CN119761552A - Distributed photovoltaic access area operation optimization system - Google Patents
Distributed photovoltaic access area operation optimization system Download PDFInfo
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Abstract
本发明涉及电网管理技术领域，具体为一种分布式光伏接入台区运行优化系统，系统包括：电力需求分析模块、输出能力计算模块、电力成本评估模块、储电需求评估模块、能源响应管理模块、电价动态响应模块。本发明中，通过集成实时监控数据和多台区用电量数据分析，有效预测电力需求的变化趋势，结合发电成本和储电成本的计算，为电力调度和管理提供数据基础，优化光伏发电设备的调度和储电设备的管理，提高了能效，实现了根据实时负荷和发电量动态调整电价，匹配市场和环境变化，优化用户的电力消费模式，提高电网对可再生能源接入的适应能力，确保电网的稳定运行，提升电网的运行性能。
The present invention relates to the field of power grid management technology, specifically to a distributed photovoltaic access area operation optimization system, the system includes: a power demand analysis module, an output capacity calculation module, a power cost assessment module, a power storage demand assessment module, an energy response management module, and a power price dynamic response module. In the present invention, by integrating real-time monitoring data and multi-area power consumption data analysis, the changing trend of power 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, matching market and environmental changes, optimizing users' power consumption patterns, improving the adaptability of the power grid to renewable energy access, ensuring the stable operation of the power grid, and improving the operation performance of the power grid.
Description
Technical Field
The invention relates to the technical field of power grid management, in particular to a distributed photovoltaic access platform region operation optimization system.
Background
The technical field of power grid management is focused on monitoring, scheduling and optimizing of a power system, ensures the safety, reliability and economy of power supply, utilizes various information technologies, automation technologies and power engineering technologies to conduct real-time monitoring and dynamic management of the power grid, comprises load prediction, power scheduling, fault detection, integration and optimization management of distributed energy resources, effectively improves the running efficiency of the power grid, responds to market and environment changes, and improves the adaptability of the power system to renewable energy access.
The distributed photovoltaic access platform region operation optimization system is focused on improving the economical efficiency and the efficiency of distributed photovoltaic power generation in a platform region level power grid, improves the energy efficiency and the economic benefit of the power grid by optimizing the dispatching and the management of the photovoltaic power generation, reduces the operation cost, improves the power generation efficiency, ensures the stability of the power grid, adapts to and utilizes renewable energy sources through intelligent power grid management, helps power grid operators to realize refined energy management and allocation, and enhances the adaptability of the power grid to renewable energy source fluctuation and uncertainty.
The conventional photovoltaic access platform region operation optimization technology lacks enough flexibility and adaptability when processing a large-scale distributed photovoltaic power generation system, relies on a conventional power scheduling strategy in management of distributed energy resource integration, has insufficient capability in predicting and adapting to fluctuation and uncertainty of renewable energy sources, has difficulty in processing high-proportion renewable energy source access, has difficulty in predicting and controlling output of renewable energy sources, has insufficient capability in optimizing operation of a power grid, has insufficient dynamic adjustment mechanism in electricity price setting and load management, causes insufficient supply and demand in high-demand time and resource waste in low-demand time, causes insufficient safety of power supply, and increases operation risk and cost.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a distributed photovoltaic access area operation optimization system.
In order to achieve the above object, the present invention adopts the following technical scheme that a distributed photovoltaic access area operation optimization system includes:
The power demand analysis module collects power consumption data of a plurality of areas based on the area position information, analyzes the influence of a plurality of factors on power consumption, evaluates the change trend of the energy demand and generates an energy demand prediction result;
The output capacity calculation module predicts the power generation capacity of the photovoltaic power generation equipment by analyzing the influence of various factors on the output capacity of the photovoltaic power generation equipment based on the energy demand prediction result, and generates power generation capacity prediction data;
the power cost evaluation module calculates the production cost of unit power by calculating the deployment, operation and maintenance cost of the photovoltaic equipment by using the power generation capacity prediction data, and generates a power generation cost calculation result;
The electricity storage demand assessment module assesses electricity storage demands of a plurality of areas and calculates electricity storage cost according to electricity consumption demands and electricity generation modes in the target areas based on the electricity generation cost calculation result, and generates electricity storage cost assessment information;
The energy response management module is used for adjusting the energy output and storage configuration of the photovoltaic equipment based on the electricity storage cost evaluation information and combining the energy demand prediction information of a plurality of areas to generate resource scheduling optimization parameters;
and the electricity price dynamic response module is used for analyzing the demand and supply condition of the electric power market according to the real-time load and the photovoltaic power generation amount of the power grid based on the resource scheduling optimization parameters, adjusting the electricity price to optimize the power consumption and generating a power price adjustment record of the power grid.
As a further aspect of the present invention, the energy demand prediction result includes predicted peak load data and average load data, the power generation capability prediction data includes power generation capability at a plurality of time points, photovoltaic equipment specification parameters, and an effect analysis result of performance degradation on the power generation capability, the power generation cost calculation result includes a production cost of unit power, operation cost data, and an effect analysis result of maintenance and equipment replacement on cost, the power storage cost evaluation information includes predicted battery replacement cost, battery capacity decrease trend analysis result, and power storage demand data, the resource scheduling optimization parameters include a photovoltaic power generation amount adjustment proportion parameter, energy storage equipment usage priority information, and a load demand response parameter, and the power grid electricity price adjustment record includes an electricity price list, adjustment region data, and seasonal electricity price change adjustment record for a plurality of time periods.
As a further aspect of the present invention, the power demand analysis module includes:
The power consumption data acquisition submodule acquires power consumption data of a plurality of areas based on the area position information, and performs standardization and formatting processing on the data to generate a power consumption data set;
The power consumption data analysis submodule analyzes the influence of various factors on the power consumption based on the power consumption data set, wherein the influence comprises industrial activities, climate change and regional population quantity, and generates an influence factor identification result;
and the electricity demand assessment sub-module assesses the fluctuation mode and the change trend of the energy demand by utilizing time sequence analysis based on the influence factor identification result, and generates an energy demand prediction result.
As a further aspect of the present invention, the output capability calculation module includes:
the influence factor analysis submodule calculates influence of various factors on illumination intensity based on the energy demand prediction result, and the influence factor analysis submodule evaluates output capacity of photovoltaic equipment at a plurality of positions including geographic positions, seasons and time points and generates output influence evaluation information;
The performance degradation evaluation submodule is used for evaluating the performance degradation trend of the equipment and the influence on the output capacity by carrying out time series analysis on the power generation amount data of the plurality of photovoltaic equipment based on the output influence evaluation information, and generating a performance degradation analysis result;
And the output quantity prediction submodule predicts the power output quantity of the photovoltaic power generation equipment in the power grid in a plurality of time periods and seasons based on the performance degradation analysis result and generates power generation capacity prediction data.
As a further scheme of the invention, the specific formula for calculating the influence of various factors on the illumination intensity is as follows:
Wherein x i represents a specific value of a geographic location, season or point in time, Represents the average value of x i, y i represents the corresponding illumination intensity value,Representing the average value of y i, the result r represents the correlation strength between the two variables, and i is the index sign of the target factor under consideration.
As a further aspect of the present invention, the power cost evaluation module includes:
The deployment cost calculation submodule calculates the deployment cost of the photovoltaic equipment by utilizing the power generation capacity prediction data, wherein the deployment cost comprises equipment cost and installation cost, and an equipment cost data set is generated;
The operation demand analysis sub-module is used for evaluating the maintenance frequency and the replacement demand of a plurality of devices based on the device cost data set in consideration of the influence of the device performance degradation on the power generation efficiency, calculating the maintenance cost of the plurality of devices, and generating operation cost information by combining the labor cost required by the device operation;
The unit cost analysis submodule calculates the production cost of unit power by considering the deployment cost, the maintenance cost and the change of the power generation efficiency based on the operation cost information, and generates a power generation cost calculation result.
As a further aspect of the present invention, the electricity storage demand assessment module includes:
The power generation and power consumption analysis submodule evaluates the power storage requirements of a plurality of areas by analyzing the power consumption requirements and the power generation modes in the target areas based on the power generation cost calculation result, and generates power storage requirement information;
The battery aging influence analysis submodule predicts maintenance and operation costs of the energy storage equipment and generates electricity storage maintenance cost information by analyzing influence of aging and performance attenuation of battery materials on the electricity storage capacity based on the electricity storage demand information;
and the electricity storage cost calculation submodule calculates the electricity storage total cost of the power grid by combining the electricity storage requirements of a plurality of transformer areas based on the electricity storage maintenance cost information, and generates electricity storage cost evaluation information.
As a further aspect of the present invention, the specific formula for calculating the total electricity storage cost of the power grid is:
Ctotal=a+b×Q+c×V+d×P
Wherein, C total represents the total electricity storage cost of the grid, Q represents the total electricity storage demand of the whole grid, the electricity storage demand of all the areas and the operation efficiency of the energy storage device are covered, V represents the battery maintenance cost variable, considering the cost effect caused by the battery performance attenuation and the increase of the maintenance frequency, P represents the policy change influence parameter including the direct influence of policy factors such as tax, patch and the like on the cost, ensuring that the model can adapt to the influence of economic policy change, a is intercept, reflects the basic electricity storage cost when no demand and other influence factors exist, b is the influence coefficient of the demand Q, represents how the electricity storage demand is changed by one unit, C is the influence coefficient of the maintenance cost V, reflects how the battery maintenance cost is increased by one unit, d is the influence coefficient of the policy factor P, shows how the electricity storage cost is responded when the policy adjustment of tax, patch and the like changes by one unit.
As a further aspect of the present invention, the energy response management module includes:
the demand pattern analysis submodule analyzes the energy use patterns and peaks Gu Chayi of the multiple areas based on the electricity storage cost evaluation information and combines the energy demand prediction information of the multiple areas, evaluates the output demands of the power grid in multiple time periods and generates demand difference information;
The power storage configuration optimizing submodule adjusts power storage configurations of a plurality of areas based on the demand difference information, and comprises the steps of adjusting the energy storage capacity of the areas to match consumption demands of a plurality of time periods, optimizing charge and discharge parameters, optimizing the working efficiency and the service life of a battery and generating a storage configuration optimizing record;
and the power resource scheduling sub-module adjusts power distribution in consideration of energy supply and demand balance based on the storage configuration optimization record, optimizes load response and resource distribution of the power grid and generates resource scheduling optimization parameters.
As a further aspect of the present invention, the electricity price dynamic response module includes:
The power market analysis submodule analyzes the demand and supply conditions of the power market based on the resource scheduling optimization parameters, predicts the supply and demand states of a plurality of time periods and seasons and generates a market demand analysis result;
the adjustment demand calculation submodule evaluates the electric power price to be adjusted in various supply and demand states according to the market demand analysis result and considering consumer behaviors and market reactions, and generates price adjustment analysis data;
and the price list updating sub-module is used for adjusting the step power prices of a plurality of time periods according to the predicted power consumption mode based on the price adjustment analysis data, constructing a price list, recording adjustment operation and generating a power grid power price adjustment record.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, through integrating real-time monitoring data and multi-zone electricity consumption data analysis, the change trend of the power demand is effectively predicted, the calculation of the power generation cost and the power storage cost is combined, a data base is provided for power dispatching and management, the dispatching of the photovoltaic power generation equipment and the management of the power storage equipment are optimized, the energy efficiency is improved, the dynamic adjustment of electricity price according to the real-time load and the generated energy is realized, the market and environment change are matched, the power consumption mode of a user is optimized, the adaptability of the power grid to the access of renewable energy sources is improved, the stable operation of the power grid is ensured, and the operation performance of the power grid is improved.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flow chart of the power demand analysis module of the present invention;
FIG. 4 is a flow chart of an output capability calculation module according to the present invention;
FIG. 5 is a flow chart of a power cost assessment module according to the present invention;
FIG. 6 is a flow chart of a power storage demand assessment module according to the present invention;
FIG. 7 is a flow chart of an energy response management module according to the present invention;
FIG. 8 is a flow chart of the electricity price dynamic response module of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1 to 2, a distributed photovoltaic access area operation optimization system includes:
The power demand analysis module collects power consumption data of a plurality of areas based on the area position information, analyzes the influence of a plurality of factors on power consumption, evaluates the change trend of the energy demand and generates an energy demand prediction result;
the output capacity calculation module predicts the power generation capacity of the photovoltaic power generation equipment by analyzing the influence of various factors on the output capacity of the photovoltaic power generation equipment based on the energy demand prediction result, and generates power generation capacity prediction data;
The power cost evaluation module utilizes the power generation capacity prediction data to calculate the production cost of unit power by calculating the deployment, operation and maintenance costs of the photovoltaic equipment, so as to generate a power generation cost calculation result;
The electricity storage demand assessment module assesses electricity storage demands of a plurality of areas and calculates electricity storage cost according to electricity consumption demands and electricity generation modes in the target areas based on the electricity generation cost calculation result, and generates electricity storage cost assessment information;
the energy response management module is used for adjusting the energy output and storage configuration of the photovoltaic equipment based on the electricity storage cost evaluation information and combining the energy demand prediction information of the plurality of areas to generate resource scheduling optimization parameters;
the electricity price dynamic response module is used for analyzing the demand and supply condition of the electric power market according to the real-time load and the photovoltaic power generation amount of the power grid based on the resource scheduling optimization parameters, adjusting the electricity price to optimize the power consumption, and generating a power price adjustment record of the power grid.
The energy demand prediction results comprise predicted peak load data and average load data, the energy demand modes of a plurality of areas, the power generation capacity prediction data comprise power generation capacity at a plurality of time points, photovoltaic equipment specification parameters and influence analysis results of performance degradation on the power generation capacity, the power generation cost calculation results comprise production cost of unit power, operation cost data, influence analysis results of maintenance and equipment replacement on cost, the power storage cost evaluation information comprises predicted battery replacement cost, battery capacity descending trend analysis results and power storage demand data, the resource scheduling optimization parameters comprise photovoltaic power generation capacity adjustment proportion parameters, energy storage equipment use priority information and load demand response parameters, the power grid power price adjustment records comprise power price lists of a plurality of periods, adjustment area data and seasonal power price change adjustment records.
Referring to fig. 2 and 3, the power demand analysis module includes:
The power consumption data acquisition submodule acquires power consumption data of a plurality of areas based on the area position information, and performs standardization and formatting processing on the data to generate a power consumption data set;
In the electricity data acquisition sub-module, based on the location information of the areas, electricity data of a plurality of areas are acquired, data standardization processing is carried out, the standardization process involves data cleaning, abnormal values and missing values are removed, the formatting processing comprises the steps of converting all data into a unified data format, such as standardizing a timestamp into an ISO 8601 format, unifying the electricity data into kilowatt-hour, and the scaling processing is combined, so that the electricity data is convenient to use in a subsequent model, the scaling adopts a Min-Max standardization method, the data is scaled to between 0 and 1, the influence caused by different units is eliminated, a data set is suitable for machine learning processing, an electricity data set is generated, accurate baseline data is provided for subsequent analysis by the data set, and the accuracy and reliability of the subsequent analysis are ensured.
The power consumption data analysis submodule analyzes the influence of various factors on the power consumption based on the power consumption data set, wherein the influence comprises industrial activities, climate change and regional population quantity, and generates an influence factor identification result;
In the power consumption data analysis submodule, based on a power consumption data set, analyzed factors comprise power requirements of industrial activities, seasonal changes of power consumption influence caused by climate change and power consumption requirement changes caused by regional population increase and decrease, used technologies comprise regression analysis and variance analysis, historical power usage data of the industrial activities are related to regional economic activity indexes through target analysis, the relation strength and characteristic importance between the historical power usage data and the regional economic activity indexes are determined by utilizing a linear regression model, the power consumption difference under different seasons or climate conditions is evaluated by adopting variance analysis, climate factors with the largest influence on the power consumption are identified, long-term influence of population change on the power requirements is evaluated through correlation analysis of demographic data and power consumption, target steps are all carried out through pandas and scikit-learn libraries in Python, data processing and calculation accuracy of an analysis process are ensured, an influence factor identification result is generated, and a scientific basis is provided for follow-up demand prediction.
The power consumption demand evaluation sub-module evaluates fluctuation modes and variation trends of the energy demands by utilizing time sequence analysis based on the influence factor identification result to generate an energy demand prediction result;
In the power consumption demand evaluation submodule, based on the influence factor identification result, the steps of executing time series analysis comprise data preprocessing, model selection, parameter adjustment and trend prediction, wherein the data preprocessing comprises data seasonal processing and stability detection, the stability of a time series is ensured, a technology is used for ADF test, a proper time series model is selected according to data characteristics, the applicability of the model is evaluated through AIC and BIC criteria, optimization adjustment of model parameters is carried out, an optimal parameter combination is searched by using a grid search method, the prediction accuracy of the model is ensured, future energy demands are predicted by using the selected model, the prediction result is converted back to an original proportion through reverse conversion, an energy demand prediction result is generated, and a decision maker is helped to know the fluctuation mode and the change trend of the future energy demands, and support is provided for power grid operation and planning.
Referring to fig. 2 and 4, the output capability calculation module includes:
The influence factor analysis submodule calculates influence of various factors on illumination intensity based on the energy demand prediction result, and the influence factor analysis submodule evaluates output capacity of the photovoltaic devices at the various positions including geographic positions, seasons and time points and generates output influence evaluation information;
the specific formula for calculating the influence of various factors on the illumination intensity is as follows:
Wherein x i represents a specific value of a geographic location, season or point in time, Represents the average value of x i, y i represents the corresponding illumination intensity value,Representing the average value of y i, the result r represents the correlation strength between the two variables, and i is the index sign of the target factor under consideration.
The formula:
formula details and formula calculation derivation process:
the formula is used for calculating a correlation coefficient r between latitude and illumination intensity, and the result is used for evaluating the influence of position change on illumination intensity.
Parameter meaning and setting value:
x i is the latitude of each place, reflecting the position change from the equator to the pole, y i is the illumination intensity of the corresponding latitude, assuming that the illumination intensity is=900W/m 2 at 10 ° north latitude, 600W/m2 at 20 ° north latitude, 300W/m2 at 30 ° north latitude, and 100W/m2 at 40 ° north latitude;
The average value of latitude is 25 degrees, and the average latitude position is reflected;
the average illumination intensity is 475W/m2, and reflects the average illumination intensity;
Substituting the parameters into a formula to calculate:
Calculation of
Calculation of
Calculation of
Calculating r:
The result-0.443 shows that there is a negative correlation between latitude and illumination intensity, indicating that an increase in latitude results in a decrease in illumination intensity, and as a result is used to predict the power generation capability of the photovoltaic device based on the geographic arrangement location.
The performance degradation evaluation submodule is used for evaluating the performance degradation trend of the equipment and the influence on the output capacity based on the output influence evaluation information by carrying out time series analysis on the generated energy data of the plurality of photovoltaic equipment and generating a performance degradation analysis result;
In the performance attenuation evaluation submodule, based on output influence evaluation information, time series analysis is performed to track and predict performance degradation trend of the photovoltaic equipment, seasonal fluctuation and trend data are processed by utilizing a seasonal adjustment autoregressive moving average model, historical power generation data of the equipment are analyzed through a SARIMA model, degradation trend and periodic mode are identified, parameter selection of the model is optimized based on a red pool information criterion and a Bayesian information criterion, accuracy and applicability of the model are ensured, how the performance of the equipment is evaluated and attenuated along with time change, equipment specific adjustment factors such as temperature compensation and load adjustment are introduced in consideration of the difference between different equipment and installation environments, calculation of the target factors is based on actual operation data and technical specifications of equipment manufacturers, and the generated performance degradation analysis result reveals health conditions and expected service lives of each equipment and provides basis for maintenance planning and performance optimization.
The output quantity prediction submodule predicts the power output quantity of the photovoltaic power generation equipment in the power grid in a plurality of time periods and seasons based on the performance degradation analysis result, and generates power generation capacity prediction data;
In the output quantity prediction submodule, the future output quantity of the photovoltaic power generation equipment is predicted based on a performance degradation analysis result, a random forest algorithm in machine learning is used for processing large-scale input variables and considering nonlinear relations of the large-scale input variables, a random forest model estimates the power generation capacity of different equipment in each future season and different time periods through a training data set, model inputs comprise historical performance data of the equipment, predicted illumination intensity, climate conditions and performance degradation indexes, the optimal number and depth of trees are determined through training and verification of the model, prediction accuracy and generalization capacity are ensured, the process comprises performance testing on a control and verification set of the model overfitting, the actual application effect of the model is estimated, the generated power generation capacity prediction data presents possible output conditions of each future time period, and accurate look-ahead information is provided for demand response and capacity planning of a power grid.
Referring to fig. 2 and 5, the power cost evaluation module includes:
The deployment cost calculation submodule calculates the deployment cost of the photovoltaic equipment by utilizing the power generation capacity prediction data, wherein the deployment cost comprises equipment cost and installation cost, and an equipment cost data set is generated;
In the deployment cost calculation sub-module, cost accounting of the photovoltaic equipment is performed based on the power generation capacity prediction data, purchase and installation costs of all parts of equipment are calculated by adopting a cost analysis method, the cost information of common equipment on the market is collected, discounts of batch purchase are estimated according to the expected installation scale, the logistics and installation costs, including transportation cost, site preparation work and labor cost of professional installation staff are estimated, additional safety measures and insurance cost are required to be adjusted in consideration of installation standards and regulations of different areas, the target data is managed and calculated by an enterprise resource planning system, and the equipment cost data set generated by combining project management software is used for listing various equipment and related costs thereof, so that accurate basis is provided for budget formulation and financial analysis.
The operation demand analysis submodule is used for evaluating maintenance frequency and replacement demand of a plurality of devices based on the device cost data set in consideration of the influence of device performance degradation on the power generation efficiency, calculating maintenance cost of the plurality of devices and generating operation cost information by combining labor cost required by device operation;
In the operation demand analysis submodule, analysis of equipment maintenance cost is carried out based on an equipment cost data set, equipment management software is used for tracking equipment performance and predicting maintenance demand, a performance degradation curve provided by equipment manufacturers is combined, the expected service life and replacement period of key equipment components such as an inverter and a photovoltaic panel are estimated, the life cycle cost analysis method is adopted to estimate the maintenance cost in consideration of the performance difference of equipment under different climatic conditions, the method considers the influence of maintenance activities on operation efficiency, including the influence of planned and unplanned maintenance on power generation interruption and the calculation of labor cost, the related labor cost comprises the labor cost of periodic inspection, component replacement and emergency repair work, the data is collected and analyzed through a maintenance management system, the accuracy and timeliness of cost estimation are ensured, the generated operation cost information lists the frequency and cost of various maintenance activities, and important basis is provided for operation decision and cost control.
The unit cost analysis submodule calculates the production cost of unit power based on the operation cost information in consideration of the deployment cost, the maintenance cost and the power generation efficiency change, and generates a power generation cost calculation result;
In the unit cost analysis submodule, the production cost of unit power is calculated based on operation cost information, deployment and maintenance cost data are collected and integrated, a cost accounting model is used, total cost is distributed to expected power output, the power generation efficiency of photovoltaic equipment is monitored in real time and analyzed through historical data, the average power generation efficiency of the photovoltaic equipment in a life cycle is estimated, a dynamic adjustment mechanism is applied to update cost prediction, periodic reevaluation of equipment maintenance frequency and cost is considered, the influence of seasonal change of power generation amount on cost allocation is considered, the generated power generation cost calculation result provides accurate cost of power per kilowatt-hour, the result is important for setting electricity price and conducting financial planning, and the energy efficiency of power production and feasibility of projects are ensured.
Referring to fig. 2 and 6, the electricity storage requirement assessment module includes:
The power generation and power consumption analysis submodule is used for estimating the power storage requirements of a plurality of areas and generating power storage requirement information by analyzing the power consumption requirements and the power generation modes in the target areas based on the power generation cost calculation result;
In the power generation and power consumption analysis submodule, based on a power generation cost calculation result, a power demand analysis technology is utilized to evaluate power consumption requirements and a power generation mode in a target platform area, a mode of combining real-time data monitoring and historical data analysis is adopted, the power consumption history record and a prediction model of the platform area are collected, trend analysis is carried out on data through statistical analysis software, peak power consumption time periods and valley time periods are identified, the power demand mode in the target time period is analyzed, influence of seasonal change on the power consumption mode is considered, multiple linear regression models are utilized to predict power consumption in future time periods, input variables comprise time, air temperature, industrial activity indexes and other influence factors, the predicted power consumption is output, the power storage requirements of each platform area are evaluated according to the target analysis result, power storage requirement information is generated by combining the power generation capacity of the platform area, and decision support is provided for power grid scheduling and energy storage strategies.
The battery aging influence analysis submodule predicts maintenance and operation costs of the energy storage equipment and generates electricity storage maintenance cost information by analyzing influence of aging and performance attenuation of battery materials on the electricity storage capacity based on electricity storage demand information;
In the battery aging influence analysis submodule, based on the electricity storage demand information, the battery aging and performance attenuation are analyzed, the battery life analysis model is utilized to simulate the battery attenuation under different working conditions, the model combines the charge and discharge cycle, the temperature change and the influence on the chemical properties of the battery, the parameters such as the initial capacity, the attenuation rate per cycle and the temperature attenuation coefficient of the battery are included in the calculation model, the target parameters are corrected through experimental data, the accuracy of the model is ensured, the specific influence of the battery performance attenuation on the energy storage capacity is analyzed, the dynamic programming technology is adopted to optimize the maintenance and replacement strategy of the battery, the maintenance cost and the operation cost are predicted, the target cost data are summarized through the cost analysis tool, and the electricity storage maintenance cost information is generated, so that practical information about battery health management and cost control is provided for a power grid operator.
The electricity storage cost calculation submodule calculates the electricity storage total cost of the power grid based on the electricity storage maintenance cost information by combining the electricity storage requirements of a plurality of areas, and generates electricity storage cost evaluation information;
The specific formula for calculating the total electricity storage cost of the power grid is as follows:
Ctotal=a+b×Q+c×V+d×P
Wherein, C total represents the total electricity storage cost of the grid, Q represents the total electricity storage demand of the whole grid, the electricity storage demand of all the areas and the operation efficiency of the energy storage device are covered, V represents the battery maintenance cost variable, considering the cost effect caused by the battery performance attenuation and the increase of the maintenance frequency, P represents the policy change influence parameter including the direct influence of policy factors such as tax, patch and the like on the cost, ensuring that the model can adapt to the influence of economic policy change, a is intercept, reflects the basic electricity storage cost when no demand and other influence factors exist, b is the influence coefficient of the demand Q, represents how the electricity storage demand is changed by one unit, C is the influence coefficient of the maintenance cost V, reflects how the battery maintenance cost is increased by one unit, d is the influence coefficient of the policy factor P, shows how the electricity storage cost is responded when the policy adjustment of tax, patch and the like changes by one unit.
The formula:
Ctotal=a+b×Q+c×V+d×P
formula details and formula calculation derivation process:
the formula calculates the total electricity storage cost of the grid by combining maintenance costs, electricity storage requirements, policy factors with the base costs.
Parameter meaning and setting value:
a is the basic electricity storage cost, which is set to 500,000 yuan and reflects the lowest cost required to be born by the power grid under the condition of no electricity storage requirement and policy adjustment;
b is a power storage demand coefficient, which is set to 200 yuan/megawatt hour, and represents that the total cost is increased by 200 yuan when the power storage demand is increased by one megawatt hour;
c is a maintenance cost coefficient, and is set to be 50 yuan/megawatt hour, which indicates that the electricity storage requirement per megawatt hour increases the maintenance cost by 50 yuan;
d is a policy adjustment coefficient, which is set to 100 yuan/policy unit, considering the influence of each unit added to the policy adjustment on the total cost;
q is the total electricity storage demand of the whole power grid, and the total electricity storage demand is assumed to be 2000 megawatt hours;
v represents a battery maintenance cost variable assuming an average maintenance cost of 40 yuan per megawatt hour;
P represents a policy change influencing parameter, assuming that the policy adjustment factor is 3;
Substituting the parameters into a formula to calculate:
Ctotal=500000+200×2000+50×2000+100×3
Ctotal=500000+400000+100000+300
Ctotal=1000300
the result 1000300 element shows that after considering the current electricity demand, maintenance cost and policy influence, the electricity total cost of the power grid is calculated to help evaluate and adjust the energy storage strategy, and the efficiency of the power grid operation is ensured.
Referring to fig. 2 and 7, the energy response management module includes:
The demand pattern analysis submodule is used for analyzing the energy use patterns and peaks Gu Chayi of the multiple areas based on the electricity storage cost evaluation information and combining the energy demand prediction information of the multiple areas, evaluating the output demands of the power grid in multiple time periods and generating demand difference information;
in the above, the demand pattern analysis sub-module calculates the demand difference information by using a peak-valley difference analysis model according to the electricity storage cost evaluation information and the energy demand prediction, wherein, Where ΔE represents the total demand difference, D peak,i and D valley,i represent the peak demand and the valley demand of the ith zone, respectively,
Formula details and formula calculation derivation process:
Assuming that the peak demand D peak for the three zones is 1200kW, 1000kW, 800kW, respectively, the valley demand D valley is 300kW, 200kW, 100kW, respectively, Δe is calculated:
ΔE=1200-300+1000-200+800-100=2400kW
The result Δe=2400 kW indicates that the sum of the energy usage pattern and the peak-valley difference between the areas is 2400kW, and the target information will be used to evaluate the output requirements of the grid in different periods, and support the decision process of the grid adjustment measures, ensuring the effective allocation of resources and the optimal use of energy.
The power storage configuration optimizing submodule adjusts power storage configurations of a plurality of areas based on the demand difference information, and comprises the steps of adjusting the energy storage capacity of the areas to match consumption demands of a plurality of time periods, optimizing charge and discharge parameters to optimize the working efficiency and the service life of a battery, and generating a storage configuration optimizing record;
The power storage configuration optimization sub-module plays a vital role in improving the operation efficiency of a power grid and reducing the cost, and based on the demand difference information obtained by the previous analysis, the power storage configuration of a plurality of areas is adjusted, the optimal energy storage capacity of each area is determined by adopting a linear programming and nonlinear optimization technology, the consumption requirements of different time periods can be met, the charge and discharge characteristics and the service life optimization of a battery are considered in the implementation, the charge and discharge parameters are adjusted and optimized by adopting a genetic algorithm and a simulated annealing technology, the target parameters comprise the charge rate, the discharge depth and the temperature control, the response speed and the adaptability of the system are improved, a real-time data processing and feedback mechanism is introduced, the state of energy storage equipment is monitored in real time through a sensor and the Internet of things technology, the configuration is adjusted accordingly, the working efficiency of the battery is improved, the service life is prolonged, and the energy supply continuity and the load balance of the power grid are ensured.
The power resource scheduling sub-module is used for adjusting power distribution by considering energy supply and demand balance based on the storage configuration optimization record, optimizing load response and resource distribution of a power grid and generating resource scheduling optimization parameters;
The power resource scheduling sub-module balances the energy supply and demand of the power grid through an advanced scheduling algorithm and an automation technology, utilizes a storage configuration optimization record obtained from the power storage configuration optimization sub-module, combines real-time energy market data, performs power resource allocation and scheduling, adopts a multi-objective optimization framework, integrates a deterministic and stochastic programming method, processes the uncertainty of energy supply and the fluctuation of demand, optimizes the power flow across a plurality of areas by using a network flow algorithm, ensures effective response in the peak period and the valley period of the demand, and has parameters including the total load of the power grid, the generation capacity and energy storage state of each area and market electricity price, optimizes the load response of the power grid by adjusting the power allocation strategy in real time, improves the efficiency of resource allocation, and the generated resource scheduling optimization parameters provide scientific data support and decision basis for the power grid operation.
Referring to fig. 2 and 8, the electricity price dynamic response module includes:
the power market analysis submodule analyzes the demand and supply conditions of the power market based on the resource scheduling optimization parameters, predicts the supply and demand states of a plurality of time periods and seasons, and generates a market demand analysis result;
In the power market analysis submodule, based on resource scheduling optimization parameters, the supply and demand conditions of a power market are analyzed, a time sequence analysis and market supply and demand balance model is adopted to conduct market prediction, target data are input into an ARIMA model through collecting power market transaction data, weather data, economic indexes and policy changes in each period and season of the past year, seasonal and periodic changes in the time sequence are captured, a supply and demand balance model is used for analyzing market demands in future periods, the model considers various key factors of power consumption mode changes, differences of load distribution of industries and residential areas and market demand elasticity, the target model results are integrated to obtain prediction supply and demand conditions of each period and season, and a market demand analysis result is generated, wherein the supply and demand prediction data, supply tension and market capacity data content of different periods are included, and scientific market pre-judgment basis is provided for price strategy adjustment.
The adjustment demand calculation sub-module is used for evaluating the electric power price to be adjusted under various supply and demand states by considering consumer behaviors and market reactions based on the market demand analysis result, and generating price adjustment analysis data;
In the above, the price adjustment analysis data is calculated according to the formula P adj＝Pbase +α× (Δd- Δs) in consideration of consumer behavior and market reaction to evaluate the price of electric power to be adjusted in various supply and demand states,
Wherein P adj represents the adjusted power price, P base represents the base power price, alpha represents the price adjustment coefficient, the sensitivity of supply and demand changes to price is reflected, deltaD and DeltaS represent the change amounts of demand and supply respectively,
Formula details and formula calculation derivation process:
Assuming that the basic power price P base is 1 yuan/kwh, the demand increment Δd is 3000 kwh, the supply increment Δs is 1500 kwh, the price adjustment coefficient α is set to 0.03, and the adjusted power price is calculated:
Padj=1+0.03×(3000-1500)
Padj=1+0.03×1500
Padj=1+45
Padj=1.45
The result of 1.45 yuan/kwh indicates that in the case of current market demand greater than supply, the power price needs to be up-regulated to market balance, and this calculation shows the direct effect of supply and demand status on power price adjustment, providing a quantification tool to help power market operators make data-driven pricing decisions.
The price list updating submodule adjusts the step power price of a plurality of time periods according to the predicted power consumption mode based on price adjustment analysis data, builds a price list, records adjustment operation and generates a power grid power price adjustment record;
In the price list updating sub-module, based on price adjustment analysis data, step electricity prices of a plurality of time periods are adjusted according to a predicted power consumption mode, power demand prediction data of each time period is associated with price adjustment suggestions, a step electricity price structure is constructed, power demands of different time periods are met by setting different price intervals, peak time period price improvement regulation and control demands are ensured, price downregulation of valley time periods is promoted, consumption is promoted, price adjustment information is recorded through a data management system, price changes of each time period are recorded, analysis and comparison are carried out on implementation effects of historical price adjustment, future price setting is optimized, power grid electricity price adjustment records are generated, and basic data support is provided for subsequent price strategy evaluation and optimization.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (10)
1. A distributed photovoltaic access area operation optimization system is characterized by comprising:
The power demand analysis module collects power consumption data of a plurality of areas based on the area position information, analyzes the influence of a plurality of factors on power consumption, evaluates the change trend of the energy demand and generates an energy demand prediction result;
The output capacity calculation module predicts the power generation capacity of the photovoltaic power generation equipment by analyzing the influence of various factors on the output capacity of the photovoltaic power generation equipment based on the energy demand prediction result, and generates power generation capacity prediction data;
the power cost evaluation module calculates the production cost of unit power by calculating the deployment, operation and maintenance cost of the photovoltaic equipment by using the power generation capacity prediction data, and generates a power generation cost calculation result;
The electricity storage demand assessment module assesses electricity storage demands of a plurality of areas and calculates electricity storage cost according to electricity consumption demands and electricity generation modes in the target areas based on the electricity generation cost calculation result, and generates electricity storage cost assessment information;
The energy response management module is used for adjusting the energy output and storage configuration of the photovoltaic equipment based on the electricity storage cost evaluation information and combining the energy demand prediction information of a plurality of areas to generate resource scheduling optimization parameters;
and the electricity price dynamic response module is used for analyzing the demand and supply condition of the electric power market according to the real-time load and the photovoltaic power generation amount of the power grid based on the resource scheduling optimization parameters, adjusting the electricity price to optimize the power consumption and generating a power price adjustment record of the power grid.
2. The distributed photovoltaic access district operation optimization system according to claim 1, wherein the energy demand prediction result includes predicted peak load data, average load data, energy demand patterns of a plurality of districts, the power generation capability prediction data includes power generation capability at a plurality of time points, photovoltaic equipment specification parameters, and analysis results of influence of performance degradation on power generation capability, the power generation cost calculation result includes production cost of unit power, operation cost data, analysis results of influence of maintenance and equipment replacement on cost, the power storage cost evaluation information includes predicted battery replacement cost, analysis results of battery capacity decrease trend, and power storage demand data, the resource scheduling optimization parameters include photovoltaic power generation amount adjustment proportion parameters, energy storage equipment use priority information, and load demand response parameters, and the power grid power rate adjustment record includes a list of power rates for a plurality of time periods, adjustment region data, and seasonal power rate change adjustment record.
3. The distributed photovoltaic access bay operation optimization system of claim 1, wherein the power demand analysis module comprises:
The power consumption data acquisition submodule acquires power consumption data of a plurality of areas based on the area position information, and performs standardization and formatting processing on the data to generate a power consumption data set;
The power consumption data analysis submodule analyzes the influence of various factors on the power consumption based on the power consumption data set, wherein the influence comprises industrial activities, climate change and regional population quantity, and generates an influence factor identification result;
and the electricity demand assessment sub-module assesses the fluctuation mode and the change trend of the energy demand by utilizing time sequence analysis based on the influence factor identification result, and generates an energy demand prediction result.
4. The distributed photovoltaic access bay operation optimization system of claim 1, wherein the output capability calculation module comprises:
the influence factor analysis submodule calculates influence of various factors on illumination intensity based on the energy demand prediction result, and the influence factor analysis submodule evaluates output capacity of photovoltaic equipment at a plurality of positions including geographic positions, seasons and time points and generates output influence evaluation information;
The performance degradation evaluation submodule is used for evaluating the performance degradation trend of the equipment and the influence on the output capacity by carrying out time series analysis on the power generation amount data of the plurality of photovoltaic equipment based on the output influence evaluation information, and generating a performance degradation analysis result;
And the output quantity prediction submodule predicts the power output quantity of the photovoltaic power generation equipment in the power grid in a plurality of time periods and seasons based on the performance degradation analysis result and generates power generation capacity prediction data.
5. The distributed photovoltaic access bay operation optimization system of claim 4, wherein the specific formula for calculating the impact of multiple factors on the illumination intensity is:
Wherein x i represents a specific value of a geographic location, season or point in time, Represents the average value of x i, y i represents the corresponding illumination intensity value,Representing the average value of y i, the result r represents the correlation strength between the two variables, and i is the index sign of the target factor under consideration.
6. The distributed photovoltaic access bay operation optimization system of claim 1, wherein the power cost assessment module comprises:
The deployment cost calculation submodule calculates the deployment cost of the photovoltaic equipment by utilizing the power generation capacity prediction data, wherein the deployment cost comprises equipment cost and installation cost, and an equipment cost data set is generated;
The operation demand analysis sub-module is used for evaluating the maintenance frequency and the replacement demand of a plurality of devices based on the device cost data set in consideration of the influence of the device performance degradation on the power generation efficiency, calculating the maintenance cost of the plurality of devices, and generating operation cost information by combining the labor cost required by the device operation;
The unit cost analysis submodule calculates the production cost of unit power by considering the deployment cost, the maintenance cost and the change of the power generation efficiency based on the operation cost information, and generates a power generation cost calculation result.
7. The distributed photovoltaic access bay operation optimization system of claim 1, wherein the electricity storage requirement assessment module comprises:
The power generation and power consumption analysis submodule evaluates the power storage requirements of a plurality of areas by analyzing the power consumption requirements and the power generation modes in the target areas based on the power generation cost calculation result, and generates power storage requirement information;
The battery aging influence analysis submodule predicts maintenance and operation costs of the energy storage equipment and generates electricity storage maintenance cost information by analyzing influence of aging and performance attenuation of battery materials on the electricity storage capacity based on the electricity storage demand information;
and the electricity storage cost calculation submodule calculates the electricity storage total cost of the power grid by combining the electricity storage requirements of a plurality of transformer areas based on the electricity storage maintenance cost information, and generates electricity storage cost evaluation information.
8. The distributed photovoltaic access bay operation optimization system of claim 7, wherein the specific formula for calculating the total electricity storage cost of the electrical grid is:
Ctotal=a+b×Q+c×V+d×P
Wherein, C total represents the total electricity storage cost of the grid, Q represents the total electricity storage demand of the whole grid, the electricity storage demand of all the areas and the operation efficiency of the energy storage device are covered, V represents the battery maintenance cost variable, considering the cost effect caused by the battery performance attenuation and the increase of the maintenance frequency, P represents the policy change influence parameter including the direct influence of policy factors such as tax, patch and the like on the cost, ensuring that the model can adapt to the influence of economic policy change, a is intercept, reflects the basic electricity storage cost when no demand and other influence factors exist, b is the influence coefficient of the demand Q, represents how the electricity storage demand is changed by one unit, C is the influence coefficient of the maintenance cost V, reflects how the battery maintenance cost is increased by one unit, d is the influence coefficient of the policy factor P, shows how the electricity storage cost is responded when the policy adjustment of tax, patch and the like changes by one unit.
9. The distributed photovoltaic access bay operation optimization system of claim 1, wherein the energy response management module comprises:
the demand pattern analysis submodule analyzes the energy use patterns and peaks Gu Chayi of the multiple areas based on the electricity storage cost evaluation information and combines the energy demand prediction information of the multiple areas, evaluates the output demands of the power grid in multiple time periods and generates demand difference information;
The power storage configuration optimizing submodule adjusts power storage configurations of a plurality of areas based on the demand difference information, and comprises the steps of adjusting the energy storage capacity of the areas to match consumption demands of a plurality of time periods, optimizing charge and discharge parameters, optimizing the working efficiency and the service life of a battery and generating a storage configuration optimizing record;
and the power resource scheduling sub-module adjusts power distribution in consideration of energy supply and demand balance based on the storage configuration optimization record, optimizes load response and resource distribution of the power grid and generates resource scheduling optimization parameters.
10. The distributed photovoltaic access bay operation optimization system of claim 1, wherein the electricity price dynamic response module comprises:
The power market analysis submodule analyzes the demand and supply conditions of the power market based on the resource scheduling optimization parameters, predicts the supply and demand states of a plurality of time periods and seasons and generates a market demand analysis result;
the adjustment demand calculation submodule evaluates the electric power price to be adjusted in various supply and demand states according to the market demand analysis result and considering consumer behaviors and market reactions, and generates price adjustment analysis data;
and the price list updating sub-module is used for adjusting the step power prices of a plurality of time periods according to the predicted power consumption mode based on the price adjustment analysis data, constructing a price list, recording adjustment operation and generating a power grid power price adjustment record.
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