WO2019196427A1 - 基于支撑故障事件约束机组组合的备用优化方法和装置 - Google Patents
基于支撑故障事件约束机组组合的备用优化方法和装置 Download PDFInfo
- Publication number
- WO2019196427A1 WO2019196427A1 PCT/CN2018/118375 CN2018118375W WO2019196427A1 WO 2019196427 A1 WO2019196427 A1 WO 2019196427A1 CN 2018118375 W CN2018118375 W CN 2018118375W WO 2019196427 A1 WO2019196427 A1 WO 2019196427A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- lolp
- event
- marginal
- scene
- standby
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000005457 optimization Methods 0.000 claims abstract description 70
- 230000001186 cumulative effect Effects 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 description 6
- 230000014509 gene expression Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000009194 climbing Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007152 ring opening metathesis polymerisation reaction Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000010977 unit operation Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q10/067—Enterprise or organisation modelling
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
-
- 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
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
Definitions
- the invention belongs to the field of rotating standby optimization, and particularly relates to a standby optimization method and device for constraining a unit combination based on a supporting fault event.
- Rotating standby is an important resource in the power system.
- the rotary standby is mainly provided by a network-operated generator and can be input into the system within a specified time to cope with power fluctuations caused by load fluctuations and component failures in the system, thereby avoiding system load loss.
- a sufficient rotation reserve can reduce the possibility of loss of load and improve the reliability of the power system.
- there is a cost associated with providing a spin reserve because a new genset may be required to access the system or force the unit being commissioned to deviate from its optimal operating point. Therefore, the rotation reserve needs scientific and reasonable planning, taking into account the economic and reliability of the system.
- the rotating standby configuration uses a deterministic approach that determines the number of rotating spares based on a ratio of total load to maximum on-line unit capacity. This method is simple and easy to operate, but it is easy to cause the alternate configuration to be conservative or aggressive.
- Literature [4] establishes an alternate cost model based on storage theory, and combines the probability of spare capacity utilization in historical data, and uses the decision-making algorithm to solve the optimal spare capacity, which can obtain the optimal economy under the premise of ensuring the security of the system. Spare capacity.
- Spare capacity the risk analysis of the rotating reserve scheme is carried out from the perspective of power generation system.
- the utility function and utility value are used to reflect the satisfaction degree of different types of decision makers on the rotating reserve profit and loss, and the utility expectation value decision model of rotating reserve profit and loss is proposed. These two alternative configuration schemes are more in line with economic laws, to a certain extent, taking into account the economics and reliability of the system, and more adapt to the power system in the market environment. With the continuous access of new energy sources, the uncertainty in the system is gradually increasing, which makes the probabilistic backup optimization method pay more attention.
- the probabilistic backup optimization method mainly includes an optimization model with reliability index constraints and an optimization model based on cost-benefit compromise.
- An optimization model with reliability index constraints refers to adding a reliability indicator not exceeding a certain set value as a constraint to the scheduling model.
- the optimization model based on the cost-benefit compromise refers to quantifying the loss caused by the loss of load and adding it to the objective function, which is minimized together with the running cost, so that the standby optimization can automatically balance the economy and reliability.
- VOLL lost load
- Loss of load probability (LOLP) is the probability of a user's power outage due to various disturbances in the system at a given time. This indicator directly reflects the reliability of the system operation, the concept is simple and clear, intuitive and reasonable.
- the LOLP can be accurately expressed as a function of the generator's start-stop state, output, output reserve, system rotation reserve, expected events, and probability of occurrence of an expected event.
- LOLP expressions are highly nonlinear and combined, and contain not only many continuous variables, but also a large number of 0/1 variables, not only related to the scheduling results, but also to the expected event scenarios considered.
- the number of scenes has a combination of features and a large scale.
- the present invention provides an alternate optimization method and apparatus for constraining unit combinations based on supporting fault events, converting highly nonlinear and combined LOLP constraints into a series of linear expressions, only Optimization based on the constraints of some of the key marginal scenarios, effectively improving the efficiency of standby optimization.
- the present invention adopts the following technical solutions:
- An alternate optimization method based on supporting fault events to constrain a combination of units includes the following steps:
- Step 1 Run a basic unit combination backup optimization model to obtain the basic unit combination scheduling results
- Step 2 Establish a running capacity missing table based on the scheduling result, calculate LOLP, and find a marginal event therefrom;
- Step 3 Add the linear constraint corresponding to the marginal event to the alternate optimization model to obtain a new scheduling result, and return to step 2 until the result meets the LOLP requirement.
- the basic unit combination standby optimization model in the step 1 is a rotation standby optimization model that does not include the LOLP constraint.
- the row of the operational capacity loss table represents a fault event that may occur in the unit, and the column represents the missing capacity, the probability of failure, and the cumulative probability.
- LOLP is expressed as:
- n is the number of rows of CCOPT, which indicates the number of fault events that may occur in the unit during t-time;
- p i,t represents the probability of failure of event i;
- b i,t is the variable of 0/1, and it is judged whether the corresponding fault scene in t period Loss of load, b i, t is 1 means that if the scene will cause loss of load, b i, t is 0 means that the scene will not cause load loss if it occurs.
- ⁇ CC i,t is the missing capacity of the fault event i in the t period, indicating the sum of the power and the standby of all the units in the event
- SSR t is the total standby of the system in the t period
- ⁇ * indicates the fault event that will not cause the loss of load
- s represents a marginal event.
- the LOLP sum caused by the fault scene with the number of rows greater than or equal to i in CCOPT does not exceed LOLP max , but the number of rows is greater than or equal to i-1
- the sum of LOLP does not exceed LOLP max ;
- the i-1th line scene is a marginal scene, and the same type of fault scene as the marginal scene is also a marginal scene.
- the present invention also discloses a backup optimization apparatus for constraining a unit combination based on a support failure event, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, The processor executes:
- Step 1 Run a basic unit combination backup optimization model to obtain the basic unit combination scheduling results
- Step 2 Establish a running capacity missing table based on the scheduling result, calculate LOLP, and find a marginal event therefrom;
- Step 3 Add the linear constraint corresponding to the marginal event to the alternate optimization model to obtain a new scheduling result, and return to step 2 until the result meets the LOLP requirement.
- a third object of the present invention there is also a computer readable storage medium having stored thereon a computer program executed by a processor to perform:
- Step 1 Run a basic unit combination backup optimization model to obtain the basic unit combination scheduling results
- Step 2 Establish a running capacity missing table based on the scheduling result, calculate LOLP, and find a marginal event therefrom;
- Step 3 Add the linear constraint corresponding to the marginal event to the alternate optimization model to obtain a new scheduling result, and return to step 2 until the result meets the LOLP requirement.
- the present invention is based on a LOLP constrained alternate optimization model that converts highly nonlinear and combined LOLP constraints into a series of linear expressions. Since most of the series of equivalent linear constraints are slack constraints, only a small number of key marginal scene constraints can be found. Only based on representative scene constraints can improve the efficiency of standby optimization.
- the present invention proposes a constraint addition method for a UC model with representative scene constraints. Specifically, in combination with CCOPT, iteratively, the marginal scene is searched successively and optimized as constraints until the result satisfies the LOLP constraint.
- the present invention takes into account multiple compromises in the problem and simplifies the LOLP constraint so that the model can be solved accurately and efficiently.
- the optimization method of the invention has good accuracy and effectiveness in single-time and multi-machine multi-time systems.
- FIG. 1 is a flow chart of an alternate optimization method for constraining a unit combination based on a support fault event according to the present invention
- Figure 2 shows the standby at different reliability levels
- Figure 3 shows the optimized spares for different sized systems
- Figure 4 shows the comparison between the different sizes of the system.
- the LOLP constraint is expressed as a series of linear constraints, and most of the constraints in this series of equivalent linear constraints are relaxation constraints, so only a small number of tight constraints can be considered.
- This article gradually adds constraints in an iterative manner. Starting from a basic unit combination problem, a committed capacity outage probability table (CCOPT) is established based on the scheduling result, and a marginal event is sought from it. Add the linear constraint corresponding to the marginal event to the next alternate optimization model. As the iteration progresses, constraints are added until the results meet the LOLP requirements.
- CCOPT committed capacity outage probability table
- the constrained addition method proposed in this paper solves the alternate optimization problem with LOLP constraints, considers the multiple compromises in the problem, and simplifies the LOLP constraint so that the model can be solved accurately and efficiently.
- the objective function in the ROMP-based rotation reserve optimization model is the sum of the running cost and the standby cost:
- N T is the number of periods in a study period
- N G is the number of generators that can be dispatched
- U i,t is the start-stop state of unit i in period t
- P i,t is the unit i of period t Output
- q i,t is the reserve price of unit i in period t
- R i,t is the reserve capacity of unit i in period t
- C it (P it ,U it ) is the running cost of unit i in period t
- Three-stage linear function representation SUC i is the starting cost of unit i
- K i,t is 0/1 variable, satisfying
- P t D is the load value at time t.
- P i max is the maximum output of unit i; It is the climbing speed of unit i; ⁇ is the time taken for the unit to release the standby. In this paper, ⁇ is set to 0.5h.
- the upper type constraint usually includes the upper and lower limit constraints of the genset output power, the minimum start and stop time constraint, the initial condition constraint, and the unit output power rate constraint.
- the fault can be divided into first-order, second-order, third-order and other faults according to the number of simultaneous faults of the unit.
- the following only gives the expression of the first two orders of LOLP:
- p i,t is the probability that unit i will fail during t period
- p i,j,t is the probability that units i and j will simultaneously fail during t period
- SSR t is the total backup of the system at time t, which satisfies:
- Formulas (8) and (9) can be linearized according to the method of [7, 19].
- equation (8) can be linearized as:
- the probability of failure p i,t ,p i,j,t can be expressed as:
- u i is the fault replacement rate, equal to r i ⁇ T in the ⁇ T period
- r i is the failure rate of unit i, where ⁇ T is 1h.
- This embodiment discloses an alternate optimization method for constraining a unit combination based on a support fault event, including the following steps:
- Step 1 Run a basic unit combination backup optimization model to obtain the basic unit combination scheduling results
- Step 2 Establish a running capacity missing table based on the scheduling result, calculate LOLP, and find a marginal event therefrom;
- Step 3 Add the linear constraint corresponding to the marginal event to the alternate optimization model to obtain a new scheduling result, and return to step 2 until the result meets the LOLP requirement.
- the basic unit combination standby optimization model the objective function is as in formula (1), and the constraint conditions are as in formulas (2)-(5).
- the operation capacity loss table in the step 2 includes a missing capacity, a failure probability, and a cumulative probability.
- CCOPT is established according to the scheduling result, as shown in Table 1.
- n is the number of CCOPT lines, and indicates the number of fault events that may occur in the unit during t-time;
- p i,t represents the probability of failure of event i. From equation (12-13), p i,t is greater than CCOPT. 0;
- b i, t is a 0/1 variable, and it is judged whether the fault scene corresponds to a fault situation in the t period.
- b i, t is 1 means that if the scene occurs, the load will be lost, and b i, t is 0, indicating that the scene occurs. Will not cause loss of load.
- ⁇ CC i,t is the missing capacity of the t-time fault event i, which represents the sum of the power and the standby of all the units in the event.
- event i is the simultaneous failure of the x and y units
- ⁇ CC i,t P x +R x +P y +R y
- SSR t is the total system backup for the t period.
- the fault event is divided into two parts, one is a fault event that will not cause loss of load, and constitutes a set ⁇ * ; part of it is a fault event that will cause loss of load, constitute a set ⁇ * and
- the complete set of fault events may occur when the optimal scheduling of the system is formed, and the probability sum is 1. Therefore, all missing volumes in the optimal solution that do not cause LOLP and cause LOLP are satisfied:
- Equation (16) Equation (16) with Are all parameters, ⁇ * and The events in the event are also ok. Obviously the optimal solution can't be known in advance, but if you can determine ⁇ * and The event in which you can know in advance which events cause LOLP and which events do not cause LOLP, Equation (16) can be converted to:
- Equation (17) ⁇ * and The events in the event are deterministic, but ⁇ CC s, t and SSR t are variables. Substituting equation (17) for LOLP constraint (7), after optimization, it is clear that the optimal solution can be obtained.
- a large number of constraints in equation (18) are slack.
- the fault capacity of many events in the optimal solution is significantly smaller than the standby, and the constraint in equation (18) corresponding to these events is slack. That is to say, most of the events in ⁇ * are slack and can be covered by a small number of events in ⁇ * . Therefore, it is only necessary to find a few key events in ⁇ * to form the constraint (19), and the optimal solution can be obtained after optimization.
- the key to dealing with alternate optimization problems with LOLP constraints translates into finding a few key events in ⁇ * .
- the missing capacity of this small number of key events is at Nearby, it can be called a marginal event, and the corresponding constraint is called a marginal constraint.
- the method for finding the marginal event in the step 2 is:
- CCOPT is established based on the scheduling result.
- the meaning of the above formula is that the LOLP sum caused by the fault scene on the ith line and below in the COPT does not exceed LOLP max , but if the probability of the fault scene in the i-1th line is added, the LOLP sum will be greater than LOLP max .
- the i-th line is the boundary line that the system is allowed to not cause LOLP, and the minimum external demand for the system to meet the reliability requirements is reflected.
- the scene in the i-1th line in CCOPT is a marginal scene.
- the system has the same type of fault scene as the marginal scene (the same type of scene, that is, the scene contains the same type of unit). If it is above the i-1th line in CCOPT, the same type of scene is also a marginal scene.
- the present invention also provides a backup optimization apparatus for constraining a unit combination based on a support fault event, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, The processor executes:
- Step 1 Run a basic unit combination backup optimization model to obtain the basic unit combination scheduling results
- Step 2 Establish a running capacity missing table based on the scheduling result, calculate LOLP, and find a marginal event therefrom;
- Step 3 Add the linear constraint corresponding to the marginal event to the alternate optimization model to obtain a new scheduling result, and return to step 2 until the result meets the LOLP requirement.
- the present invention also provides a computer readable storage medium having stored thereon a computer program, wherein the program is executed by the processor:
- Step 1 Run a basic unit combination backup optimization model to obtain the basic unit combination scheduling results
- Step 2 Establish a running capacity missing table based on the scheduling result, calculate LOLP, and find a marginal event therefrom;
- Step 3 Add the linear constraint corresponding to the marginal event to the alternate optimization model to obtain a new scheduling result, and return to step 2 until the result meets the LOLP requirement.
- the steps involved in the above two devices are corresponding to the method embodiments.
- the term "computer-readable storage medium” shall be taken to mean a single medium or a plurality of mediums including one or more sets of instructions; it should also be understood to include any medium that can be stored, encoded or carried for use by a processor.
- the set of instructions executed and causes the processor to perform any of the methods of the present invention.
- the system contains 26 units.
- the unit combination data and the climbing rate limit are obtained by the literature [20].
- the starting cost and reliability data of the generator set are obtained by the literature [21].
- the reserve price is equal to 10% of the maximum incremental cost of power generation.
- the output of the unit at the initial time is determined by the economic dispatch when the load in the first period is 1700 MW. Considering a time period, when LOLP max is 0.001, the problem proposed in this paper is used to solve the alternate optimization problem with LOLP constraints.
- the marginal combination is found. Since the LOLP max is 0.001, the cumulative probability of the 15th line in CCOPT is 0.00182014, the cumulative probability of the 15th line is 0.000916849, 0.000916849 ⁇ 0.001 ⁇ 0.00182014, so the 25th generator in the 15th line is faulty. It is the marginal scene.
- the fault scenario contained in ⁇ is the failure of the 25th unit.
- CCOPT the method of this paper, find the marginal scene, you can get the marginal scene for the 24th unit to fail, and add it to the set ⁇ to establish the constraint of the form (18).
- the optimized scheduling result is shown in Appendix A2.
- the backup is gradually increased with the iteration, because the marginal scene is added to the set ⁇ successively, and the corresponding constraints are more and more, which increases the requirements for system backup and optimizes each iteration.
- the post system backup is always equal to the missing capacity of the newly added marginal scene.
- the process of standby growth is also a process of gradual decline in economy, and moves toward the direction of reliability improvement, and finally meets the reliability requirements.
- the LOLPmax is transformed, and the calculation system satisfies the cost corresponding to different LOLP constraints.
- two methods are used to solve the same problem. The first method uses the original model to solve, and the second method uses the method proposed in this paper. The results are shown in Table 4.
- the method of this paper can solve the problem that cannot be solved by the original model. Also taking the IEEE-RTS system as an example, considering the 26-machine system, the optimization period is 24 hours, and it is necessary to find the marginal unit for each time period. For the different LOLP max , the alternate obtained by the method of this paper is shown in Figure 2. Considering the second-order fault, the original model and the proposed method are used for the alternate optimization under different LOLP max , and the time used is as shown in Table 5.
- the model used in this paper is coded in GAM.
- the calculation tool is the large-scale MILP solver CPLEX combined with Visual C.
- the dual interval of MILP is 0.1%.
- the computer CPU used is 3.6GHz and the running memory is 4G.
- the present invention is based on a LOLP constrained alternate optimization model that converts highly nonlinear and combined LOLP constraints into a series of linear expressions. Since most of the series of equivalent linear constraints are slack constraints, only a small number of key marginal scene constraints can be found. Only based on representative scene constraints can improve the efficiency of standby optimization.
- the present invention proposes a constraint addition method for a UC model with representative scene constraints. Specifically, in combination with CCOPT, iteratively, the marginal scene is searched successively and optimized as constraints until the result satisfies the LOLP constraint.
- the present invention takes into account multiple compromises in the problem and simplifies the LOLP constraint so that the model can be solved accurately and efficiently.
- the optimization method of the invention has good accuracy and effectiveness in single-time and multi-machine multi-time systems.
- modules or steps of the present invention described above can be implemented in a general-purpose computer device. Alternatively, they can be implemented in program code executable by a computing device so that they can be stored in storage.
- the devices are implemented by computing devices, or they are separately fabricated into individual integrated circuit modules, or a plurality of modules or steps thereof are fabricated into a single integrated circuit module.
- the invention is not limited to any particular combination of hardware and software.
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Mathematical Optimization (AREA)
- Computing Systems (AREA)
- Mathematical Analysis (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Algebra (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Educational Administration (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
Description
Claims (9)
- 一种基于支撑故障事件约束机组组合的备用优化方法,其特征在于,包括以下步骤:步骤1:运行一个基本的机组组合备用优化模型,获取基本机组组合调度结果;步骤2:基于所述调度结果建立投运容量缺失表,计算LOLP,并从中寻找边际事件;步骤3:将边际事件对应的线性约束添加到备用优化模型,得到新的调度结果,返回步骤2,直至结果满足LOLP要求。
- 如权利要求1所述的基于支撑故障事件约束机组组合的备用优化方法,其特征在于,所述步骤1中基本的机组组合备用优化模型为不包括LOLP约束的旋转备用优化模型。
- 如权利要求1所述的基于支撑故障事件约束机组组合的备用优化方法,其特征在于,所述投运容量缺失表的行代表机组可能发生的故障事件,列代表缺失容量、故障概率和累计概率。
- 如权利要求5所述的基于支撑故障事件约束机组组合的备用优化方法,其特征在于,所述寻找边际事件方法为:在CCOPT中找出第i-1行和第i行,累计概率满足:在CCOPT中行数大于等于i的故障场景造成的LOLP总和不超过LOLP max,但行数大于等于i-1的故障场景造成的LOLP总和不超过LOLP max;第i-1行场景为边际场景,与边际场景同类型的故障场景也是边际场景。
- 一种基于支撑故障事件约束机组组合的备用优化装置,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,其特征在于,所述处理器执行如权利要求1-7任一项所述的方法。
- 一种计算机可读存储介质,其上存储有计算机程序,其特征在于,该程序被处理器执行时执行如权利要求1-7任一项所述的基于支撑故障事件约束机组组合的备用优化方法。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16/959,908 US20200334562A1 (en) | 2018-04-11 | 2018-11-30 | Reserve optimization method and apparatus based on support outage event constrained unit commitment |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810321864.1 | 2018-04-11 | ||
CN201810321864.1A CN108334997B (zh) | 2018-04-11 | 2018-04-11 | 基于支撑故障事件约束机组组合的备用优化方法和装置 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2019196427A1 true WO2019196427A1 (zh) | 2019-10-17 |
Family
ID=62932950
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2018/118375 WO2019196427A1 (zh) | 2018-04-11 | 2018-11-30 | 基于支撑故障事件约束机组组合的备用优化方法和装置 |
Country Status (3)
Country | Link |
---|---|
US (1) | US20200334562A1 (zh) |
CN (1) | CN108334997B (zh) |
WO (1) | WO2019196427A1 (zh) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114221391A (zh) * | 2021-12-13 | 2022-03-22 | 清华四川能源互联网研究院 | 一种电力系统备用容量确定方法、装置及相关设备 |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108334997B (zh) * | 2018-04-11 | 2020-12-29 | 山东大学 | 基于支撑故障事件约束机组组合的备用优化方法和装置 |
CN109193617B (zh) * | 2018-07-30 | 2020-04-14 | 山东大学 | 基于紧约束识别的电力系统脆弱点评价方法及系统 |
CN113537703B (zh) * | 2021-06-04 | 2022-12-02 | 广东电网有限责任公司广州供电局 | 配电网灾前应急资源部署方法、装置和计算机设备 |
CN113659621B (zh) * | 2021-08-15 | 2023-04-04 | 国网福建省电力有限公司 | 计及机组启停特性的区域电量传输可行域计算方法 |
CN114243687A (zh) * | 2021-12-09 | 2022-03-25 | 国网甘肃省电力公司电力科学研究院 | 一种基于成本和效益的风电提供旋转备用服务定价方法 |
CN115347570B (zh) * | 2022-10-17 | 2023-01-24 | 国网浙江省电力有限公司宁波供电公司 | 一种基于主配协同的区域停电范围分析方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105322566A (zh) * | 2015-11-06 | 2016-02-10 | 山东大学 | 考虑预测误差时序分布的含风电机组组合模型建立方法 |
CN106253351A (zh) * | 2016-08-11 | 2016-12-21 | 中国电力科学研究院 | 一种基于简化失负荷概率约束式的电力系统旋转备用优化方法 |
CN106295226A (zh) * | 2016-08-26 | 2017-01-04 | 山东电力工程咨询院有限公司 | 统筹考虑电力系统可靠性与经济性的备用决策方法 |
CN108334997A (zh) * | 2018-04-11 | 2018-07-27 | 山东大学 | 基于支撑故障事件约束机组组合的备用优化方法和装置 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7305282B2 (en) * | 2003-05-13 | 2007-12-04 | Siemens Power Transmission & Distribution, Inc. | Very short term load prediction in an energy management system |
WO2010129059A1 (en) * | 2009-05-08 | 2010-11-11 | Consert Inc. | System and method for estimating and providing dispatchable operating reserve energy capacity through use of active load management |
CN104362673B (zh) * | 2014-10-29 | 2016-04-13 | 国网甘肃省电力公司 | 基于调峰裕度的风电并网协调调度优化方法 |
-
2018
- 2018-04-11 CN CN201810321864.1A patent/CN108334997B/zh not_active Expired - Fee Related
- 2018-11-30 WO PCT/CN2018/118375 patent/WO2019196427A1/zh active Application Filing
- 2018-11-30 US US16/959,908 patent/US20200334562A1/en not_active Abandoned
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105322566A (zh) * | 2015-11-06 | 2016-02-10 | 山东大学 | 考虑预测误差时序分布的含风电机组组合模型建立方法 |
CN106253351A (zh) * | 2016-08-11 | 2016-12-21 | 中国电力科学研究院 | 一种基于简化失负荷概率约束式的电力系统旋转备用优化方法 |
CN106295226A (zh) * | 2016-08-26 | 2017-01-04 | 山东电力工程咨询院有限公司 | 统筹考虑电力系统可靠性与经济性的备用决策方法 |
CN108334997A (zh) * | 2018-04-11 | 2018-07-27 | 山东大学 | 基于支撑故障事件约束机组组合的备用优化方法和装置 |
Non-Patent Citations (1)
Title |
---|
WANG, M. Q. ET AL.: "Spinning Reserve Optimization Using Reliability Constrained Unit Commitment", 2013 IEEE INTERNATIONAL CONFERENCE OF IEEE REGION 10 (TENCON 2013), 1 November 2015 (2015-11-01), pages 1 - 5, XP032844825 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114221391A (zh) * | 2021-12-13 | 2022-03-22 | 清华四川能源互联网研究院 | 一种电力系统备用容量确定方法、装置及相关设备 |
CN114221391B (zh) * | 2021-12-13 | 2024-02-06 | 清华四川能源互联网研究院 | 一种电力系统备用容量确定方法、装置及相关设备 |
Also Published As
Publication number | Publication date |
---|---|
CN108334997A (zh) | 2018-07-27 |
US20200334562A1 (en) | 2020-10-22 |
CN108334997B (zh) | 2020-12-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2019196427A1 (zh) | 基于支撑故障事件约束机组组合的备用优化方法和装置 | |
Eldali et al. | Employing ARIMA models to improve wind power forecasts: A case study in ERCOT | |
CN107239863B (zh) | 一种电网安全约束的鲁棒机组组合方法 | |
CN110266031B (zh) | 储能并网充放电量控制方法、装置、服务器及存储介质 | |
Chen et al. | A distributed framework for solving and benchmarking security constrained unit commitment with warm start | |
CN112104005B (zh) | 一种考虑新能源预测不确定性的电网调度方法和系统 | |
US20180284706A1 (en) | Gas turbine dispatch optimizer | |
CN112308411B (zh) | 基于动态碳交易模型的综合能源站随机规划方法及系统 | |
JP6582758B2 (ja) | 発電計画作成装置、発電計画作成プログラム及び発電計画作成方法 | |
CN113779874B (zh) | 一种离网微电网建设的多目标优化方法 | |
JP2016143336A (ja) | 分散型エネルギーシステムの構成最適化方法及び装置 | |
Yu et al. | A dynamic lot sizing model with carbon emission constraint and multi-mode production: A general property and a polynomially solvable case | |
AU2020200488A1 (en) | Operation plan creating apparatus, operation plan creating method, and program | |
CN109787217B (zh) | 基于风电多状态模型及机会成本修正的备用出清方法 | |
CN116070757A (zh) | 一种综合能源系统优化调度方法、装置、设备及存储介质 | |
CN112134275B (zh) | 一种计算含风电场电力系统的可靠性方法及系统 | |
Ke et al. | Uncertain resource leveling problem | |
AU2020200492A1 (en) | Operation plan creating apparatus, operation plan creating method, and program | |
Liu et al. | Two-stage robust optimal dispatch method considering wind power and load correlation | |
CN115065102B (zh) | 一种火电机组启停调度的方法及装置 | |
CN116646994B (zh) | 一种电力系统优化调度方法及装置 | |
Khan | Data Center Load Forecast Using Dependent Mixture Model | |
CN113642937B (zh) | 风机集群的运维排程方法、装置、电子设备及存储介质 | |
US20240104589A1 (en) | Prediction of consumer demand for a supply in a geographic zone based on unreliable and non-stationary data | |
CN114386236A (zh) | 一种新能源消纳合理利用率的规划方法及系统 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 18914536 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 18914536 Country of ref document: EP Kind code of ref document: A1 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 19/03/2021) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 18914536 Country of ref document: EP Kind code of ref document: A1 |