Peak regulation method and system for public cost sharing of household rooftop photovoltaic power generation

Abstract

The application discloses a peak regulation method and system for public cost sharing of household roof photovoltaic power generation, and belongs to the field of power grid peak regulation. The method comprises the following steps: calculating equivalent hours of abandoned electricity of each transformer substation area according to historical data; calculating abandonment contribution based on the equivalent hours of abandoned electricity, calculating power generation benefit and return period based on electricity price and cost; setting fairness weight according to the abandonment contribution, the power generation benefit and the return period; obtaining the abandoned electricity quantity of the current time of the transformer substation area, and predicting the abandoned electricity quantity of each transformer substation area in a set time window in the future, and combining the fairness weight to construct a target function; constructing a plurality of constraint conditions, the constraint conditions comprising a constraint condition of a hot spot temperature of a transformer of the transformer substation area, wherein the hot spot temperature is solved through a transformer temperature model; and constructing a peak regulation optimization model based on the target function and the temperature constraint condition to carry out peak regulation. The application ensures fair cost distribution in the peak regulation process, and improves the willingness of residents to participate in peak regulation.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation technology, and more specifically, relates to a peak regulation method and system for fair cost sharing of residential rooftop photovoltaic power generation. Background Technology

[0002] In rural areas, at midday, excess rooftop photovoltaic (RPV) power generation leads to reverse overload, forcing utility companies to shut down inverters to peak the load and ensure the safe operation of the main power grid. However, the target and scale of rooftop photovoltaic peak shaving present an unprecedented challenge, involving residents' economic interests and fairness, especially given the randomness of rooftop photovoltaic power generation.

[0003] CN120454185A discloses a method for frequency regulation, peak shaving, and power distribution in a smart microgrid based on photovoltaic power generation, relating to the field of smart microgrids. The method includes: analyzing and obtaining predicted volatility and power supply deviation amplitude; calculating volatility by combining predicted volatility and historical volatility; configuring frequency regulation coefficients and peak shaving distribution coefficients based on volatility and power supply deviation amplitudes; randomly generating power distribution schemes; performing peak shaving and frequency regulation analysis based on predicted photovoltaic power generation power sequences and predicted demand load power sequences; calculating distribution fitness according to the frequency regulation coefficients and peak shaving distribution coefficients; performing iterative optimization evaluation; and obtaining the optimal distribution scheme for power distribution. However, this patent does not consider fairness issues and ignores differences in users' historical contributions and economic capabilities, which may cause dissatisfaction among high-contribution users in practical applications. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a peak regulation method and system for fair cost sharing in residential rooftop photovoltaic power generation.

[0005] The present invention adopts the following technical solution.

[0006] The first aspect of this invention proposes a peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation, comprising the following:

[0007] Obtain the curtailment periods and installed capacity of residential rooftop photovoltaic systems in each substation area from historical data, and calculate the equivalent number of curtailment hours for each substation area;

[0008] The waste contribution is calculated based on the equivalent hours of abandoned electricity, and the power generation benefit and payback period are calculated based on electricity price and cost; the fairness weight of household photovoltaic in each substation area is set according to the waste contribution, power generation benefit and payback period.

[0009] Obtain the current amount of abandoned electricity in the substation area, predict the amount of abandoned electricity in the future within a set time window for each substation area, and construct an objective function by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weight.

[0010] Multiple constraints are constructed, including constraints on the hot spot temperature of the transformer in the substation area. A transformer temperature model characterizing the relationship between photovoltaic output power and hot spot temperature is constructed, and the hot spot temperature is obtained by solving the transformer temperature model.

[0011] A peak-shaving optimization model is constructed based on the objective function and temperature constraints to solve for the optimal peak-shaving strategy, which is to adjust the output power of the residential rooftop photovoltaic system in each substation area.

[0012] Preferably, the calculation of the equivalent hours of power curtailment for each substation area specifically involves:

[0013] Obtain the theoretical photovoltaic output power curve from historical data, as well as the corresponding actual photovoltaic output power curve and curtailment period from historical data. If the actual photovoltaic output power at a certain moment is less than the set output threshold and the difference between the theoretical photovoltaic output power and the actual photovoltaic output power is greater than the set difference threshold, then the corresponding moment is considered to be in a curtailment period.

[0014] The total amount of power curtailed during the curtailment period is obtained, which is the integral of the difference between the theoretical photovoltaic output power and the actual photovoltaic output power during the curtailment period; the total amount of power curtailed is divided by the installed capacity of the corresponding power station area to obtain the equivalent number of hours of power curtailment.

[0015] Preferably, the method of setting the fairness weight of household photovoltaic systems in each substation area based on waste contribution, power generation efficiency, and payback period specifically involves:

[0016] The formula for calculating fairness weights is:

[0017]

[0018] Where, N s γ represents the total number of substation areas; i CE represents the fairness weight for household photovoltaic systems in the i-th substation area; i IRR i and IRP i These represent the waste contribution, power generation efficiency, and payback period of residential photovoltaic systems in the i-th substation area; L i S represents the equivalent hours of power curtailment in the i-th substation area; i Let m be the household photovoltaic capacity of the i-th substation area; i pi These refer to the feed-in tariff and subsidy price for photovoltaic power generation, respectively; W i y i c represents the annual power generation and number of years of operation of the i-th substation area, respectively; b,i c s,i These represent the photovoltaic construction cost and installation subsidy for the i-th substation area, respectively.

[0019] Preferably, the objective function constructed by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weights is as follows:

[0020] Calculate the peak-shaving cost at the current time t0 for each substation area and the predicted peak-shaving cost for all peak-shaving steps within the set time window L in the future; the peak-shaving cost is the normalized fairness weight multiplied by the peak-shaving efficiency factor and then multiplied by the current time t0; the predicted peak-shaving cost at time t is the normalized fairness weight multiplied by the peak-shaving efficiency factor and then multiplied by the predicted amount of abandoned electricity at time t based on the prediction model that takes uncertainty into account; t = [t0 + δ, t0 + 2δ, ..., t0 + δ + L], where δ is the peak-shaving step interval; L is the set time window;

[0021] The rolling peak-shaving cost of each substation area is obtained by adding the peak-shaving cost of all predicted peak-shaving costs. The objective function is to minimize the sum of the rolling peak-shaving costs of all substation areas divided by the total number of substation areas.

[0022] Preferably, the amount of electricity wasted at time t is predicted according to a prediction model that takes uncertainty into account, specifically as follows:

[0023] The photovoltaic output power at the current time t0 is obtained, as well as the meteorological data predicted by the meteorological station at time t. The meteorological data includes weather type, total horizontal irradiance, and ambient temperature. The weather type includes clear sky, scattered clouds, and cloudy.

[0024] Different perturbation distributions are set for different weather types. A set number of random values ​​are extracted from the perturbation distribution corresponding to the current weather type. The average value of all random values ​​is calculated as the perturbation term, and the random values ​​satisfy the set time constraints. The total irradiance of the water surface plus the perturbation term is used as the total irradiance of the horizontal surface for uncertainty correction. The total irradiance of the horizontal surface for uncertainty correction, the ambient temperature, and the photovoltaic output power at the current time t0 are input into the pre-trained Transformer algorithm to predict the amount of power wasted.

[0025] Preferably, the random value satisfies a set time constraint, specifically:

[0026] When t equals t0+δ, the time constraint is that the ratio of the disturbance term at time t to the amount of electricity wasted at time t0 is less than the set proportional coefficient.

[0027] When t is greater than t0+δ, calculate the correlation coefficient between the sequence formed by sorting all random values ​​at time t in descending order and the sequence formed by sorting all random values ​​at time t-δ in descending order. The time constraint is that the correlation coefficient is greater than the set correlation coefficient threshold. If the set time constraint is not met, random values ​​are re-generated.

[0028] Preferably, the construction of multiple constraints specifically involves:

[0029] The constraints include the hot spot temperature constraints of the transformers in the substation area, the photovoltaic output power constraints, and the load constraints.

[0030] The constraint condition for the hot spot temperature of transformers in the substation area is that the hot spot temperature of transformers in all substation areas at the current moment is less than the set maximum temperature threshold. The constraint condition for photovoltaic output power is that the photovoltaic output power of all substation areas at the current moment is greater than or equal to 0 and less than or equal to the set maximum power threshold. The load constraint condition is that the sum of the thermal power output power and photovoltaic output power of all substation areas is equal to the total load demand.

[0031] Preferably, the construction of the transformer temperature model characterizing the relationship between photovoltaic output power and hotspot temperature specifically involves:

[0032] θ t =θ O +Δθ h1 -Δθ h2

[0033]

[0034] Where, θ t For hotspot temperature; θ O Δθ represents the initial value of the transformer top oil temperature. h1 , Δθ h2 These are the first and second components of the hotspot temperature rise, respectively; θ a , Δθ hr , Δθ Or These represent the temperature rise relative to ambient temperature, top oil temperature, and hot spot temperature, respectively; δ is the peak shaving step interval; x is the oil temperature index; y is the hot spot temperature rise index; C1, C2, and C3 are all integration constants; k 11 The oil time constant weighting coefficient; k 21 k is the hotspot temperature rise ratio coefficient. 22 τ is the dynamic correction coefficient for hot spot temperature rise; τ0 is the oil thermal time constant; τ w P is the thermal time constant of the winding; L Total electricity demand; PPV S represents the current photovoltaic output power. TN R represents the installed capacity; R is the set dynamic correction coefficient for the photovoltaic peak-shaving effect on the transformer thermal model.

[0035] Preferably, after solving for the optimal peak-shaving strategy through the peak-shaving optimization model, the Gini coefficient between substation areas is calculated as the peak-shaving fairness assessment index among households in all substation areas. If the peak-shaving fairness assessment index exceeds the set fairness threshold, historical data is re-acquired to calculate the fairness weight of household photovoltaics in each substation area.

[0036] A second aspect of this invention proposes a peak regulation system for fair cost sharing of residential rooftop photovoltaic power generation based on the method described in the first aspect of this invention, comprising a module for calculating the equivalent hours of curtailed power, a module for setting fairness weights, a module for constructing an objective function, a module for constructing constraints, and a module for generating peak regulation strategies, including:

[0037] The module for calculating the equivalent hours of curtailed electricity: retrieves the curtailed electricity periods and installed capacity of residential rooftop photovoltaic systems in each substation area from historical data, and calculates the equivalent hours of curtailed electricity for each substation area;

[0038] Fairness weight setting module: Calculates the waste contribution based on the equivalent hours of abandoned electricity, and calculates the power generation benefit and payback period based on electricity price and cost; sets the fairness weight of household photovoltaic in each substation area according to the waste contribution, power generation benefit and payback period;

[0039] Objective function construction module: Obtain the current amount of abandoned electricity in the substation area, predict the amount of abandoned electricity in the future within a set time window for each substation area, and construct the objective function by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weight;

[0040] Constraint Construction Module: Constructs multiple constraints, including constraints on the hot spot temperature of the transformer in the substation area, and constructs a transformer temperature model characterizing the relationship between photovoltaic output power and hot spot temperature. The hot spot temperature is obtained by solving the transformer temperature model.

[0041] Peak shaving strategy generation module: Based on the objective function and temperature constraints, a peak shaving optimization model is constructed to solve for the optimal peak shaving strategy, which is to adjust the output power of the residential rooftop photovoltaic system in each substation area.

[0042] The beneficial effects of this invention are as follows: Compared with the prior art, this invention proposes a multi-dimensional fairness weight, which clearly considers the historical contribution of households to ensure fair cost allocation and increases residents' willingness to participate in peak shaving; this invention combines the actual peak shaving cost value with the predicted value for future periods to form a rolling peak shaving cost as the objective function of the peak shaving model, and takes uncertainty into account when predicting, thus solving the impact of power generation uncertainty on the optimal solution within the set time window; this invention proposes a safety constraint based on transformer hot spot temperature, which can conveniently calculate the hot spot temperature and ensure the safety of the peak shaving process. Attached Figure Description

[0043] Figure 1 This is a flowchart of the present invention;

[0044] Figure 2 A schematic diagram for calculating the equivalent hours of abandoned electricity;

[0045] Figure 3 This is a graph showing the Gini coefficient under different time windows. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0047] like Figure 1 As shown, Embodiment 1 of the present invention provides a peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation, specifically as follows:

[0048] Obtain the curtailment periods and installed capacity of residential rooftop photovoltaic systems in each substation area from historical data, and calculate the equivalent number of curtailment hours for each substation area;

[0049] The waste contribution is calculated based on the equivalent hours of abandoned electricity, and the power generation benefit and payback period are calculated based on electricity price and cost; the fairness weight of household photovoltaic in each substation area is set according to the waste contribution, power generation benefit and payback period.

[0050] Obtain the current amount of abandoned electricity in the substation area, predict the amount of abandoned electricity in the future within a set time window for each substation area, and construct an objective function by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weight.

[0051] Multiple constraints are constructed, including constraints on the hot spot temperature of the transformer in the substation area. A transformer temperature model characterizing the relationship between photovoltaic output power and hot spot temperature is constructed, and the hot spot temperature is obtained by solving the transformer temperature model.

[0052] A peak-shaving optimization model is constructed based on the objective function and temperature constraints to solve for the optimal peak-shaving strategy, which is to adjust the output power of the residential rooftop photovoltaic system in each substation area.

[0053] It should be noted that the peak shaving optimization model can employ various algorithms, including but not limited to particle swarm optimization and genetic algorithms.

[0054] In this preferred embodiment, the calculation of the equivalent hours of power curtailment for each substation area specifically involves:

[0055] like Figure 2 As shown, the theoretical photovoltaic output power curve in the historical data, as well as the actual photovoltaic output power curve and the curtailment period in the corresponding historical data, are obtained. If the actual photovoltaic output power at a certain moment is less than the set output threshold and the difference between the theoretical photovoltaic output power and the actual photovoltaic output power is greater than the set difference threshold, it is considered that the corresponding moment is in the curtailment period.

[0056] The total amount of power curtailed during the curtailment period is obtained, which is the integral of the difference between the theoretical photovoltaic output power and the actual photovoltaic output power during the curtailment period; the total amount of power curtailed is divided by the installed capacity of the corresponding power station area to obtain the equivalent number of hours of power curtailment.

[0057] It should be noted that this embodiment measures historical data within one year, dividing a day into multiple time periods according to the peak-shaving step size; both the theoretical photovoltaic output power curve and the actual photovoltaic output power curve are curves composed of photovoltaic data at different times of the day; the theoretical photovoltaic output power curve is the ideal power generation capacity calculated using current meteorological data and a photovoltaic physical model; the calculated equivalent hours of curtailed power are the average curtailed power hours equivalent to the data within one year, representing the historical contribution to the power station area. The larger the equivalent hours of curtailed power, the greater the curtailment contribution, and the smaller the historical contribution to peak shaving.

[0058] Specifically, in this embodiment, the curtailment period refers to the period during which the grid-connected switch of the residential rooftop photovoltaic system remains closed, and the set output threshold is 10. -5 .

[0059] In this preferred embodiment, the step of setting the fairness weight of household photovoltaic systems in each substation area based on waste contribution, power generation efficiency, and payback period specifically involves:

[0060] The formula for calculating fairness weights is:

[0061]

[0062] Where, N s γ represents the total number of substation areas; i CE represents the fairness weight for household photovoltaic systems in the i-th substation area; i IRR i and IRP i These represent the waste contribution, power generation efficiency, and payback period of residential photovoltaic systems in the i-th substation area; L i This represents the equivalent number of hours of abandoned power in the i-th substation area. It should be noted that a larger abandoned power contribution indicates a smaller historical contribution to peak shaving, resulting in more peak shaving tasks being allocated currently. Greater power generation efficiency translates to stronger economic efficiency, leading to more daily peak shaving tasks. A longer payback period means a longer time required for the benefits of photovoltaic peak shaving to exceed the cost of power generation; its higher weighting compensates for the proportion of long-payback-period photovoltaic peak shaving, avoiding prioritizing only fast-turnover peak shaving. S i Let m be the household photovoltaic capacity of the i-th substation area; i p i These refer to the feed-in tariff and subsidy price for photovoltaic power generation, respectively; W i y i c represents the annual power generation and number of years of operation of the i-th substation area, respectively; b,i c s,i These represent the photovoltaic construction cost and installation subsidy for the i-th substation area, respectively.

[0063] It should be noted that the photovoltaic (PV) operation period in this embodiment is 25 years. The PV construction cost of the substation is 8,000 yuan. The installation subsidy cost of the substation is 2,000 yuan. The solar energy data comes from daily power generation data in rural areas, with a peak-shaving step size of 60 minutes. This embodiment considers four substation areas (substation area A, substation area B, substation area C, and substation area D) connected to the same distribution network. Each substation area includes several residential rooftop PV systems and transformers. The substation parameters are shown in Table 1.

[0064] Table 1 Substation Parameters

[0065]

[0066] This embodiment uses different fairness weights to test the rationality of the fairness weights in substation area B. The different fairness weights are: no fairness weight, using only a single normalized CE. i Normalized IRR i and normalized IRP iAs a fairness weight; or two of them can be used as fairness weights, and combined with the normalized CE of this embodiment. i Normalized IRR i and normalized IRP i The results are shown in Table 2, where the weights are added together as fairness weights.

[0067] Table 2 shows the results of fair peak shaving with different fairness weights.

[0068]

[0069] It can be seen that the single-weight benchmark yield slightly improves fairness but exacerbates the reduction loss, while the dual-weight benchmark further optimizes the allocation, but still has the problem of incomplete dimensional coverage; the fairness weight in this embodiment has the lowest peak-shaving cost and the smallest Gini coefficient, that is, it is the fairest.

[0070] In this preferred embodiment, the objective function constructed by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weights is specifically as follows:

[0071] Calculate the peak-shaving cost at the current time t0 for each substation area and the predicted peak-shaving cost for all peak-shaving steps within the set time window L in the future; the peak-shaving cost is the normalized fairness weight multiplied by the peak-shaving efficiency factor and then multiplied by the current time t0; the predicted peak-shaving cost at time t is the normalized fairness weight multiplied by the peak-shaving efficiency factor and then multiplied by the predicted amount of abandoned electricity at time t based on the prediction model that takes uncertainty into account; t = [t0 + δ, t0 + 2δ, ..., t0 + δ + L], where δ is the peak-shaving step interval; L is the set time window;

[0072] The rolling peak-shaving cost of each substation area is obtained by adding the peak-shaving cost of all predicted peak-shaving costs. The objective function is to minimize the sum of the rolling peak-shaving costs of all substation areas divided by the total number of substation areas.

[0073] Specifically, the formula for the objective function is:

[0074]

[0075] in, Let be the peak-shaving cost of the i-th substation area at time t0; Let t be the predicted peak-shaving cost for the i-th substation area at time t; t = [t0 + δ, t0 + 2δ, ..., t0 + δ + L], where δ is the peak-shaving step size; and L is the set time window. Let t0 be the amount of electricity wasted in the i-th substation area; To predict the amount of power wasted in the i-th substation area at time t; λ PVCost per unit of abandoned electricity; Let be the peak-shaving efficiency factor for the i-th substation area at time t0; it should be noted that the peak-shaving efficiency factor is obtained based on line loss and response delay, and the formula is:

[0076]

[0077] in, Let be the line loss power at time t0 of the i-th substation; Let be the total active power of the line at time t0 of the i-th substation; α is a set coefficient between 0 and 1; μ i Let be the response delay time of the i-th substation.

[0078] It should be noted that the selection of the time window L in this embodiment has both adaptability and stability; the impact of different time windows on peak shaving costs is shown in Table 3; the Gini coefficient under different time windows L is as follows: Figure 3 As shown;

[0079] Table 3. Impact of different time windows on peak shaving costs

[0080] It can be seen that when the time window varies from 3 to 9, the peak-shaving cost does not change significantly. When L is larger, more time points are predicted, resulting in smoother control actions and a smaller Gini coefficient, but the difference is not significant. This indicates that the peak-shaving optimization model of this invention has real-time adaptive prediction due to the incorporation of uncertainties, thus maintaining consistency in the overall peak-shaving decision when the time window changes. It should be noted that Tables 3 and 2 are results obtained from simulation tests based on data from different scheduling days; therefore, the total peak-shaving cost of substation area B in Table 2 differs from that in Table 3.

[0081] It should be noted that due to the uncertainty of photovoltaic power generation, especially the possibility of rapid cloud movement on a minute-by-minute scale causing sudden changes in the total horizontal irradiance predicted by the weather station, uncertainty is introduced through a random term.

[0082] In this preferred embodiment, the amount of electricity to be abandoned at time t is predicted according to a prediction model that takes uncertainty into account, specifically as follows:

[0083] The photovoltaic output power at the current time t0 is obtained, as well as the meteorological data predicted by the meteorological station at time t. The meteorological data includes weather type, total horizontal irradiance, and ambient temperature. The weather type includes clear sky, scattered clouds, and cloudy.

[0084] Different perturbation distributions are set for different weather types. A set number of random values ​​are extracted from the perturbation distribution corresponding to the current weather type. The average value of all random values ​​is calculated as the perturbation term, and the random values ​​satisfy the set time constraints. The total irradiance of the water surface plus the perturbation term is used as the total irradiance of the horizontal surface for uncertainty correction. The total irradiance of the horizontal surface for uncertainty correction, the ambient temperature, and the photovoltaic output power at the current time t0 are input into the pre-trained Transformer algorithm to predict the amount of power wasted.

[0085] Specifically, the number of iterations is set to 1000. Different perturbation distributions are set for different weather types: clear skies are set to a normal distribution, scattered clouds are set to a truncated normal distribution with a fixed range, and cloudy skies are set to a Laplace distribution. It should be noted that the predicted power curtailment at time t is a prediction under the condition that the peak-shaving command remains the same as the peak-shaving command at time t0. Therefore, the input value includes the photovoltaic output power at the current time t0.

[0086] In this preferred embodiment, the random value satisfies a set time constraint, specifically:

[0087] When t equals t0+δ, the time constraint is that the ratio of the disturbance term at time t to the amount of electricity wasted at time t0 is less than the set proportional coefficient.

[0088] When t is greater than t0+δ, calculate the correlation coefficient between the sequence formed by sorting all random values ​​at time t in descending order and the sequence formed by sorting all random values ​​at time t-δ in descending order. The time constraint is that the correlation coefficient is greater than the set correlation coefficient threshold. If the set time constraint is not met, random values ​​are re-generated.

[0089] It should be noted that the correlation coefficient threshold in this embodiment is set to 0.7.

[0090] In this preferred embodiment, the construction of multiple constraints specifically involves:

[0091] The constraints include the hot spot temperature constraints of the transformers in the substation area, the photovoltaic output power constraints, and the load constraints.

[0092] The constraint condition for the hot spot temperature of transformers in the substation area is that the hot spot temperature of transformers in all substation areas at the current moment is less than the set maximum temperature threshold. The constraint condition for photovoltaic output power is that the photovoltaic output power of all substation areas at the current moment is greater than or equal to 0 and less than or equal to the set maximum power threshold. The load constraint condition is that the sum of the thermal power output power and photovoltaic output power of all substation areas is equal to the total load demand.

[0093] It should be noted that the maximum temperature threshold set in this embodiment is 120°C.

[0094] In this preferred embodiment, the construction of the transformer temperature model characterizing the relationship between photovoltaic output power and hotspot temperature specifically involves:

[0095] θ t =θ O +Δθ h1 -Δθ h2

[0096]

[0097] Where, θ t For hotspot temperature; θ O Δθ represents the initial value of the transformer top oil temperature. h1 , Δθ h2 These are the first and second components of the hotspot temperature rise, respectively; θ a , Δθ hr , Δθ Or These represent the temperature rise relative to ambient temperature, top oil temperature, and hot spot temperature, respectively; δ is the peak shaving step interval; x is the oil temperature index; y is the hot spot temperature rise index; C1, C2, and C3 are integration constants, determined by the initial value of the transformer top oil temperature and the initial hot spot temperature of the transformer; k 11 The oil time constant weighting coefficient; k 21 k is the hotspot temperature rise ratio coefficient. 22 τ is the dynamic correction coefficient for hot spot temperature rise; τ0 is the oil thermal time constant; τ w P is the thermal time constant of the winding; L Total electricity demand; P PV S represents the current photovoltaic output power. TN R represents the installed capacity; R is the set dynamic correction coefficient for the photovoltaic peak-shaving effect on the transformer thermal model.

[0098] Specifically, the operating parameters of the transformers in the substation are shown in Table 4;

[0099] Table 4 Operating parameters of transformers in substations

[0100]

[0101]

[0102] In this preferred embodiment, after solving for the optimal peak-shaving strategy through the peak-shaving optimization model, the Gini coefficient between substation areas is calculated as the peak-shaving fairness assessment index among households in all substation areas. If the peak-shaving fairness assessment index exceeds the set fairness threshold, historical data is re-acquired to calculate the fairness weight of household photovoltaics in each substation area.

[0103] It should be noted that the Gini coefficient G is calculated using the following formula:

[0104]

[0105] in, Let be the peak-shaving cost of the i-th and j-th substation areas at time t0; N represents the average peak-shaving cost for all substation areas at time t0; s This represents the total number of substation areas.

[0106] Embodiment 2 of this invention proposes a peak regulation system for fair cost sharing of residential rooftop photovoltaic power generation based on the method described in Embodiment 1 of this invention. The system includes a module for calculating the equivalent hours of curtailed power, a module for setting fairness weights, a module for constructing an objective function, a module for constructing constraints, and a module for generating peak regulation strategies.

[0107] The module for calculating the equivalent hours of curtailed electricity: retrieves the curtailed electricity periods and installed capacity of residential rooftop photovoltaic systems in each substation area from historical data, and calculates the equivalent hours of curtailed electricity for each substation area;

[0108] Fairness weight setting module: Calculates the waste contribution based on the equivalent hours of abandoned electricity, and calculates the power generation benefit and payback period based on electricity price and cost; sets the fairness weight of household photovoltaic in each substation area according to the waste contribution, power generation benefit and payback period;

[0109] Objective function construction module: Obtain the current amount of abandoned electricity in the substation area, predict the amount of abandoned electricity in the future within a set time window for each substation area, and construct the objective function by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weight;

[0110] Constraint Construction Module: Constructs multiple constraints, including constraints on the hot spot temperature of the transformer in the substation area, and constructs a transformer temperature model characterizing the relationship between photovoltaic output power and hot spot temperature. The hot spot temperature is obtained by solving the transformer temperature model.

[0111] Peak shaving strategy generation module: Based on the objective function and temperature constraints, a peak shaving optimization model is constructed to solve for the optimal peak shaving strategy, which is to adjust the output power of the residential rooftop photovoltaic system in each substation area.

[0112] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not 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 modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation, characterized in that, Includes the following: Obtain the curtailment periods and installed capacity of residential rooftop photovoltaic systems in each substation area from historical data, and calculate the equivalent number of curtailment hours for each substation area; The waste contribution is calculated based on the equivalent hours of abandoned electricity, and the power generation benefit and payback period are calculated based on electricity price and cost; the fairness weight of household photovoltaic in each substation area is set according to the waste contribution, power generation benefit and payback period. The formula for calculating fairness weights is: in, This represents the total number of substation areas. For the first Fairness weighting of household solar power systems in each substation area; , and The first Waste contribution, power generation benefits and payback period of household photovoltaic systems in a substation area; For the first The equivalent number of hours of power curtailment in each substation area; For the first The household photovoltaic capacity in the substation area; , These are the feed-in tariff and subsidy price for photovoltaic power generation, respectively. , The first Annual power generation and number of years of operation of each substation area; , The first The cost of photovoltaic construction and installation subsidies for each substation area; Obtain the current amount of abandoned electricity in the substation area, predict the amount of abandoned electricity in the future within a set time window for each substation area, and construct an objective function by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weight. Specifically, the formula for the objective function is: in, For the first Substation area Peak shaving costs at any given time; For the predicted first Substation area Peak shaving costs at any given time; , This is the step size for peak shaving; For the set time window; For the first Substation area The amount of electricity wasted at any given moment; To predict the first Substation area The amount of electricity wasted at any given moment; Cost per unit of abandoned electricity; For the first Substation area The peak-shaving efficiency factor at any given time; it should be noted that the peak-shaving efficiency factor is obtained based on line loss and response delay, and the formula is: in, For the first Substation Line power loss at any given time; For the first Substation The total active power of the line at any given time; The coefficient is set between 0 and 1; For the first The response delay time of each substation; Multiple constraints are constructed, including constraints on the hot spot temperature of the transformer in the substation area. A transformer temperature model characterizing the relationship between photovoltaic output power and hot spot temperature is constructed, and the hot spot temperature is obtained by solving the transformer temperature model. A peak-shaving optimization model is constructed based on the objective function and temperature constraints to solve for the optimal peak-shaving strategy, which is to adjust the output power of the residential rooftop photovoltaic system in each substation area.

2. The peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation according to claim 1, characterized in that: The calculation of the equivalent hours of power curtailment for each substation area is specifically as follows: Obtain the theoretical photovoltaic output power curve from historical data, as well as the corresponding actual photovoltaic output power curve and curtailment period from historical data. If the actual photovoltaic output power at a certain moment is less than the set output threshold and the difference between the theoretical photovoltaic output power and the actual photovoltaic output power is greater than the set difference threshold, then the corresponding moment is considered to be in a curtailment period. The total amount of power curtailed during the curtailment period is obtained, which is the integral of the difference between the theoretical photovoltaic output power and the actual photovoltaic output power during the curtailment period; the total amount of power curtailed is divided by the installed capacity of the corresponding power station area to obtain the equivalent number of hours of power curtailment.

3. The peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation according to claim 2, characterized in that: The objective function is constructed by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weight, specifically as follows: Calculate the current time for each substation area Peak shaving costs at specific times and the time window set in the future The predicted peak-shaving cost is calculated by multiplying the normalized fairness weight by the peak-shaving efficiency factor and then by the current time step. The amount of electricity wasted at any given moment; The predicted peak-shaving cost at any given time is calculated by multiplying the normalized fairness weight by the peak-shaving efficiency factor, and then by the cost predicted using a prediction model that takes uncertainty into account. The amount of electricity wasted at any given moment; , This is the step interval for peak adjustment; For the set time window; The rolling peak-shaving cost of each substation area is obtained by adding the peak-shaving cost of all predicted peak-shaving costs. The objective function is to minimize the sum of the rolling peak-shaving costs of all substation areas divided by the total number of substation areas.

4. The peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation according to claim 3, characterized in that: Predictions based on a forecasting model that takes uncertainty into account The amount of electricity wasted at any given time is as follows: Get the current time The photovoltaic output power at any given time, and the weather station forecast. The meteorological data at any given time includes weather type, total horizontal irradiance, and ambient temperature. Weather type includes clear sky, scattered clouds, and cloudy. Different disturbance distributions are set for different weather types. A set number of random values ​​are extracted from the disturbance distribution corresponding to the current weather type. The average of all random values ​​is calculated as the disturbance term, and the random values ​​satisfy the set time constraints. The total horizontal irradiance is added to the disturbance term to obtain the total horizontal irradiance for uncertainty correction. The total horizontal irradiance for uncertainty correction, the ambient temperature, and the current time are then compared. The photovoltaic output power at any given time is input into the pre-trained Transformer algorithm to predict the amount of power wasted.

5. The peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation according to claim 4, characterized in that: The random value satisfies the set time constraint, specifically: when equal At that time, the time constraint is The perturbation term at time and The ratio of the amount of electricity wasted at any given time is less than the set proportional coefficient; when Greater than At that time, the calculation will All random values ​​at time t are sorted in descending order to form a sequence, and then... All random values ​​at time points are sorted from largest to smallest to form the correlation coefficient between sequences. The time constraint is that the correlation coefficient must be greater than a set correlation coefficient threshold. If the set time constraint is not met, random values ​​are re-generated.

6. The peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation according to claim 3, characterized in that: The construction of multiple constraints is specifically as follows: The constraints include the hot spot temperature constraints of the transformers in the substation area, the photovoltaic output power constraints, and the load constraints. The constraint condition for the hot spot temperature of transformers in the substation area is that the hot spot temperature of transformers in all substation areas at the current moment is less than the set maximum temperature threshold. The constraint condition for photovoltaic output power is that the photovoltaic output power of all substation areas at the current moment is greater than or equal to 0 and less than or equal to the set maximum power threshold. The load constraint condition is that the sum of the thermal power output power and photovoltaic output power of all substation areas is equal to the total load demand.

7. The peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation according to claim 3, characterized in that: The construction of the transformer temperature model characterizing the relationship between photovoltaic output power and hotspot temperature is specifically as follows: in, For hotspot temperature; This is the initial value of the top oil temperature of the transformer; , These are the first and second components of the hotspot temperature rise, respectively. , , These are the temperature rise relative to ambient temperature, top oil temperature, and hot spot temperature, respectively. This is the step interval for peak adjustment; This refers to the oil temperature index; The hotspot temperature rise index; , , All are integral constants; The oil time constant weighting coefficient; This refers to the hotspot temperature rise ratio coefficient. This is a dynamic correction coefficient for hotspot temperature rise; The oil heating time constant; The winding thermal time constant; Total electricity demand; This represents the current photovoltaic output power. This refers to the installed capacity; This is the dynamic correction coefficient for the photovoltaic peak-shaving effect on the transformer thermal model.

8. The peak regulation method for fair cost sharing of residential rooftop photovoltaic power generation according to claim 3, characterized in that: After solving for the optimal peak-shaving strategy through the peak-shaving optimization model, the Gini coefficient between substation areas is calculated as the peak-shaving fairness assessment index among households in all substation areas. If the peak-shaving fairness assessment index exceeds the set fairness threshold, historical data is re-acquired to calculate the fairness weight of household photovoltaics in each substation area.

9. A peak regulation system for fair cost sharing of residential rooftop photovoltaic power generation based on the method of any one of claims 1-8, comprising a module for calculating the equivalent hours of curtailed power, a module for setting fairness weights, a module for constructing an objective function, a module for constructing constraints, and a module for generating peak regulation strategies, characterized in that: The module for calculating the equivalent hours of curtailed electricity: retrieves the curtailed electricity periods and installed capacity of residential rooftop photovoltaic systems in each substation area from historical data, and calculates the equivalent hours of curtailed electricity for each substation area; Fairness weight setting module: Calculates the waste contribution based on the equivalent hours of abandoned electricity, and calculates the power generation benefit and payback period based on electricity price and cost; sets the fairness weight of household photovoltaic in each substation area according to the waste contribution, power generation benefit and payback period; Objective function construction module: Obtain the current amount of abandoned electricity in the substation area, predict the amount of abandoned electricity in the future within a set time window for each substation area, and construct the objective function by combining the current amount of abandoned electricity, the predicted amount of abandoned electricity, and the fairness weight; Constraint Construction Module: Constructs multiple constraints, including constraints on the hot spot temperature of the transformer in the substation area, and constructs a transformer temperature model characterizing the relationship between photovoltaic output power and hot spot temperature. The hot spot temperature is obtained by solving the transformer temperature model. Peak shaving strategy generation module: Based on the objective function and temperature constraints, a peak shaving optimization model is constructed to solve for the optimal peak shaving strategy, which is to adjust the output power of the residential rooftop photovoltaic system in each substation area.

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