CN110110937B - Intelligent dispatching automatic cruising method for cross-region alternating-current and direct-current large power grid - Google Patents
Intelligent dispatching automatic cruising method for cross-region alternating-current and direct-current large power grid Download PDFInfo
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Abstract
The invention discloses a cross-region alternating current and direct current large power grid intelligent scheduling automatic cruise method which comprises the steps of firstly constructing a large power grid multi-target safety constraint day-ahead scheduling plan model, solving a day-ahead scheduling plan based on information such as unit operation data, maintenance plans and source load prediction, generating an economic operation domain and a visual graph thereof, and sending the economic operation domain and the visual graph thereof to a whole network analysis decision center; then, taking the current scheduling target and the main assessment indexes into consideration, constructing an economic post-assessment index system, and performing post-assessment by using the actual operation data of the power grid; and finally, selecting a cruise mode according to the actual scheduling requirement of the power grid, and realizing rolling calculation and updating of the power generation plan based on the index scores and the economic operation domain. The invention can realize the self-adaption and fine rolling update of the dispatching plan, realize the automatic cruise of the power grid dispatching plan and ensure that the power grid dispatching in a large area is more flexible.
Description
Technical Field
The invention relates to an automatic power grid dispatching method, in particular to a day-ahead and day-inside dispatching plan rolling updating and closed-loop method for a cross-region alternating-current and direct-current large power grid.
Background
With the construction of AC/DC transmission projects, the change of energy structures and the reform of power markets, the characteristics of power grids in China are deeply changed, and the dispatching of economic operation faces new challenges. The dispatching plan is used as one of basic functions used by each stage of dispatching center every day, and the operation mode and the operation result of the dispatching plan have great influence on the operation state and the operation economy of the power grid. The flexible and automatic intelligent scheduling method can improve the self-adaptive level of power grid scheduling, and can actively correct the operation direction when the power grid operation deviates, which is particularly important in cross-region alternating current and direct current large power grid scheduling.
The cross-regional large power grid intelligent dispatching method based on automatic cruise can generate dispatching plans under various dispatching preferences, assists dispatching personnel in mastering economic operation boundaries of a power grid, improves refinement degree of dispatching strategies, and can adaptively correct dispatching targets according to post-evaluation results of the dispatching plans, so that a cruising function of the dispatching plans is achieved, and the method is expected to become an effective means for solving the problem of intelligently improving dispatching of an extra-high voltage alternating current and direct current power grid.
The basic idea of cross-regional large power grid intelligent scheduling is to try to decompose a daily scheduling decision into a series of scheduling plans in different time periods, periodically obtain the operating state of a power grid, and perform alternate updating and coordinated optimization of the scheduling plans according to the actual operating result of the power grid, wherein the implementation methods are roughly divided into three types at present.
The first method divides the intelligent scheduling plan into different periods of month (week), day ahead, day in and the like, and performs continuous dynamic optimization, information feedback and closed-loop correction on each period scheduling plan, as described in the document "intelligent power grid scheduling power generation plan system architecture and key technology" (power grid technology, page 1 of 2009, volume 33, 20). However, the method still depends on manual experience to complete the closed loop of the dispatching plan, and the requirement of intelligent dispatching automation is not met.
The second method realizes comprehensive control of the development trend of the power grid operation state based on the situation awareness technology and the power grid operation track, and actively generates a scheduling aid decision suggestion by judging the power grid operation track, as described in the document "power grid automatic intelligent scheduling architecture and key technology based on situation awareness" (power grid technology, vol.38, page 33 of vol.1, 2014). However, situation awareness indexes constructed by the method are more applied to safety and long-term economy, so that the application scenes are limited, and the guidance on short-term scheduling of the power grid is insufficient.
In the third method, an index system covering aspects of power grid operation safety, economy, high quality, environmental protection and the like is constructed, and a closed-loop scheduling mode of rolling circulation is formed through links such as monitoring, early warning, analysis and control, as described in the literature "technical research on a complex power system operation cockpit" (power system automation, page 100 of 38 vol. 9 in 2014). However, the indexes provided by the method are not matched with the dispatching target, so that the practicability of the indexes is greatly reduced, the dispatching result is always conservative, and the resource allocation capability of the power grid cannot be fully exerted.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides the cross-region alternating current and direct current large power grid intelligent scheduling automatic cruise method which is suitable for the hierarchical scheduling operation and management mode in China.
The technical scheme is as follows: the invention adopts the technical scheme that an intelligent dispatching automatic cruising method for a cross-region alternating current-direct current large power grid is adopted, and comprises the following steps:
(1) constructing a large power grid multi-target safety constraint day-ahead scheduling plan model, converting multi-target weighting into a single-target problem through a multi-target linear weighting method, and discretizing weight coefficients by using a weight equal division method to solve the model so as to generate an economic operation domain; wherein the step 1 comprises the following processes:
(1.1) constructing a multi-target safety constraint day-ahead power generation plan model, wherein the specific mathematical expression is as follows:
in the formula: x is a power grid operation optimization variable; f. of 1 (x) The total power generation cost of the system; f. of 2 (x) The wind and light abandoning rate is adopted; f. of 3 (x) The discharge amount of pollutants; g, (x) and h (x) are equality and inequality constraints of the system, including power balance constraint, unit output constraint, unit climbing constraint, unit start-stop constraint, wind power output constraint, tie line transmission power constraint and section tide constraint;
(1.2) converting the multi-target problem into a single-target problem by a multi-target linear weighting method, wherein the specific mathematical expression is as follows:
in the formula: alpha is alpha 1 、α 2 、α 3 Satisfy alpha for multiple target weight coefficients 1 +α 2 +α 3 ＝1；
(1.3) discretizing weight coefficients by using a weight equal division method, and solving a plurality of single-target optimization problems to form an economic operation domain, wherein a specific mathematical expression is as follows:
and solving the model by adopting a planning method to obtain 66 discrete large power grid multi-target safety constraint day-ahead power generation plans so as to form an economic operation domain.
(2) Constructing an economic post-evaluation index system reflecting the current scheduling target and the main power grid operation parameters, and performing post-evaluation by using the actual power grid operation data; the evaluation index is an index which can be changed by a scheduling means and can directly guide the scheduling operation to adjust the direction; the indexes specifically comprise a power generation cost same-ratio increase rate, a network loss same-ratio increase rate, a new energy consumption rate, a third electricity abandonment index completion rate, a power pollutant emission same-ratio increase rate and an emission reduction index completion rate;
the calculation formula of the electricity generation cost proportional growth rate is as follows:
in the formula: f 1,d And F 1,d-1 Represents the cost of electricity generation on the current day and the previous day; w d And W d-1 Represents the total power generation amount on the current day and the previous day;
the calculation formula of the network loss ratio growth rate is as follows:
in the formula: p loss,d And P loss,d-1 Actual network loss values of the current day and the previous day respectively;
the calculation formula of the new energy consumption rate is as follows:
in the formula: p r,j,t And P cut,j,t The available power and the abandoned power of the jth new energy source in the t period are respectively.
The calculation formula of the index completion rate of the three-power-abandon quantity is as follows:
in the formula: w is a group of cut Three electric quantities discarded in the same day, W ave The electric quantity can be discarded every day; w total The total electric quantity of the abandoned wind is allowed for the year; w cut,d The electricity amount is abandoned for three years by the current day; d is the time days, and the average electricity-saving quantity of the last day is the residual electricity quantity of the annual three-time-saving index;
the calculation formula of the electrical pollutant emission proportional growth rate is as follows:
in the formula: c E And C S The unit electricity quantity pollutant discharge amount and the average discharge amount in the same period are shown on the current day.
The specific calculation formula of the emission reduction index completion rate is as follows:
in the formula: c ave Predicting the discharge amount for the current day; c total Is annual emission total amount; c d The total amount has been discharged by the day.
(3) The method comprises the steps of selecting a cruise mode according to the actual scheduling requirement of a power grid, and realizing rolling calculation and updating of a power generation plan based on index scores and an economic operation domain, wherein the cruise mode comprises (a) a constant-speed cruise mode for automatically correcting multi-target weight coefficients so as to complete full-automatic calculation of a day-ahead power generation plan, (b) a variable-speed cruise mode for actively selecting a day-ahead power generation plan from the economic operation domain, and (c) an event cruise mode for recomposing the day-ahead power generation plan after a trigger event occurs.
In the constant-speed cruise mode, the specific mathematical expression of the automatic correction multi-target weight coefficient is as follows:
in the formula: alpha's' 1 、α′ 2 、α′ 3 Setting a weight coefficient for the power grid, and keeping the weight coefficient unchanged in a constant-speed cruise mode;
evaluating an index value for the economy; g 1 (·)、g 2 (·)、g 3 (. C) is a weight correction function satisfying g 1 (·)+g 2 (·)+g 3 (·)＝1；η c The consumption rate of the renewable energy is ideal; g' i (α′ i Eta) and g i (α′ i And eta) are the weight coefficients of the ith target before and after normalization respectively.
In the variable speed cruise mode, the specific determination flow of the day-ahead power generation plan is as follows:
(1) mastering the economic operation boundary of the power grid according to the economic operation domain;
(2) judging the overall completion condition of the power grid dispatching index according to the post-evaluation result;
(3) and screening the plan operation points meeting the requirements in the economic operation domain according to the scheduling preference to be used as the day-ahead scheduling plan to be issued.
In the event cruise mode, the specific determination process of the day-ahead power generation plan comprises the following steps: when a trigger event is monitored, a unit start-stop and output plan in the remaining time period of the day is compiled based on the source load ultra-short term predicted value and the current scheduling preference, and therefore an intra-day power generation plan is generated in a rolling mode; and if no disturbance event occurs, executing a day-ahead power generation plan, wherein the triggering event comprises a random event that the deviation of the predicted value of the new energy in the day-ahead is greater than a certain threshold value or an event that the change of the scheduling preference occurs suddenly.
Has the advantages that: the invention comprehensively considers a plurality of dispatching targets and dispatching plans under different dispatching preferences, and realizes the self-adaption, fine rolling generation and updating of the dispatching plans according to the economic post-evaluation result of the actual operation state of the power grid. Compared with the prior art, the invention has the main advantages that: the method considers the comprehensive benefits of the power grid in the aspects of operation power generation cost, new energy consumption rate and pollutant emission, and realizes the integral optimization of a power grid dispatching plan; secondly, an economic operation domain composed of scheduling plans under different scheduling preferences is constructed by using a discretization weight coefficient method, the economic operation boundary of the power grid is represented, and the scheduling conservatism is reduced; thirdly, a post-evaluation index system matched with the scheduling target is constructed by the method, and the evaluation result can be directly used for guiding the adjustment direction of scheduling operation; and fourthly, the method provides three correction modes of the scheduling plan under different scheduling requirements, automatically or semi-automatically corrects the scheduling preference according to the economic operation domain and the post-evaluation result, and realizes the refined self-adaptive generation of the scheduling plan in the day and the day.
Drawings
FIG. 1 is a schematic diagram of a practical power grid system employing the method of the present invention for dispatching auto-cruise;
FIG. 2 is a block diagram of the present invention;
FIG. 3 is a flow chart of the present invention;
FIG. 4 is a schematic diagram of economic operation domain generation according to the present invention;
FIG. 5 is a schematic illustration of the visualization of the economic operation domain according to the present invention;
FIG. 6 is a diagram of a post-evaluation index system according to the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Before describing the specific steps, the system architecture and mode upon which the present invention is based will first be described. The invention is applied to a whole-network analysis decision center, realizes data interaction through an information highway, each level of regulation and control centers and a whole-network model cloud platform, and has a schematic structure diagram as shown in figure 1. Each level of regulation and control center sends the operation data to a whole network analysis decision, and sends the grid model data of each regional power grid to a whole network model cloud platform; the whole network model cloud platform forms a complete whole network grid structure model through integration of the partitioned power grid structure data and sends the complete whole network grid structure model to the analysis decision center; the whole-network analysis decision center realizes automatic cruising of a daily scheduling plan through the system and the method according to the power grid operation data and the whole-network model data, periodically and automatically sends a power generation plan, an economic operation domain and a multi-target weight coefficient to the regulation and control center, and closed-loop of the scheduling plan is realized. FIG. 2 shows a relationship diagram among key functional modules in the invention, the intelligent dispatching automatic cruise is mainly composed of an economic operation domain, an after-economic evaluation module and an optimal operation point module, the economic operation domain module carries out multi-objective optimization dispatching calculation based on information such as maintenance plans, unit data, source load prediction and the like, generates an economic operation domain and a visual view thereof and sends the economic operation domain and the visual view thereof to a whole network analysis decision center; the post-economy evaluation module is combined with the current scheduling target and the main evaluation indexes to construct a post-evaluation index system and evaluate the operation result of the power grid, and the index score is sent to the optimal operation point module for use; and the optimal operation point module realizes rolling calculation and updating of the power generation plan based on each index score and the economic operation domain. Fig. 3 shows a flow chart of an integrated auto cruise method, which may incorporate constraints of event cruise to ultimately determine the optimal operating point of the power generation plan after the constant cruise or variable speed cruise. Alternatively, a cruise mode alone may be used to determine the power generation plan. The specific steps for performing the automatic cruise are as follows:
Step 101, constructing a multi-target safety constraint day-ahead power generation plan model:
comprehensively considering the power generation cost of the whole network, the consumption rate of new energy and the pollutant emission level, constructing a multi-target safety constraint day-ahead power generation plan model, wherein the specific mathematical expression is as follows:
in the formula: x is a power grid operation optimization variable; f. of 1 (x) The total power generation cost of the system; f. of 2 (x) The wind and light abandoning rate is adopted; f. of 3 (x) The discharge amount of pollutants; g, (x) and h (x) are equality and inequality constraints of the system, including power balance constraint, unit output constraint, unit climbing constraint, unit start-stop constraint, wind power output constraint, tie line transmission power constraint, section tide constraint and the like;
step 102, converting the multi-objective problem into a single-objective optimization problem:
the method is converted into a single-target problem through a multi-target linear weighting technology, and a specific mathematical expression is as follows:
in the formula: alpha is alpha 1 、α 2 、α 3 Are multi-objective weight coefficients.Satisfies alpha 1 +α 2 +α 3 ＝1。
Step 103, discretizing the multi-target weight coefficient and solving a single-target optimization problem to generate an economic operation domain:
discretizing weight coefficients by using a weight equal division method, and solving a plurality of single-target optimization problems to form an economic operation domain, wherein a specific mathematical expression is as follows:
and solving the discretized single-target problem for multiple times by adopting different weight coefficients. And solving the single-target problem by adopting a planning method, such as an interior point method, and forming an economic operation domain in a target space by using the 66 large power grid multi-target safety constraint day-ahead power generation plans, as shown by star points in fig. 4. And projecting the star point in fig. 4 to a two-dimensional target plane to obtain a visual view of the economic operation domain, as shown in fig. 5, wherein a circle point in the view represents a currently selected planned operation point, a triangle point represents a selectable planned operation point, and a black curve is a fitted economic operation domain boundary.
And 2, constructing a large power grid economic post-evaluation index system and evaluating the running state of the power grid.
Step 201, constructing an evaluation index system after economy:
comprehensively considering a current scheduling objective function and main assessment indexes of power grid operation, and constructing an after-economy assessment index system from three aspects of thermal power economy, new energy consumption and pollutant emission, wherein the indexes comprise: the grid loss co-proportional increase rate, the new energy consumption rate, the third electricity abandonment index completion rate, the grid loss co-proportional increase rate, the grid loss co-increase rate, the grid loss rate, the rate of the grid loss co-rate, the rate of the grid loss rate of the grid loss rate, the grid loss rate of the grid, the grid loss rate, the rate of the grid loss rate of the grid, the grid loss rate of the grid loss rate of the same, the rate of the grid, the rate of the grid, the rate of the grid, the rate of the grid, the rate of.
Step 202, calculating the indexes of the power generation economic index set according to the actual operation data of the power grid:
the power generation cost proportional increase rate has the following calculation formula:
in the formula: f 1,d And F 1,d-1 Represents the cost of electricity generation on the current day and the previous day; w d And W d-1 Indicating the total power generation on the day and the day before.
The network loss ratio growth rate is calculated according to the following formula:
in the formula: p loss,d And P loss,d-1 The actual loss values of the current day and the previous day are respectively.
Step 203, calculating new energy consumption index set indexes according to the actual operation data of the power grid:
the new energy consumption rate is calculated according to the following formula:
in the formula: p r,j,t And P cut,j,t The available power and the abandoned power of the jth new energy source in the t period are respectively.
The three-discard electric quantity index completion rate is calculated according to the following formula:
in the formula: w cut Three electric quantities discarded in the same day, W ave The electric quantity can be discarded every day; w total The total electric quantity of the abandoned wind is allowed for the year; w cut,d The electricity amount is abandoned for three years by the current day; d is the time days, and the average electricity-abandoning quantity of the last day is the residual electricity quantity of the annual three-abandoning index.
Step 204, calculating pollutant emission index set indexes according to the actual operation data of the power grid:
the equivalent growth rate of the discharge of the electric pollutants is calculated according to the following formula:
in the formula: c E And C S The unit electricity quantity pollutant discharge amount and the average discharge amount in the same period are shown on the current day.
The emission reduction index completion rate is calculated by the following specific formula:
in the formula: c ave Predicting the discharge amount for the current day; c total Is annual emission total amount; c d The total amount has been discharged by the day.
Step 205, updating the evaluation index value after economy:
and acquiring the latest operation data and the accumulated operation data of the power grid every 24 hours, calculating various index values according to the model, and updating the power grid evaluation result.
And 3, realizing rolling calculation and updating of the power generation plan based on the index scores and the economic operation domain.
Selecting a cruise mode according to the actual dispatching requirement of the power grid:
the method provides three auto cruise modes including constant cruise, variable cruise and event cruise. If the dispatching plan is expected to be generated and updated fully automatically, a constant-speed cruise mode is selected, and the multi-target weight coefficient is automatically corrected according to the index evaluation value, so that the day-ahead power generation plan is updated; if the power grid is expected to run in a specific mode, selecting a variable speed cruise mode, and selecting a proper running point in an economic running domain as a day-ahead power generation plan to be issued according to the scheduling running requirement; and if an accidental disturbance event is monitored, selecting an event cruise mode, and automatically calculating and updating a day-to-day power generation plan according to the latest running state of the power grid after the event occurs.
Updating the day-ahead power generation plan in the constant-speed cruise mode:
and firstly, correcting the multi-target weight coefficient according to the index value, and then substituting the corrected weight into a multi-target solving formula to calculate the power generation plan. And automatically increasing or decreasing the value of the weight according to the size of each index value evaluated after economy, wherein the specific mathematical expression is as follows:
in the formula: alpha's' 1 、α′ 2 、α′ 3 Setting a weight coefficient for the power grid, keeping the weight coefficient unchanged in the constant-speed cruise mode, and indicating that the power grid continuously operates according to the target preference if no operation deviation exists;
evaluating an index value for the economy; g 1 (·)、g 2 (·)、g 3 (. cndot.) is a weight correction function determined by the weight coefficient and the post-evaluation result, satisfying g 1 (·)+g 2 (·)+g 3 (·)＝1；η c The consumption rate of the renewable energy is ideal; g' i (α′ i Eta) and g i (α′ i And eta) are the weight coefficients of the ith target before and after normalization respectively. As described above, a set of weight coefficients can be uniquely determined from equations (12) and (13), and further substituted into equation (2) in step 1, and a unique future power generation plan is calculated.
In the constant-speed-cruise mode of operation,
updating the day-ahead power generation plan in the variable speed cruise mode:
the day-ahead power generation plan under variable speed cruising is actively selected from an economic operation domain by a dispatcher according to working experience and actual requirements. The specific implementation process comprises the following steps: 1) mastering the economic operation boundary of the power grid according to the economic operation domain; 2) judging the overall completion condition of the power grid dispatching index according to the post-evaluation result; 3) and making final judgment, and selecting a plan operation point meeting the requirement in the economic operation domain as a day-ahead scheduling plan for issuing. As shown in fig. 5, the circles in the graph represent currently selected planned operating points, the triangular points represent selectable planned operating points, and the black curve is the fitted economic operating region boundary.
Intra-day power generation plan update in the event cruise mode:
triggering events for event cruising include: 1) predicting random events such as the deviation of the output force value is larger than a certain threshold value by the new energy in the day ahead; 2) scheduling preference changes, etc. After a trigger event is monitored, updating the source load predicted value and the weight coefficient in the model (2) in the step 1 based on the source load ultra-short-term predicted value and the current scheduling preference (reflected in the weight coefficient), and further calculating a unit start-stop and output plan in the remaining time period of the day by adopting the same calculation method, so that an in-day power generation plan is generated in a rolling mode; and if the disturbance event does not occur, executing a day-ahead power generation plan.
Claims (1)
1. An intelligent dispatching automatic cruising method for a cross-region alternating current-direct current large power grid is characterized by comprising the following steps:
step 1: constructing a large power grid multi-target safety constraint day-ahead scheduling plan model, converting multi-target weighting into a single-target problem through a multi-target linear weighting method, and discretizing weight coefficients by using a weight equal division method to solve the model so as to generate an economic operation domain;
and 2, step: constructing an economic post-evaluation index system reflecting the current scheduling target and the main power grid operation parameters, and performing post-evaluation by using the actual power grid operation data; the evaluation index is an index which can be changed by a scheduling means and can directly guide the scheduling operation to adjust the direction;
and step 3: selecting a cruise mode according to the actual scheduling requirement of a power grid, and realizing rolling calculation and updating of a power generation plan based on an index score and an economic operation domain, wherein the cruise mode comprises (a) a constant-speed cruise mode for automatically correcting a multi-target weight coefficient so as to complete full-automatic calculation of a day-ahead power generation plan, (b) a variable-speed cruise mode for actively selecting a day-ahead power generation plan from the economic operation domain, and (c) an event cruise mode for recomposing the day-ahead power generation plan after a trigger event occurs;
step 1 comprises the following processes:
(1) constructing a multi-target safety constraint day-ahead power generation plan model, wherein the specific mathematical expression is as follows:
in the formula: x is a power grid operation optimization variable; f. of 1 (x) The total power generation cost of the system; f. of 2 (x) The wind and light abandoning rate is adopted; f. of 3 (x) The discharge amount of pollutants; g, (x) and h (x) are equality and inequality constraints of the system, including power balance constraint, unit output constraint, unit climbing constraint, unit start-stop constraint, wind power output constraint, tie line transmission power constraint and section tide constraint;
(2) converting the multi-target problem into a single-target problem by a multi-target linear weighting method, wherein the specific mathematical expression is as follows:
in the formula: alpha is alpha 1 、α 2 、α 3 Satisfy alpha for multiple target weight coefficients 1 +α 2 +α 3 ＝1；
(3) Discretizing weight coefficients by using a weight equal division method, and solving a plurality of single-target optimization problems to form an economic operation domain, wherein a specific mathematical expression is as follows:
solving the model by adopting a planning method to obtain 66 discrete large power grid multi-target safety constraint day-ahead power generation plans so as to form an economic operation domain;
the indexes in the step 2 comprise the same-proportion increase rate of the kilowatt-hour power generation cost, the same-proportion increase rate of the network loss, the new energy consumption rate, the third-power-abandon index completion rate, the same-proportion increase rate of the kilowatt-hour pollutant emission and the emission reduction index completion rate;
the calculation formula of the electricity generation cost proportional growth rate is as follows:
in the formula: f 1，d And F 1，d-1 Represents the cost of electricity generation on the current day and the previous day; w d And W d-1 Represents the total power generation amount on the current day and the previous day;
the calculation formula of the network loss ratio growth rate is as follows:
in the formula: p loss，d And P loss，d-1 Actual network loss values of the current day and the previous day respectively;
the calculation formula of the new energy consumption rate is as follows:
in the formula: p r，j，t And P cut，j，t Respectively the available power and the abandoned power of the jth new energy source in the t period;
the calculation formula of the index completion rate of the three electricity curtailment is as follows:
in the formula: w cut Three electric quantities discarded in the same day, W ave The electric quantity can be discarded every day; w is a group of total The total electric quantity of the abandoned wind is allowed for the year; w cut，d The electricity amount is abandoned for three years by the current day; d is the time days, and the average electricity-abandoning quantity of the last day is the residual electricity quantity of the annual three-abandoning index;
the calculation formula of the electrical pollutant emission proportional growth rate is as follows:
in the formula: c E And C S The unit electric quantity pollutant discharge amount and the average discharge amount in the same period on the current day are shown;
the specific calculation formula of the emission reduction index completion rate is as follows:
in the formula: c ave Predicting the discharge amount for the current day; c total Annual emission total credits; c d Total amount discharged by the present day;
in the constant speed cruise mode in the step 3, the specific mathematical expression of the automatic correction multi-target weight coefficient is as follows:
in the formula: alpha's' 1 、α′ 2 、α′ 3 Setting a weight coefficient for the power grid, and keeping the weight coefficient unchanged in a constant-speed cruise mode;
evaluating an index value for the economy; g 1 (·)、g 2 (·)、g 3 (. a) is a weight correction function satisfying g 1 (·)+g 2 (·)+g 3 (·)＝1；η c The consumption rate of the renewable energy is ideal; g' i (α′ i Eta) and g i (α′ i Eta) are the weighting coefficients of the ith target before and after normalization respectively;
in the variable speed cruise mode described in step 3, the specific determination flow of the day-ahead power generation plan is:
(1) mastering the economic operation boundary of the power grid according to the economic operation domain;
(2) judging the overall completion condition of the power grid dispatching index according to the post-evaluation result;
(3) screening a plan operation point meeting the requirement in an economic operation domain according to the scheduling preference to serve as a day-ahead scheduling plan to be issued;
in the event cruise mode described in step 3, the specific determination flow of the day-ahead power generation plan is as follows: when a trigger event is monitored, a unit start-stop and output plan in the remaining time period of the day is compiled based on the source load ultra-short term predicted value and the current scheduling preference, and therefore an intra-day power generation plan is generated in a rolling mode; and if no disturbance event occurs, executing a day-ahead power generation plan, wherein the triggering event comprises a random event that the deviation of the predicted value of the new energy in the day-ahead is greater than a certain threshold value or an event that the change of the scheduling preference occurs suddenly.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102156815A (en) * | 2011-04-07 | 2011-08-17 | 国电南瑞科技股份有限公司 | Method for optimizing power generation plan in mode of tracing scheduling of annual electric quantity progress |
| CN104330977A (en) * | 2014-09-29 | 2015-02-04 | 贵州电网公司电力调度控制中心 | Power scheduling driving cabin cruise simulation method and system |
| CN106992556A (en) * | 2017-05-24 | 2017-07-28 | 南方电网科学研究院有限责任公司 | A kind of Optimization Scheduling complementary based on AC-battery power source Multiple Time Scales |
-
2019
- 2019-05-14 CN CN201910396998.4A patent/CN110110937B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102156815A (en) * | 2011-04-07 | 2011-08-17 | 国电南瑞科技股份有限公司 | Method for optimizing power generation plan in mode of tracing scheduling of annual electric quantity progress |
| CN104330977A (en) * | 2014-09-29 | 2015-02-04 | 贵州电网公司电力调度控制中心 | Power scheduling driving cabin cruise simulation method and system |
| CN106992556A (en) * | 2017-05-24 | 2017-07-28 | 南方电网科学研究院有限责任公司 | A kind of Optimization Scheduling complementary based on AC-battery power source Multiple Time Scales |
Non-Patent Citations (3)
| Title |
|---|
| 基于层次分析和变权重机制的电网安全指标计算及展示方法;徐鹏等;《电力***自动化》;20150425(第08期);全文 * |
| 基于态势感知的电网自动智能调度架构及关键技术;杨胜春等;《电网技术》;20140105(第01期);全文 * |
| 支撑一体化大电网的调度控制***架构及关键技术;许洪强等;《电力***自动化》;20180325(第06期);全文 * |
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