CN111626509B - A method and system for evaluating the effective supply capacity of regional new energy - Google Patents
A method and system for evaluating the effective supply capacity of regional new energy Download PDFInfo
- Publication number
- CN111626509B CN111626509B CN202010461687.4A CN202010461687A CN111626509B CN 111626509 B CN111626509 B CN 111626509B CN 202010461687 A CN202010461687 A CN 202010461687A CN 111626509 B CN111626509 B CN 111626509B
- Authority
- CN
- China
- Prior art keywords
- new energy
- time
- analysis
- load
- distribution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Educational Administration (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明公开了一种区域新能源有效供给能力的评价方法和系统，所述评价方法包括数据预处理、负荷特性分析、分析样本选取和评估新能源有效供给能力步骤；所述的评价系统包括：计算机客户终端、Ethernet网络、应用服务器、数据库服务器，计算机客户终端通过Ethernet网络分别与应用服务器、数据库服务器连接实现数据交换。本发明提出了基于概率统计分析的区域新能源有效供给能力的评估方法和系统，能够计算不同所属区域、典型场景、概率水平下新能源参与电力平衡的容量系数，有效准确评估新能源有效供给能力。
The invention discloses an evaluation method and system for the effective supply capacity of regional new energy. The evaluation method includes the steps of data preprocessing, load characteristic analysis, analysis sample selection and evaluation of the effective supply capability of new energy; the evaluation system includes: Computer client terminal, Ethernet network, application server, database server, and computer client terminal are respectively connected with the application server and database server through the Ethernet network to realize data exchange. The invention proposes a method and system for evaluating the effective supply capacity of regional new energy based on probability and statistical analysis, which can calculate the capacity coefficient of new energy participating in power balance under different regions, typical scenarios and probability levels, and effectively and accurately evaluate the effective supply capacity of new energy. .
Description
Technical Field
The invention belongs to the technical field of new energy supply, and particularly relates to a method and a system for evaluating the effective supply capacity of regional new energy.
Background
The new energy is an important component of an energy production system, and the rapid development of the new energy becomes an important way for promoting the clean and low-carbon development of the energy. With the rapid development of new energy, the proportion of new energy output to electric load continuously rises, and the load peak clipping effect of the new energy at noon is more and more obvious, so that on one hand, the new energy gradually becomes an important component part of energy supply at a large load period; on the other hand, the risk of new energy consumption in some regions is increasingly highlighted due to the influence of new energy resources, seasons and day-night difference characteristics. In traditional power planning, a principle that new energy participates in power balance is mostly selected according to a typical sunrise characteristic curve and an empirical value, wind power does not participate in power balance at a large load moment in summer and daytime, a photovoltaic installation participates in 20% of balance, the overall power output rate of the new energy is 39.8% at the large load moment in summer and daytime in Henan of 2019, and the selection of a traditional coefficient is obviously low. Considering that the output characteristics of the new energy in each region are different, in order to improve the utilization efficiency of power grid equipment and improve the power investment benefit, the proportion of the new energy participating in power balance in the whole province, the subareas and the time intervals needs to be reasonably determined so as to better serve the power planning and construction of the whole province.
The Chinese patent application with the publication number of CN110266059A discloses a novel energy supply system optimal configuration method based on triangular comprehensive evaluation, which comprises the steps of firstly selecting a load loss rate, a system annual cost and an energy surplus rate as evaluation indexes of the novel energy supply system in three aspects of electric heating supply stability, economy and new energy utilization, establishing a triangular comprehensive evaluation model, and setting the area of the triangular comprehensive evaluation model as a target function of the optimal configuration method; then, establishing an optimized mathematical model of the novel energy supply system by taking the maximum area of the triangular comprehensive evaluation model as a target and obeying constraint conditions; and finally, solving an optimal configuration scheme of the novel energy supply system by adopting a layered iterative algorithm, verifying the correctness and feasibility of the optimization method through an actual example, wherein the optimal configuration scheme can meet the rigid requirement of electric power and heat in remote areas and is favorable for the economic operation of the energy supply system and the efficient utilization of renewable energy. However, the patent only calculates the configuration scheme of the electric power and the thermal capacity according to the power and efficiency indexes of each energy module, does not consider the historical characteristics and the prediction analysis of the new energy output, does not consider the differences of the new energy output characteristics caused by the factors such as the located area, weather (photovoltaic irradiance, wind speed, temperature and the like), analysis time and the like, is relatively conservative, and has low evaluation accuracy; and the installed capacity of the photovoltaic power generation unit is only 4.4M, and the photovoltaic power generation unit aims at a small-sized regional combined heat and power system, and is generally an industrial park or an intelligent building provided with new energy power generation, energy storage and heat storage equipment.
Chinese patent application publication No. CN110854922A discloses an ant colony algorithm-based system and method for evaluating new energy accepting capability of a regional power grid, and the ant colony algorithm-based system for evaluating new energy accepting capability of a regional power grid includes: the system comprises an energy monitor, a power grid monitor, a computer, a grid connection module, a load monitoring module, a load distribution module and an evaluation module. According to the invention, the existing power transmission network resources are optimally distributed and utilized through the grid-connected module, the consumption capacity of new energy is improved, and the structure of a power grid is perfected; meanwhile, the total cost of the micro-grid power generation is minimized while the conditions of the micro-grid safe operation constraints and the load requirements are met through the load distribution module. Meanwhile, loads are classified and refined according to different requirements of users on electric energy supply, diversified electric energy supply in the micro-grid can be effectively utilized, and the running economy of the micro-grid system is optimized. However, historical characteristics and prediction analysis of new energy output are not considered, and differences of new energy output characteristics caused by factors of areas, weather conditions (photovoltaic irradiance, wind speed, temperature and the like) and analysis time periods are not considered, so that the method is relatively conservative and low in evaluation accuracy; the ant colony algorithm aims at a small micro-grid, and when large-scale and complex systems such as provincial-level and even national large-area-level power grids and the like are involved, the problems of complex modeling, long calculation time and low efficiency exist, and the ant colony algorithm does not basically have the value of application in the actual power grid operation in consideration of the complexity of the actual power grid load and power supply configuration scheme and the high requirement on the response speed.
Disclosure of Invention
In order to overcome the defects, the invention aims to provide a method and a system for evaluating the effective supply capacity of new energy in a region based on probability statistical analysis by means of accurately pushing research time periods suitable for different analysis requirements through refined analysis of load characteristics on the basis of integrating real-time operation, development planning and consumption evaluation of all-chain data of the new energy, so that capacity coefficients of the new energy participating in power balance in different affiliated regions, typical scenes and probability levels can be calculated, and the effective supply capacity of the new energy can be effectively and accurately evaluated.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for evaluating the effective supply capacity of regional new energy comprises the following steps:
(1) data preprocessing: setting sampling intervals, and performing discretization pretreatment on the new energy output coefficient, station weather and regional load real-time data;
(2) and (3) load characteristic analysis: screening out the maximum load values in different power supply region time periods of nearly ten years, and taking out the time labels corresponding to the maximum load values;
(3) selecting an analysis sample: firstly, determining analysis time distribution through the occurrence time of the maximum load value, then determining analysis duration through the duration time of the maximum load value, and finally determining an analysis time interval and an analysis sample through the analysis time distribution and the analysis duration;
(4) evaluating the effective supply capacity of new energy: firstly, calculating the probability distribution and the accumulative probability distribution of the new energy output coefficient, then selecting a new energy capacity coefficient, and finally evaluating the effective supply capacity of the new energy.
Preferably, the discretization preprocessing method in the step (1) is as follows: the new energy output coefficient:
ORT＝U{R 11 ,R 12 ,…,R 1j ,R 21 ,R 22 ,…,R 2j ,……,R i1 ,R i2 ,…,R ij }
R ij ＝U(r ij0 ,r ij1 ,r ij2 ,……,r ijn )
wherein R is ij Represents the output coefficient set r of the ith power supply area in the jth year ijn Representing the nth output coefficient of the ith power supply area in the jth year;
0 represents: 00:00, 1 denotes 00:05, 2 denotes: 00: 10.... i.e. the sample period time interval is 5 min;
weather data of the new energy station:
OPP＝U{P 111 ,P 112 ,…,P 11d ,P 121 ,P 122 ,…,P 12d ,……,P ij1 ,P ij2 ,…,P ijd }
P ijd ＝U(p ijd0 ,p ijd1 ,p ijd2 ,……,p ijdn )
wherein p is ijd D-th meteorological index data set, p, representing the ith power supply area in the j-th year ijdn The nth number of the d-th meteorological indexes of the ith power supply area in the j year is represented;
0 represents: 00:00, 1 denotes 00:05, 2 denotes: 00: 10.... i.e. the sample period time interval is 5 min;
regional load data:
L＝U{L 11 ,L 12 ,…L 1j ,L 21 ,L 22 ,…,L 2j ,……,L i1 ,L i2 ,…,L ij }
L ij ＝U(l ij0 ,l ij1 ,l ij2 ,……,l ijn )
wherein L is ij Represents the discrete load set of the ith power supply region in the jth year, l ijn The nth load of the ith power supply area in the jth year is shown;
0 represents: 00:00, 1 denotes 00:05, 2 denotes: 00: 10.. said, i.e. the sample period time interval is 5 min.
Preferably, the time period in step (2) includes natural year, season, natural month, natural day, early morning/noon/late hour, wherein natural year refers to 1 month and 1 day of the gregorian calendar to 31 months and 12 months of the gregorian calendar; the seasons are defined as follows: the spring is 3-5 months of the gregorian calendar, the summer is 6-8 months of the gregorian calendar, the autumn is 9-11 months of the gregorian calendar, and the winter is 12 months and 1-2 months; natural months mean 1 day per month to the end of the month in the lunar calendar; the natural day means zero point to 24 points in the morning every day; the initial values of the corresponding time intervals of the early morning, the noon and the late are respectively set as: 00:00-05:00, 11: 00-15:00, 16:00-21: 00.
Preferably, the selecting of the analysis sample in step (3) includes: firstly, the selected time periods of year, season, month, nature day, morning/noon/evening are analyzed according to the load characteristicsThe maximum load value in the inter-period is calculated, and the current year maximum load value ratio r of the maximum load values in different seasons is calculated 1 The percentage of the current season load maximum value of the load maximum values in different months r 2 The ratio r of the load maximum value of the current month to the load maximum value of different natural daily loads 3 The ratio r of the maximum load value of the load in different time periods to the maximum load value of the load in the same day 4 Defining the time of day analysis weight coefficient phi 1 Set to 0.9, screen out r 1 Greater than phi 1 The time label group corresponding to the sample is the seasonal distribution of the maximum load value, and in the same way, the month distribution, the date distribution and the time distribution of the maximum load value of different power supply areas in the last decade are obtained; then, according to the annual load maximum value screened by the load characteristic analysis, the annual load maximum value ratio r of the load values at different times is calculated 5 Defining a time interval analysis weight coefficient phi 2 Set to 0.99, screen out r 5 Greater than phi 2 Obtaining the duration time of the maximum load value, namely the analysis duration time, of the time tag group corresponding to the sample; and finally, according to the distribution of the analysis time and the analysis duration, when the maximum load time tag number in the analysis starting time and the analysis ending time is the maximum, the maximum load time tag number is the recommended analysis time period, and the corresponding meteorological data and output coefficient data of the new energy field station after discretization pretreatment are analysis samples.
Preferably, the evaluating the new energy efficient supply capacity in the step (4) includes: firstly, calculating the probability distribution of the new energy output coefficient, and fitting the probability density distribution according to the output characteristic; then, calculating the new energy output coefficient more than or equal to a certain specific level p i Obtaining an accumulative probability density curve of the new energy output coefficient, and calculating the accumulative probability density curve by using the fitted probability density distribution; defining the probability level p by planning 5% of the extreme cases in combination with meteorological data i The probability level is 0.95, the new energy output coefficient corresponding to the probability level on the cumulative probability density curve is selected as the capacity coefficient of the regional new energy participating in the power balance under the analysis scene if the difference value between the probability level and the probability level on the fitted cumulative probability density curve is minimum; finally, the installed capacity of the regional new energy and the capacity of the new energyAnd the product of the coefficients is the effective new energy supply capacity of the area under the analysis scene.
Preferably, the probability distribution formula of the new energy output coefficient is as follows:
where j is a certain supply area, m is a certain selected time range, x jm For a new energy output coefficient, r, of a certain power supply area within a selected time range i-1 To set the lower limit of the range of the output coefficient, r i To set the upper limit of the output coefficient interval, P (x) jm ) Is shown when x jm R is greater than or equal to i-1 And is less than r i Probability of 0. ltoreq. r i-1 ＜r i ≤1，1≤i≤10；
When j and m are fixed, [ integral ] P (x) jm )＝1；
count(r i-1 ≤x jm ＜r i ) When x is jm The number of samples within the upper limit and the lower limit of the set output coefficient interval; count (x) jm ) Total number of samples in a selected time range for a certain power supply region;
the accumulated probability density curve of the new energy output coefficient is as follows:
wherein p is i For the set ith output coefficient level, i is more than or equal to 0 and less than or equal to 1000, j represents a certain power supply area, m is a selected time range, count (x) jm ≥p i ) When x is jm A value greater than or equal to the set output coefficient level p i Number of samples, count (x) jm ) The total number of samples in a selected time range for selecting a certain power supply area; r (x) jm ) Is shown when x jm Greater than the level of force p i The cumulative probability of (c).
A system for evaluating the effective supply capacity of regional new energy comprises: the system comprises a computer client terminal, an Ethernet network, an application server and a database server, wherein the computer client terminal is respectively connected with the application server and the database server through the Ethernet network to realize data exchange;
the computer client terminal is used for carrying out interactive operation with the interactive module of the application server and setting related parameters; the Ethernet network provides physical connection between the computer client terminal and the application server, and between the computer client terminal and the database server, and is used for exchanging, transmitting and sharing various data; the database server is used for storing and calling basic data, parameter data and result data, performing interactive operation with the application server and displaying data and results;
the application server comprises an interaction module, a calculation module and a display module, wherein the interaction module comprises: receiving a first interactive operation set based on a sampling interval, a second interactive operation set based on a power supply area and a time period, a third interactive operation set based on a time analysis weight coefficient and a time period analysis weight coefficient, a fourth interactive operation set based on a probability level, and a load characteristic, an analysis sample, an accumulated probability density curve and a new energy output coefficient which are correspondingly displayed according to the interactive operation;
a computing module, comprising: the first computing unit is used for carrying out discretization preprocessing on the real-time data according to a sampling interval set by the first interactive operation of the interactive module; the second computing unit is used for screening out the maximum load value in the specified time period of the specified power supply area in the last decade according to the power supply area and the time period set by the second interactive operation of the interactive module, and taking out the time tag corresponding to the maximum load value; the third calculating unit is used for calculating season distribution, month distribution, date distribution and time distribution of the maximum load value of the specified power supply area in the last decade according to the selected time labels and the time analysis weight coefficient set by the interactive module through third interactive operation; the fourth calculating unit is used for calculating the duration of the maximum load value of the designated power supply area according to the time interval analysis weight coefficient set by the third interactive operation of the interactive module to obtain analysis duration; the time distribution and the analysis duration are used for selecting an analysis time interval and analyzing a sample; the fifth calculating unit is used for calculating frequency distribution and cumulative probability density curves of the analysis samples and calculating corresponding new energy sources to ensure output coefficients and effective supply capacity of regional new energy sources according to the probability level set by the fourth interactive operation of the interactive module;
and the display module displays the load characteristics, the moment distribution, the duration of the maximum load value, the new energy output rate within the typical day distribution moment, the probability distribution of the new energy output coefficient, the cumulative probability density curve of the new energy output coefficient and the new energy guarantee output coefficients of different areas under different probability levels according to the determined attribute dimensions.
Preferably, the interaction module is further configured to: clicking a storage trigger interactive operation, and storing the artificially configured analysis duration and the accumulated probability distribution data; and clicking the switching of the icons to trigger and display the corresponding table data and the corresponding graphs.
Preferably, the Ethernet network comprises a switch and a plurality of communication units, each communication unit comprises two Ethernet network connectors and an unshielded twisted pair, and two ends of the unshielded twisted pair are respectively connected with one Ethernet network connector; the computer client terminal, the application server and the database server are respectively connected to the switchboard through corresponding communication units.
Preferably, the database server comprises a plurality of data storage modules, and each data storage module comprises a basic data unit, a parameter data unit and a result data unit;
the basic data unit is used for acquiring and storing real-time data of new energy station weather, output coefficient and regional load; the parameter data unit is used for acquiring and storing sampling intervals, power supply areas, time periods, time analysis weight coefficients, time period analysis weight coefficients and probability level parameter data set by the computer client terminal through the interactive module; the result data unit is used for acquiring and storing various items of result data of the computing module of the application server, and comprises: the method comprises the steps of discretizing preprocessed basic data, load characteristics, analysis time distribution, load maximum value duration distribution, probability distribution and cumulative probability density curve of new energy output coefficients and new energy guaranteed output coefficients of different regions under different probability levels.
The invention has the following positive beneficial effects:
1. the traditional method for evaluating the effective supply capacity of the new energy in the region by power planning is rough, the capacity coefficient of the new energy is often selected according to a typical sunrise curve and the experience value of a planner, the regional characteristic of the capacity coefficient of the new energy is often ignored, the intermittence, the fluctuation and the basic uncontrollable property of the new energy output determine that the output has the statistical probability distribution characteristic, the maximum output of the new energy is often not matched with the occurrence time of the maximum power load, the load peak is not supplied, the load valley is difficult to be consumed, the neglected or rough evaluation of the capacity value of the new energy can underestimate the available capacity of the new energy in power balance or consider the extreme condition in the regional new energy consumption evaluation, so that a conservative strategy is adopted for the installation of the traditional unit, particularly along with the rapid development of the installation scale of the new energy in recent years, credibility and accuracy of evaluation of the effective supply capacity of the new energy directly influence measurement and calculation of the matching investment scale of the power supply side and the power grid side in the planning year.
The method for evaluating the effective supply capacity of the new energy in the region is based on the real-time output of a large number of station levels and the fine analysis of load data, evaluates the effective supply capacity of the new energy in different power supply regions, different probability levels and typical scenes, considers the personalized characteristics of different power supply regions and the probability distribution characteristics of the output of the new energy, realizes the accurate pushing of an analysis time period, provides detailed and reliable data for the selection of a capacity coefficient of the new energy, can effectively and accurately evaluate the effective supply capacity of the new energy, is beneficial to promoting the optimization and adjustment of an electric power planning idea under the background of strictly controlling the investment of a power grid, and helps to realize customized, scientific and accurate electric power planning.
2. The system for evaluating the effective supply capacity of the regional new energy can be applied to the field of power planning, a convenient and fast new energy supply capacity analysis model tool is established, the refined pushing of analysis samples for different analysis requirements (regions, seasons, analysis time periods and new energy types) is realized through a convenient and fast human-computer interaction interface based on real-time load and new energy field station output data, the working efficiency of workers is greatly improved, data support is provided for selection of a new energy participation power balance principle in the traditional power planning, and the personalized and customized scientific planning is assisted. After the method is applied, taking a summer heavy load analysis scene of a certain provincial power grid as an example, referring to a specific embodiment, the photovoltaic output coefficient at the peak noon is 35%, and is improved by 15% compared with the traditional planning; the late peak wind power output coefficient is 10%, which is 10% higher than that of the traditional wind power output coefficient.
3. The method aims at the reasonable evaluation of the new energy supply capacity in provincial or even national regional power system planning, the installed capacity of the new energy is large, the influence of the large-scale distribution factors of the new energy field group on the whole new energy output level of the region is integrated on the basis of considering the intermittence and the fluctuation of the new energy output, the influence on wind power is particularly obvious, the fluctuation of the new energy resources at different positions of the region is mutually counteracted due to the time delay effect, the filtering effect and the spatial distribution effect along with the expansion of the scale of the region or the increase of the distance between regional individuals and the number of regional individuals, and the obvious region smoothing effect is presented.
Drawings
FIG. 1 is a flowchart of a method for evaluating the effective supply capacity of new energy in a region according to the present invention;
FIG. 2 is a block diagram of a system for evaluating the regional new energy efficient supply capability of the present invention;
fig. 3 is a monthly load characteristic curve diagram of the province in 2018 of a certain province in the embodiments 1-2 of the present invention;
FIG. 4 is a time distribution graph of the load maximum value of the single day in the 7-8 months of the province in 2018-2019 in the embodiments 1-2 of the present invention (r) 3 >90％)；
FIG. 5 is a frequency distribution diagram of the new energy (photovoltaic) output coefficient according to example 1 of the present invention;
FIG. 6 is a graph of cumulative probability density of new energy (photovoltaic) output coefficient according to example 1 of the present invention;
fig. 7 is a frequency distribution diagram of the new energy (wind power) output coefficient in embodiment 2 of the present invention;
fig. 8 is a cumulative probability density curve diagram of the new energy (wind power) output coefficient in embodiment 2 of the present invention.
Detailed Description
The invention is further illustrated below with reference to some specific examples.
Referring to fig. 1, a method for evaluating the effective supply capacity of regional new energy includes the following steps:
(1) data preprocessing: combining the requirement of analysis precision, setting a sampling interval, and carrying out discretization pretreatment on the new energy output coefficient, station weather and regional load real-time data, wherein the output coefficient is the ratio of the new energy station output to the actual grid-connected capacity, and specifically comprises the following steps:
the new energy output coefficient:
ORT＝U{R 11 ,R 12 ,…,R 1j ,R 21 ,R 22 ,…,R 2j ,……,R i1 ,R i2 ,…,R ij }
Rij＝U(rij0,rij1,rij2,……,rijn)
wherein R is ij Represents the output coefficient set r of the ith power supply area in the jth year ijn Representing the nth output coefficient of the ith power supply area in the jth year;
0 represents: 00:00, 1 denotes 00:05, 2 denotes: 00: 10.... i.e. the sample period time interval is 5 min;
weather data of the new energy station:
OPP＝U{P 111 ,P 112 ,…,P 11d ,P 121 ,P 122 ,…,P 12d ,……,P ij1 ,P ij2 ,…,P ijd }
P ijd ＝U(p ijd0 ,p ijd1 ,p ijd2 ,……,p ijdn )
wherein p is ijd D-th meteorological index data set, p, representing the ith power supply area in the j-th year ijdn The nth number of the d-th meteorological indexes of the ith power supply area in the j year is represented;
0 represents: 00:00, 1 denotes 00:05, 2 denotes: 00: 10.... i.e. the sample period time interval is 5 min;
regional load data:
L＝U{L 11 ,L 12 ,…L 1j ,L 21 ,L 22 ,…,L 2j ,……,L i1 ,L i2 ,…,L ij }
L ij ＝U(l ij0 ,l ij1 ,l ij2 ,……,l ijn )
wherein L is ij Represents the discrete load set of the ith power supply region in the jth year, l ijn Represents the nth load of the ith power supply area in the jth year;
0 represents: 00:00, 1 denotes 00:05, 2 denotes: 00:10, i.e. the sample period time interval is 5 min;
(2) and (3) load characteristic analysis: screening out the maximum load values in different power supply area time periods of nearly ten years, and taking out the time labels corresponding to the maximum load values, namely:
T＝f(MAX(R ijm ))，
wherein T is a time stamp, i is a certain power supply area, j is a year, m is a month, f (MAX (R) ijm ) Date and time at which the maximum load was returned;
the time period comprises a natural year, a season, a natural month, a natural day, a morning/noon/evening period, wherein the natural year refers to 1 month and 1 day of the gregorian calendar to 31 months and 12 months of the gregorian calendar; the seasons are defined as follows: the spring is 3-5 months of the Gregorian calendar, the summer is 6-8 months of the Gregorian calendar, the autumn is 9-11 months of the Gregorian calendar, and the winter is 12 months and 1-2 months; natural months mean 1 day per month to the end of the month in the lunar calendar; the natural day means zero point to 24 points in the morning every day; the initial values of the corresponding time intervals of the early morning, the noon and the late are respectively set as: 00:00-05:00, 11: 00-15:00, 16:00-21: 00.
(3) Selecting an analysis sample: firstly, determining analysis time distribution through the occurrence time of the maximum load value, then determining analysis duration through the duration time of the maximum load value, and finally determining an analysis time interval and an analysis sample through the analysis time distribution and the analysis duration, wherein the details are as follows:
first, year and season selected by the load characteristic analysis are analyzedLoad maximum values in time periods of month, natural day, morning/noon/evening, and load maximum value ratio r in current year of load maximum values in different seasons 1 The percentage of the current season load maximum value of the load maximum values in different months r 2 The ratio r of the load maximum value of the current month to the load maximum value of different natural daily loads 3 The ratio r of the maximum load value of the load in different time periods to the maximum load value of the load in the same day 4 Defining the time of day analysis weight coefficient phi 1 Set to 0.9, screen out r 1 Greater than phi 1 The time label group corresponding to the sample is the seasonal distribution of the maximum load value, and in the same way, the month distribution, the date distribution and the time distribution of the maximum load value of different power supply areas in the last decade are obtained; then, according to the annual load maximum value screened by the load characteristic analysis, the annual load maximum value ratio r of the load values at different times is calculated 5 Defining a time interval analysis weight coefficient phi 2 Set to 0.99, screen out r 5 Greater than phi 2 Obtaining the duration time of the maximum load value, namely the analysis duration time, of the time tag group corresponding to the sample; finally, determining an analysis time period and an analysis sample through analysis time distribution and analysis duration, adjusting analysis starting time until the maximum load time label number in the analysis starting time and the analysis ending time is maximum, namely the analysis time period, and obtaining corresponding new energy station meteorological data and output coefficient data which are subjected to discretization pretreatment as the analysis sample;
seasonal distribution, month distribution, date distribution and time distribution of the maximum load values of different power supply areas in the last decade are as follows:
wherein: l is a radical of an alcohol ijs Is a load set in a certain power supply area and in a certain seasonal time period of a certain year,
L ij is a load set in a certain power supply area and a certain year.
Wherein: l is a radical of an alcohol ijm Is a load set in a certain power supply area and in a certain month and time period in a certain year,
L ijs the maximum load set in a certain power supply region and a certain year in a certain quarter.
Wherein: l is ijd Is a load set in a certain power supply area and in a certain day of a certain year,
L ijm is a load set in a certain power supply area and in a certain month of a certain year.
Wherein: l is ijo Is a load set of a certain power supply area and a certain time period on a certain day of a certain year,
L ijd is a load set in a certain power supply area and in a certain day of a certain year.
Wherein: l is ijo Is a load set of a certain power supply area and a certain time period on a certain day of a certain year,
L ij a certain power supply area, a load set of a certain year;
the duration is:
DT＝g(T ij )
wherein: g (T) ij ) To satisfy the condition r for a certain power supply area within a certain year 5 ≥Φ 2 The time period of (a).
(4) Evaluating the effective supply capacity of new energy: firstly, calculating the probability distribution and the accumulative probability distribution of the new energy output coefficient, then selecting a new energy capacity coefficient, and finally evaluating the effective supply capacity of the new energy, wherein the detailed description is as follows:
firstly, calculating the probability score of the new energy output coefficientDistributing to obtain distribution probabilities in different output coefficient ranges, and fitting the probability density distribution according to the output characteristics; then, calculating the new energy output coefficient more than or equal to a certain specific level p i Obtaining an accumulative probability density curve of the new energy output coefficient, and calculating the accumulative probability density curve by using the fitted probability density distribution; defining the probability level p by planning 5% of the extreme cases in combination with meteorological data i The probability level is 0.95, the new energy output coefficient corresponding to the probability level on the cumulative probability density curve is selected as the capacity coefficient of the regional new energy participating in the power balance in the analysis scene if the probability level difference value on the cumulative probability density curve matched with the probability level is minimum; finally, evaluating the effective supply capacity of the new energy, wherein the product of the installed capacity of the regional new energy and the capacity coefficient of the new energy is the effective supply capacity of the regional new energy under the analysis scene, namely the probability that the actual output of the regional new energy exceeds the effective supply capacity is greater than or equal to the set probability level of 0.95;
the probability distribution formula of the new energy output coefficient is as follows:
wherein j is a certain power supply region, m is a certain selected time range, x jm For a new energy output coefficient, r, of a certain power supply area within a selected time range i-1 To set the lower limit of the range of the output coefficient, r i To set the upper limit of the output coefficient interval, P (x) jm ) Is shown when x jm R is greater than or equal to i-1 And is less than r i Probability of 0. ltoreq. r i-1 ＜r i ≤1，1≤i≤10；
When j and m are fixed, [ integral ] P (x) jm )＝1；
count(r i-1 ≤x jm ＜r i ) When x is jm The number of samples within the upper limit and the lower limit of the set output coefficient interval; count (x) jm ) A total number of samples in a selected time range for a power supply region;
the accumulated probability density curve of the new energy output coefficient is as follows:
wherein p is i For the set ith output coefficient level, i is more than or equal to 0 and less than or equal to 1000, j represents a certain power supply region, m is a selected time range, count (x) jm ≥p i ) When x is jm A value greater than or equal to the set output coefficient level p i Number of samples, count (x) jm ) The total number of samples in a selected time range for selecting a certain power supply area; r (x) jm ) Is shown when x jm Greater than the level of force p i The cumulative probability of (c).
Referring to fig. 2, a system for evaluating an effective supply capacity of regional new energy includes: the system comprises a computer client terminal, an Ethernet network, an application server and a database server, wherein the computer client terminal is respectively connected with the application server and the database server through the Ethernet network to realize data exchange;
the computer client terminal is used for carrying out interactive operation with the interactive module of the application server and setting related parameters; the Ethernet network provides physical connection between the computer client terminal and the application server and between the computer client terminal and the database server, and is used for exchanging, transmitting and sharing various data; the database server is used for storing and calling basic data, parameter data and result data, performing interactive operation with the application server and displaying data and results.
The Ethernet network comprises an exchanger and a plurality of communication units; the communication unit comprises two Ethernet network connectors and an unshielded twisted pair, and two ends of the unshielded twisted pair are respectively connected with one Ethernet network connector; the computer client terminal, the application server and the database server are respectively connected to the switchboard through corresponding communication units.
The database server comprises a plurality of data storage modules, and each data storage module comprises a basic data unit, a parameter data unit and a result data unit;
the basic data unit is used for acquiring and storing real-time data of weather, output coefficient and area load of the new energy station; the parameter data unit is used for acquiring and storing the region sampling interval, the power supply region, the time period, the time analysis weight coefficient, the time period analysis weight coefficient and the probability level parameter data set by the interactive module through the computer client terminal; the result data unit is used for acquiring and storing various items of result data of the computing module of the application server, and comprises: the method comprises the steps of discretizing preprocessed basic data, load characteristics, time distribution, duration of maximum load, probability distribution of new energy output coefficients, cumulative probability density curves of the new energy output coefficients and new energy guarantee output coefficients of different regions under different probability levels.
The application server comprises an interaction module, a calculation module and a display module, wherein the interaction module comprises a first interaction operation unit, a second interaction operation unit and a third interaction operation unit, and the calculation module comprises a first calculation unit, a second calculation unit, a third calculation unit, a fourth calculation unit and a fifth calculation unit;
an interactive module comprising: receiving a first interactive operation set based on a sampling interval, a second interactive operation set based on a power supply area and a time period, a third interactive operation set based on a time analysis weight coefficient and a time period analysis weight coefficient, a fourth interactive operation set based on a probability level, and a load characteristic, an analysis sample, an accumulated probability density curve and a new energy guarantee output coefficient which are correspondingly displayed according to the interactive operation; the interaction module is further configured to: clicking a storage trigger interactive operation, and storing the artificially configured analysis time length and the accumulated probability distribution data; clicking the switching of the icons to trigger and display corresponding table data and graphs;
a computing module, comprising: the first computing unit is used for carrying out discretization pretreatment on the real-time data according to a sampling interval set by the first interactive operation of the interactive module; the second computing unit screens out the maximum load value in the specified time period of the specified power supply area in the last decade according to the power supply area and the time period set by the second interactive operation of the interactive module, and takes out the time label corresponding to the maximum load value; the third calculating unit is used for calculating season distribution, month distribution, date distribution and time distribution of the maximum load value of the specified power supply area in the last decade according to the selected time labels and the time analysis weight coefficient set by the interactive module in the third interactive operation; the fourth calculation unit is used for calculating the duration of the maximum load value of the specified power supply area according to the time interval analysis weight coefficient set by the third interactive operation of the interactive module to obtain analysis duration; selecting an analysis time interval and an analysis sample according to the time distribution and the analysis duration; the fifth calculating unit is used for calculating frequency distribution and cumulative probability density curves of the analysis samples and calculating corresponding new energy sources to ensure output coefficients and the effective supply capacity of regional new energy sources according to the probability level set by the fourth interactive operation of the interactive module;
and the display module displays the load characteristics, the analysis time distribution, the load maximum value duration distribution, the new energy output rate in the typical day distribution time, the probability distribution of the new energy output coefficient, the cumulative probability density curve of the new energy output coefficient and the new energy guarantee output coefficients of different areas under different probability levels according to the determined attribute dimensions.
Example 1
Taking the evaluation of the new energy effective supply capacity at the annual heavy load moment of a certain power saving network in 2020 as an example.
In this embodiment, the switch is a Cisco SF100D-08 type switch, the Ethernet network connector is an RJ45 type Ethernet network connector, and the unshielded twisted pair is a CAT5E type unshielded twisted pair.
The minimum configuration requirements for a computer client terminal are as follows: an operating system Centos Linux 6.8, a processor 1GHz, a memory 1GB, a graphics card supporting DirectX 9128M, a hard disk space 16G, a network interface equipped with Ethernet, and a Google browser version 72 or more; configuring an application server: 8-core CPU, 16G memory and 100G hard disk; database server configuration: 4-core CPU, 256G memory and 5T hard disk.
A method for evaluating the effective supply capacity of regional new energy comprises the following steps:
(1) reading load, new energy output and meteorological data of each area in the province in 2009-2019 years, setting a sampling interval to be 5min through a first interactive operation of an interactive module, calling a first computing unit of a computing module, and performing discretization preprocessing on the data;
(2) through second interactive operation of the interactive module, the power supply area is set to be full province, the time period is set to be year, a second computing unit of the computing module is called to start the annual load characteristic analysis of the full province, the year-by-year evolution characteristic of the load characteristic of the power saving network is obvious, and the largest annual load value mostly appears in summer noon; setting a power supply area as a whole province, setting an analysis year (taking 2018 as an example), setting a time period as month, calling a second computing unit of a computing module to start load characteristic analysis of months in different years, and finding that a load curve of the power saving network has the characteristic of double peaks in summer and winter; setting a power supply area as a whole province, setting an analysis date (taking the date corresponding to the maximum load value in 2019 as an example), setting a time period as a day, calling a second calculation unit of a calculation module to start daily load characteristic analysis, finding that a load curve of the power saving network has the characteristic of 'double peaks at noon and night', and obtaining an analysis result shown in figure 3;
(3) setting a time analysis weight coefficient to be 0.9 through a third interactive operation of the interactive module, calling a third calculating unit of the calculating module, and screening the distribution of seasons, months, dates and times with the maximum load of the province; in the aspect of seasonal distribution, except that the annual load maximum value in 2009 appears in winter, the rest of years appear in summer; in terms of distribution of months and dates, the maximum value of the summer load appears from the beginning of 7 months to the end of 8 months, the maximum value of the winter load appears from the beginning of 12 months to the end of 1 month in the next year, the maximum value of the load appears in the summer year, and the proportion of the maximum value of the winter load to the maximum value of the summer load is between 79 and 96 percent; in the aspect of time distribution, except that the load maximum values of 3, 7 and 8 months occur in the noon, the load maximum values of the rest months all occur in the evening, and the result is shown in a figure 4; pushing the time labels of the month, the date and the moment corresponding to the maximum annual load value to a fourth calculating unit of a calculating module to serve as initial values of analysis time periods;
(4) setting a time interval analysis weight coefficient to be 0.99 through a third interactive operation of an interactive module, calling a fourth calculation unit of a calculation module, setting an analysis year (2019 as an example), calculating the duration of the total province load maximum value to be 1 hour, and ensuring that the analysis duration of the first time is equal to the analysis ending time-analysis starting time by using the initial value of the analysis time interval calculated in the step 3; the time labels corresponding to the maximum load values in different time periods are not earlier than the respective analysis starting time; thirdly, on the premise that the time labels corresponding to the maximum load values in different time periods are not later than the respective analysis ending time, adjusting the analysis starting time until the maximum load value time labels in the analysis starting time and the analysis ending time are the most, wherein the recommended analysis time period is 7-8 months 12:30-13: 30;
(5) calling a fifth calculation unit of a calculation module, calculating frequency distribution and cumulative probability density curves of new energy output coefficients corresponding to analysis time intervals, setting new energy types (taking photovoltaic as an example) through fourth interactive operation of an interactive module, setting a probability level to be 0.95, calculating a new energy guarantee output coefficient to be 35%, calculating according to the photovoltaic installed capacity of 1100 ten thousand kilowatts in 2020, setting the photovoltaic effective supply capacity to 385 ten thousand kilowatts at a heavy load moment in summer, and setting the probability that the actual output of regional new energy exceeds the effective supply capacity to be greater than or equal to the set probability level (generally to be 0.95), wherein the result is shown in fig. 5 and fig. 6.
Example 2
Considering that the probability of the load appearing at the late peak increases year by year, the effective new energy supply capacity at the time of heavy load in late time of a certain power saving network year in 2020 is taken as an example. In this embodiment, the steps (1) - (2) of the device model, the minimum configuration requirement of the computer client terminal, and the evaluation method are the same as those in embodiment 1, except that:
a method for evaluating the effective supply capacity of regional new energy comprises the following steps:
(3) setting a time analysis weight coefficient to be 0.9 through a third interactive operation of the interactive module, calling a third calculating unit of the calculating module, and screening the distribution of seasons, months, dates and times with the maximum load of the province; in the aspect of seasonal distribution, except that the annual load maximum value appears in winter in 2009, the rest of the years appear in summer; in terms of distribution of months and dates, the maximum value of the summer load appears from the beginning of 7 months to the end of 8 months, the maximum value of the winter load appears from the beginning of 12 months to the end of 1 month in the next year, the maximum value of the load appears in the summer year, and the proportion of the maximum value of the winter load to the maximum value of the summer load is between 79 and 96 percent; in the aspect of time distribution, except that the load maximum values of 3, 7 and 8 months occur in the noon, the load maximum values of the rest months all occur in the evening, and the result is shown in a figure 4; pushing time labels of months, dates and moments corresponding to the load maximum value of the late time period of the year to a fourth calculation unit of the calculation module to serve as initial values of the analysis time period;
(4) setting a time interval analysis weight coefficient to be 0.99 through a third interactive operation of an interactive module, calling a fourth calculation unit of a calculation module, setting an analysis year (2019 as an example), calculating the duration of the total province load maximum value to be 1 hour, and ensuring that the analysis duration of the first time is equal to the analysis ending time-analysis starting time by using the initial value of the analysis time interval calculated in the step 3; the time labels corresponding to the maximum load values in different time periods are not earlier than the respective analysis starting time; thirdly, on the premise that the time labels corresponding to the maximum load values in different time periods are not later than the respective analysis ending time, adjusting the analysis starting time until the maximum load value time labels in the analysis starting time and the analysis ending time are the most, wherein the recommended analysis time period is 7-8 months, and 20:00-22: 00;
(5) calling a fifth calculation unit of a calculation module, calculating frequency distribution and cumulative probability density curves of new energy output coefficients corresponding to the analysis time period, setting new energy types (taking wind power as an example) through fourth interactive operation of an interactive module, setting a probability level to be 0.95, calculating a new energy guarantee output coefficient to be 10%, calculating according to the wind power installed capacity of 1500 ten thousand kilowatts in 2020, setting the wind power effective supply capacity of 150 ten thousand kilowatts at a heavy load moment in the late summer time period, and setting the probability that the actual output of regional new energy exceeds the effective supply capacity to be more than or equal to the set probability level (generally to be 0.95), wherein the result is shown in fig. 7 and 8.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited, and other modifications or equivalent substitutions made by the technical solutions of the present invention by the persons skilled in the art should be covered within the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.
Claims (9)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010461687.4A CN111626509B (en) | 2020-05-27 | 2020-05-27 | A method and system for evaluating the effective supply capacity of regional new energy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010461687.4A CN111626509B (en) | 2020-05-27 | 2020-05-27 | A method and system for evaluating the effective supply capacity of regional new energy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111626509A CN111626509A (en) | 2020-09-04 |
| CN111626509B true CN111626509B (en) | 2022-09-09 |
Family
ID=72272308
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010461687.4A Active CN111626509B (en) | 2020-05-27 | 2020-05-27 | A method and system for evaluating the effective supply capacity of regional new energy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111626509B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116796926B (en) * | 2023-06-30 | 2024-03-15 | 国家电网有限公司华东分部 | Low-voltage side new energy resource aggregation capacity assessment method and devices, media and equipment |
| CN120073701B (en) * | 2025-02-26 | 2025-12-09 | 中国电力科学研究院有限公司 | Wind power actual capacity coefficient evaluation method, system and equipment under medium-long scale |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005051048A (en) * | 2003-07-29 | 2005-02-24 | Atsushi Iga | Method of evaluating amount of power generated by field photovoltaic power generating system, computer-readable recording medium recording evaluation program, and evaluation system |
| JP2005137143A (en) * | 2003-10-31 | 2005-05-26 | Osaka Gas Co Ltd | Energy supply evaluation system |
| CN103955765A (en) * | 2014-04-25 | 2014-07-30 | 广东工业大学 | Micro power source capacity configuring method based on power generation capability evaluation and load classification |
| JP2017050972A (en) * | 2015-09-01 | 2017-03-09 | 富士電機株式会社 | Power generation plan creation apparatus, power generation plan creation program, and power generation plan creation method |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110071882A1 (en) * | 2009-09-22 | 2011-03-24 | International Business Machines Corporation | Method and system for intermediate to long-term forecasting of electric prices and energy demand for integrated supply-side energy planning |
| CN103346563B (en) * | 2013-07-15 | 2015-04-29 | 国家电网公司 | Method for evaluating maximum permeability of distributed generation based on time scene access analysis |
| CN104868505B (en) * | 2015-06-03 | 2017-03-29 | 中国能源建设集团甘肃省电力设计院有限公司 | Cluster output characteristic analysis method for large-scale solar photovoltaic power generation base |
| CN105719058A (en) * | 2016-01-15 | 2016-06-29 | 国网江西省电力科学研究院 | Electric power demand-side management auxiliary decision support system |
| CN205665726U (en) * | 2016-03-31 | 2016-10-26 | 国家电网公司 | Anticipated risk assessment system to extensive planning electric wire netting |
| CN107769271B (en) * | 2017-10-30 | 2020-07-28 | 国网能源研究院有限公司 | A new energy comprehensive absorption capacity evaluation method |
| CN110648249B (en) * | 2019-09-23 | 2022-03-29 | 广西电网有限责任公司 | Annual power balance measuring and calculating method, device and equipment |
-
2020
- 2020-05-27 CN CN202010461687.4A patent/CN111626509B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005051048A (en) * | 2003-07-29 | 2005-02-24 | Atsushi Iga | Method of evaluating amount of power generated by field photovoltaic power generating system, computer-readable recording medium recording evaluation program, and evaluation system |
| JP2005137143A (en) * | 2003-10-31 | 2005-05-26 | Osaka Gas Co Ltd | Energy supply evaluation system |
| CN103955765A (en) * | 2014-04-25 | 2014-07-30 | 广东工业大学 | Micro power source capacity configuring method based on power generation capability evaluation and load classification |
| JP2017050972A (en) * | 2015-09-01 | 2017-03-09 | 富士電機株式会社 | Power generation plan creation apparatus, power generation plan creation program, and power generation plan creation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111626509A (en) | 2020-09-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105260803B (en) | A system power consumption forecast method | |
| CN103714426B (en) | Medium and small radial-flow type GROUP OF HYDROPOWER STATIONS integrated dispatching system | |
| CN110097220B (en) | A method for forecasting the monthly electricity quantity of wind power generation | |
| CN114977330B (en) | Building distributed photovoltaic-storage battery system continuous operation scheduling method and platform | |
| CN106651200A (en) | Electrical load management method and system for industrial enterprise aggregate user | |
| CN118739276A (en) | A control method and system based on distributed photovoltaic energy storage | |
| CN110796307B (en) | A distributed load forecasting method and system for an integrated energy system | |
| CN112950098A (en) | Energy planning method and device based on comprehensive energy system and terminal equipment | |
| CN110109971A (en) | A kind of low-voltage platform area user power utilization Load Characteristic Analysis method | |
| CN108196317B (en) | Meteorological prediction method for micro-grid system | |
| CN111626509B (en) | A method and system for evaluating the effective supply capacity of regional new energy | |
| CN118137500B (en) | Photovoltaic power generation prediction method and device | |
| CN117458543A (en) | Electric energy optimization method and system for micro-grid group energy storage device | |
| CN119168568A (en) | An integrated energy management system based on big data | |
| Jeong et al. | Research on development and implementation of integrated energy management system for buildings | |
| CN108346009A (en) | A kind of power generation configuration method and device based on user model self study | |
| CN116293982A (en) | Load prediction and decision control system for ice storage | |
| CN120087571B (en) | A method and system for optimizing power dispatching of a virtual power plant | |
| CN115906408A (en) | Energy scheduling system and method based on building load prediction | |
| CN120031335B (en) | A method for constructing an accurate power load prediction model using big data | |
| CN210776794U (en) | Device for carrying out load analysis and prediction aiming at temperature change | |
| CN113869573B (en) | Public transformer load forecasting method based on industry expansion data | |
| CN118673463B (en) | Multi-source data-based power supply quantity prediction method, system, equipment and storage medium | |
| CN117273795B (en) | Industrial and commercial energy storage capacity configuration and profit measurement method and system | |
| CN120706948B (en) | Auxiliary decision-making method, device, equipment and medium for combined configuration of photovoltaic and energy storage |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |