Wind and light storage load bearing capacity evaluation method based on random production simulation

Abstract

The invention discloses a wind and light storage load bearing capacity evaluation method based on random production simulation, and the method comprises the steps: carrying out the variable time scale filtering processing of a load curve through building a target function of a variable time scale rolling filtering optimization model, and employing the peak load shifting effect of energy storage; further obtaining a reliability index of the system after the load increment is increased according to the curve, obtaining three types of load increments when the equal reliability principle is met, and finally obtaining the improved load bearing capacity after the thermal power generation system is connected to wind storage, the improved load bearing capacity after the thermal power generation system is connected to light storage and the improved load bearing capacity after the thermal power generation system is connected to wind and light storage. On the premise of considering wind and light output volatility and configuring certain energy storage capacity, quantitative calculation of the improved load bearing capacity of a system after wind and light are connected and certain energy storage capacity is configured is achieved, and the wind and light storage load bearing capacity is evaluated based on random production simulation.

Description

Technical Field

[0001] The present invention relates to the field of load carrying capacity assessment in power systems, and in particular to a method for assessing the load carrying capacity of a wind, solar, thermal and energy storage system based on random production simulation. Background Art

[0002] Load carrying capacity is defined as the difference in the power load that a power system can carry at the same reliability level before and after the integration of renewable energy. This approach can assess the contribution of renewable energy to power system reliability. As the proportion of renewable energy in the power system continues to increase, the volatility and uncertainty of its output pose new challenges to the stable operation of the power system. Due to the time-series coupling characteristics of energy storage, traditional power system load carrying capacity assessment methods based on stochastic production simulations cannot account for these time-series characteristics and therefore struggle to accurately reflect the impact of energy storage integration on system reliability. Summary of the Invention

[0003] The present invention discloses a method for evaluating the wind-solar storage load carrying capacity based on random production simulation to overcome the above technical problems.

[0004] In order to achieve the above object, the technical solution of the present invention is:

[0005] A method for evaluating the wind, solar, and storage load carrying capacity based on random production simulation includes the following steps:

[0006] S1: According to the capacity of the thermal power generating units, the power generation capacity of the thermal power generation system is obtained, including the power generation capacity of the thermal power generation system and the probability distribution of the power generation capacity of the thermal power generation system;

[0007] S2: According to the wind power time sequence output curve and the photovoltaic time sequence output curve, obtain the load curve after subtracting the wind power output, the load curve after subtracting the photovoltaic output, and the load curve after subtracting the wind power output and the photovoltaic output;

[0008] S3: establishing an objective function and constraint conditions of the variable time scale rolling filter optimization model to obtain a filtered load curve after subtracting wind power output, a filtered load curve after subtracting photovoltaic output, and a filtered load curve after subtracting wind power output and photovoltaic output based on the load curve after subtracting wind power output, the load curve after subtracting photovoltaic output, and the load curve after subtracting wind power output and photovoltaic output;

[0009] S4. According to the power generation capacity of the thermal power generation system, obtain the reliability index of the thermal power generation system under the original load, the reliability index of the wind-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power output, the reliability index of the solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the photovoltaic output, and the reliability index of the wind-solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power and photovoltaic outputs, so as to obtain the load increment of the load curve after subtracting the wind power output, the load increment of the load curve after subtracting the photovoltaic output, and the load increment of the load curve after subtracting the wind power output and photovoltaic output when the equal reliability principle is satisfied;

[0010] S5. Based on the load increment of the load curve after subtracting wind power output, the load increment of the load curve after subtracting photovoltaic output, and the load increment of the load curve after subtracting wind power output and photovoltaic output when the equal reliability principle is met, the load carrying capacity improved after the thermal power generation system is connected to wind storage, the load carrying capacity improved after the thermal power generation system is connected to photovoltaic storage, and the load carrying capacity improved after the thermal power generation system is connected to wind, photovoltaic and storage, so as to evaluate the load carrying capacity of wind, photovoltaic and storage.

[0011] Beneficial effect: The present invention provides a method for evaluating the load carrying capacity of wind, solar and storage based on random production simulation. By establishing the objective function of a variable time scale rolling filter optimization model, the filtered load curve after subtracting wind power output, the filtered load curve after subtracting photovoltaic output, and the filtered load curve after subtracting wind power output and photovoltaic output are obtained, and then the reliability index of the system after the load increment is increased according to the curve is obtained. Based on the principle of equal reliability, the load increment of the load curve after subtracting wind power output, the load increment of the load curve after subtracting photovoltaic output, and the load increment of the load curve after subtracting wind power output and photovoltaic output when the equal reliability principle is satisfied are obtained. Finally, the load carrying capacity improved after the thermal power generation system is connected to wind storage, the load carrying capacity improved after the thermal power generation system is connected to photovoltaic storage, and the load carrying capacity improved after the thermal power generation system is connected to wind, solar and storage is obtained, and the wind, solar and storage load carrying capacity is evaluated. The present invention first utilizes the peak-shaving and valley-filling effect of energy storage to perform variable time-scale filtering on the load curve, and then evaluates the load carrying capacity based on random production simulation. This allows for quantitative calculation of the improved load carrying capacity of the system after connecting to wind and solar power and configuring a certain capacity of energy storage, while taking into account the volatility of wind and solar power output and configuring a certain capacity of energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0013] Figure 1 This is a flow chart of the wind-solar-storage load carrying capacity assessment method based on random production simulation of the present invention;

[0014] Figure 2 Schematic diagram of the load bearing capacity evaluation process in an embodiment of the present invention;

[0015] Figure 3 Schematic diagram of rolling filter net load in an embodiment of the present invention;

[0016] Figure 4 Schematic diagram of iteration based on the dichotomy method in an embodiment of the present invention;

[0017] Figure 5 is a wind power net load timing curve in an embodiment of the present invention;

[0018] Figure 6 is a photovoltaic net load timing curve in an embodiment of the present invention;

[0019] Figure 7 This is the wind and solar net load timing curve in the embodiment of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] This embodiment introduces a method for evaluating the wind, solar and storage load carrying capacity based on random production simulation. Figure 1 and Figure 2 As shown, the following steps are included:

[0022] S1: Obtain the power generation capacity of the thermal power generation system, including the power generation capacity C of the thermal power generation system * and the probability distribution of the power generation capacity of the thermal power generation system P(C * =k), k is the possible power generation capacity of the thermal power generation system;

[0023] Preferably, the formula used to obtain the power generation capacity of the thermal power generation system is as follows:

[0024]

[0025] Where: C * Indicates the power generation capacity of the thermal power generation system; Represents the generating capacity of the first thermal power unit, that is, the generating unit capacity vector The first component in ; Represents the generating capacity of the second thermal power unit, that is, the generating unit capacity vector The second component of ; Represents the generating capacity of the Nth thermal power unit, that is, the generating unit capacity vector The Nth component in ; N represents the total number of thermal power units in the thermal power system;

[0026] The formula used to obtain the probability distribution of the power generation capacity of the thermal power generation system is as follows:

[0027]

[0028] Where: P(C * =k) represents the probability distribution of the thermal power generation system when the generating capacity is k, that is, the sum of the probabilities of all combinations that can make the total capacity of the subset S of thermal power generation units equal to k; S represents the subset of thermal power generation units with a total capacity equal to k; k is the possible generating capacity of the thermal power generation system; C * Indicates the power generation capacity of the thermal power generation system; and They represent the probability that the generating capacity of the i-th thermal power generating unit and the j-th thermal power generating unit is 0 respectively; where i∈S means that the i-th thermal power generating unit is in the set S and is in operation, and its probability is Indicates that the jth thermal power generating unit is not in S, and the probability of forced shutdown of the unit is i and j are both thermal power generating unit indexes.

[0029] Specifically, this embodiment first obtains the basic data required to calculate the new energy load carrying capacity, including the generator capacity vector of each thermal power unit in the system, that is, the set of N thermal power unit capacities in the system. Forced outage rate vector That is, the set of forced outage rates corresponding to N thermal power units in the system; the load time series curve p load , Wind power timing output curve p wind , Photovoltaic timing output curve p solar , Load timing curve Maximum load p load,max , Wind power installed capacity Cwind , Photovoltaic installed capacity C solar , the maximum storage capacity S e , Maximum charging power P e,ch,max , maximum discharge power P e ,dis,max .

[0030] Specifically, this embodiment calculates the power generation capacity of the thermal power generation system through convolution.

[0031] According to the generator capacity vector and the forced outage rate vector The power generation capacity of the thermal power generation system is calculated by convolution, that is, the power generation capacity C of the thermal power generation system * The probability distribution of the power generation capacity of the thermal power generation system P(C * =k). The power generation capacity of each thermal power generating unit can be expressed as a random variable. There are two possible power generation capacities: one is shut down, with a power generation capacity of 0, and the other is turned on, with a power generation capacity equal to the power generation capacity of the unit. The probability distribution of its power generation capacity is P(C * =k)=p i,k , Where k represents the possible power generation capacity of the thermal power generation system; p i,k represents the probability of the i-th thermal power generating unit being under the power generation capacity k; Represents the capacity of the i-th thermal power generating unit, that is, the generating unit capacity vector The i-th component in ;

[0032] Initially, the power generation capacity C of the thermal power generation system * The probability function of the power generation capacity of the thermal power generation system P(C * =k), which is given by the power generation capacity C1 of the first thermal power generating unit and the power generation capacity probability function. The possible power generation capacity of the thermal power generation system is the possible power generation capacity of the first thermal power generating unit, that is, The probability distribution of its power generation capacity P(C * =k) is the same as the probability distribution of power generation capacity of thermal power unit 1 P(C1=k), where P(C * =0) represents the probability when the power generation capacity of the thermal power generation system is 0; p 1,0 That is, the probability that the generating capacity of the first thermal power generator is 0, and its value is is the forced outage rate vector The first component of ; The power generation capacity of the thermal power generation system is The probability of time.

[0033] After the second thermal power generating unit is added, the power generation capacity of the thermal power generation system is the combination of the possible power generation capacities of thermal power generating unit 1 and thermal power generating unit 2, that is, Based on this, we can obtain the probability distribution of power generation capacity of thermal power generation system P(C * =k) is calculated as shown in formula (2); after adding N thermal power generating units, the probability distribution P(C * =k), that is, the probability corresponding to the power generation capacity of the thermal power generation system is k, where the power generation capacity C * is the possible combination of power generation capacity of N thermal power generating units.

[0034] S2: Obtain the load curve p after subtracting wind power output net,wind , load curve after subtracting photovoltaic output p net ,solar , and the load curve p after subtracting wind power output and photovoltaic output net ;

[0035] Preferably, in said S2, the load curve p after subtracting the wind power output is obtained net,wind , load curve after subtracting photovoltaic output p net,solar , and the load curve p after subtracting wind power output and photovoltaic output net The formula used is as follows:

[0036] p net,wind =p load -p wind (3)

[0037] p net,solar =p load -p solar (4)

[0038] p net =p load -p wind -p solar (5)

[0039] Where: p net,wind represents the load curve after subtracting wind power output; p net,solar represents the load curve after subtracting the photovoltaic output; p net represents the load curve after subtracting wind power output and photovoltaic output; p load represents the load timing curve; p wind Represents the wind power timing output curve; p solar Indicates the photovoltaic timing output curve;

[0040] Specifically, in this embodiment, the load curve p after subtracting the wind power output is obtained. net,wind , load curve after subtracting photovoltaic output p net,solar , and the load curve p after subtracting wind power output and photovoltaic output net Finally, the three types of load curves are subjected to variable time scale rolling filtering using the peak-shaving and valley-filling capability of energy storage, and the three types of load curves after using energy storage to smooth out the volatility are obtained.

[0041] S3: Establish the objective function and constraint conditions of the variable time scale rolling filter optimization model, according to the load curve p after subtracting the wind power output net,wind , load curve after subtracting photovoltaic output p net,solar , load curve after subtracting wind power output and photovoltaic output p net Perform variable time scale rolling filtering on the above three types of load curves to obtain the filtered load curve after subtracting wind power output, the filtered load curve after subtracting photovoltaic output, and the filtered load curve after subtracting wind power output and photovoltaic output;

[0042] Specifically, the overall optimization goal of the variable time scale rolling filter optimization model is to filter out the larger and smaller values in the three types of load curves through the peak-shaving and valley-filling effect of energy storage under a given energy storage capacity and maximum charge and discharge power, so as to minimize the fluctuations of these three types of load curves.

[0043] Preferably, the objective function of the variable time scale rolling filter optimization model is established as follows:

[0044]

[0045] Where z net,wind Minimize p net,wind The objective function value of the curve fluctuation; z net,soalr Minimize p net ,solar The objective function value of the curve fluctuation; z net Minimize p net The objective function value of the curve fluctuation; represents the charging power of the energy storage when filtering the load curve after subtracting the wind power output during period t; represents the charging power of the energy storage when filtering the load curve after subtracting the photovoltaic output during period t; represents the charging power of the energy storage when filtering the load curve after subtracting the wind power output and photovoltaic output in period t; represents the discharge power of energy storage when filtering the load curve after subtracting wind power output in period t; It represents the discharge power of energy storage when filtering the load curve after subtracting the photovoltaic output in period t; represents the discharge power of the energy storage when filtering the load curve after subtracting the wind power output and photovoltaic output in time period t; T represents the total number of time periods in the optimization cycle, and t represents the time period index within the cycle; Indicates that p in the optimization period T net,wind The average value of the curve; Indicates p in period T net,solar The average value of the curve; Indicates p in period T net The average value of the curve;

[0046] in,

[0047]

[0048] Specifically, the objective function of the variable time-scale rolling filter optimization model expressed in Equation (1) explicitly incorporates the charging and discharging process of the energy storage system, automatically achieving peak shaving and valley filling by adjusting the charging and discharging power. The model will prioritize compensating for extreme high loads (peaks) and low loads (valleys), making the utilization of energy storage resources more efficient. Furthermore, the objective function adjusts the net load based on the average value of the entire iteration cycle (24 hours, 48 hours, or longer), avoiding the model's over-reliance on a single adjustment period and being limited to short-term instantaneous demand, thereby balancing the charging and discharging operations of the energy storage.

[0049] Preferably, the constraints of the objective function are established as follows:

[0050] Among them, the constraints can be summarized as the constraints composed of the variable time scale rolling filter optimization model, the model is as follows:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] Where: It represents the amount of electricity stored in the energy storage during the period t; It represents the amount of energy stored in the energy storage system during the t-1 period; △T represents the time granularity, which is one hour; represents the discharge power of the energy storage in period t; η e Indicates the charging and discharging efficiency of energy storage; P te,ch represents the charging power of the energy storage during period t; It represents the storage capacity of the electric energy storage at the initial moment, and its value is equal to the storage capacity of the energy storage at the last time point of the previous optimization cycle; S0 represents the storage capacity of the electric energy storage at the last time point of the optimization cycle; S e Indicates the maximum storage capacity of electric energy; P e,dis,max Indicates the maximum discharge power of the electric energy storage; P e,ch,max Indicates the maximum charging power of the electric energy storage; It represents the charging state decision variable of the energy storage at time period t. The value of 1 indicates that the energy storage is in the charging state, and the value of 0 indicates that the energy storage is in the discharging state. It represents the decision variable of the discharge state of the energy storage at time period t. The value of 1 indicates that the energy storage is in the discharge state, and the value of 0 indicates that the energy storage is in the charge state.

[0058] Specifically, formula (8) represents the coupling transfer relationship of the storage capacity between adjacent time periods of the energy storage; formula (9) indicates that the storage capacity of the energy storage in the first time period of the optimization cycle should be equal to the storage capacity of the energy storage in the last time period of the previous optimization cycle, so as to achieve the connection and transfer of storage capacity; formula (10) represents the storage capacity boundary of each time period of the energy storage, which is between 0 and the maximum storage capacity; formula (11) and formula (12) respectively represent the operating boundaries of the energy storage during discharge and charging: during discharge, Take 1, When 0 is selected, the energy storage discharge power is between 0 and the maximum discharge power, and the charging power is 0; when charging, Take 1, When 0 is taken, the energy storage charging power is between 0 and the maximum charging power, and the discharge power is 0. Formula (13) shows that the energy storage cannot be charged and discharged at the same time.

[0059] Specifically, in this embodiment, the objective function of the variable time scale rolling filter optimization model is solved to obtain the filtered load curve after subtracting the wind power output, the filtered load curve after subtracting the photovoltaic output, and the filtered load curve after subtracting the wind power output and photovoltaic output. The method used is a conventional technology in the field and will not be described in detail here.

[0060] Preferably, the filtered load curve after subtracting wind power output, the filtered load curve after subtracting photovoltaic output, and the filtered load curve after subtracting wind power output and photovoltaic output are obtained as follows:

[0061] In this embodiment, the energy storage charging and discharging power of each period is obtained by using the established variable time scale rolling filter optimization model. and After that, we can get the three types of loads in each period after using energy storage to smooth out the fluctuations: The calculation formula is as follows:

[0062]

[0063] Where: p t net,Swind represents the load curve after filtering minus wind power output; p t net,Ssolar represents the load curve after filtering minus the photovoltaic output; p t Snet Represents the load curve after filtering, minus wind power output and photovoltaic output;

[0064] Specifically, the initial filtering cycle is 1 day. After the current cycle filtering is completed, it is necessary to check whether the energy storage capacity is fully utilized. If the energy storage capacity is not fully utilized, the optimization time span is extended (such as from 24 hours to 48 hours) until the energy storage is fully utilized. If the first optimization cycle is N days, the second round of optimization starts from the data of the N+1th day, and the above steps are repeated until the net load data of 365 days of the year is covered. The flow chart is as follows Figure 3 shown.

[0065] S4. Obtain the reliability index of the thermal power generation system under the original load, the reliability index of the wind-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power output, the reliability index of the solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the photovoltaic output, and the reliability index of the wind-solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power and photovoltaic outputs, so as to obtain the load curve p after subtracting the wind power output when the equal reliability principle is met based on the equal reliability principle. t net,Swind Load increment, load curve after subtracting photovoltaic output p t net,Ssolar Load increment, load curve after subtracting wind power output and photovoltaic output p t Snet Load increment;

[0066] Specifically, this embodiment uses the equal reliability principle to quantify the improved load carrying capacity of the above three types of thermal power generation systems after they are connected to new energy sources and equipped with energy storage.

[0067] S41. First, calculate the reliability index LOLE0 under the original load. LOLE, or loss of load expectation, is the expected time (hours or days) that the system load demand exceeds the available generation capacity within a given time period (usually one year). It measures the time period in which the power system cannot meet the load demand due to insufficient generation capacity. The formula for calculating the reliability index LOLE0 under the original load is as follows:

[0068]

[0069] Where: LOLE 0 Represents the reliability index of the thermal power generation system under the original load; F c represents the cumulative distribution function of the power generation capacity of thermal power generating units; represents the load during period t; Indicates that the power generation capacity provided by the thermal power generation system during period t is less than The probability of accumulating all time periods within the period T That is, the reliability index within the considered time period can be obtained. k represents the possible power generation capacity of the thermal power generation system; C * Indicates the power generation capacity of the thermal power generation system;

[0070] S42, using the bisection method to iteratively calculate the filtered load curve p after subtracting the wind power output. t net,Swind The load increment and the filtered load curve after subtracting the photovoltaic output p t net,Ssolar The load increment, filtered load curve after subtracting wind power output and photovoltaic output p t Snet Load increment;

[0071] Specifically, this embodiment uses the bisection method to iteratively calculate the load increments of the three types of loads, such as Figure 4 As shown. The algorithm first needs to define a search capacity upper limit C high and the search capacity lower bound C min , the C corresponding to the three types of load curves initialized min Take 0, C high Take the installed wind power capacity C wind , Photovoltaic installed capacity C solar , and C wind +C solar , to ensure that the optimal solution can be found. The calculation formula for the first iteration point is

[0072]

[0073] Where: c v,wind The load curve p after filtering minus wind power output t net,Swind Load increment; c v,solar The load curve p after filtering minus photovoltaic output t net,Ssolar Load increment; c v,new The load curve p after filtering minus wind power output and photovoltaic output is shown tnet,Snet Load increment; C high Indicates the upper limit of search capacity; C min Indicates the lower limit of search capacity; C solar Represents photovoltaic installed capacity; C wind Indicates installed wind power capacity.

[0074] S43: Obtain the reliability index of the wind-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power output, the reliability index of the solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the photovoltaic output, and the reliability index of the wind-solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power and photovoltaic outputs; adjust the load increment of the filtered load curve after subtracting the wind power output, the load increment of the filtered load curve after subtracting the photovoltaic output, and the load increment of the filtered load curve after subtracting the wind power output and photovoltaic output, and the load increment of the filtered load curve after subtracting the wind power output and photovoltaic output, and the adopted formula is as follows:

[0075]

[0076]

[0077]

[0078] Where: LOLE1 wind Represents the reliability index of the wind-thermal storage system after adding the load increment to the load curve after filtering and subtracting wind power output; LOLE1 solar Represents the reliability index of the solar-thermal-storage system after adding the load increment to the load curve after filtering and subtracting the photovoltaic output; LOLE1 net It represents the reliability index of the wind-solar-thermal-storage system after adding the load increment to the load curve after filtering, minus the wind power and photovoltaic output; p load,max Indicates the maximum load of the load timing curve;

[0079] Specifically, in this embodiment, it is determined whether the reliability index of the three types of load curves after the load is increased is equal to the reliability index LOLE0 of the thermal power generation system under the original load. If they are equal, the iteration is stopped. This principle is called the equal reliability principle and can be expressed as:

[0080] LOLE1 wind =LOLE1 solar =LOLE1 net =LOLE0 (20)

[0081] Specifically, if the calculated load increment does not meet the reliability standard, the upper and lower limits of the search capacity need to be adjusted: If LOLE1 wind LOLE1 solar LOLE1net If it is larger than LOLE0, it means that the corresponding c v,wind 、c v,solar 、c v,new The load increment is too large, and the search trend needs to move in the direction of decreasing, that is, c v,wind 、c v,solar 、c v,new Assign a value to the search capacity upper limit; similarly, if it is too small, it means that c v,wind 、c v,solar 、c v,new The load increment is too small, and the search trend needs to move in the direction of increasing, that is, c v,wind 、c v,solar 、c v,new Assign a value to the search capacity lower limit. After a finite number of iterations, it finally converges to the load increment c of the three types of load curves that meet the equal reliability principle. v,wind 、c v,solar 、c v,new .

[0082] S5. Load curve p after subtracting wind power output when the reliability principle is met t net,Swind Load increment, load curve after subtracting photovoltaic output p t net,Ssolar Load increment, load curve after subtracting wind power output and photovoltaic output p t Snet The load increment is used to obtain the load carrying capacity η increased by the thermal power generation system after it is connected to the wind storage. wind , the load carrying capacity η increased after access to solar storage solar , the load carrying capacity increased after connecting to wind, solar and storage η new , to evaluate the wind, solar and storage load carrying capacity;

[0083] Specifically, this embodiment calculates the load carrying capacity η under three scenarios wind ,η solar ,η new , evaluate the load carrying capacity of the thermal power generation system after connecting to new energy and configuring a certain proportion of energy storage. Load carrying capacity η wind ,η solar ,η new Is the reliability index LOLE1 wind LOLE1 solar LOLE1 net The load increment c obtained when LOLE0 and LOLE1 satisfy the equal reliability principle v,wind 、c v,solar 、c v,new The ratio to the installed capacity of new energy is calculated as follows:

[0084]

[0085] Where: η wind It represents the increased load carrying capacity of the thermal power generation system after the wind power storage is connected; η solar It represents the increased load carrying capacity of the thermal power generation system after the solar energy storage is connected; η new Indicates the increased load carrying capacity of the thermal power generation system after it is connected to wind, solar and storage; C wind represents wind power installed capacity; C solar represents the photovoltaic installed capacity; c′ v,wind represents the load increment of the load curve after subtracting the wind power output when the equal reliability principle is met; c′ v,solar represents the load increment of the load curve after subtracting the photovoltaic output when the equal reliability principle is met; c′ v,new Indicates the load increment of the load curve after subtracting wind power output and photovoltaic output when the equal reliability principle is met;

[0086] A specific embodiment of the present invention is as follows:

[0087] Based on the power data of Liaoning Province from July 2019 to June 2020, the hourly load, wind power and photovoltaic output information of the province are integrated, and the load capacity is calculated by combining 70 conventional thermal power units with a total capacity of 28,200MW, 9,810MW wind power installed capacity, and 3,996MW photovoltaic installed capacity. Among them, the wind power net load timing curve, photovoltaic net load timing curve, and wind and solar net load timing curve are as follows: Figure 5 、 Figure 6 、 Figure 7 shown.

[0088] The calculation results are shown in Table 1, where LOLE0 refers to the reliability index of the thermal power generation system under original load, and LOLE1 refers to the reliability index of the power generation system after connecting to renewable energy and configuring a certain proportion of energy storage.

[0089] Table 1 shows that wind power with storage, photovoltaic power with storage, and wind-solar power with storage improve load carrying capacity by 24.3%, 21.1%, and 27.6%, respectively, compared to thermal power generation systems. While energy storage only accounts for 20% of installed renewable energy capacity, it increases the credible capacity of wind power and photovoltaic power to 24.3% and 21.1%, respectively, demonstrating that energy storage achieves high efficiency with a small capacity. The combined wind-solar-storage system improves load carrying capacity by 27.6% compared to thermal power generation systems, exceeding the performance of a single energy source combined with storage, highlighting the amplifying effect of energy storage on the complementary nature of multiple energy sources.

[0090] To illustrate the beneficial effects of this embodiment, this embodiment also calculates the traditional load carrying capacity index, namely the equivalent reliable capacity, to evaluate the load carrying capacity (c in Table 1). R), it is clearly seen in the table that c R Slightly lower than c v This shows that the system load carrying capacity evaluation method based on random production simulation in this embodiment considers more comprehensive factors and is more accurate in calculation.

[0091] Table 1 Calculation results

[0092]

[0093] The present invention provides a method for evaluating the load carrying capacity of wind, solar and storage based on random production simulation. By establishing the objective function of a variable time scale rolling filter optimization model, the load curve after filtering minus wind power output, the load curve after filtering minus photovoltaic output, and the load curve after filtering minus wind power output and photovoltaic output are obtained, and then the reliability index of the system after the load increment is increased according to the curve is obtained. Based on the equal reliability principle, the load increment of the load curve after subtracting wind power output, the load increment of the load curve after subtracting photovoltaic output, and the load increment of the load curve after subtracting wind power output and photovoltaic output when the equal reliability principle is satisfied are obtained. Finally, the load carrying capacity improved after the thermal power generation system is connected to wind storage, the load carrying capacity improved after the thermal power generation system is connected to photovoltaic storage, and the load carrying capacity improved after the thermal power generation system is connected to wind, solar and storage is obtained, and the wind, solar and storage load carrying capacity is evaluated. The present invention first utilizes the peak-shaving and valley-filling effect of energy storage to perform variable time-scale filtering on the load curve, and then evaluates the load carrying capacity based on random production simulation. It can achieve quantitative calculation of the load carrying capacity improved by the system after connecting to wind and solar power and configuring a certain capacity of energy storage, under the premise of given thermal power generator set capacity and forced outage rate, original load timing curve, wind and solar power generation timing curve, wind and solar power installed capacity, energy storage capacity, energy storage charging and discharging rate, and energy storage charging and discharging efficiency, taking into account the volatility of wind and solar power output and configuring a certain energy storage capacity.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating wind, solar and storage load carrying capacity based on random production simulation, characterized in that: The steps include: S1: According to the capacity of the thermal power generating units, the power generation capacity of the thermal power generation system is obtained, including the power generation capacity of the thermal power generation system and the probability distribution of the power generation capacity of the thermal power generation system; S2: According to the wind power time sequence output curve and the photovoltaic time sequence output curve, obtain the load curve after subtracting the wind power output, the load curve after subtracting the photovoltaic output, and the load curve after subtracting the wind power output and the photovoltaic output; S3: establishing an objective function and constraint conditions of the variable time scale rolling filter optimization model to obtain a filtered load curve after subtracting wind power output, a filtered load curve after subtracting photovoltaic output, and a filtered load curve after subtracting wind power output and photovoltaic output based on the load curve after subtracting wind power output, the load curve after subtracting photovoltaic output, and the load curve after subtracting wind power output and photovoltaic output; S4. According to the power generation capacity of the thermal power generation system, obtain the reliability index of the thermal power generation system under the original load, the reliability index of the wind-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power output, the reliability index of the solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the photovoltaic output, and the reliability index of the wind-solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power and photovoltaic outputs, so as to obtain the load increment of the load curve after subtracting the wind power output, the load increment of the load curve after subtracting the photovoltaic output, and the load increment of the load curve after subtracting the wind power output and photovoltaic output when the equal reliability principle is satisfied; S5. Based on the load increment of the load curve after subtracting wind power output, the load increment of the load curve after subtracting photovoltaic output, and the load increment of the load curve after subtracting wind power output and photovoltaic output when the equal reliability principle is met, the load carrying capacity improved after the thermal power generation system is connected to wind storage, the load carrying capacity improved after the thermal power generation system is connected to photovoltaic storage, and the load carrying capacity improved after the thermal power generation system is connected to wind, photovoltaic and storage, so as to evaluate the load carrying capacity of wind, photovoltaic and storage.

2. The method for evaluating wind-solar-storage load carrying capacity based on random production simulation according to claim 1 is characterized in that: In S3, the objective function of the variable time scale rolling filter optimization model is established as follows: Where z net,wind Minimize p net,wind The objective function value of the curve fluctuation; z net,soalr Minimize p net,solar The objective function value of the curve fluctuation; z net Minimize p net The objective function value of the curve fluctuation; represents the charging power of the energy storage when filtering the load curve after subtracting the wind power output during period t; represents the charging power of the energy storage when filtering the load curve after subtracting the photovoltaic output during period t; represents the charging power of the energy storage when filtering the load curve after subtracting the wind power output and photovoltaic output in period t; represents the discharge power of energy storage when filtering the load curve after subtracting wind power output in period t; It represents the discharge power of energy storage when filtering the load curve after subtracting the photovoltaic output in period t; represents the discharge power of the energy storage when filtering the load curve after subtracting the wind power output and photovoltaic output in time period t; T represents the total number of time periods in the optimization cycle, and t represents the time period index within the cycle; Indicates that p in the optimization period T net,wind The average value of the curve; Indicates p in period T net,solar The average value of the curve; Indicates p in period T net The average value of the curve; in, 3. The method for evaluating wind, solar and storage load carrying capacity based on random production simulation according to claim 2 is characterized in that: The constraints of the objective function are established as follows: Where: It represents the amount of electricity stored in the energy storage during the period t; It represents the amount of energy stored in the energy storage system during the t-1 period; △T represents the time granularity; represents the discharge power of the energy storage during period t; η e Indicates the charging and discharging efficiency of energy storage; P t e,ch represents the charging power of the energy storage during period t; Indicates the storage capacity of the energy storage at the initial moment; S0 indicates the storage capacity of the energy storage in the last period of the optimization cycle; S e Indicates the maximum storage capacity of electric energy; P e,dis,max Indicates the maximum discharge power of the electric energy storage; P e,ch,max Indicates the maximum charging power of the electric energy storage; represents the decision variable of the charging state of the energy storage at time t; represents the decision variable of the discharge state of the energy storage in period t.

4. The method for evaluating wind, solar and storage load carrying capacity based on random production simulation according to claim 3 is characterized in that: The filtered load curve after subtracting wind power output, the filtered load curve after subtracting photovoltaic output, and the filtered load curve after subtracting wind power output and photovoltaic output are obtained as follows: Where: p t net,Swind represents the load curve after filtering minus wind power output; p t net,Ssolar represents the load curve after filtering minus the photovoltaic output; p t Snet It represents the load curve after filtering after subtracting wind power output and photovoltaic output.

5. The method for evaluating wind, solar and storage load carrying capacity based on random production simulation according to claim 1 is characterized in that: The formula used to obtain the power generation capacity of the thermal power generation system is as follows: Where: C * Indicates the power generation capacity of the thermal power generation system; Indicates the generating capacity of the first thermal power unit; Indicates the generating capacity of the second thermal power unit; It represents the generating capacity of the Nth thermal power unit; N represents the total number of thermal power units in the thermal power system.

6. The method for evaluating wind, solar and storage load carrying capacity based on random production simulation according to claim 1 is characterized in that: The formula used to obtain the probability distribution of the power generation capacity of the thermal power generation system is as follows: Where: P(C * =k) represents the probability distribution of the thermal power generation system when the generating capacity is k, that is, the sum of the probabilities of all combinations that can make the total capacity of the subset S of thermal power generation units equal to k; S represents the subset of thermal power generation units with a total capacity equal to k; k is the possible generating capacity of the thermal power generation system; C * Indicates the power generation capacity of the thermal power generation system; and They represent the probability that the power generation capacity of the i-th thermal power generating unit and the j-th thermal power generating unit is 0 respectively; i and j are the indexes of the thermal power generating units.

7. The method for evaluating wind, solar and storage load carrying capacity based on random production simulation according to claim 1 is characterized in that: In S2, the load curve after subtracting the wind power output, the load curve after subtracting the photovoltaic output, and the load curve after subtracting the wind power output and the photovoltaic output are obtained using the following formula: p net,wind =p load -p wind p net,solar =p load -p solar p net =p load -p wind -p solar Where: p net,wind represents the load curve after subtracting wind power output; p net,solar represents the load curve after subtracting the photovoltaic output; p net represents the load curve after subtracting wind power output and photovoltaic output; p load represents the load timing curve; p wind Represents the wind power timing output curve; p solar Represents the photovoltaic timing output curve.

8. The method for evaluating wind, solar and storage load carrying capacity based on random production simulation according to claim 1 is characterized in that: The S4 includes: S41, first calculate the reliability index LOLE0 under the original load; Where: LOLE0 represents the reliability index of the thermal power generation system under the original load; F c represents the cumulative distribution function of the power generation capacity of thermal power generating units; represents the load during period t; Indicates that the power generation capacity provided by the thermal power generation system during period t is less than The probability of accumulating all time periods within the period T That is, the reliability index within the considered time period can be obtained; k represents the possible power generation capacity of the thermal power generation system; C * Indicates the power generation capacity of the thermal power generation system; S42, iteratively calculating, using a dichotomy method, a load increment of the filtered load curve after subtracting wind power output, a load increment of the filtered load curve after subtracting photovoltaic output, and a load increment of the filtered load curve after subtracting wind power output and photovoltaic output; Where: c v,wind The load curve p after filtering minus wind power output t net,Swind Load increment; c v,solar The load curve p after filtering minus photovoltaic output t net,Ssolar Load increment; c v,new The load curve p after filtering minus wind power output and photovoltaic output is shown t net,Snet Load increment; C high Indicates the upper limit of search capacity; C min Indicates the lower limit of search capacity; C solar Represents photovoltaic installed capacity; C wind represents the installed capacity of wind power; S43: Obtain the reliability index of the wind-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power output, the reliability index of the solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the photovoltaic output, and the reliability index of the wind-solar-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power and photovoltaic outputs. The formula used is as follows: Where: It represents the reliability index of the wind-thermal storage system after adding the load increment to the filtered load curve after subtracting the wind power output; It represents the reliability index of the solar-thermal-storage system after adding the load increment to the filtered load curve after subtracting the photovoltaic output; It represents the reliability index of the wind-solar-thermal-storage system after adding the load increment to the load curve after filtering, minus the wind power and photovoltaic output; p load,max Indicates the maximum load of the load timing curve.

9. The method for evaluating wind, solar and storage load carrying capacity based on random production simulation according to claim 1 is characterized in that: The formulas used to obtain the load carrying capacity increased after the thermal power generation system is connected to wind storage, the load carrying capacity increased after the thermal power generation system is connected to solar storage, and the load carrying capacity increased after the thermal power generation system is connected to wind, solar and storage are as follows: Where: η wind It represents the increased load carrying capacity of the thermal power generation system after the wind power storage is connected; η solar It represents the increased load carrying capacity of the thermal power generation system after the solar energy storage is connected; η new Indicates the increased load carrying capacity of the thermal power generation system after it is connected to wind, solar and storage; C wind represents wind power installed capacity; C solar represents the photovoltaic installed capacity; c′ v,wind represents the load increment of the load curve after subtracting the wind power output when the equal reliability principle is met; c′ v,solar represents the load increment of the load curve after subtracting the photovoltaic output when the equal reliability principle is met; c′ v,new It represents the load increment of the load curve after subtracting wind power output and photovoltaic output when the equal reliability principle is met.

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