CN114884134A - Thermal power generating unit flexibility adjusting and scheduling method based on interval optimization - Google Patents
Thermal power generating unit flexibility adjusting and scheduling method based on interval optimization Download PDFInfo
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Abstract
The invention discloses a thermal power generating unit flexibility adjusting and scheduling method based on interval optimization, and belongs to the technical field of power system optimization. Step 1: establishing a flexibility regulation certainty model of the thermal power generating unit; step 2: establishing a flexibility adjusting interval optimization model of the thermal power generating unit; and step 3: solving the thermal power generating unit flexibility adjusting interval optimization model established in the step 2 by using a GSOMP algorithm; using a GSOMP algorithm to cooperatively optimize the mean value and the deviation value of the target interval and generating a candidate scheme with the characteristics of the comprehensive cost intervals in different years; and 4, step 4: and (3) evaluating the annual comprehensive cost intervals in the candidate schemes in the step (3) by using a multi-attribute decision method, and selecting a group of optimal schemes according to the comprehensive performance of the characteristic quantities of each interval. The method can better adapt to the uncertainty of the power flow under the high wind power access proportion, effectively relieves the blocking phenomenon of the power flow, and reduces the abandoned wind volume under each uncertain scene.
Description
Technical Field
The invention relates to the technical field of power system optimization, in particular to a thermal power generating unit flexibility adjusting and scheduling method based on interval optimization.
Background
Under the promotion of rapid development of economy, the proportion of renewable energy sources is continuously increased in consideration of environmental protection and persistent development, and the utilization and development of the renewable energy sources become important problems in the world, so that the energy consumption of users is more economical and low-carbon. Renewable energy is accessed into a power grid in a large scale, higher requirements are provided for the peak regulation capacity of the power grid, the existing power supply structure in China is used, the installed capacity and the generated energy are mainly thermal power, and the main mode for realizing the renewable energy consumption of the power grid is also the regulation of a thermal power generating unit at present. The thermal power generating unit has more flexibility and better peak regulation and frequency modulation capability and has years of peak regulation operation experience of the coal-fired unit.
In order to solve the problem of renewable energy consumption, improve the flexibility of a power system, assist in achieving the carbon neutralization goal, and China actively explores a thermal power generating unit flexibility adjusting and optimizing scheduling method. The flexibility of the power system not only comprises definition and analysis of the flexibility, but also comprises various contents such as related evaluation indexes of the flexibility of the power system, related products of the flexibility and the like, and the existing related research mainly takes large-scale renewable intermittent power supply grid connection as a background, discusses how to better maintain the active balance of the power system in the aspects of concepts, evaluation systems and the like, and whether the flexibility regulation and optimization scheduling is economical and reasonable, and how to have difference of comprehensive benefits among different methods, and further research and discussion are needed. The economic evaluation system for the flexibility regulation and optimization scheduling technology of the thermal power generating unit is researched, a flexibility regulation comprehensive evaluation model of the thermal power generating unit is constructed, the reasonable adjustment of the power supply structure layout in China is facilitated, the consumption and storage capacity of renewable energy sources are improved, and the efficient utilization of the renewable energy sources is promoted. The research on the flexibility of the power system not only has academic value, but also has important significance on the grid-connected access and market consumption of renewable energy sources in China, and a scientific and reasonable flexibility evaluation system of the power system is constructed, so that the transformation of the energy sources in China can be better assisted.
When the thermal power generating unit participates in deep peak shaving, flexibility adjustment and optimization of the thermal power generating unit are required to be performed in advance so as to reduce the lowest output limit of the thermal power generating unit. In the past, only a flexible adjustment optimization model suitable for a thermal power generating unit in a large-scale wind power access scene is considered, the system economy of the thermal power generating unit under different flexible adjustment schemes is analyzed, and only main factors influencing the peak regulation capacity of the thermal power generating unit and influences of the thermal power generating unit on the system under different peak regulation depths are analyzed. Because the unit can constantly carry out large-scale regulation and reduction power and frequent start and stop, the running cost is increased, the service life is reduced, and the problems faced by the current peak regulation are difficult to solve only by depending on the current peak regulation capability of the system. Therefore, the influence of a network topological structure on flexibility adjustment and optimization scheduling needs to be deeply excavated, and peak shaving of the thermal power generating unit is assisted by combining the adaptability of the scheme under multiple scenes, so that the wind and light abandoning amount is reduced.
Aiming at the problem that the unit power generation cost is greatly fluctuated due to the fact that the power generation cost of a thermal power generating unit is greatly increased in the deep peak regulation stage, the operation cost optimization of multiple scenes needs to be achieved through a full-scene optimization method, and a scheme with better comprehensive economy is provided. In the aspect of the trend adaptability, considering that a power grid needing flexibility modification usually contains high-permeability wind power, a full-scene optimization method is adopted to process the trend uncertainty under the high-permeability wind power, so that a scheme with better trend adaptability is provided.
Disclosure of Invention
The invention aims to provide a thermal power generating unit flexibility adjusting and scheduling method based on interval optimization, which is characterized by comprising the following steps of:
step 1: comprehensively considering flexibility adjusting cost and operating cost of the thermal power generating unit, and establishing a flexibility adjusting certainty model of the thermal power generating unit by taking annual comprehensive cost as an optimization target;
step 2: the method comprises the steps of limiting the boundary of a scene set by limiting the fluctuation range of uncertain scenes of each node, and establishing a thermal power generating unit flexibility adjusting interval optimization model;
and step 3: solving the thermal power generating unit flexibility adjusting interval optimization model established in the step 2 by using a GSOMP algorithm; using a GSOMP algorithm to cooperatively optimize the mean value and the deviation value of the target interval and generating a candidate scheme with the characteristics of the comprehensive cost intervals in different years;
and 4, step 4: and (3) evaluating the annual comprehensive cost intervals in the candidate schemes in the step (3) by using a multi-attribute decision method, and selecting a group of optimal schemes according to the comprehensive performance of the characteristic quantities of each interval.
The thermal power unit flexibility adjustment certainty model in the step 1 is specifically as follows:
an objective function: the minimum annual comprehensive cost is taken as an optimization target
min(C B +C O ) In the formula (1), C B And C O Respectively adjusting the annual flexibility adjusting cost and the annual operation cost of the thermal power generating unit;
the annual transformation cost of the thermal power generating unit is as follows (2):
in the formula, P i G,min And P i G,min0 Respectively representing the lowest output of the thermal power generating unit i before and after modification; c y_rebuild Annual flexibility modification costs per unit capacity; m G For system medium fireThe number of the motor sets;
the annual flexibility transformation cost per unit capacity is converted into the following formula (3):
in the formula, C rebuild Cost for flexibility modification per unit capacity; y is the unit life; eta is annual interest rate;
the middle-aged operating cost of the system is as follows:
in the formula, D m Days of month m; t is the number of hours of operation in each typical day;
the output of the thermal power generating unit i in a typical day period t in a month m is obtained; lambda [ alpha ] w Punishment cost of unit air volume abandon; m W The number of wind generating sets in the system;
the wind power is the wind curtailment quantity of the wind turbine generator j in the typical day period t in the month m;
and power balance constraint:
in the formula (I), the compound is shown in the specification,
the output of the wind turbine generator j is the output of the wind turbine generator j in a typical day period t in a month m, and the wind turbine generator output refers to the predicted output of the wind turbine generator;
for load, node k is active power at typical day time t in month m; m D Is the number of load nodes in the system;
output restraint of the thermal power generating unit:
in the formula, P i G,max The maximum output of the thermal power generating unit i is obtained;
flexibility modification constraint of the thermal power generating unit:
in the formula, P i G,min1 And P i G,min2 Respectively carrying out non-oil-feeding peak regulation transformation and oil-feeding peak regulation transformation on the thermal power generating unit i to obtain the lowest output of the thermal power generating unit; x is the number of i,0 、x i,1 、x i,2 The variable of the transformation scheme decision of the unit i is 0-1 variable, x i,0 ＝1、x i,1 ＝1、x i,2 When the unit i is 1, the unit i is not transformed, is not transformed in an oil feeding mode, and is transformed in an oil feeding mode;
the climbing of thermal power generating unit restricts:
in the formula (I), the compound is shown in the specification,
and
the maximum upward climbing speed and the maximum downward climbing speed of the thermal power generating unit i are respectively set;
and (3) line power flow constraint:
in the formula, G n-i 、G n-j 、G n-k Power transfer factors of a thermal power generating unit i, a wind power generating unit j and a load node k to a line n are respectively set; m L Is the number of lines in the system;
is the maximum allowed transmission power of line n;
abandoned air volume constraint of wind turbine generator
The thermal power generating unit flexibility adjusting interval optimization model in the step 2 is specifically as follows:
the system operating cost is as follows:
in the formula: c D The system operating cost;
and
respectively representing the lower limit and the upper limit of the output of the thermal power generating unit i in a typical day period t in a month m;
and
respectively setting a lower limit and an upper limit of the wind curtailment quantity of the wind turbine generator j in a typical day period t in a month m;
lower limit and upper limit of annual running cost interval:
in the formula:X D and
respectively the lower limit and the upper limit of the system operation cost;
power balance equation:
output restraint of the thermal power generating unit:
and (3) climbing restraint:
and (3) line power flow constraint:
wind abandon restraint:
the invention has the beneficial effects that:
the invention can effectively improve the comprehensive economy and the trend adaptability of the scheme; the model adopts a full scene optimization method, so that the running cost under the scene of large wind power output is obviously improved, the low running cost under the conventional wind power output scene is maintained, and the comprehensive economy of the scheme is improved; the full-scene optimization mode can better adapt to the tidal current uncertainty under the high wind power access proportion, effectively relieves the tidal current blocking phenomenon, and reduces the air abandonment rate under each uncertain scene, thereby obtaining a group of thermal power generating unit flexibility adjusting schemes with optimal comprehensive economy.
Drawings
Fig. 1 is a flowchart of a thermal power generating unit flexibility adjustment scheduling method based on interval optimization.
Detailed Description
The invention provides a thermal power generating unit flexibility adjusting and scheduling method based on interval optimization, and the method is further explained by combining an attached drawing and a specific embodiment.
Fig. 1 is a flowchart of a thermal power generating unit flexibility adjustment scheduling method based on interval optimization. The thermal power generating unit flexibility adjusting model based on interval optimization provided by the invention takes the annual comprehensive cost interval of thermal power generating unit flexibility adjusting cost and multi-scene operation cost as an optimization target, extracts the mean value and the deviation value of the target interval as the characteristic quantity of the interval, and selects the characteristic quantity as 2 optimization sub-targets in the model. In order to comprehensively measure the overall characteristics of a target interval and screen out a scheme which can meet the user requirements to the maximum extent, 2 steps of multi-target optimization and multi-attribute decision are used in model solution. Firstly, a GSOMP algorithm is used for cooperatively optimizing the mean value and the deviation value of a target interval, so that a plurality of candidate schemes are generated, and each scheme has different annual comprehensive cost interval characteristics. And secondly, evaluating annual comprehensive cost intervals in each scheme by using a multi-attribute decision method, and selecting an optimal scheme according to comprehensive performance of characteristic quantities in each interval. The method comprises the following specific steps:
1. establishing a flexibility regulation certainty model of a traditional thermal power generating unit
The established thermal power unit flexibility regulation deterministic model comprehensively considers flexibility regulation cost and operation cost of the thermal power unit. In order to facilitate the unification of long-term planning and calculation periods in operation, the optimization period of the model is selected to be 1 year, and the annual comprehensive cost is used as the optimization target of the model. The model is specifically expressed as follows:
min(C B +C O ) (1)
wherein: c B And C O Respectively adjusting the annual flexibility adjusting cost and the annual operation cost of the thermal power generating unit; p i G,min And P i G ,min0 Respectively representing the lowest output of the thermal power generating unit i before and after modification; c y_rebuild Cost for annual flexibility modification per unit volume; c rebuild Transforming the cost for the flexibility of unit capacity, and converting the cost into annual cost; y is the unit life; eta is annual interest rate; d m Days of month m; t is the number of hours of operation in each typical day;
and
respectively outputting power of a thermal power generating unit i and a wind power generating unit j in a typical day time t in a month m, wherein the output of the wind power generating unit refers to the predicted output of the wind power generating unit;
for load, node k is active power at a typical day period t in month m;
the wind power is the wind curtailment quantity of the wind turbine generator j in the typical day period t in the month m; p i G,max The maximum output of the thermal power generating unit i is obtained; p i G,min1 And P i G,min2 The minimum output of the thermal power generating unit i after non-oil-feeding peak regulation transformation and oil-feeding peak regulation transformation are respectively carried out; x is the number of i,0 、x i,1 、x i,2 The variable of the transformation scheme decision of the unit i is 0-1 variable, x i,0 ＝1、x i,1 ＝1、x i,2 When the unit i is 1, the unit i is not transformed, is not transformed in an oil feeding mode, and is transformed in an oil feeding mode; m is a group of G 、M W 、M D 、M L Respectively as a system medium-temperature motorThe number of groups, wind turbines, load nodes and lines; lambda [ alpha ] w Punishment cost of unit air volume abandon;
and
the maximum upward climbing speed and the maximum downward climbing speed of the thermal power generating unit i are respectively set;
is the maximum allowed transmission power of line n; g n-i 、G n-j 、G n-k And power transfer factors of the thermal power generating unit i, the wind power generating unit j and the load node k to the line n are respectively.
The optimization goal of the model represented by formula (1) is to minimize the annual combined cost, including the annual modification cost and the annual operation cost. And the formula (2) gives a formula of annual transformation cost of the thermal power generating unit. Equation (3) gives a conversion equation for the annual reform cost per unit volume. Equation (4) gives the equation for the annual operating costs in the system. Equation (5) is the power balance constraint in the system. And the formula (6) is the output constraint of the thermal power generating unit. The formula (7) and the formula (8) are flexibility modification constraints of the thermal power generating unit. Formula (9) is the climbing restraint of thermal power unit. Equation (10) is the line flow constraint. And the formula (11) is the wind abandoning amount constraint of the wind turbine generator.
The model can be represented in the general form:
wherein U is a scheme decision variable
The method is used for expressing the optimal flexibility transformation scheme of each thermal power generating unit; c is an optimization target; x is a state variable and is a state variable,
the method comprises the steps of obtaining various operation variables in optimal power flow calculation, such as the output of each thermal power generating unit and the air curtailment quantity of each wind power plant; g (X, U) and h (X, U) are equality constraint functions and inequality constraint functions respectively. The optimal lower output limit P of each thermal power generating unit can be obtained according to the decision variable U i G,min0 。
2. Establishing thermal power generating unit flexibility adjusting interval optimization model
Compared with the traditional thermal power generating unit, the output of the wind power generating unit has stronger fluctuation, which causes higher uncertainty of the output of the wind power in actual calculation. When a plurality of wind power plants fluctuate within the output range interval of each time period, a plurality of uncertain scenes are generated, wherein the uncertain scene s can be expressed as:
in the formula:
the output of a wind generating set j in a month M under an uncertain scene s in a time period t, wherein j is 1,2, …, M W ，t＝1,2,…,T；P s,m And (4) the wind turbine generator set output vector in the month m under the uncertain scene s.
From a given scene s expression P s，m It can be seen that a large number of uncertain scenes can be derived after considering the volatility of a plurality of wind power plants. Under the condition that a large number of uncertain scenes exist in the system, the interval method limits the boundary of the scene set by limiting the fluctuation range of the uncertain scenes of each node, and the value in all transition scenes can be covered in the boundary range. The proposed model aims to find a group of thermal power generating unit flexibility modification schemes, so that the feasible power flow of a power grid in a wind power fluctuation range in multiple operation days can be met, and the optimal economy is achieved.
The expressions (4) to (6) and (9) to (11) in the original model can be converted into interval forms, and the expression forms and the corresponding scene reduction methods are as follows.
1) System operating costs
In the formula: c D The system operating cost;
and
respectively representing the lower limit and the upper limit of the output of the thermal power generating unit i in a typical day period t in a month m;
and
respectively is the lower limit and the upper limit of the wind curtailment quantity of the wind turbine generator j in the typical day period t in the month m.
The upper and lower limits of the annual running cost interval are shown in the formula (15) and the formula (16), respectively.
In the formula:C D and
respectively, the lower limit and the upper limit of the system operating cost.
2) Power balance equation
The power balance equation in the system consists of 4 parts, namely thermal power unit output, wind power unit output, load and air abandon amount. Because the output and the load power of the wind turbine generator are both known quantities, the known quantities can be transferred to the equal sign right side of an equation, and the output range of unknown quantities is reflected.
By respectively combining the upper limit value and the lower limit value of the 2 intervals on the left side of the equal sign of the equation (18), 2 limit scenes can be obtained, and the interval equation can be converted into the following power balance equation under the 2 limit scenes.
3) Thermal power unit output constraint
Equation (21) can be converted into the following 2 inequalities by strict constraints on the upper and lower limits of the interval inequality.
4) Climbing restraint
By restricting the upper and lower limits of these 2 intervals, equation (24) can be converted into the following 2 inequalities.
5) Line flow constraint
Transferring the known quantity and the unknown quantity of the interval inequality to two sides of the inequality respectively, and converting the two-sided inequality into 2 single-sided inequalities to solve conveniently, thus obtaining:
for the known quantity on the right side of the equation, the interval boundary is shrunk according to a low boundary theory, the minimum quantity on the right side of the inequality is selected as a shrinking condition of the boundary, the condition is the tightest constraint added to the line power flow, and the power flow under all possible scenes can be guaranteed to be feasible. And for the unknown quantity on the left side of the equation, carrying out constraint according to the upper limit and the lower limit of the interval. Through the above process, the 2 inequalities can be converted as follows.
6) Wind curtailment
For the interval inequality, it can be ensured that all scenes can meet the requirements as long as the upper limit and the lower limit of the 2 intervals are respectively constrained. This inequality can be converted into the following 2 constraints.
In summary, the constraint conditions and the objective function in the original model may be expressed in an interval form, and full scene reduction may be performed through rigorous mathematical derivation, so as to ensure that each interval range covers all possible transition scenes. The protomodel can be expressed in the following general form:
in the formula: w, W,P w 、
Wind power output, a wind power output lower limit and a wind power output upper limit vector are respectively;Xand
the lower and upper limits of X, respectively. The optimization target of the proposed model comprises 2 parts in total, wherein one part is the year of the thermal power generating unitThe flexibility modification cost is a single-value target; the other part is an annual operation cost interval formed under a given wind power output prediction interval, and is an interval target. For ease of calculation, the cost of combining these 2 parts results in an annual combined cost interval. In order to comprehensively measure the overall characteristics of the target interval, the interval mean value and the deviation value are used as the characteristic quantity of the interval to carry out collaborative optimization. The master model can be converted into:
in the formula: a (C (X, U, W)) and J (C (X, U, W)) are the average value and the deviation value of the target section C (X, U, W), respectively;C(X, U, W) and
(X, U, W) are the lower and upper limits of the target interval C (X, U, W), respectively.
3. Solving interval optimization model by GSOMP algorithm
The GSOMP algorithm is a multi-objective optimization method based on group search, and the method simulates a swarm intelligent search technology in the optimization process, so that the GSOMP algorithm has good performance in the application of searching a non-dominated solution set. And (3) solving a group of non-dominated optimal solution sets with different sub-targets in focus by adopting a GSOMP algorithm, wherein the solution sets comprise a plurality of candidate schemes with different annual comprehensive cost interval characteristics.
4. Target interval evaluation method based on evidence reasoning for evaluation
An optimal scheme is selected from a plurality of candidate schemes by adopting an evidence reasoning method, the evidence reasoning method is a three-layer heuristic evaluation method based on attributes, and the basic attributes to be evaluated, namely the mean value and the deviation value of the annual comprehensive cost interval, are defined at the bottom layer of an evaluation frame. In the middle layer of the evaluation framework, 5 evaluation levels are set, wherein the evaluation levels are excellent, good, general, marginal and poor, the layer divides each basic attribute into 5 evaluation levels and generates corresponding level confidence, and comprehensive evaluation results of each scheme on the 5 evaluation levels can be obtained. And combining the evaluation results of all the grades and the corresponding grade demand degrees at the top layer of the evaluation framework to obtain the comprehensive evaluation result of the scheme.
The model in the embodiment can effectively improve the comprehensive economy and the trend adaptability of the scheme, the model adopts a full-scene optimization method, the running cost under the scene with large wind power output is obviously improved, the low running cost under the conventional wind power output scene is maintained, and the comprehensive economy of the scheme is improved. In the aspect of flow adaptability, the full-scene optimization mode can better adapt to the flow uncertainty under the high wind power access proportion, effectively relieves the flow blocking phenomenon and reduces the air abandonment rate under each uncertain scene, thereby obtaining a group of thermal power generating unit flexibility adjusting schemes with optimal comprehensive economy.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (3)
1. The thermal power generating unit flexibility adjusting and scheduling method based on interval optimization is characterized by comprising the following steps of:
step 1: comprehensively considering flexibility adjusting cost and operating cost of the thermal power generating unit, and establishing a flexibility adjusting certainty model of the thermal power generating unit by taking annual comprehensive cost as an optimization target;
and 2, step: the method comprises the steps of limiting the boundary of a scene set by limiting the fluctuation range of uncertain scenes of each node, and establishing a thermal power generating unit flexibility adjusting interval optimization model;
and step 3: solving the thermal power generating unit flexibility adjusting interval optimization model established in the step 2 by using a GSOMP algorithm; using a GSOMP algorithm to cooperatively optimize the mean value and the deviation value of the target interval and generating a candidate scheme with the characteristics of the comprehensive cost intervals in different years;
and 4, step 4: and (3) evaluating the annual comprehensive cost intervals in the candidate schemes in the step (3) by using a multi-attribute decision method, and selecting a group of optimal schemes according to the comprehensive performance of the characteristic quantities of each interval.
2. The thermal power generating unit flexibility regulation scheduling method based on interval optimization according to claim 1, wherein the thermal power generating unit flexibility regulation certainty model in the step 1 is specifically as follows:
an objective function: the minimum annual comprehensive cost is taken as an optimization target
min(C B +C O ) (1)
In the formula, C B And C O Respectively adjusting the annual flexibility adjusting cost and the annual operation cost of the thermal power generating unit;
the annual transformation cost of the thermal power generating unit is as follows (2):
in the formula, P i G,min And P i G,min0 Respectively representing the lowest output of the thermal power generating unit i before and after modification; c y_rebuild Cost for annual flexibility modification per unit volume; m G The number of the thermoelectric generator sets in the system;
the annual flexibility transformation cost per unit capacity is converted into the following formula (3):
in the formula, C rebuild Cost for flexibility modification per unit capacity; y is the unit life; eta is annual interest rate;
the middle-aged operating cost of the system is as follows:
in the formula, D m Days of month m; t is the number of hours of operation in each typical day;
the output of the thermal power generating unit i in a typical day period t in a month m is obtained; lambda [ alpha ] w Punishment cost of unit air volume abandon; m is a group of W The number of wind generating sets in the system;
and power balance constraint:
in the formula (I), the compound is shown in the specification,
the output of the wind turbine generator j in a typical day time t in a month m refers to the predicted output of the wind turbine generator;
for load, node k is active power at a typical day period t in month m; m D Is the number of load nodes in the system;
output restraint of the thermal power generating unit:
in the formula, P i G,max The maximum output of the thermal power generating unit i is obtained;
flexibility modification constraint of the thermal power generating unit:
in the formula, P i G,min1 And P i G,min2 Respectively carrying out non-oil-feeding peak regulation transformation and oil-feeding peak regulation transformation on the thermal power generating unit i to obtain the lowest output of the thermal power generating unit; x is the number of i,0 、x i,1 、x i,2 The variable of the transformation scheme decision of the unit i is 0-1 variable, x i,0 ＝1、x i,1 ＝1、x i,2 When the unit i is 1, the unit i is not transformed, is not transformed in an oil feeding mode, and is transformed in an oil feeding mode;
and (3) climbing restraint of the thermal power generating unit:
in the formula (I), the compound is shown in the specification,
and
the maximum upward climbing speed and the maximum downward climbing speed of the thermal power generating unit i are respectively set;
and (3) line power flow constraint:
in the formula, G n-i 、G n-j 、G n-k Power transfer factors of a thermal power generating unit i, a wind power generating unit j and a load node k to a line n are respectively set; m L Is the number of lines in the system;
is the maximum allowed transmission power of line n;
abandoned air volume constraint of wind turbine generator
3. The thermal power generating unit flexibility adjusting and scheduling method based on interval optimization according to claim 1, wherein the thermal power generating unit flexibility adjusting interval optimization model in the step 2 is specifically as follows:
the system operating cost is as follows:
in the formula: c D The system operating cost;
and
respectively representing the lower limit and the upper limit of the output of the thermal power generating unit i in a typical day period t in a month m;
and
respectively setting a lower limit and an upper limit of the wind curtailment quantity of the wind turbine generator j in a typical day period t in a month m;
lower limit and upper limit of annual running cost interval:
in the formula:C D and
respectively the lower limit and the upper limit of the system operation cost;
power balance equation:
output restraint of the thermal power generating unit:
and (3) climbing restraint:
and (3) line power flow constraint:
wind abandon restraint:
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116454890A (en) * | 2023-04-20 | 2023-07-18 | 中国南方电网有限责任公司 | Combined control method, device and equipment for unit based on SCUC model |
| CN117744410A (en) * | 2024-02-19 | 2024-03-22 | 中汽研汽车检验中心（天津）有限公司 | Interval multi-objective optimization method, equipment and medium based on vehicle stability requirement |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116454890A (en) * | 2023-04-20 | 2023-07-18 | 中国南方电网有限责任公司 | Combined control method, device and equipment for unit based on SCUC model |
| CN116454890B (en) * | 2023-04-20 | 2024-02-06 | 中国南方电网有限责任公司 | Combined control method, device and equipment for unit based on SCUC model |
| CN117744410A (en) * | 2024-02-19 | 2024-03-22 | 中汽研汽车检验中心（天津）有限公司 | Interval multi-objective optimization method, equipment and medium based on vehicle stability requirement |
| CN117744410B (en) * | 2024-02-19 | 2024-05-07 | 中汽研汽车检验中心（天津）有限公司 | Interval multi-objective optimization method, equipment and medium based on vehicle stability requirement |
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Effective date of registration: 20231208 Address after: 102206 No. 2 Nong Road, Changping District, Beijing Applicant after: NORTH CHINA ELECTRIC POWER University Applicant after: HUANENG NANJING JINLING POWER GENERATION Co.,Ltd. Applicant after: HUANENG GROUP TECHNOLOGY INNOVATION CENTER Co.,Ltd. Applicant after: Xi'an Thermal Power Research Institute Co.,Ltd. Address before: 102206 No. 2 Nong Road, Changping District, Beijing Applicant before: NORTH CHINA ELECTRIC POWER University Applicant before: HUANENG GROUP TECHNOLOGY INNOVATION CENTER Co.,Ltd. Applicant before: Xi'an Thermal Power Research Institute Co.,Ltd. |