CN108549958A - Ordinal utility theory-based day-ahead low-carbon scheduling decision method considering wind power access - Google Patents
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Abstract
The invention provides a ordinal utility theory-based day-ahead low-carbon scheduling decision method considering wind power access, which is characterized in that a multi-scene method is used for describing uncertainty of wind power output, a power generation scheduling decision problem is constructed into a two-stage coordination optimization model, and the model can depict day-ahead clearing and real-time balancing processes of scheduling decisions, so that scheduling decision results have stronger adaptability under the uncertain wind power access.
Description
Technical Field
The invention relates to the technical field of power generation optimization scheduling, in particular to a day-ahead low-carbon scheduling decision method based on ordinal utility theory considering wind power access.
Background
The energy-saving power generation scheduling changes the traditional power generation scheduling mode, cancels the method of distributing the generated energy index according to the administrative plan, takes energy conservation and environmental protection as targets, takes power generation, transmission and power supply equipment in the full power system as scheduling objects, preferentially schedules renewable and clean power generation resources, and sequentially calls fossil power generation resources from low to high according to the energy consumption and pollutant emission level, thereby reducing the energy and resource consumption and pollutant emission to the maximum extent and promoting the efficient and clean operation of the power system.
Energy-saving power generation scheduling requires that renewable power generation resources are output according to the reporting arrangement of the renewable power generation resources, but because the output of the renewable energy resources has uncertainty, the actual output and the reported value have deviation, but because the wind power output is usually processed into a determined form, the traditional power generation optimization decision method in wind power integration usually adopts multi-objective optimizationChemical method for considering both economy of power generation dispatching and CO2And (4) discharging the targets, wherein the multi-target optimization problem is generally converted into a single-target optimization problem, and then each single-target optimization problem is solved by using a relevant mathematical optimization algorithm.
Because wind power output has randomness and volatility, optimizing and scheduling based on the power generation optimizing decision method based on the determined form can cause a serious wind abandon situation in real-time operation, and meanwhile, the processing mode of converting a multi-target problem into a single target to solve is insufficient for describing preference information of a decision maker among a plurality of targets, so that the decision maker is inconvenient to obtain an optimizing scheme according with self preference.
In summary, a low-carbon scheduling decision method which has stronger adaptability under uncertain wind power access and can describe preference information of each target more sufficiently is urgently needed to be provided in the prior art.
Disclosure of Invention
To solve the above problems, embodiments of the present invention provide a day-ahead low-carbon scheduling decision method that overcomes or at least partially solves the above problems.
According to a first aspect of the embodiments of the present invention, a day-ahead low-carbon scheduling decision method is provided, including:
expected cost function and CO based on pre-established scheduling decisions according to decision maker preferences2An expected emission function, namely constructing a target function of a scheduling decision by taking the effectiveness of the maximum scheduling decision as a principle;
establishing a day-ahead low-carbon scheduling decision model according to a target function of a scheduling decision and a pre-constructed constraint condition of the scheduling decision, and solving the day-ahead low-carbon scheduling decision model to obtain a scheduling decision scheme; the constraint conditions of the scheduling decision comprise constraint conditions of the day-ahead clearing of the first stage and constraint conditions of real-time balance in the second stage.
Further, the expected cost function and the CO based on pre-established scheduling decisions according to the decision maker preference2The expected emission function constructs an objective function of the scheduling decision by taking the effectiveness of the maximized scheduling decision as a principle, and the method also comprises the following steps: CO for respectively establishing expected cost function and scheduling decision of scheduling decision2The desired emissions function.
Further, the expected cost function and the CO are based on pre-established scheduling decisions according to decision maker preferences2The method comprises the following steps of constructing an objective function of a scheduling decision by taking the utility of the maximized scheduling decision as a principle, and further comprising the following steps:
constructing an initial objective function:
wherein F is the desired cost;is CO2The expected discharge amount; u represents the desired cost F, CO2The expected discharge amount isThe utility of the time; obtaining a utility function of the initial objective function as follows:
wherein a and b are coefficients of a utility function, a is more than or equal to 0, b is more than or equal to 0, and ab is not equal to 0;
further, obtaining an absolute value of a slope of a utility non-difference curve corresponding to the utility function as a marginal substitution rate epsilon, and obtaining a converted target function based on epsilon:
further, the establishing an expected cost function of the scheduling decision further includes:
wherein F is the desired cost; n is a radical ofTIs the number of scheduling periods; n is a radical ofGThe number of conventional units; n is a radical ofWThe number of wind power plants; n is a radical ofSThe number of scenes of wind power output is obtained; n is a radical ofDIs the number of loads;starting cost of a conventional unit g in a time period t; cg(. C) is a quote function for a conventional unit ggExpressed as a quadratic functionAnd a isg，bg，cgThe parameters are the quotation curve parameters of the unit g;punishing cost for wind power station w;penalty cost for unit load shedding of load d; u. ofg,tThe starting and stopping state of the conventional unit g in the time period t is a variable of 0-1, ug,t1 denotes the operating state, ug,t0 represents an outage state; pg,tThe output of the conventional unit g in the first stage in the time period t; rhosThe probability of occurrence of a wind power output scene s is shown; r isg,t,sAdjusting the output of the second-stage conventional unit g in a time t scene s;the wind curtailment power of the second-stage wind power plant w in a time period t scene s is obtained;the amount of load shedding for the second stage load d over time t scene s.
Further, the constraint conditions of the first-stage day-ahead clearing comprise: system power balance constraints, line transmission capacity constraints, wind farm output constraints and conventional unit operation constraints.
Further, the conventional unit operation constraint further includes: output constraints, minimum on-off time constraints, startup cost constraints, and hill climbing constraints.
Further, the constraint conditions of real-time balance in the second stage day include: each scene equality constraint, line transmission capacity constraint, constraint of a conventional unit under each scene, wind curtailment constraint and load shedding constraint.
Further, the constraint of the conventional unit under each scenario further includes: force constraints and hill climbing constraints.
According to a second aspect of embodiments of the present invention, there is provided an electronic apparatus, including:
a processor; and a memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, and the processor calls the program instructions to perform the future low-carbon scheduling decision method provided by any one of the various possible implementations of the first aspect.
According to a third aspect of embodiments of the present invention, there is provided a non-transitory computer-readable storage medium, which stores computer instructions for causing a computer to execute a method for day-ahead low-carbon scheduling decision provided in any one of various possible implementations of the first aspect.
The embodiment of the invention provides a day-ahead low-carbon scheduling decision method, wherein a multi-scene method is used for describing uncertainty of wind power output, a power generation scheduling decision problem is constructed into a two-stage coordination optimization model, the model can depict day-ahead clearing and real-time balancing processes of scheduling decisions, so that scheduling decision results have stronger adaptability under the condition of uncertain wind power access, and meanwhile, the invention provides that an ordinal utility theory is used for considering both economic performance and carbon emission targets, so that a decision maker can more fully depict preference information of each target.
Drawings
Fig. 1 is a schematic flow chart of a day-ahead low-carbon scheduling decision method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a utility non-difference curve corresponding to a utility function in a day-ahead low-carbon scheduling decision method according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Wind power is a renewable energy source with large resource potential and basically mature technology, is increasingly emphasized by countries in the world under the new situation of reducing greenhouse gas emission and coping with climate change, and is developed and utilized on a large scale in the world. In recent years, Chinese wind power is rapidly developed, the installed capacity of the wind power is continuously doubled, the manufacturing capacity of equipment is rapidly improved, a relatively complete industrial system is formed, and a good foundation is laid for large-scale wind power development. With the progress of wind power technology and the continuous reduction of cost, China also holds great hope on the role of wind power in the future energy structure adjustment and the strategic new industry cultivation process. However, as wind energy resources have the characteristics of randomness, intermittence, uncontrollable property and the like, along with the enlargement of the wind power scale, a power generation optimization decision method in wind power integration becomes one of important factors restricting the development of wind power.
The embodiment of the invention provides a method for taking the economic efficiency and CO of system operation into consideration by using the utility theory2And (3) the emission target describes the clearing and real-time balancing process of the day-ahead scheduling decision by using a two-stage model, so that the day-ahead low-carbon scheduling decision of the generator set is realized.
Fig. 1 shows an overall flow chart of a day-ahead low-carbon scheduling decision according to an embodiment of the present invention. In general, the following steps are included.
S1, expected cost function and CO based on pre-established scheduling decision according to decision maker preference2And (4) an expected emission function, and constructing a target function of the scheduling decision by taking the effectiveness of the maximized scheduling decision as a principle.
S2, establishing a day-ahead low-carbon scheduling decision model according to a target function of a scheduling decision and a pre-constructed constraint condition of the scheduling decision, and solving the day-ahead low-carbon scheduling decision model to obtain a scheduling decision scheme; the constraint conditions of the scheduling decision comprise constraint conditions of the day-ahead clearing of the first stage and constraint conditions of real-time balance in the second stage.
First, the above embodiments of the present invention propose to utilize the ordinal utility theory and consider two objectives by respectively constructing the economic and carbon emission objective functions in advance, so that the decision maker can more fully characterize the scheduling economic and carbon emission preferences. Secondly, constructing a power generation scheduling decision problem into a two-stage coordination optimization model, wherein the first stage of the optimization model describes the clearing process of the market at the day before, and the stage comprises the optimization and load distribution of the starting and stopping states of a conventional unit; and in the second stage of the optimization model, under the condition of keeping the starting and stopping states of the conventional unit in the first stage, a real-time balance process under uncertain wind power output is simulated, and output adjustment, wind abandon, load shedding and the like of the conventional unit based on the day-ahead optimized output under each wind power output scene are related. The model enables the scheduling decision result to have stronger adaptability under the condition of uncertain wind power access.
Specifically, a day-ahead low-carbon scheduling decision model based on the ordinal number utility theory considering uncertain wind power access is established in S2 according to the objective function obtained in S1 and a pre-established constraint condition of the scheduling decision, wherein the model is a mixed integer quadratic programming model.
Based on the above embodiments of the present invention, a day-ahead low-carbon scheduling decision method is provided, which is based on an expected cost function and CO of a pre-established scheduling decision according to the preference of a decision maker2The expected emission function constructs an objective function of the scheduling decision by taking the effectiveness of the maximized scheduling decision as a principle, and the method also comprises the following steps: CO for respectively establishing expected cost function and scheduling decision of scheduling decision2The desired emissions function.
Based on the above embodiments of the present invention, a method for making a day-ahead low-carbon scheduling decision is provided, which is based on an expected cost function and a CO of a pre-established scheduling decision according to the preference of a decision maker2The method comprises the following steps of constructing an objective function of a scheduling decision by taking the utility of the maximized scheduling decision as a principle, and further comprising the following steps:
constructing an initial objective function:
wherein F is the desired cost;is CO2The expected discharge amount; u represents the desired cost F, CO2The expected discharge amount isThe utility of the time; obtaining a utility function of the initial objective function as follows:
wherein a and b are coefficients of a utility function, a is more than or equal to 0, b is more than or equal to 0, and ab is not equal to 0;
further, obtaining an absolute value of a slope of a utility non-difference curve corresponding to the utility function as a marginal substitution rate epsilon, and obtaining a converted target function based on epsilon:
specifically, as shown in fig. 2, a utility indifference curve corresponding to the utility function is obtained, wherein the direction indicated by the arrow is the direction of increasing utility, the absolute value of the slope of the indifference curve is a marginal substitution rate epsilon, and b ═ epsilon a, the larger the marginal substitution rate is, the larger the marginal substitution rate represents the decision maker for reducing CO2The stronger the preference for emissions, the more the transformed objective function is obtained based on epsilon.
On the basis of the foregoing embodiments of the present invention, a method for a day-ahead low-carbon scheduling decision is provided, where the establishing of an expected cost function for a scheduling decision further includes:
wherein F is the desired cost; n is a radical ofTIs the number of scheduling periods; n is a radical ofGThe number of conventional units; n is a radical ofWThe number of wind power plants; n is a radical ofSThe number of scenes of wind power output is obtained; n is a radical ofDIs the number of loads;starting cost of a conventional unit g in a time period t; cg(. C) is a quote function for a conventional unit ggExpressed as a quadratic functionAnd a isg，bg，cgThe parameters are the quotation curve parameters of the unit g;punishing cost for wind power station w;penalty cost for unit load shedding of load d; u. ofg,tThe starting and stopping state of the conventional unit g in the time period t is a variable of 0-1, ug,t1 denotes the operating state, ug,t0 represents an outage state; pg,tThe output of the conventional unit g in the first stage in the time period t; rhosThe probability of occurrence of a wind power output scene s is shown; r isg,t,sAdjusting the output of the second-stage conventional unit g in a time t scene s;the wind curtailment power of the second-stage wind power plant w in a time period t scene s is obtained;the amount of load shedding for the second stage load d over time t scene s.
Based on the above embodiments of the present invention, a day-ahead low-carbon scheduling decision method is provided, where the CO for establishing a scheduling decision2A desired emissions function, further comprising:
wherein,is CO2desired amount of discharge, αg，βg，χgRespectively are parameters of the carbon emission characteristic curve of the unit g.
On the basis of the foregoing specific embodiments of the present invention, a method for scheduling and deciding a day-ahead low carbon is provided, where the constraint condition of day-ahead clearing in the first stage includes:
system power balance constraints, line transmission capacity constraints, wind farm output constraints and conventional unit operation constraints.
Specifically, the system power balance constraint is:
wherein L isd,tLoad prediction value of the load point d in the time period t; pw,tAnd the output of the wind power plant w in the time period t.
Specifically, wherein the line transmission capacity constraint is:
wherein, T is a power transmission distribution coefficient matrix; pt injAn injection power column vector of a time period t system;andFrespectively the column vectors of the upper and lower limits of the transmission capacity of the line.
Specifically, the wind farm output constraint is as follows:
wherein,and (4) the output predicted value of the wind power plant w in the time period t.
On the basis of the above specific embodiment of the present invention, a day-ahead low-carbon scheduling decision method is provided, where the conventional unit operation constraint further includes: output constraints, minimum on-off time constraints, startup cost constraints, and hill climbing constraints.
Specifically, wherein the output constraint is:
wherein,andP grespectively the upper and lower output limits of the conventional unit g.
Specifically, wherein the minimum on/off time constraint is:
wherein,andrespectively the minimum on-time and off-time duration of the conventional unit g.
Specifically, wherein the start-up cost constraint is:
wherein,the starting cost of the conventional unit g.
Specifically, wherein the hill climbing constraint is:
wherein,andrespectively the up-down climbing speed of the conventional unit g.
On the basis of the foregoing specific embodiments of the present invention, a day-ahead low-carbon scheduling decision method is provided, where the constraint conditions of real-time balance in the second stage day include:
each scene equality constraint, line transmission capacity constraint, constraint of a conventional unit under each scene, wind curtailment constraint and load shedding constraint.
Specifically, the scene equation constraints are as follows:
wherein, Pw,t,sAnd (4) the output predicted value of the wind power plant w under the time period t scene s.
Specifically, wherein the line transmission capacity constraint is:
wherein,is the injection power column vector of the system in time period tset.
Specifically, the curtailment wind constraint is:
specifically, wherein the shear load constraint is:wherein, k is the proportion of the maximum allowable load cut of the system to the total load.
On the basis of the above specific embodiments of the present invention, a day-ahead low-carbon scheduling decision method is provided, where the constraints of the conventional unit in each scene further include: force constraints and hill climbing constraints.
Specifically, wherein the output constraint is:
specifically, wherein the hill climbing constraint is:
based on the above specific embodiments, an electronic device is provided. Referring to fig. 3, the electronic device includes: a processor (processor)301, a memory (memory)302, and a bus 303;
the processor 301 and the memory 302 respectively complete communication with each other through a bus 303;
the processor 301 is configured to call the program instructions in the memory 302 to execute the day-ahead low carbon scheduling decision method provided by the foregoing embodiment, for example, including: based on decision maker preferenceExpected cost function and CO of pre-established scheduling decisions2An expected emission function, namely constructing a target function of a scheduling decision by taking the effectiveness of the maximum scheduling decision as a principle; establishing a day-ahead low-carbon scheduling decision model according to a target function of a scheduling decision and a pre-constructed constraint condition of the scheduling decision, and solving the day-ahead low-carbon scheduling decision model to obtain a scheduling decision scheme; the constraint conditions of the scheduling decision comprise constraint conditions of the day-ahead clearing of the first stage and constraint conditions of real-time balance in the second stage.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable a computer to execute the method for scheduling and deciding low carbon in the day-ahead provided by the foregoing embodiment, for example, the method includes: expected cost function and CO based on pre-established scheduling decisions according to decision maker preferences2An expected emission function, namely constructing a target function of a scheduling decision by taking the effectiveness of the maximum scheduling decision as a principle; establishing a day-ahead low-carbon scheduling decision model according to a target function of a scheduling decision and a pre-constructed constraint condition of the scheduling decision, and solving the day-ahead low-carbon scheduling decision model to obtain a scheduling decision scheme; the constraint conditions of the scheduling decision comprise constraint conditions of the day-ahead clearing of the first stage and constraint conditions of real-time balance in the second stage.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the information interaction device and the like are merely illustrative, where units illustrated as separate components may or may not be physically separate, and components displayed as units may or may not be physical units, may be located in one place, or may also be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute the various embodiments or some parts of the methods of the embodiments.
Finally, the method of the present application is only a preferred embodiment and is not intended to limit the scope of the embodiments of the present invention. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.
Claims (10)
1. A day-ahead low-carbon scheduling decision method is characterized by comprising the following steps:
expected cost function and CO based on pre-established scheduling decisions according to decision maker preferences2An expected emission function, namely constructing a target function of a scheduling decision by taking the effectiveness of the maximum scheduling decision as a principle;
establishing a day-ahead low-carbon scheduling decision model according to a target function of a scheduling decision and a pre-constructed constraint condition of the scheduling decision, and solving the day-ahead low-carbon scheduling decision model to obtain a scheduling decision scheme; the constraint conditions of the scheduling decision comprise constraint conditions of the day-ahead clearing of the first stage and constraint conditions of real-time balance in the second stage.
2. The method of claim 1, wherein the desired cost function and the CO are based on pre-established scheduling decisions according to decision maker preferences2The expected emission function constructs an objective function of the scheduling decision by taking the effectiveness of the maximized scheduling decision as a principle, and the method also comprises the following steps:
CO for respectively establishing expected cost function and scheduling decision of scheduling decision2The desired emissions function.
3. The method of claim 1, wherein the expected cost function and the CO are based on pre-established scheduling decisions according to decision maker preferences2The method comprises the following steps of constructing an objective function of a scheduling decision by taking the utility of the maximized scheduling decision as a principle, and further comprising the following steps:
constructing an initial objective function:
wherein F is the desired cost;is CO2The expected discharge amount; u represents the desired cost F, CO2The expected discharge amount isThe utility of the time; obtaining a utility function of the initial objective function as follows:
wherein a and b are coefficients of a utility function, a is more than or equal to 0, b is more than or equal to 0, and ab is not equal to 0;
further, obtaining an absolute value of a slope of a utility non-difference curve corresponding to the utility function as a marginal substitution rate epsilon, and obtaining a converted target function based on epsilon:
4. the method of claim 2, wherein establishing the expected cost function for the scheduling decision further comprises:
wherein F is the desired cost; n is a radical ofTIs the number of scheduling periods; n is a radical ofGThe number of conventional units; n is a radical ofWThe number of wind power plants; n is a radical ofSThe number of scenes of wind power output is obtained; n is a radical ofDIs the number of loads;starting cost of a conventional unit g in a time period t; cg(. C) is a quote function for a conventional unit ggExpressed as a quadratic functionAnd a isg，bg，cgThe parameters are the quotation curve parameters of the unit g;punishing cost for wind power station w;penalty cost for unit load shedding of load d; u. ofg,tThe starting and stopping state of the conventional unit g in the time period t is a variable of 0-1, ug,t1 denotes the operating state, ug,t0 represents an outage state; pg,tIs the first orderThe output of the conventional unit g in the time period t is obtained; rhosThe probability of occurrence of a wind power output scene s is shown; r isg,t,sAdjusting the output of the second-stage conventional unit g in a time t scene s;the wind curtailment power of the second-stage wind power plant w in a time period t scene s is obtained;the amount of load shedding for the second stage load d over time t scene s.
5. The method of claim 1, wherein the first stage ante-natal constraints comprise:
system power balance constraints, line transmission capacity constraints, wind farm output constraints and conventional unit operation constraints.
6. The method of claim 5, wherein the conventional unit operational constraints further comprise: output constraints, minimum on-off time constraints, startup cost constraints, and hill climbing constraints.
7. The method of claim 1, wherein the constraints of real-time balance over the second phase day include:
each scene equality constraint, line transmission capacity constraint, constraint of a conventional unit under each scene, wind curtailment constraint and load shedding constraint.
8. The method of claim 7, wherein the constraints of the conventional crew under each scenario further comprise: force constraints and hill climbing constraints.
9. An electronic device, comprising:
a processor; and a memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 8.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method according to any one of claims 1 to 8.
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| CN112116476A (en) * | 2020-09-23 | 2020-12-22 | 中国农业大学 | Comprehensive energy system simulation method considering wind power and carbon transaction mechanism |
| CN112116476B (en) * | 2020-09-23 | 2024-03-01 | 中国农业大学 | Comprehensive energy system simulation method considering wind power and carbon transaction mechanism |
| CN114301071A (en) * | 2022-01-06 | 2022-04-08 | 哈尔滨工业大学 | Wind power plant plan deviation rate setting method adapting to full-scheduling period examination mode |
| CN114301071B (en) * | 2022-01-06 | 2023-10-27 | 哈尔滨工业大学 | Wind power plant planning deviation rate setting method adapting to full-scheduling period assessment mode |
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