An analysis and optimization method for a new energy DC collection system and its optimization equipment

Abstract

The present invention discloses an analysis and optimization method and device for a new energy DC collection system. The method includes: constructing a system cost function C including equipment price variables and equipment failure rate variables; constructing a system revenue function B including equipment failure rate variables; determining key equipment affecting the system and constructing an expression representing equipment price with equipment failure rate as a variable; calculating B / C values under different key equipment and different failure rates to obtain a data set (equipment failure rate, B / C value); fitting the data set by fitting to obtain a fitting function with equipment failure rates of different equipment as variables. The present invention establishes an economic evaluation model for a new energy DC collection system involving reliability, and provides a basis for the planning of future centralized new energy power stations and the optimization of related key equipment by analyzing the comprehensive influence of the failure rate and cost of key equipment on the system economy.

Description

Technical Field

[0001] The present invention belongs to the technical field of analysis and evaluation of new energy DC collection systems, and more specifically, relates to an analysis and optimization method for new energy DC collection systems and its optimization equipment. Background Art

[0002] There are mainly two types of collection systems for centralized new energy power stations, namely AC collection and DC collection. Compared with the AC collection system, the DC collection system has advantages such as low loss, no need for reactive power compensation devices, better voltage stability, and stronger transmission capacity, and is regarded as a better choice for improving the stability of centralized new energy power stations and enhancing power generation efficiency.

[0003] With the continuous development of DC technology, the DC collection system is expected to become the mainstream collection form for centralized new energy. At present, establishing an accurate evaluation model to analyze its reliability and economy, and deriving the conditions suitable for its formal application and promotion can find the direction and goal for its optimization, and has important reference value for the planning of future centralized new energy projects.

[0004] Currently, the reliability evaluation methods applied to new energy projects mainly rely on statistical methods to obtain the failure data of equipment and systems, which require a large number of actual projects for support and are not applicable to DC collection systems lacking actual projects. The economic evaluation methods rarely take reliability into account, or only use a statistical-based empirical coefficient to calculate the operation and maintenance costs, and at the same time do not consider the reduction of power generation benefits caused by failures, which is also not applicable to DC collection systems with relatively high failure rates of key equipment and lack of engineering operation data at present. Therefore, there is a need for an evaluation method that conforms to the current situation of DC collection systems to accurately evaluate their reliability and economy, and provide a basis for their planning, application, optimization, and promotion. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement requirements of the prior art, the present invention provides an analysis and optimization method for new energy DC collection systems and its optimization equipment, aiming to solve the technical problem that it is difficult to evaluate and analyze new energy DC collection systems due to the lack of engineering operation data and relatively high failure rates.

[0006] To achieve the above object, according to one aspect of the present invention, there is provided an analysis and optimization method for a new energy DC collection system, which includes:

[0007] Constructing a system cost function C, where the system cost function C includes equipment price variables and equipment failure rate variables;

[0008] Constructing a system revenue function B, where the system revenue function B includes equipment failure rate variables;

[0009] Identify the key equipment affecting the system, construct an expression representing the equipment price with the equipment failure rate as a variable, calculate the evaluation function B / C, and the evaluation function B / C is a function with the equipment failure rate as a variable;

[0010] Calculate the B / C values under different key equipment and different failure rates to obtain the data set (equipment failure rate, B / C value);

[0011] Fit the data set by fitting to obtain a fitting function with the equipment failure rates of different equipment as variables, and analyze and optimize the new energy DC collection system according to the fitting function.

[0012] Preferably, the system cost function C = α * C con + C op + C′, where C con is the total construction cost, C con(d) is the construction cost of the d-th type of equipment within the total construction cost. The construction cost includes the equipment price, D p is the number of types of equipment within the total construction cost, and α is a comprehensive coefficient; C op is the operation and maintenance cost, t is the year, T is the whole life cycle; r is the discount rate, C op_year is the mathematical expectation of the annual maintenance cost, K con(m) is the maintenance cost of the key equipment m, λ con(m) is the equipment failure rate of the key equipment m, N con(m) is the number of the key equipment m, and M is the type of key equipment;

[0013] Construct the system revenue function where q is the unit electricity price, P is the fault-free power generation, r is the discount rate, η rel is the system reliability coefficient with the equipment failure rate as a variable, η rel is equal to the ratio of the expected output power of the power station to the installed capacity of the power station;

[0014] The expression representing the equipment price with the equipment failure rate as a variable is C con(m) = F1(λ con(m) ), where λ con(m) and C con(m) are the equipment failure rate and equipment price of the key equipment m respectively.

[0015] Preferably, when constructing the system cost function, the total construction cost C con = C ini + S loan where C ini is the initial investment cost, S loanLet

[0016] be the loan amount, and the comprehensive coefficient α is obtained based on the loan amount and loan interest. std *t e *η f *τ t where std is the PV installed capacity, t e is the annual equivalent utilization hours, τ is the attenuation factor of the annual geometric ratio attenuation of the power generation capacity of the PV modules, and η f is the system power generation efficiency without faults. where i is the efficiency of the i-th link, and N ef is the number of efficiency links to be considered.

[0017] Preferably, the process of calculating the expected output power of the power station is as follows:

[0018] First, calculate the expected output power of the current equipment link based on the output power of the previous equipment link and the equipment failure rate in the current equipment link;

[0019] Then, use the expected output power of the current equipment link as the input power of the next equipment link, and calculate from the front end to the back end of the power generation in turn to obtain the expected output power of the entire system considering the equipment failure rate.

[0020] Preferably, the equipment price has a linear relationship with the equipment failure rate. Select the maximum failure rate and the corresponding price, the minimum failure rate and the corresponding price of the key equipment as the two coordinate points of the coordinate system, and solve the linear expression of the equipment price and the equipment failure rate.

[0021] Preferably, the key equipment uses its own failure rate as the maximum failure rate and the failure rate of the inverter in the AC collection system as the minimum failure rate.

[0022] Preferably, solve for the fitting function to be greater than 1 to obtain the failure rate range of different key equipment when the system needs to make a profit;

[0023] Or, calculate the B / C critical value for the collection system to make a profit, solve for the fitting function to be greater than the critical value, and obtain the failure rate range of different key equipment when the profitability of the DC collection system exceeds that of the AC collection system.

[0024] Preferably, the key equipment includes two categories: DC circuit breakers and DC step-up converters.

[0025] According to another aspect of the present invention, there is provided an analysis and optimization device for a new energy DC collection system, which includes:

[0026] A system cost function construction unit for constructing a system cost function C, where the system cost function C includes equipment price variables and equipment failure rate variables;

[0027] A system revenue function construction unit for constructing a system revenue function B, where the system revenue function B includes equipment failure rate variables;

[0028] An equipment price conversion unit for determining key equipment affecting the system and constructing an expression representing the equipment price with the equipment failure rate as a variable;

[0029] A data set collection unit for obtaining B / C values under different key equipment and different failure rates, resulting in a data set (equipment failure rate, B / C value);

[0030] A fitting function solving unit for fitting the data set by fitting to obtain a fitting function with the equipment failure rates of different equipment as variables.

[0031] Generally speaking, the present invention establishes an economic evaluation model for a new energy DC collection system involving reliability. By analyzing the comprehensive impact of the failure rates and costs of key equipment on the system economy, and by using the failure rates of key equipment to represent the costs of key equipment, the relationship between B / C and the failure rates of key equipment is obtained. By assigning various different specific values to the failure rates to obtain the corresponding B / C values, a data set of failure rates and corresponding B / C values is thus obtained, and then a specific fitting function is obtained by fitting the data set. This fitting function is the relationship between the system economy and the failure rates of each key equipment. Through the obtained fitting function, it provides a reference for the application and popularization of the DC collection system in centralized new energy projects, and provides a basis for the planning of future centralized new energy power stations and the optimization of related key equipment. Description of the Drawings

[0032] Figure 1 is the step flow chart of the new energy DC collection system analysis and optimization method in an embodiment of the present application;

[0033] Figure 2(a) is the architecture diagram of a parallel DC collection system in an embodiment of the present application;

[0034] Figure 2(b) is the architecture diagram of a cascaded DC collection system in an embodiment of the present application;

[0035] Figure 3 is the schematic diagram of the key influencing factors of the economy of the DC collection system in an embodiment of the present application;

[0036] Figure 4 The (a) in it is the diagram of the impact of the cost and failure rate of a DC converter on the economy of a parallel DC collection system in an embodiment of the present application; Figure 4Among them, (b) is the diagram showing the impact of the cost and failure rate of the DC converter in an embodiment of this application on the economy of the cascaded DC collection system;

[0037] Figure 5 Among them, (a) is the diagram showing the impact of the cost and failure rate of the DC breaker in an embodiment of this application on the economy of the parallel DC collection system; Figure 5 Among them, (b) is the diagram showing the impact of the cost and failure rate of the DC breaker in an embodiment of this application on the economy of the cascaded DC collection system;

[0038] Figure 6 is the diagram showing the comprehensive impact of the DC boost converter and the breaker on the economy of the parallel DC collection system in an embodiment of this application;

[0039] Figure 7 is the diagram showing the solution set range of the failure rates of the key equipment in the parallel DC collection system in an embodiment of this application;

[0040] Figure 8 is the diagram showing the comprehensive impact of the DC boost converter and the breaker on the economy of the cascaded DC collection system in an embodiment of this application;

[0041] Figure 9 is the diagram showing the solution set range of the failure rates of the key equipment in the cascaded DC collection system in an embodiment of this application. Detailed implementation manners

[0042] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0043] As Figure 1 shown is the step flow chart of the analysis and optimization method for the new energy DC collection system in an embodiment of this application. The method includes:

[0044] Step S100: Construct a system cost function C, which includes equipment price variables and equipment failure rate variables.

[0045] First of all, it is necessary to determine the system architecture of the new energy DC collection system.

[0046] The new energy DC collection system usually has two architectures, namely the parallel type and the cascaded type. As shown in Fig. 2(a) is the parallel architecture, and as shown in Fig. 2(b) is the cascaded architecture. The cost function of the system can be constructed according to the specific system architecture.

[0047] In one embodiment, the cost of the new energy DC collection system considers the total construction cost and operation and maintenance cost. Among them, the total construction cost is the cost required for each device when building the system. Since the upfront construction cost of a centralized new energy power station is relatively large, its funds need to be loan-financed. The total construction cost is actually composed of the initial investment cost C ini and the loan amount. In addition to the principal to be repaid for the loan, it also includes interest. The principal and interest are calculated as the loan cost C loan . Therefore, the system cost function can be expressed as C = α * C con + C op , where the comprehensive coefficient α is a coefficient calculated considering the loan situation, and α * C con = C ini + C loan . When there is no loan, α = 1, and when there is a loan, α > 1.

[0048] The calculation of each parameter in the cost function will be introduced below.

[0049] The cost C of the centralized new energy project is mainly composed of the initial investment cost, loan cost, and operation and maintenance cost, as shown in Equation (1):

[0050] C = C ini + C loan + C op (1)

[0051] In the formula, C ini is the initial investment cost, C loan is the loan cost, and C op is the operation and maintenance cost.

[0052] Since the upfront construction cost of the centralized new energy power station is relatively large, its funds need to be loan-financed. The total construction cost C con is actually composed of the initial investment cost and the loan amount, that is

[0053] C con = C ini + S loan (2)

[0054] In the formula, C ini is the initial investment cost, and S loan is the loan amount, and its expression is:

[0055] S loan = k loan · C con (3)

[0056] In the formula, k loan is the loan ratio. According to relevant engineering experience, the loan ratio of centralized new energy power stations is generally between 60% and 80%; Ccon is the total construction cost of the centralized new energy power station.

[0057] The total construction cost C con mainly includes the construction cost of the new energy array, the cost of the busbar trunking, the cost of the converter, the line cost, the circuit breaker cost, etc., that is

[0058]

[0059] In the formula, D p is the total number of projects; C con(d) is the cost of the d-th project in the construction cost;

[0060] After deducting the initial investment cost C ini from the total construction cost, the insufficient part is financed by means of loans. The loan cost C loan includes not only the principal S loan to be repaid, but also the interest. The loan interest is determined by the total construction cost, the loan ratio, the repayment method, the annual interest rate and the repayment period; the repayment period is not greater than the total life cycle period T of the project;

[0061] If the equal principal and interest repayment method is adopted, the calculation method of the loan cost C loan is shown in formula (5):

[0062]

[0063] In the formula, k loan is the loan ratio; C con is the total construction cost; t is the year; r is the discount rate; l is the annual interest rate; A is the repayment period. The loan period of the centralized new energy power station is generally 15 - 20 years, the loan interest rate is between 4.9% and 6.55%, and the discount rate is between 5% and 10%;

[0064] The operation cost C op mainly refers to the cost of equipment maintenance and replacement within the total life cycle T. Starting from the annual equivalent failure rate of each equipment link, the maintenance and replacement costs of specific equipment links are predicted, and the mathematical expectation of the maintenance cost for one year is

[0065]

[0066] In the formula, N con(m) is the total number of equipment in a certain link; λ con(m) is the failure rate of the equipment in this link; K con(m) is the maintenance cost of this type of equipment; M is the type of equipment.

[0067] The operation and maintenance cost during the whole cycle is

[0068]

[0069] Wherein, C op_year is the mathematical expectation of the maintenance cost for one year; t is the year; T is the whole life cycle; r is the discount rate.

[0070] In summary, combining formulas (1) to (7), the specific expression of the whole life cycle cost C of the centralized new energy project related to reliability is:

[0071]

[0072] In this formula,

[0073] Wherein, k is the loan ratio; t is the year; T is the whole life cycle; r is the discount rate; l is the annual interest rate; A is the repayment period; C con(d) is the cost of the d-th item in the construction cost; N con(m) is the total number of equipment in a certain link; λ con(m) is the failure rate of the equipment in this link; K con(m) is the maintenance cost of this type of equipment; M is the type of equipment. It can be seen from formula (8) that in this application, the cost C is expressed as related to the equipment failure rate λ con(m) and the equipment construction cost C con(d) is related, and the equipment construction cost C con(d) includes the equipment price, that is, the cost C varies with the two variables of the failure rate λ con(m) and the equipment construction cost C con(d) changes.

[0074] In an embodiment, the cost function may further include other costs C′ in addition to the construction cost and maintenance cost listed in the above embodiments, and no strict limitation is made here.

[0075] At the same time, in addition to constructing the cost function, it is also necessary to construct the revenue function, that is, this solution further includes:

[0076] Step S200: Construct the system revenue function B, and the system revenue function B includes the equipment failure rate variable.

[0077] The revenue B of the centralized new energy project is mainly composed of the revenue from the power generation connected to the grid and the government subsidy. However, nowadays, it is the general trend for centralized new energy to implement grid connection at parity prices. Therefore, in this embodiment, only the revenue obtained by the power station selling electricity at the benchmark electricity price is calculated. Of course, in other embodiments, other revenues B′ can also be included, and no strict limitation is made here.

[0078] In an embodiment, the system revenue function

[0079]

[0080] In the formula, t is the year; T is the full life cycle, q is the benchmark electricity price, P is the power generation without considering faults, and η rel is the reliability coefficient obtained based on the failure rate, which is the ratio of the expected output power of the power station to the installed capacity of the power station and is an index to measure the system reliability. The larger this value is, the higher the reliability of the collection system, and vice versa, the worse the reliability.

[0081] Power generation without considering faults

[0082] P = P std *t e *η f *τ t (10)

[0083] In the formula, P std is the installed capacity of the photovoltaic power generation, t e is the annual equivalent utilization hours; τ is the attenuation factor, representing that the power generation capacity of the photovoltaic module will decay geometrically year by year; η f is the power generation efficiency of the collection system without considering equipment failures.

[0084] Among them,

[0085]

[0086] In the formula, η i is the power generation efficiency of the i-th link, and N ef is the number of efficiency links to be considered.

[0087] Based on formulas (9) to (11), the system revenue function can be obtained

[0088]

[0089] It can be seen from formula (12) that the revenue function B is related to the reliability coefficient η rel and the reliability coefficient is related to the failure rate. Therefore, the revenue function B is also related to the failure rate, that is, the revenue function B is a function of the failure rate.

[0090] In an embodiment, the reliability coefficient η can be calculated in the following manner rel .

[0091] Let the input power of a certain equipment link be P eq_in ; the expected output power is P eq_out ; the equipment failure rate is λ ei , in times / year; the fault repair time is r ei , in hours. In one year, P can be equivalently represented by the probability model in Table 1 eq_out .

[0092] Table 1 Output Power Probability Model of Equipment Link

[0093]

[0094] The mathematical expectation of the output power is

[0095]

[0096] In the entire photovoltaic power station system, the expected output power of the previous equipment link is used as the input power of the next link, and it is calculated sequentially from the front end of power generation to the back end to obtain the expected output power of the entire system considering equipment failures.

[0097] η rel is the ratio of the expected output power P exp to the installed capacity P stp of the power station. It represents that within a certain period of time, assuming that all photovoltaic arrays in the power station are in the rated operating state, without considering losses, and only considering the impact of equipment failures on the power generation status of the power station.

[0098]

[0099] The execution order of the above steps S100 and S200 is not restricted.

[0100] In this application, it is also necessary to execute:[[]]

[0101] Step S300: Determine the key equipment affecting the system, construct an expression representing the equipment price with the equipment failure rate as a variable, calculate the evaluation function B / C, and the evaluation function B / C is a function with the equipment failure rate as a variable.

[0102] First, analyze the key equipment affecting the system.

[0103] Figure 3 is a schematic diagram of the key influencing factors for the economy of the DC collection system. The current economy of the DC collection system is worse than that of the AC collection system because its total construction cost, operation and maintenance cost are higher while the revenue is lower, and these three reasons are mainly closely related to the failure rate and price of two key equipment, namely the DC step-up converter and the DC circuit breaker. The initial investment cost and loan cost of the system are affected by the prices of the two key equipment, the power generation revenue is affected by the failure rate of the equipment, and the operation and maintenance cost is affected by both the price and the failure rate. Therefore, in this embodiment, the DC step-up converter and the DC circuit breaker are used as the key equipment, and the operation and maintenance costs of other non-key equipment are ignored.

[0104] Such as Figure 4 (a) in shows the impact of the cost and failure rate changes of the DC step-up converter on the economy of the system in a parallel DC collection system; such as Figure 4In (b), it shows the impact of the cost and failure rate changes of the DC boost converter on the system economy in a cascaded DC collection system. Among them, the plane with a B / C value equal to 1 is used as an auxiliary plane to judge whether the system is profitable. When the points on the surface are above this plane, the system is profitable. In Figure 4 In (b), there is also a B / C value plane for AC collection. When the points on the surface are above it, the profitability of the DC collection system exceeds that of the AC collection system.

[0105] When the failure rate and cost of the DC boost converter continue to decrease, the system gradually turns from loss to profit. When its failure rate and cost approach the levels of the corresponding equipment in the AC collection system, the profitability of the cascaded DC collection system will even exceed that of the AC collection system.

[0106] Figure 5 In (a), it shows the impact of the cost and failure rate changes of the DC circuit breaker on the economy of the DC collection system in a parallel DC collection system. Figure 5 In (b), it shows the impact of the cost and failure rate changes of the DC circuit breaker on the economy of the DC collection system in a cascaded DC collection system. Figure 5 In (a), the plane with a B / C value of 1 is used as an auxiliary plane to judge whether the system is profitable. Compared with the DC boost converter, the reduction of the failure rate and cost of the DC circuit breaker has a smaller effect on improving the system economy. Only by improving the DC circuit breaker, the cascaded DC collection system cannot even achieve profitability.

[0107] Through the above analysis, the DC circuit breaker and the DC boost converter can be regarded as key equipment.

[0108] After determining the key equipment, an empirical relationship between price and failure rate is constructed with the aim of expressing the price as a function related to the failure rate, that is, C con(m) = F1(λ con(m) ), where λ con(m) and C con(m) are the failure rate and price of the key equipment m respectively.

[0109] According to experience, as technology develops, the failure rate and price of equipment will both decline. The relationship between price and failure rate can be expressed as a linear relationship. Therefore, when constructing the relationship formula, the maximum failure rate (upper limit) of key equipment and the corresponding price, as well as the minimum failure rate (lower limit) and the corresponding price, can be selected as the two coordinate points of the coordinate system to solve the linear relationship formula. For example, a DC circuit breaker can use its own failure rate as the upper limit and the failure rate of the inverter in the AC collection system as the lower limit. A DC boost converter can use its own failure rate (0.3137 times / year) as the upper limit and the failure rate of the inverter in the AC collection system (0.3137 times / year) as the lower limit. The specific situation of equipment prices is shown in Table 2. In addition, since the trends of the failure rate and price of equipment are downward, the variables all change in a decreasing manner.

[0110] Table 2 - Range of change in failure rate and price of key equipment

[0111]

[0112]

[0113] Link the failure rate and price of the equipment and set them to change synchronously. At this time, the failure rate and price of the equipment show a linear relationship, and one of them can be expressed by the other, thus simplifying the two variables into one variable. Here, the price of the equipment is expressed by its failure rate, and the respective expressions are shown in Table 3.

[0114] Table 3 - Variable substitution situation of key equipment

[0115]

[0116] Step S400: Calculate the B / C values under different key equipment and different failure rates to obtain a data set (equipment failure rate, B / C value).

[0117] Combining formula (8), formula (12), and the above relationship between equipment price and equipment failure rate, the function of B / C can be obtained. Divide the total revenue over the entire life cycle by the total cost, and use the revenue / cost ratio (B / C value) to evaluate the economy of the project.

[0118] Step S500: Fit the data set by fitting to obtain a fitting function with the failure rate of different equipment as the variable, and analyze and optimize the new energy DC collection system according to the fitting function.

[0119] Taking the failure rates of the DC boost converter and the circuit breaker as variables, the change in the economy of the parallel-type DC collection system when the two change is as Figure 6 shown. For Figure 6The parallel-type DC collection system shown uses two planes with B / C values of 1 and 1.3505 to assist in judging the economy of the parallel-type DC collection system. Let the B / C value of the parallel-type DC collection system be Z P , and the curved surface is a set of numerical values obtained under the changes of the failure rates and prices of two key devices.

[0120] Use Matlab to find its fitting expression, as shown in Equation (13):

[0121]

[0122] In the formula, x1 and y represent the failure rates of the 500kW DC step-up converter and the DC circuit breaker respectively;

[0123] When Z P > 1, the parallel-type DC collection system makes a profit; when Z P > 1.3505, its profitability exceeds that of the AC collection system. Solving these two inequalities can obtain the range of the failure rates of the devices that meet the requirements, and then combining with the expressions in Table 1, the corresponding prices of the devices at this time can be obtained.

[0124] The solution range is as Figure 7 shown. The first region is the range of the failure rates of the key devices where the profitability of the parallel-type DC collection system exceeds that of the AC collection system; the second region and the first region are the ranges of the failure rates of the devices for which it makes a profit.

[0125] For Figure 8 the parallel-type DC collection system shown, different from the parallel-type DC collection system, let the B / C value of the cascade-type DC collection system be Z S , and use Matlab to find its fitting expression as shown in Equation (14):

[0126]

[0127] In the formula, x2 and y represent the failure rates of the 125kW DC step-up converter and the DC circuit breaker respectively;

[0128] Similarly, solve Z S > 1 and Z S > 1.3505 to obtain the ranges of the failure rates of the key devices for which the cascade-type DC collection system makes a profit and its profitability exceeds that of the AC collection system, as shown in Figure 9 .

[0129] Similar to the parallel-type system, the first region is the range of the failure rates of the key devices where the profitability of the cascade-type DC collection system exceeds that of the AC collection system; the first region and the second region are the ranges of the failure rates of the devices for which it makes a profit.

[0130] Correspondingly, the present application also relates to an analysis and optimization device for a new energy DC collection system, which includes:

[0131] A system cost function construction unit for constructing a system cost function C, where the system cost function C includes equipment price variables and equipment failure rate variables;

[0132] A system revenue function construction unit for constructing a system revenue function B, where the system revenue function B includes equipment failure rate variables;

[0133] An equipment price conversion unit for determining key equipment affecting the system and constructing an expression representing the equipment price with the equipment failure rate as a variable;

[0134] A data set collection unit for obtaining the B / C values under different key equipment and different failure rates, resulting in a data set (equipment failure rate, B / C value);

[0135] A fitting function solving unit for fitting the data set by fitting to obtain a fitting function with the equipment failure rates of different equipment as variables.

[0136] The above analysis and optimization device for the new energy DC collection system is used to implement the above analysis and optimization method for the new energy DC collection system, where each functional module corresponds to and executes each step of the above method. For details, reference can be made to the above introduction and will not be elaborated here.

[0137] Those skilled in the art can easily understand that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for analyzing and optimizing a new energy DC collection system, characterized in that including: Construct a system cost function C, which includes equipment price variables and equipment failure rate variables; Construct a system revenue function B, which includes equipment failure rate variables; Determine the key equipment affecting the system, construct an expression representing the equipment price with the equipment failure rate as a variable, calculate the evaluation function B / C, and the evaluation function B / C is a function with the equipment failure rate as a variable; Calculate the B / C values under different key equipment and different failure rates to obtain a data set (equipment failure rate, B / C value); Fit the data set by fitting to obtain a fitting function with the equipment failure rates of different equipment as variables, and analyze and optimize the new energy DC collection system according to the fitting function; wherein, The system cost function \(C = \alpha\times C\) con + C op + C', where, C con is the total construction cost, C con(d) is the construction cost of the \(d\)th type of equipment within the total construction cost. The construction cost includes the equipment price, \(D\) p is the number of types of equipment within the total construction cost, \(\alpha\) is the comprehensive coefficient converted considering loan interest; C op is the operation and maintenance cost, \(t\) is the year, \(T\) is the whole life cycle; \(r\) is the discount rate, C op_year is the mathematical expectation of the maintenance cost for one year, K con(m) is the maintenance cost of the key equipment \(m\), \(\lambda\) con(m) is the equipment failure rate of the key equipment \(m\), \(N\) con(m) is the number of the key equipment \(m\), \(M\) is the type of key equipment; C' is other costs except the total construction cost and the operation and maintenance cost; Construct the system revenue function where t is the year, T is the entire life cycle, q is the unit electricity price, P is the fault-free power generation, r is the discount rate, and η rel is the system reliability coefficient with the equipment failure rate as the variable, and η rel is equal to the ratio of the expected output power of the power station to the installed capacity of the power station, and B′ is other revenues other than the revenues obtained from selling the power generated by the power station at the benchmark electricity price The expression representing the equipment price with the equipment failure rate as a variable is C con(m) = F1(λ con(m) ), where λ con(m) and C con(m) are respectively the equipment failure rate and the equipment price of the key equipment m; wherein, the calculation process of the expected output power of the power station is: First, calculate the expected output power of the current equipment link based on the output power of the previous equipment link and the equipment failure rate in the current equipment link; Then, use the expected output power of the current equipment link as the input power of the next equipment link, and calculate from the front end to the back end of the power generation in turn to obtain the expected output power of the entire system considering the equipment failure rate.

2. The analysis and optimization method of the new energy DC collection system according to claim 1, characterized in that, When constructing the system revenue function, the power generation without faults \(P = P\) std *t e *η f *τ t , where \(P\) std is the installed photovoltaic capacity, \(t\) e is the annual equivalent utilization hours, \(\tau\) is the attenuation factor of the annual geometric attenuation of the power generation capacity of the photovoltaic modules, \(t\) is the year, and \(\eta\) f is the system power generation efficiency without faults, where \(\eta\) i is the efficiency of the \(i\)-th link, and \(N\) ef is the number of efficiency links to be considered.

3. The analysis and optimization method of the new energy DC collection system according to claim 1, characterized in that The equipment price has a linear relationship with the equipment failure rate. Select the maximum failure rate and the corresponding price, and the minimum failure rate and the corresponding price of the key equipment as the two coordinate points of the coordinate system, and solve the linear expression of the equipment price and the equipment failure rate.

4. The analysis and optimization method of the new energy DC collection system according to claim 3, wherein, The key equipment takes its own failure rate as the maximum failure rate and the failure rate of the inverter in the AC collection system as the minimum failure rate.

5. The analysis and optimization method of the new energy DC collection system according to claim 1, wherein Solve that the fitting function is greater than 1 to obtain the failure rate range of different key equipment when the system needs to make a profit; Or, calculate the B / C critical value for the collection system to make a profit, solve that the fitting function is greater than the critical value, and obtain the failure rate range of different key equipment when the profitability of the DC collection system exceeds that of the AC collection system.

6. The analysis and optimization method for a new energy DC collection system according to any one of claims 1 to 5, characterized in that The key equipment includes two categories: DC circuit breakers and DC step-up converters.

7. An analysis and optimization device for a new energy DC collection system, characterized in that, including: A system cost function construction unit for constructing a system cost function C, which includes equipment price variables and equipment failure rate variables; A system revenue function construction unit for constructing a system revenue function B, which includes equipment failure rate variables; An equipment price conversion unit for determining the key equipment affecting the system, constructing an expression representing the equipment price with the equipment failure rate as a variable, calculating the evaluation function B / C, and the evaluation function B / C is a function with the equipment failure rate as a variable; A data set collection unit for calculating the B / C values under different key equipment and different failure rates to obtain a data set (equipment failure rate, B / C value); A fitting function solving unit for fitting the data set by fitting to obtain a fitting function with the equipment failure rates of different equipment as variables, and analyzing and optimizing the new energy DC collection system according to the fitting function; wherein, System cost function \(C = \alpha\times C\) con + C op + C'\), where \(C\) con is the total construction cost, \(C\) con(d) is the construction cost of the \(d\) -th type of equipment within the total construction cost. The construction cost includes the equipment price. \(D\) p is the number of types of equipment within the total construction cost. \(\alpha\) is the comprehensive coefficient converted considering loan interest; \(C\) op is the operation and maintenance cost, \(t\) is the year, \(T\) is the whole - life cycle; \(r\) is the discount rate, \(C\) op_year is the mathematical expectation of the maintenance cost for one year, \(K\) con(m) is the maintenance cost of the key equipment \(m\), \(\lambda\) con(m) is the equipment failure rate of the key equipment \(m\), \(N\) con(m) is the number of the key equipment \(m\), \(M\) is the type of key equipment; \(C'\) is other costs except the total construction cost and the operation and maintenance cost; Construct the system revenue function where t is the year, T is the whole life cycle, q is the unit electricity price, P is the fault-free power generation, r is the discount rate, and η rel is the system reliability coefficient with the equipment failure rate as the variable, and η rel is equal to the ratio of the expected output power of the power station to the installed capacity of the power station, and B′ is other revenues except for the revenues obtained from the power generation of the power station being sold at the benchmark electricity price The expression representing the equipment price with the equipment failure rate as a variable is C con(m) = F1(λ con(m) ), where λ con(m) and C con(m) are respectively the equipment failure rate and the equipment price of the key equipment m; wherein, the calculation process of the expected output power of the power station is: First, calculate the expected output power of the current equipment link based on the output power of the previous equipment link and the equipment failure rate in the current equipment link; Then, taking the expected output power of the current equipment link as the input power of the next equipment link, calculate from the front end to the back end of power generation in sequence to obtain the expected output power of the entire system considering the equipment failure rate.

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