continuous load current flowing through the cth section of the submarine cable of the fth series feeder carried by the sth booster station of N+ redundancy, K M.sfc is the overall correction factor for the long-term allowable current carrying capacity of medium-voltage submarine cables, I M.sfc.max is the long-term current carrying capacity of the medium voltage submarine cable, S sfc S is the number set of wind turbines carried by the submarine cable; M.min ΔU is the minimum cross-section allowed for medium voltage submarine cable SFC to meet the short-circuit thermal stability standard; M.max The maximum voltage drop allowed for medium voltage submarine cables; 3) Comprehensively evaluate the alternative planning and selection schemes to obtain the best planning and selection scheme.

2. According to claim 1, a method for optimizing the planning and selection of an offshore wind farm electrical system taking into account the N+ principle is characterized in that: The difference in initial investment cost of the electrical system ΔC I The expression is: ΔC I =Σ(ΔC M.cab +ΔC inst )+ΔC H.cab +ΔC Sub Where, ΔC M.cab , ΔC inst are the difference in operating loss and laying cost of medium voltage submarine cables, ΔC H.cab is the difference in high-voltage submarine cable operation loss, ΔC sub It is the investment and construction cost of the offshore substation.

3. The method for planning and optimizing the electrical system of an offshore wind farm considering the N+ principle according to claim 1, characterized in that: The difference in electrical system operating losses ΔC O The expression is: ΔC O =ΔC H.cab +ΔC T ΔC H.cab =c[(I u 2 R u -I u+ 2 R u+ )t H Where, ΔC H.cab , ΔC T are the difference in operating losses between the high-voltage submarine cable and the transformer, c is the on-grid price of offshore wind power, I u ,I u+ are the RMS current through the high-voltage submarine cable considering N-1 redundancy and N+ redundancy, R u , R u+ Considering the resistance value of N-1 redundant high-voltage submarine cable and the resistance value of N+ redundant high-voltage submarine cable, t H is the total running time, Z T,base is the initial substation operation loss, P T,base is the initial substation capacity, P T,+ is the substation capacity under the N+ principle, and e is the scaling index.

4. The method for planning and optimizing the electrical system of an offshore wind farm considering the N+ principle according to claim 1, characterized in that: The wind curtailment loss ΔC qi Wind energy resources lost due to wind turbine output limitation caused by transmission limitation of electrical equipment C qi1 And considering the loss caused by the reduction of wind power transmission capacity due to the decrease in redundancy level when some offshore substations or high-voltage submarine cables of the wind farm fail, C qi2 The composition is: C qi =C qi1 +C qi2 Where T is the running time, P wt,N+ To consider the upper limit of the fan output to be optimized with N+ redundancy, P wt is the output power of the wind turbine at the corresponding wind speed v, t Tht is the downtime due to failure, λ is the failure simultaneity coefficient, which refers to the possibility of failure of high-voltage submarine cable or transformer, P sub,N+ The substation capacity considering N+ redundancy.

5. The method for planning and optimizing the electrical system of an offshore wind farm considering the N+ principle according to claim 4, characterized in that: The output power P of the fan wt The expression is: Among them, P R is the rated power of a single fan, P R+ is the upper limit of the fan output to be optimized considering N+ redundancy, and P R+ ≥P R,min , P R,min P R+ As the lower limit of the optimization variable, v is the instantaneous wind speed at the wind turbine installation site, V ci 、V co are the fan cut-in and cut-out speeds, V r+ is the wind speed corresponding to the upper limit of the fan output under N+ redundancy. The binomial parameters A, B, and C are the parameters of the fan power characteristic curve, which are determined only by the fan's own design parameters.

6. The method for planning and optimizing the electrical system of an offshore wind farm considering the N+ principle according to claim 4, characterized in that: The wind speed v satisfies the Weibull distribution, and its probability density function is: Among them, v is the wind speed, c and k are the quantitative parameters of the average wind speed and the shape parameters of the wind speed variation range, respectively, and both are estimated by maximum likelihood estimation.

7. The method for planning and optimizing the electrical system of an offshore wind farm considering the N+ principle according to claim 1, characterized in that: In the step 2), the initial data of the offshore wind farm includes the coordinates of the offshore wind turbines, the coordinates of the onshore booster station, the submarine cable, the transformer data and the historical wind speed data.

8. The method for planning and optimizing the electrical system of an offshore wind farm considering the N+ principle according to claim 1, characterized in that: In the step 3), the mixed strategy Nash equilibrium principle is used to conduct a comprehensive evaluation of the alternative planning schemes.

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