Distributed photovoltaic access district operation optimization system

Abstract

The invention relates to the technical field of power grid management, in particular to a distributed photovoltaic access district operation optimization system which comprises a power demand analysis module, an output capacity calculation module, a power cost evaluation module, a power storage demand evaluation module, an energy response management module and an electricity price dynamic response module. According to the invention, through integration of real-time monitoring data and analysis of power consumption data of multiple transformer areas, the change trend of power demand is effectively predicted, calculation of power generation cost and power storage cost is combined, a data basis is provided for power dispatching and management, dispatching of photovoltaic power generation equipment and management of power storage equipment are optimized, the energy efficiency is improved, and the cost is reduced. The power price is dynamically adjusted according to the real-time load and the generating capacity, market and environment changes are matched, the power consumption mode of a user is optimized, the adaptive capacity of a power grid to renewable energy access is improved, stable operation of the power grid is ensured, and the operation performance of the power grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid management, and in particular to a distributed photovoltaic access area operation optimization system. Background Art

[0002] The field of power grid management technology focuses on the monitoring, dispatching and optimization of power systems to ensure the safety, reliability and economy of power supply. It uses a variety of information technology, automation technology and power engineering technology to conduct real-time monitoring and dynamic management of power grids, including load forecasting, power dispatching, fault detection, integration and optimization management of distributed energy resources, effectively improve the operation efficiency of the power grid, respond to market and environmental changes, and improve the adaptability of the power system to the access of renewable energy.

[0003] Among them, a distributed photovoltaic access substation operation optimization system focuses on improving the economy and efficiency of distributed photovoltaic power generation in the substation-level power grid. By optimizing the scheduling and management of photovoltaic power generation, it improves the energy efficiency and economic benefits of the power grid, reduces operating costs, improves power generation efficiency, ensures the stability of the power grid, and adapts to and utilizes renewable energy through intelligent power grid management, helping power grid operators to achieve refined energy management and deployment, and enhance the power grid's ability to adapt to fluctuations and uncertainties in renewable energy.

[0004] Traditional photovoltaic access area operation optimization technology lacks sufficient flexibility and adaptability when dealing with large-scale distributed photovoltaic power generation systems. It relies on traditional power dispatching strategies in the management of distributed energy resource integration, lacks the ability to predict and adapt to the volatility and uncertainty of renewable energy, and has difficulty in handling a high proportion of renewable energy access, and has difficulty predicting and controlling the output of renewable energy. It lacks the ability to optimize the operation of the power grid, and lacks sufficient dynamic adjustment mechanisms in electricity price setting and load management, resulting in supply shortages during high-demand periods and waste of resources during low-demand periods, resulting in insufficient security of power supply and increased operational risks and costs. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a distributed photovoltaic access area operation optimization system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme, a distributed photovoltaic access area operation optimization system comprises:

[0007] The power demand analysis module collects power consumption data of multiple substations based on the substation location information, analyzes the impact of various factors on power consumption, evaluates the changing trend of energy demand, and generates energy demand forecast results;

[0008] The output capacity calculation module predicts the power generation capacity of the photovoltaic equipment based on the energy demand prediction result and generates power generation capacity prediction data by analyzing the influence of various factors on the output capacity of the photovoltaic power generation equipment;

[0009] The power cost assessment module uses the power generation capacity prediction data to calculate the deployment, operation and maintenance costs of the photovoltaic equipment, calculates the production cost per unit of power, and generates a power generation cost calculation result;

[0010] The power storage demand assessment module evaluates the power storage demand of multiple substations and calculates the power storage cost based on the power generation cost calculation result and the power consumption demand and power generation mode in the target substation, and generates power storage cost assessment information;

[0011] The energy response management module adjusts the energy output and storage configuration of the photovoltaic equipment based on the electricity storage cost assessment information and combines the energy demand forecast information of multiple substations to generate resource scheduling optimization parameters;

[0012] The electricity price dynamic response module analyzes the electricity market demand and supply conditions based on the resource scheduling optimization parameters and the real-time load and photovoltaic power generation of the power grid, adjusts the electricity price to optimize power consumption, and generates a power grid electricity price adjustment record.

[0013] As a further solution of the present invention, the energy demand forecast results include expected peak load data, average load data, and energy demand patterns of multiple substations; the power generation capacity forecast data include power generation capacity at multiple time points, photovoltaic equipment specification parameters, and analysis results of the impact of performance degradation on power generation capacity; the power generation cost calculation results include the production cost per unit of electricity, operating cost data, and analysis results of the impact of maintenance and equipment replacement on costs; the power storage cost assessment information includes predicted battery replacement costs, battery capacity decline trend analysis results, and power storage demand data; the resource scheduling optimization parameters include photovoltaic power generation adjustment ratio parameters, energy storage equipment use priority information, and load demand response parameters; the grid electricity price adjustment records include electricity price lists for multiple time periods, adjustment area data, and seasonal electricity price change adjustment records.

[0014] As a further solution of the present invention, the power demand analysis module includes:

[0015] The power consumption data collection submodule collects power consumption data of multiple substations based on the substation location information, and standardizes and formats the data to generate a power consumption data set;

[0016] The power consumption data analysis submodule analyzes the impact of various factors on power consumption based on the power consumption data set, including industrial activities, climate change, and regional population, and generates an influencing factor identification result;

[0017] The electricity demand assessment submodule uses time series analysis based on the influencing factor identification results to assess the fluctuation pattern and change trend of energy demand and generate energy demand forecast results.

[0018] As a further solution of the present invention, the output capacity calculation module includes:

[0019] The influencing factor analysis submodule calculates the influence of various factors on the light intensity based on the energy demand forecast result, including geographical location, season, and time point, evaluates the output capacity of photovoltaic equipment at multiple locations, and generates output impact evaluation information;

[0020] The performance degradation assessment submodule evaluates the performance degradation trend of the equipment and the impact on the output capacity based on the output impact assessment information, and generates a performance degradation analysis result by performing a time series analysis on the power generation data of multiple photovoltaic devices;

[0021] The output prediction submodule predicts the power output of the photovoltaic power generation equipment in the power grid in multiple time periods and seasons based on the performance degradation analysis results, and generates power generation capacity prediction data.

[0022] As a further solution of the present invention, the specific formula for calculating the influence of multiple factors on light intensity is:

[0023]

[0024] Among them, x i A specific value representing a geographic location, season, or point in time. Represents x i The average value of y i Represents the corresponding light intensity value, Represents y i The result r represents the correlation strength between the two variables, and i is the index symbol of the target factor considered.

[0025] As a further solution of the present invention, the electricity cost assessment module includes:

[0026] The deployment cost calculation submodule calculates the deployment cost of the photovoltaic equipment, including equipment cost and installation cost, using the power generation capacity prediction data, and generates an equipment cost data set;

[0027] The operation demand analysis submodule considers the impact of equipment performance degradation on power generation efficiency based on the equipment cost data set, evaluates the maintenance frequency and replacement requirements of multiple equipment, calculates the maintenance costs of multiple equipment, and generates operation cost information in combination with the labor cost required for equipment operation;

[0028] The unit cost analysis submodule calculates the production cost of unit electricity based on the operating cost information, taking into account deployment cost, maintenance cost and power generation efficiency changes, and generates a power generation cost calculation result.

[0029] As a further solution of the present invention, the power storage demand assessment module includes:

[0030] The power generation and consumption analysis submodule, based on the power generation cost calculation result, analyzes the power consumption demand and power generation mode in the target area, evaluates the power storage demand of multiple areas, and generates power storage demand information;

[0031] The battery aging impact analysis submodule is based on the power storage demand information, by analyzing the impact of battery material aging and performance degradation on power storage capacity, predicting the maintenance and operating costs of energy storage equipment, and generating power storage maintenance cost information;

[0032] The electricity storage cost calculation submodule calculates the total electricity storage cost of the power grid based on the electricity storage maintenance cost information and in combination with the electricity storage demands of multiple substations, and generates electricity storage cost assessment information.

[0033] As a further solution of the present invention, the specific formula for calculating the total electricity storage cost of the power grid is:

[0034] C total =a+b×Q+c×V+d×P

[0035] Among them, C total represents the total storage cost of the power grid, Q represents the total storage demand of the entire power grid, covering the storage demand of all substations and the operating efficiency of energy storage equipment, V represents the battery maintenance cost variable, taking into account the cost impact brought about by battery performance degradation and increased maintenance frequency, P represents the policy change impact parameter, including the direct impact of policy factors such as taxes and subsidies on costs, to ensure that the model can adapt to the impact of economic policy changes, a is the intercept, reflecting the basic storage cost when there is no demand and other influencing factors, b is the impact coefficient of demand Q, indicating how the total storage cost changes for every unit increase in storage demand, c is the impact coefficient of maintenance cost V, reflecting the amount by which the total storage cost will increase for every unit increase in battery maintenance cost, and d is the impact coefficient of policy factor P, showing how the storage cost will respond to every unit change in policy adjustments such as taxes and subsidies.

[0036] As a further solution of the present invention, the energy response management module includes:

[0037] The demand pattern analysis submodule analyzes the energy usage patterns and peak-valley differences of multiple substations based on the electricity storage cost evaluation information and the energy demand forecast information of multiple substations, evaluates the output demand of the power grid in multiple time periods, and generates demand difference information;

[0038] The power storage configuration optimization submodule adjusts the power storage configuration of multiple substations based on the demand difference information, including adjusting the energy storage capacity of multiple substations to match the consumption demand of multiple time periods, optimizing the charging and discharging parameters to optimize the working efficiency and life of the battery, and generating a storage configuration optimization record;

[0039] The power resource scheduling submodule adjusts power distribution based on the storage configuration optimization record, considers the balance between energy supply and demand, optimizes the load response and resource allocation of the power grid, and generates resource scheduling optimization parameters.

[0040] As a further solution of the present invention, the electricity price dynamic response module includes:

[0041] The power market analysis submodule analyzes the demand and supply status of the power market based on the resource scheduling optimization parameters, predicts the supply and demand status of multiple time periods and seasons, and generates market demand analysis results;

[0042] The adjustment demand calculation submodule evaluates the electricity prices that need to be adjusted under various supply and demand conditions based on the market demand analysis results, taking into account consumer behavior and market response, and generates price adjustment analysis data;

[0043] The price list updating submodule adjusts the tiered electricity prices for multiple time periods based on the price adjustment analysis data and the predicted electricity consumption pattern, constructs a price list, records the adjustment operations, and generates a grid electricity price adjustment record.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by integrating real-time monitoring data and multi-area electricity consumption data analysis, the changing trend of electricity demand is effectively predicted, and combined with the calculation of power generation cost and power storage cost, a data basis is provided for power dispatching and management, the dispatching of photovoltaic power generation equipment and the management of power storage equipment are optimized, energy efficiency is improved, and dynamic adjustment of electricity prices according to real-time load and power generation is achieved to match market and environmental changes, optimize users' power consumption patterns, improve the adaptability of the power grid to the access of renewable energy, ensure the stable operation of the power grid, and improve the operating performance of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0048] Figure 3 It is a flow chart of the power demand analysis module of the present invention;

[0049] Figure 4It is a flow chart of the output capacity calculation module of the present invention;

[0050] Figure 5 It is a flow chart of the electricity cost assessment module of the present invention;

[0051] Figure 6 It is a flow chart of the power storage demand assessment module of the present invention;

[0052] Figure 7 It is a flow chart of the energy response management module of the present invention;

[0053] Figure 8 This is a flow chart of the electricity price dynamic response module of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] Embodiment 1

[0057] See also Figure 1 to Figure 2 , a distributed photovoltaic access area operation optimization system includes:

[0058] The power demand analysis module collects power consumption data of multiple substations based on the substation location information, analyzes the impact of various factors on power consumption, evaluates the changing trend of energy demand, and generates energy demand forecast results;

[0059] The output capacity calculation module is based on the energy demand forecast results. By analyzing the impact of various factors on the output capacity of photovoltaic power generation equipment, it predicts the power generation capacity of photovoltaic equipment and generates power generation capacity forecast data.

[0060] The power cost assessment module uses the power generation capacity forecast data to calculate the deployment, operation and maintenance costs of photovoltaic equipment, calculate the production cost per unit of electricity, and generate the power generation cost calculation results;

[0061] The power storage demand assessment module evaluates the power storage demand of multiple substations and calculates the power storage cost based on the power generation cost calculation results and the power consumption demand and power generation mode in the target substation, and generates power storage cost assessment information;

[0062] The energy response management module adjusts the energy output and storage configuration of photovoltaic equipment based on the storage cost assessment information and combines the energy demand forecast information of multiple substations to generate resource scheduling optimization parameters;

[0063] The electricity price dynamic response module is based on resource scheduling optimization parameters, according to the real-time load of the power grid and photovoltaic power generation, analyzes the power market demand and supply conditions, adjusts electricity prices to optimize power consumption, and generates power grid electricity price adjustment records.

[0064] The energy demand forecast results include expected peak load data, average load data, and energy demand patterns of multiple substations. The power generation capacity forecast data include power generation capacity at multiple time points, photovoltaic equipment specifications and parameters, and analysis results of the impact of performance degradation on power generation capacity. The power generation cost calculation results include the production cost per unit of electricity, operating cost data, and analysis results of the impact of maintenance and equipment replacement on costs. The power storage cost assessment information includes the predicted battery replacement cost, battery capacity decline trend analysis results, and power storage demand data. The resource scheduling optimization parameters include photovoltaic power generation adjustment ratio parameters, energy storage equipment use priority information, and load demand response parameters. The grid electricity price adjustment records include electricity price lists for multiple time periods, adjustment area data, and seasonal electricity price change adjustment records.

[0065] See also Figure 2 and Figure 3 , the power demand analysis module includes:

[0066] The power consumption data collection submodule collects power consumption data of multiple substations based on the substation location information, and standardizes and formats the data to generate a power consumption data set;

[0067] In the electricity consumption data collection submodule, based on the substation location information, electricity consumption data of multiple substations are collected and data standardization is performed. The standardization process involves data cleaning, elimination of outliers and missing values, and formatting, including converting all data into a unified data format, such as standardizing timestamps to ISO 8601 format, and unifying electricity data into kilowatt-hours. Combined with scaling processing, it is convenient for use in subsequent models. The scaling adopts the Min-Max standardization method to scale the data to between 0 and 1, eliminating the impact of different units, making the data set suitable for machine learning processing, and generating an electricity consumption data set. The data set provides accurate baseline data for subsequent analysis to ensure the accuracy and reliability of subsequent analysis.

[0068] The power consumption data analysis submodule analyzes the impact of various factors on power consumption based on the power consumption data set, including industrial activities, climate change, and regional population, and generates influencing factor identification results;

[0069] In the power consumption data analysis submodule, based on the power consumption data set, the factors analyzed include the power demand of industrial activities, the seasonal changes in the impact of climate change on power consumption, and the changes in power demand caused by regional population increases and decreases. The techniques used include regression analysis and variance analysis. Through target analysis, the historical power usage data of industrial activities is associated with the regional economic activity index. The linear regression model is used to determine the relationship strength and feature importance between the two. Variance analysis is used to evaluate the differences in power consumption under different seasons or climatic conditions, and to identify the climate factors that have the greatest impact on power consumption. Through the correlation analysis of demographic data and power consumption, the long-term impact of population changes on power demand is evaluated. The target steps are all executed through the pandas and scikit-learn libraries in Python to ensure the data processing and calculation accuracy of the analysis process, generate the results of influencing factor identification, and provide a scientific basis for subsequent demand forecasting.

[0070] The electricity demand assessment submodule uses time series analysis to assess the fluctuation pattern and change trend of energy demand based on the results of influencing factor identification and generates energy demand forecast results;

[0071] In the electricity demand assessment submodule, based on the results of influencing factor identification, the steps of performing time series analysis include data preprocessing, model selection, parameter adjustment and trend prediction. Data preprocessing includes data deseasonalization and stationarity detection to ensure the stability of the time series. The technology used is ADF test. The appropriate time series model is selected according to the data characteristics. The applicability of the model is evaluated by AIC and BIC criteria, and the model parameters are optimized and adjusted. The grid search method is used to find the optimal parameter combination to ensure the prediction accuracy of the model. The selected model is used to predict future energy demand. The prediction results are inversely converted and converted back to the original proportion to generate energy demand forecast results. The results help decision makers understand the fluctuation pattern and change trend of future energy demand and provide support for power grid operation and planning.

[0072] See also Figure 2 and Figure 4 , the output capacity calculation module includes:

[0073] The influencing factor analysis submodule calculates the impact of multiple factors on light intensity based on the energy demand forecast results, including geographical location, season, and time point, evaluates the output capacity of photovoltaic equipment at multiple locations, and generates output impact assessment information;

[0074] The specific formula for calculating the impact of various factors on light intensity is:

[0075]

[0076] Among them, x i A specific value representing a geographic location, season, or point in time. Represents x i The average value of y i Represents the corresponding light intensity value, Represents y i The result r represents the correlation strength between the two variables, and i is the index symbol of the target factor considered.

[0077] formula:

[0078]

[0079] Detailed explanation of the formula and the process of formula calculation and derivation:

[0080] The formula is used to calculate the correlation coefficient r between latitude and light intensity, and the result is used to evaluate the impact of position changes on light intensity.

[0081] Parameter meaning and setting value:

[0082] x i is the latitude of each location, reflecting the change in position from the equator to the pole, y i is the light intensity at the corresponding latitude. Assume that at 10°N, the light intensity = 900W / m2; at 20°N, the light intensity = 600W / m2; at 30°N, the light intensity = 300W / m2; at 40°N, the light intensity = 100W / m2;

[0083] is the average value of latitude, which is 25°, reflecting the average latitudinal position;

[0084] is the average value of light intensity, which is 475W / m2, reflecting the average light intensity;

[0085] Substitute the parameters into the formula for calculation:

[0086] calculate

[0087]

[0088] calculate

[0089]

[0090] calculate

[0091]

[0092] Calculate r:

[0093]

[0094] The result -0.443 shows that there is a negative correlation between latitude and light intensity, indicating that an increase in latitude will lead to a decrease in light intensity. The results are used to predict the power generation capacity of photovoltaic equipment based on the geographical layout location.

[0095] The performance degradation assessment submodule is based on the output impact assessment information. By performing time series analysis on the power generation data of multiple photovoltaic devices, the performance degradation trend of the equipment and its impact on the output capacity are evaluated, and the performance degradation analysis results are generated.

[0096] In the performance degradation assessment submodule, based on the output impact assessment information, time series analysis is performed to track and predict the performance degradation trend of photovoltaic equipment. The seasonally adjusted autoregressive moving average model is used to process data with seasonal fluctuations and trends. The historical power generation data of the equipment is analyzed through the SARIMA model to identify degradation trends and periodic patterns. The parameter selection of the model is optimized based on the Akaike Information Criterion and the Bayesian Information Criterion to ensure the accuracy and applicability of the model. It evaluates how the equipment performance decays over time. Taking into account the differences between different equipment and installation environments, equipment-specific adjustment factors such as temperature compensation and load regulation are introduced. The calculation of the target factor is based on actual operating data and the technical specifications of the equipment manufacturer. The generated performance degradation analysis results reveal the health status and life expectancy of each device, providing a basis for maintenance planning and performance optimization.

[0097] The output prediction submodule predicts the power output of photovoltaic power generation equipment in the power grid in multiple time periods and seasons based on the performance degradation analysis results, and generates power generation capacity prediction data;

[0098] In the output prediction submodule, the future output of photovoltaic power generation equipment is predicted based on the results of performance degradation analysis. The random forest algorithm in machine learning is used to process large-scale input variables and consider their nonlinear relationships. The random forest model estimates the power generation capacity of different equipment in future seasons and different time periods through training data sets. The model input includes the historical performance data of the equipment, predicted light intensity, climatic conditions and performance degradation indicators. Through model training and verification, the optimal number and depth of trees are determined to ensure the accuracy and generalization ability of the prediction. The process includes control of model overfitting and performance testing on the verification set to evaluate the actual application effect of the model. The generated power generation capacity prediction data presents the possible output conditions in each future time period, providing accurate forward-looking information for demand response and capacity planning of the power grid.

[0099] See also Figure 2 and Figure 5 , the electricity cost assessment module includes:

[0100] The deployment cost calculation submodule uses the power generation capacity prediction data to calculate the deployment cost of photovoltaic equipment, including equipment cost and installation cost, and generates an equipment cost data set;

[0101] In the deployment cost calculation submodule, the cost accounting of photovoltaic equipment is performed based on the power generation capacity forecast data. The cost analysis method is used to calculate the purchase and installation costs of each part of the equipment, including solar panels, inverters, support systems and necessary electrical connection materials. The price information of common equipment on the market is collected, and the discount for bulk purchases is estimated based on the expected installation scale. The logistics and installation costs are estimated, including transportation costs, site preparation work and the labor costs of professional installers. Taking into account the installation standards and regulations in different regions, additional safety measures and insurance costs need to be adjusted. The target data is managed and calculated through the enterprise resource planning system. Combined with the project management software, an equipment cost data set is generated. The data set lists various equipment and their related costs, providing an accurate basis for budget formulation and financial analysis.

[0102] The operation demand analysis submodule is based on the equipment cost data set, considers the impact of equipment performance degradation on power generation efficiency, evaluates the maintenance frequency and replacement requirements of multiple equipment, calculates the maintenance costs of multiple equipment, and generates operation cost information in combination with the labor cost required for equipment operation;

[0103] In the operation demand analysis submodule, the equipment maintenance cost is analyzed based on the equipment cost data set. The equipment management software is used to track equipment performance and predict maintenance needs. The expected life and replacement cycle of key equipment components such as inverters and photovoltaic panels are estimated in combination with the performance degradation curves provided by the equipment manufacturer. Taking into account the performance differences of equipment under different climatic conditions, the life cycle cost analysis method is used to estimate the maintenance cost. The method considers the impact of maintenance activities on operational efficiency, including the impact of planned and unplanned maintenance on power generation interruptions, and the calculation of labor costs. The labor costs involved include regular inspections, component replacements, and labor costs for emergency repairs. Data is collected and analyzed through the maintenance management system to ensure the accuracy and timeliness of cost estimates. The generated operating cost information lists the frequency and cost of various maintenance activities, providing an important basis for operational decision-making and cost control.

[0104] The unit cost analysis submodule calculates the production cost of unit electricity based on the operating cost information, taking into account the deployment cost, maintenance cost and power generation efficiency changes, and generates the power generation cost calculation results;

[0105] In the unit cost analysis submodule, the production cost per unit of electricity is calculated based on operating cost information, deployment and maintenance cost data are collected and integrated, and the total cost is allocated to the expected electricity output using a cost accounting model, including real-time monitoring of the power generation efficiency of photovoltaic equipment and historical data analysis, estimating its average power generation efficiency over its life cycle, taking into account that equipment performance may fluctuate due to aging and changes in the external environment, and applying a dynamic adjustment mechanism to update cost forecasts, including regular re-evaluation of equipment maintenance frequency and costs, and taking into account the impact of seasonal changes in power generation on cost allocation. The generated power generation cost calculation results provide an accurate cost per kilowatt-hour of electricity, which is crucial for setting electricity prices and conducting financial planning to ensure the energy efficiency of power production and the feasibility of the project.

[0106] See also Figure 2 and Figure 6 , the power storage demand assessment module includes:

[0107] The power generation and consumption analysis submodule, based on the power generation cost calculation results, analyzes the power consumption demand and power generation mode in the target area, evaluates the power storage demand of multiple areas, and generates power storage demand information;

[0108] In the power generation and consumption analysis submodule, based on the power generation cost calculation results, the power demand analysis technology is used to evaluate the power consumption demand and power generation pattern in the target substation. A combination of real-time data monitoring and historical data analysis is adopted, including collecting the power consumption history records and prediction models of the substation, performing trend analysis on the data through statistical analysis software, identifying peak power consumption periods and trough periods, analyzing the power demand pattern in the target period, considering the impact of seasonal changes on power consumption patterns, and using a multivariate linear regression model to predict power consumption in future time periods. Input variables include time, temperature, industrial activity index and other influencing factors, and output predicted power consumption. According to the target analysis results, the power storage demand of each substation is evaluated, and combined with the power generation capacity of the substation, power storage demand information is generated. The information provides decision support for power grid scheduling and energy reserve strategies.

[0109] The battery aging impact analysis submodule is based on the power storage demand information. It analyzes the impact of battery material aging and performance degradation on power storage capacity, predicts the maintenance and operating costs of energy storage equipment, and generates power storage maintenance cost information.

[0110] In the battery aging impact analysis submodule, battery aging and performance degradation are analyzed based on the power storage demand information. The battery life analysis model is used to simulate the battery degradation under different working conditions. The model combines the battery's charge and discharge cycles, temperature changes, and the impact on the battery's chemical properties. The calculation model includes parameters such as the battery's initial capacity, per-cycle decay rate, and temperature decay coefficient. The target parameters are corrected through experimental data to ensure the accuracy of the model, analyze the specific impact of battery performance degradation on energy storage capacity, and use dynamic programming technology to optimize battery maintenance and replacement strategies. The maintenance cost and operating cost are predicted. The target cost data is summarized through the cost analysis tool, and the power storage maintenance cost information is generated, providing grid operators with practical information on battery health management and cost control.

[0111] The power storage cost calculation submodule calculates the total power storage cost of the power grid based on the power storage maintenance cost information and the power storage demand of multiple substations, and generates power storage cost evaluation information;

[0112] The specific formula for calculating the total cost of electricity storage in the power grid is:

[0113] C total =a+b×Q+c×V+d×P

[0114] Among them, C total represents the total storage cost of the power grid, Q represents the total storage demand of the entire power grid, covering the storage demand of all substations and the operating efficiency of energy storage equipment, V represents the battery maintenance cost variable, taking into account the cost impact brought about by battery performance degradation and increased maintenance frequency, P represents the policy change impact parameter, including the direct impact of policy factors such as taxes and subsidies on costs, to ensure that the model can adapt to the impact of economic policy changes, a is the intercept, reflecting the basic storage cost when there is no demand and other influencing factors, b is the impact coefficient of demand Q, indicating how the total storage cost changes for every unit increase in storage demand, c is the impact coefficient of maintenance cost V, reflecting the amount by which the total storage cost will increase for every unit increase in battery maintenance cost, and d is the impact coefficient of policy factor P, showing how the storage cost will respond to every unit change in policy adjustments such as taxes and subsidies.

[0115] formula:

[0116] C total =a+b×Q+c×V+d×P

[0117] Detailed explanation of the formula and the process of formula calculation and derivation:

[0118] The formula calculates the total cost of power storage for the grid by combining maintenance costs, power storage requirements, policy factors and base costs.

[0119] Parameter meaning and setting value:

[0120] a is the basic storage cost, set at RMB 500,000, reflecting the minimum cost that the grid needs to bear in the absence of storage demand and policy adjustments;

[0121] b is the power storage demand coefficient, which is set at 200 yuan / MWh, indicating that the total cost increases by 200 yuan for every additional MWh of power storage demand;

[0122] c is the maintenance cost coefficient, which is set at 50 yuan / MWh, indicating that the maintenance cost increases by 50 yuan for each MWh of storage demand;

[0123] d is the policy adjustment coefficient, which is set at 100 yuan / policy unit, taking into account the impact of each additional unit of policy adjustment on the total cost;

[0124] Q is the total power storage demand of the entire power grid, assuming that the total power storage demand is 2000 MWh;

[0125] V represents the battery maintenance cost variable, assuming the average maintenance cost is 40 yuan per MWh;

[0126] P represents the policy change impact parameter, assuming the policy adjustment factor is 3;

[0127] Substitute the parameters into the formula for calculation:

[0128] C total =500000+200×2000+50×2000+100×3

[0129] C total =500000+400000+100000+300

[0130] C total =1000300

[0131] The result 1000300 yuan shows the total cost of energy storage for the power grid after considering the current energy storage demand, maintenance costs and policy impacts. The calculation is used to help evaluate and adjust energy storage strategies to ensure the efficiency of power grid operation.

[0132] See also Figure 2 and Figure 7 , the energy response management module includes:

[0133] The demand pattern analysis submodule is based on the storage cost assessment information and combined with the energy demand forecast information of multiple substations to analyze the energy usage patterns and peak-valley differences of multiple substations, evaluate the output demand of the power grid in multiple time periods, and generate demand difference information;

[0134] In the above content, the demand pattern analysis submodule uses the peak-valley difference analysis model to calculate the demand difference information based on the electricity storage cost assessment information and energy demand forecast, where: Where ΔE represents the total demand difference, D peak,i With D valley,i Represent the peak demand and valley demand of the ith station area respectively,

[0135] Detailed explanation of the formula and the process of formula calculation and derivation:

[0136] Assume that the peak demand D of the three substations peak They are 1200kW, 1000kW, 800kW respectively, and the valley demand is D valley They are 300kW, 200kW, and 100kW respectively, and ΔE is calculated as:

[0137] ΔE=1200-300+1000-200+800-100=2400kW

[0138] The result ΔE=2400kW indicates that the total difference between the energy usage patterns and peak-to-valley differences between the substations is 2400kW. The target information will be used to evaluate the output demand of the power grid at different time periods and support the decision-making process for grid adjustment measures to ensure the effective allocation of resources and the optimal use of energy.

[0139] The power storage configuration optimization submodule adjusts the power storage configuration of multiple substations based on demand difference information, including adjusting the energy storage capacity of multiple substations to match the consumption demand of multiple time periods, optimizing the charging and discharging parameters to optimize the working efficiency and life of the battery, and generating storage configuration optimization records;

[0140] The power storage configuration optimization submodule plays a vital role in improving the efficiency of power grid operation and reducing costs. Based on the demand difference information obtained from the previous analysis, the power storage configuration of multiple substations is adjusted, including the use of linear programming and nonlinear optimization techniques to determine the optimal energy storage capacity of each substation to ensure that the consumption needs of different time periods can be met. During implementation, the battery charging and discharging characteristics and life optimization are taken into account, and genetic algorithms and simulated annealing techniques are used to adjust and optimize the charging and discharging parameters. The target parameters include charging rate, discharge depth and temperature control. In order to improve the response speed and adaptability of the system, real-time data processing and feedback mechanisms are introduced. Sensors and Internet of Things technologies are used to monitor the status of energy storage equipment in real time and adjust the configuration accordingly. The generated storage configuration optimization records improve the working efficiency of the battery and extend its service life, ensuring the continuity of energy supply and the load balance of the power grid.

[0141] The power resource scheduling submodule adjusts power distribution based on storage configuration optimization records, considers energy supply and demand balance, optimizes load response and resource allocation of the power grid, and generates resource scheduling optimization parameters;

[0142] The power resource scheduling submodule balances the energy supply and demand of the power grid through advanced scheduling algorithms and automation technologies. It uses the storage configuration optimization records obtained from the power storage configuration optimization submodule and combines real-time energy market data to perform power resource allocation and scheduling. It adopts a multi-objective optimization framework and integrates deterministic and stochastic planning methods to deal with the uncertainty of energy supply and the volatility of demand. It uses a network flow algorithm to optimize the flow of electricity across multiple substations to ensure effective response during peak and trough periods of demand. Parameters include the total load of the power grid, the generation capacity and energy storage status of each substation, and the market electricity price. By adjusting the power distribution strategy in real time, the load response of the power grid is optimized and the efficiency of resource allocation is improved. The generated resource scheduling optimization parameters provide scientific data support and decision-making basis for power grid operations.

[0143] See also Figure 2 and Figure 8 , the electricity price dynamic response module includes:

[0144] The power market analysis submodule analyzes the demand and supply status of the power market based on resource scheduling optimization parameters, predicts the supply and demand status of multiple time periods and seasons, and generates market demand analysis results;

[0145] In the power market analysis submodule, the supply and demand situation of the power market is analyzed based on the resource scheduling optimization parameters, and the time series analysis and market supply and demand balance model are used for market forecasting. By collecting power market transaction data, weather data, economic indicators and policy changes in various time periods and seasons of previous years, the target data is input into the ARIMA model to capture the seasonal and cyclical changes in the time series, and the supply and demand balance model is used to analyze the market demand in future time periods. The model takes into account multiple key factors such as changes in power consumption patterns, differences in load distribution between industrial and residential areas, and market demand elasticity. By combining the results of the target model, the predicted supply and demand status of each time period and season is obtained, and the market demand analysis results are generated, including supply and demand forecast data, supply tension, and market capacity data content for different time periods, providing a scientific market prediction basis for price strategy adjustments.

[0146] The demand adjustment calculation submodule is based on the market demand analysis results, considers consumer behavior and market response, evaluates the electricity prices that need to be adjusted under various supply and demand conditions, and generates price adjustment analysis data;

[0147] In the above content, the electricity price that needs to be adjusted under various supply and demand conditions is evaluated by considering consumer behavior and market response. According to the formula P adj =P base +α×(ΔD-ΔS), calculate the price adjustment analysis data,

[0148] Where P adjrepresents the adjusted electricity price, P base is the basic electricity price, α is the price adjustment coefficient, which reflects the sensitivity of supply and demand changes to price, ΔD and ΔS represent the changes in demand and supply, respectively.

[0149] Detailed explanation of the formula and the process of formula calculation and derivation:

[0150] Assuming the basic electricity price P base The electricity price after adjustment is 1 yuan / kWh, the demand increment ΔD is 3000 kWh, the supply increment ΔS is 1500 kWh, and the price adjustment coefficient α is set to 0.03.

[0151] P adj =1+0.03×(3000-1500)

[0152] P adj =1+0.03×1500

[0153] P adj =1+45

[0154] P adj =1.45

[0155] The result of 1.45 yuan / kWh indicates that when the current market demand exceeds the supply, the electricity price needs to be raised to achieve market balance. This calculation shows the direct impact of supply and demand conditions on electricity price adjustments and provides a quantitative tool to help electricity market operators make data-driven pricing decisions.

[0156] The price list update submodule adjusts the tiered electricity prices for multiple time periods and constructs a price list based on the price adjustment analysis data and the predicted electricity consumption pattern, records the adjustment operations, and generates a grid electricity price adjustment record;

[0157] In the price list update submodule, based on the price adjustment analysis data, the tiered electricity prices of multiple time periods are adjusted according to the predicted electricity consumption pattern, the electricity demand forecast data of each time period is associated with the price adjustment suggestions, and a tiered electricity price structure is constructed. By setting different price ranges to cope with the electricity demand in different time periods, it is ensured that prices are increased during peak hours to regulate demand, and prices are reduced during trough hours to promote consumption. The price adjustment information is entered into the data management system, the price changes of each time period are recorded, and the implementation effects of historical price adjustments are analyzed and compared to optimize future price settings. A record of grid electricity price adjustments is generated, including tiered price information for each time period, price adjustment effective time, and recorded market feedback data, to provide basic data support for subsequent price strategy evaluation and optimization.

[0158] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A distributed photovoltaic access area operation optimization system, characterized by: The system comprises: The power demand analysis module collects power consumption data of multiple substations based on the substation location information, analyzes the impact of various factors on power consumption, evaluates the changing trend of energy demand, and generates energy demand forecast results; The output capacity calculation module predicts the power generation capacity of the photovoltaic equipment based on the energy demand prediction result and generates power generation capacity prediction data by analyzing the influence of various factors on the output capacity of the photovoltaic power generation equipment; The power cost assessment module uses the power generation capacity prediction data to calculate the deployment, operation and maintenance costs of the photovoltaic equipment, calculates the production cost per unit of power, and generates a power generation cost calculation result; The power storage demand assessment module evaluates the power storage demand of multiple substations and calculates the power storage cost based on the power generation cost calculation result and the power consumption demand and power generation mode in the target substation, and generates power storage cost assessment information; The energy response management module adjusts the energy output and storage configuration of the photovoltaic equipment based on the electricity storage cost assessment information and combines the energy demand forecast information of multiple substations to generate resource scheduling optimization parameters; The electricity price dynamic response module analyzes the electricity market demand and supply conditions based on the resource scheduling optimization parameters and the real-time load and photovoltaic power generation of the power grid, adjusts the electricity price to optimize power consumption, and generates a power grid electricity price adjustment record.

2. The distributed photovoltaic access area operation optimization system according to claim 1 is characterized in that: The energy demand forecast results include expected peak load data, average load data, and energy demand patterns of multiple substations; the power generation capacity forecast data include power generation capacity at multiple time points, photovoltaic equipment specification parameters, and analysis results of the impact of performance degradation on power generation capacity; the power generation cost calculation results include the production cost per unit of electricity, operating cost data, and analysis results of the impact of maintenance and equipment replacement on costs; the power storage cost assessment information includes predicted battery replacement costs, battery capacity decline trend analysis results, and power storage demand data; the resource scheduling optimization parameters include photovoltaic power generation adjustment ratio parameters, energy storage equipment use priority information, and load demand response parameters; the grid electricity price adjustment records include electricity price lists for multiple time periods, adjustment area data, and seasonal electricity price change adjustment records.

3. The distributed photovoltaic access area operation optimization system according to claim 1 is characterized in that: The power demand analysis module includes: The power consumption data collection submodule collects power consumption data of multiple substations based on the substation location information, and standardizes and formats the data to generate a power consumption data set; The power consumption data analysis submodule analyzes the impact of various factors on power consumption based on the power consumption data set, including industrial activities, climate change, and regional population, and generates an influencing factor identification result; The electricity demand assessment submodule uses time series analysis based on the influencing factor identification results to assess the fluctuation pattern and change trend of energy demand and generate energy demand forecast results.

4. The distributed photovoltaic access area operation optimization system according to claim 1 is characterized in that: The output capacity calculation module comprises: The influencing factor analysis submodule calculates the influence of various factors on the light intensity based on the energy demand forecast result, including geographical location, season, and time point, evaluates the output capacity of photovoltaic equipment at multiple locations, and generates output impact evaluation information; The performance degradation assessment submodule evaluates the performance degradation trend of the equipment and the impact on the output capacity based on the output impact assessment information, and generates a performance degradation analysis result by performing a time series analysis on the power generation data of multiple photovoltaic devices; The output prediction submodule predicts the power output of the photovoltaic power generation equipment in the power grid in multiple time periods and seasons based on the performance degradation analysis results, and generates power generation capacity prediction data.

5. The distributed photovoltaic access area operation optimization system according to claim 4 is characterized in that: The specific formula for calculating the influence of various factors on light intensity is: Among them, x i A specific value representing a geographic location, season, or point in time. Represents x i The average value of y i Represents the corresponding light intensity value, Represents y i The result r represents the correlation strength between the two variables, and i is the index symbol of the target factor considered.

6. The distributed photovoltaic access area operation optimization system according to claim 1 is characterized in that: The electricity cost assessment module includes: The deployment cost calculation submodule calculates the deployment cost of the photovoltaic equipment, including equipment cost and installation cost, using the power generation capacity prediction data, and generates an equipment cost data set; The operation demand analysis submodule considers the impact of equipment performance degradation on power generation efficiency based on the equipment cost data set, evaluates the maintenance frequency and replacement requirements of multiple equipment, calculates the maintenance costs of multiple equipment, and generates operation cost information in combination with the labor cost required for equipment operation; The unit cost analysis submodule calculates the production cost of unit electricity based on the operating cost information, taking into account deployment cost, maintenance cost and power generation efficiency changes, and generates a power generation cost calculation result.

7. The distributed photovoltaic access area operation optimization system according to claim 1 is characterized in that: The electricity storage demand assessment module includes: The power generation and consumption analysis submodule, based on the power generation cost calculation result, analyzes the power consumption demand and power generation mode in the target area, evaluates the power storage demand of multiple areas, and generates power storage demand information; The battery aging impact analysis submodule is based on the power storage demand information, by analyzing the impact of battery material aging and performance degradation on power storage capacity, predicting the maintenance and operating costs of energy storage equipment, and generating power storage maintenance cost information; The electricity storage cost calculation submodule calculates the total electricity storage cost of the power grid based on the electricity storage maintenance cost information and in combination with the electricity storage demands of multiple substations, and generates electricity storage cost assessment information.

8. The distributed photovoltaic access area operation optimization system according to claim 7 is characterized in that: The specific formula for calculating the total cost of power storage in the power grid is: C total =a+b×Q+c×V+d×P Among them, C total represents the total storage cost of the power grid, Q represents the total storage demand of the entire power grid, covering the storage demand of all substations and the operating efficiency of energy storage equipment, V represents the battery maintenance cost variable, taking into account the cost impact brought about by battery performance degradation and increased maintenance frequency, P represents the policy change impact parameter, including the direct impact of policy factors such as taxes and subsidies on costs, to ensure that the model can adapt to the impact of economic policy changes, a is the intercept, reflecting the basic storage cost when there is no demand and other influencing factors, b is the impact coefficient of demand Q, indicating how the total storage cost changes for every unit increase in storage demand, c is the impact coefficient of maintenance cost V, reflecting the amount by which the total storage cost will increase for every unit increase in battery maintenance cost, and d is the impact coefficient of policy factor P, showing how the storage cost will respond to every unit change in policy adjustments such as taxes and subsidies.

9. The distributed photovoltaic access area operation optimization system according to claim 1 is characterized in that: The energy response management module includes: The demand pattern analysis submodule analyzes the energy usage patterns and peak-valley differences of multiple substations based on the electricity storage cost evaluation information and the energy demand forecast information of multiple substations, evaluates the output demand of the power grid in multiple time periods, and generates demand difference information; The power storage configuration optimization submodule adjusts the power storage configuration of multiple substations based on the demand difference information, including adjusting the energy storage capacity of multiple substations to match the consumption demand of multiple time periods, optimizing the charging and discharging parameters to optimize the working efficiency and life of the battery, and generating a storage configuration optimization record; The power resource scheduling submodule adjusts power distribution based on the storage configuration optimization record, considers the balance between energy supply and demand, optimizes the load response and resource allocation of the power grid, and generates resource scheduling optimization parameters.

10. The distributed photovoltaic access area operation optimization system according to claim 1, characterized in that: The electricity price dynamic response module includes: The power market analysis submodule analyzes the demand and supply status of the power market based on the resource scheduling optimization parameters, predicts the supply and demand status of multiple time periods and seasons, and generates market demand analysis results; The adjustment demand calculation submodule evaluates the electricity prices that need to be adjusted under various supply and demand conditions based on the market demand analysis results, taking into account consumer behavior and market response, and generates price adjustment analysis data; The price list update submodule adjusts the tiered electricity prices for multiple time periods based on the price adjustment analysis data and the predicted electricity consumption pattern, constructs a price list, records the adjustment operations, and generates a grid electricity price adjustment record.

Images