CN110957717A - Multi-target day-ahead optimal scheduling method for multi-power-supply power system - Google Patents
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- 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
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- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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Abstract
The invention belongs to the technical field of power system automation, and particularly relates to a multi-target day-ahead optimal scheduling method for a multi-power-supply power system. The invention comprises the following steps: (1) acquiring a system operation mode, load prediction data and renewable energy power generation prediction data of a future day; (2) establishing a multi-target day-ahead optimization scheduling model of a multi-power-supply power system; (3) solving the model by adopting a particle swarm optimization algorithm; (4) and generating a day-ahead scheduling strategy according to the solving result. The method can comprehensively consider the operation characteristics of various generator sets, formulate a day-ahead optimized scheduling scheme with the safety meeting the requirements, fully utilizing the renewable energy and reasonable economy, ensure the safe and economic operation of the power system and improve the consumption level of the renewable energy at the same time.
Description
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a multi-target day-ahead optimal scheduling method for a multi-power-supply power system.
Background
In recent years, renewable energy sources are rapidly developed, wind power generation and photovoltaic power generation technologies are mature day by day, and installed capacity is continuously improved. Among them, wind power is one of the most mature, scaled and commercially valuable power generation modes in technology. However, because the primary energy of renewable energy has uncertainty, the output power of wind power and photovoltaic power can fluctuate randomly in a large range. The continuous improvement of the new energy power generation penetration rate can bring new challenges to the peak shaving of a power system, the stability of a power grid and the economic operation. In areas with rich wind energy, illumination and water resources, if effective and reasonable scheduling is lacked, the phenomena of wind abandonment, light abandonment and water abandonment are likely to occur, and resource waste is caused. With the continuous adjustment of power supply structures and the continuous access of new energy in the power industry in China, the joint scheduling and optimized operation of multiple power supplies including water, electricity, thermal power, wind power and water pumping and energy storage become necessary trends of power grid development.
Because the output characteristics, economic characteristics and time characteristics of various types of power supplies are greatly different, the characteristics of various types of power supplies are necessarily combined to carry out the combined optimal scheduling of multiple power supplies so as to ensure the safe and economic operation of a power grid. The optimal scheduling of the power system is a process of reasonably and economically distributing the active power of each generator set to realize active balance and safe operation of the power system, and needs to span different time scales to carry out multi-level coordination and stepwise refinement on the power distribution. Based on the results of the load prediction and the renewable energy power generation prediction in the day, the day-ahead optimization scheduling mainly has the functions of making the unit combination of the thermal power generating units, the operation condition of the pumped storage power station, the output distribution of each generating unit, the effective reserve capacity distribution and the like within 24 hours in the future, and the contradiction between the limited peak regulation capacity of the power grid and the random fluctuation of wind power can be solved through the coordinated optimization scheduling of each power supply, so that the method is the basis of the multi-power supply combined optimization scheduling.
Currently, the optimization scheduling model of a single target is difficult to satisfy the safe and economic operation of the power system, so the research and development are continuously needed by those skilled in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a multi-target day-ahead optimal scheduling method for a multi-power-supply power system, and aims to provide a day-ahead optimal scheduling scheme which can comprehensively consider the conditions of water, incoming wind and load requirements and the operation characteristics of various types of generator sets, makes safety meeting requirements, fully utilizes renewable energy sources and is reasonable in economy, ensures the safe and economic operation of the power system and improves the consumption level of the renewable energy sources.
Based on the above purpose, the invention is realized by the following technical scheme:
a multi-target day-ahead optimization scheduling method for a multi-power-supply power system comprises the following steps:
step 1: acquiring a system operation mode, load prediction data and renewable energy power generation prediction data within a set time range;
step 2: establishing a multi-target day-ahead optimization scheduling model of the multi-power-supply power system according to the system operation mode, the load prediction data and the renewable energy power generation prediction data;
and step 3: solving the multi-target day-ahead optimization scheduling model of the multi-power-supply electric power system by adopting a particle swarm optimization algorithm;
and 4, step 4: and generating a day-ahead scheduling strategy according to the solving result.
Further, the multi-target day-ahead optimization scheduling model of the multi-power-supply power system comprises a target function and constraint conditions.
Further, the control variables of the objective function include at least one of:
starting and stopping state variables of the thermal power generating unit at each time interval; output plan and effective spinning reserve capacity; reservoir water level state variables of the hydropower station in each time period; the output plan and the effective rotation reserve capacity of the hydroelectric generating set at each time interval; output plans of the wind power plant at each time interval; the operating condition state variable, the output plan and the effective rotation reserve capacity of the pumped storage power station at each time interval.
Further, the objective function is:
f＝min{f1，f2，f3} (4)
in the formula: f. of1Representing an economic objective function; f. of2Representing a security objective function; f. of3Representing the renewable energy consumption objective function.
Further, the economic objective function of the system operation comprises the operation cost and the starting cost of the conventional thermal power generating unit
Wherein T is the total scheduling duration; n is the number of thermal power generating units; a isi、bi、ciThe operation cost coefficient of the thermal power generating unit i is obtained; e.g. of the typei、fi、τiThe starting cost coefficient is the starting cost coefficient of the thermal power generating unit i; pi tThe active power output of the thermal power generating unit i in the time period t is obtained; Δ t is the duration of each time period;
the method comprises the following steps that 1 indicates that a thermal power generating unit i is in a starting state in a time period t, and 0 indicates that the thermal power generating unit i is in a stopping state;
for thermal power generating units iWhether the starting operation occurs in the time period t is 1, which indicates that the unit is started from the shutdown operation, and 0 indicates that the unit is not started;
and (4) continuously stopping the thermal power generating unit i in the time period t.
Further, a safety objective function for system operation is defined as a confidence level that wind, water, and load shedding of the system do not occur, as determined by the effective spare capacity of the system, as follows:
in the formula (I), the compound is shown in the specification,
for the total effective positive rotation of the system in the time period t as standby
The probability of the system being under-loaded,
for the total effective negative rotation of the system in the time period t to be standby
The probability of the system generating wind or water abandon.
Furthermore, the renewable energy consumption level objective function is represented by the sum of the abandoned wind electric quantity and the abandoned water electric quantity in the scheduling duration, and the more the abandoned wind and the abandoned water are, the weaker the consumption capability of the renewable energy is;
wherein the content of the first and second substances,
curtailment wind Power, N, for wind farm w occurring at time period tWIs the total number of the wind farm,
water power curtailment for hydropower station h occurring at time period t, NhTo the total number of hydro-power plants, Δ t is the duration of each time period.
Furthermore, the constraint conditions comprise the self operation constraint conditions of the generator set, the operation constraint conditions of the system and the safety constraint conditions of the power network; the generator set operation constraints comprise thermal power unit operation constraints, hydroelectric power unit operation constraints, wind power unit operation constraints and pumped storage power station operation constraints; the power system level operation constraints comprise power balance constraints and standby capacity constraints; the power grid security constraints comprise network static security constraints and N-1 security check constraints.
Further, the solving method is as follows: converting the multi-objective optimization model into a single-objective optimization model by adopting a linear weighting method, wherein the method comprises the following steps:
minF＝min(w1f1+w2f2+w3f3) (5)
in the formula, w1、w2、w3Is a weighting coefficient;
and solving the optimization model by adopting a particle swarm optimization algorithm to obtain a day-ahead optimization scheduling strategy.
The invention also relates to a multi-target day-ahead optimization scheduling device for the multi-power-supply power system, which comprises the following steps:
the acquisition module is used for acquiring the system operation mode, the load prediction data and the renewable energy power generation prediction data within a set time range;
the modeling module is used for establishing a multi-target day-ahead optimization scheduling model of the multi-power-supply power system according to the system operation mode, the load prediction data and the renewable energy power generation prediction data;
the calculation module is used for solving the multi-target day-ahead optimization scheduling model of the multi-power-supply power system by adopting a particle swarm optimization algorithm;
and the output module is used for generating a day-ahead scheduling strategy according to the solving result.
The invention has the following advantages and beneficial effects:
the multi-objective optimization scheduling model comprehensively considering the system operation economy, safety and renewable energy consumption level can provide guidance for the optimization operation of the power system with various power supplies.
Firstly, acquiring a system operation mode, load prediction data and renewable energy power generation prediction data in a future day; then establishing a multi-target day-ahead optimization scheduling model of the multi-power-supply power system; solving the model by adopting a particle swarm optimization algorithm; and finally, generating a day-ahead scheduling strategy according to the result. The method can comprehensively consider the operation characteristics of various generator sets, formulate a day-ahead optimized scheduling scheme with the safety meeting the requirements, fully utilizing the renewable energy and reasonable economy, ensure the safe and economic operation of the power system and improve the consumption level of the renewable energy at the same time.
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Detailed Description
The technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiment of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. Other embodiments, which can be derived by one of ordinary skill in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention relates to a multi-target day-ahead optimization scheduling method for a multi-power-supply power system. The method aims to improve the running economy, safety and renewable energy consumption level of a multi-power-supply power system, and establishes a multi-target day-ahead optimization scheduling model by taking the running constraint of a generator set, the running constraint of the system, the power network safety constraint and the like as constraint conditions. And solving the model by adopting a particle swarm optimization algorithm to obtain a day-ahead optimized scheduling strategy.
As follows:
(1) acquiring system operation modes, load prediction data and renewable energy power generation prediction data in one day in the future, wherein the data acquisition work is preparation work of a multi-target day-ahead optimization scheduling method of the multi-power-supply power system;
(2) establishing a multi-target day-ahead optimization scheduling model of the multi-power-supply power system according to the system operation mode, the load prediction data and the renewable energy power generation prediction data, wherein the multi-target day-ahead optimization scheduling model comprises the following steps 1-3 in the multi-power-supply power system multi-target day-ahead optimization scheduling method;
(3) solving the multi-target day-ahead optimization scheduling model of the multi-power-supply electric power system by adopting a particle swarm optimization algorithm, wherein the step 4 of the multi-target day-ahead optimization scheduling method of the multi-power-supply electric power system is as follows;
(4) and generating a day-ahead scheduling strategy according to the solving result, as shown in the step 5 of the multi-target day-ahead optimization scheduling method of the multi-power-supply electric power system.
The invention provides a multi-target day-ahead optimization scheduling method for a multi-power-supply power system, which comprises the following steps:
step 1: optimizing a scheduling model and controlling variables.
The control variables include: the system comprises a thermal power generating unit, a hydroelectric generating unit, a wind power plant, a pumped storage power station, a thermal power generating unit, a water level state variable of a reservoir of the hydroelectric generating unit, an output plan of the hydroelectric generating unit and an effective rotation reserve capacity of the hydroelectric generating unit in each time period, an output plan of the wind power plant in each time period, and an operation working condition state variable of the pumped storage power station, an output plan of the pumped storage power station and an effective rotation reserve capacity of the pumped storage power station in each.
Step 2: an objective function.
Step 2-1: economy of system operation.
The economical efficiency of the system operation mainly comprises the operation cost and the starting cost of the conventional thermal power generating unit, namely:
wherein T is the total scheduling duration; n is the number of thermal power generating units; a isi、bi、ciThe operation cost coefficient of the thermal power generating unit i is obtained; e.g. of the typei、fi、τiThe starting cost coefficient is the starting cost coefficient of the thermal power generating unit i; pi tThe active power output of the thermal power generating unit i in the time period t is obtained; Δ t is the duration of each time period;
the method comprises the following steps that 1 indicates that a thermal power generating unit i is in a starting state in a time period t, and 0 indicates that the thermal power generating unit i is in a stopping state;
whether the thermal power generating unit i is subjected to starting operation in a time period t is judged, wherein 1 represents that the thermal power generating unit is started from shutdown operation, and 0 represents that the thermal power generating unit is not subjected to starting operation;
and (4) continuously stopping the thermal power generating unit i in the time period t.
Step 2-2: and the safety of system operation.
The safety index of the system operation is defined as the confidence level that the system does not generate wind, water and load shedding, which is determined by the effective spare capacity of the system, and is as follows:
in the formula (I), the compound is shown in the specification,
for the total effective positive rotation of the system in the time period t as standby
The probability of the system being under-loaded,
for the total effective negative rotation of the system in the time period t to be standby
The probability of the system generating wind or water abandon.
Step 2-3: renewable energy consumption level.
The renewable energy consumption level is represented by the sum of the abandoned wind power and the abandoned water power in the scheduling duration, as shown in formula (3). The more the wind and water are abandoned, the weaker the absorption capacity of the renewable energy source is.
Wherein the content of the first and second substances,
curtailment wind Power, N, for wind farm w occurring at time period tWIs the total number of the wind farm,
water power curtailment for hydropower station h occurring at time period t, NhTo the total number of hydro-power plants, Δ t is the duration of each time period.
The objective function is integrated as:
f＝min{f1，f2，f3} (4)
in the formula: f. of1Expressing an economic index, such as formula (1); f. of2Representing a safety index, such as formula (2); f. of3And expressing the renewable energy consumption index, such as formula (3).
And step 3: a constraint condition.
In the multi-power supply combined optimization scheduling, the day-ahead optimization scheduling mainly considers the operation constraint conditions of the generator set, the operation constraint conditions of the system and the safety constraint conditions of the power network. The full constraint is shown in figure 1. Specifically, the generator set operation constraints comprise thermal power unit operation constraints, hydroelectric power unit operation constraints, wind power unit operation constraints and pumped storage power station operation constraints; the power system level operation constraints comprise power balance constraints and standby capacity constraints; the power grid security constraints comprise network static security constraints and N-1 security check constraints.
And 4, step 4: and (5) solving the method.
Converting the multi-objective optimization model into a single-objective optimization model by adopting a linear weighting method, namely:
minF＝min(ω1f1+ω2f2+ω3f3) (5)
in the formula, w1、w2、w3Are weighting coefficients.
And then solving the single-target optimization model by adopting a particle swarm optimization algorithm to obtain a date optimization scheduling strategy, wherein the method comprises the following steps of:
① initializing the position and velocity of the particles;
② performing transient time domain simulation, and calculating the fitness of each particle according to formula (5);
③ updating particle individual optima;
④ updating the population global optimum;
and 5: and issuing the optimal scheduling strategy to each thermal power generating unit, each hydroelectric generating unit, each wind power plant and each pumped storage power station to finish the implementation of the scheduling strategy.
The invention also relates to a multi-target day-ahead optimization scheduling device for the multi-power-supply power system, which comprises the following steps:
the acquisition module is used for acquiring the system operation mode, the load prediction data and the renewable energy power generation prediction data within a set time range;
the modeling module is used for establishing a multi-target day-ahead optimization scheduling model of the multi-power-supply power system according to the system operation mode, the load prediction data and the renewable energy power generation prediction data;
the calculation module is used for solving the multi-target day-ahead optimization scheduling model of the multi-power-supply power system by adopting a particle swarm optimization algorithm;
and the output module is used for generating a day-ahead scheduling strategy according to the solving result.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A multi-target day-ahead optimization scheduling method for a multi-power-supply power system is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring a system operation mode, load prediction data and renewable energy power generation prediction data within a set time range;
step 2: establishing a multi-target day-ahead optimization scheduling model of the multi-power-supply power system according to the system operation mode, the load prediction data and the renewable energy power generation prediction data;
and step 3: solving the multi-target day-ahead optimization scheduling model of the multi-power-supply electric power system by adopting a particle swarm optimization algorithm;
and 4, step 4: and generating a day-ahead scheduling strategy according to the solving result.
2. The multi-target day-ahead optimization scheduling method of the multi-power-supply power system as claimed in claim 1, wherein the method comprises the following steps: the multi-target day-ahead optimization scheduling model of the multi-power-supply power system comprises a target function and constraint conditions.
3. The multi-target day-ahead optimization scheduling method of the multi-power-supply power system as claimed in claim 2, wherein: the control variables of the objective function include at least one of:
starting and stopping state variables of the thermal power generating unit at each time interval; output plan and effective spinning reserve capacity; reservoir water level state variables of the hydropower station in each time period; the output plan and the effective rotation reserve capacity of the hydroelectric generating set at each time interval; output plans of the wind power plant at each time interval; the operating condition state variable, the output plan and the effective rotation reserve capacity of the pumped storage power station at each time interval.
4. The multi-target day-ahead optimization scheduling method of the multi-power-supply power system as claimed in claim 2, wherein: the objective function is:
f＝min{f1，f2，f3} (4)
in the formula: f. of1Representing an economic objective function; f. of2Representing a security objective function; f. of3Representing the renewable energy consumption objective function.
5. The multi-target day-ahead optimization scheduling method of the multi-power-supply power system as claimed in claim 4, wherein the method comprises the following steps: the economic objective function comprises the operation cost and the starting cost of the conventional thermal power generating unit, and is represented by the following formula:
wherein T is the total scheduling duration; n is the number of thermal power generating units; a isi、bi、ciThe operation cost coefficient of the thermal power generating unit i is obtained; e.g. of the typei、fi、τiThe starting cost coefficient is the starting cost coefficient of the thermal power generating unit i; pi tThe active power output of the thermal power generating unit i in the time period t is obtained; Δ t is the duration of each time period;
the method comprises the following steps that 1 indicates that a thermal power generating unit i is in a starting state in a time period t, and 0 indicates that the thermal power generating unit i is in a stopping state;
whether the thermal power generating unit i is subjected to starting operation in a time period t is judged, wherein 1 represents that the thermal power generating unit is started from shutdown operation, and 0 represents that the thermal power generating unit is not subjected to starting operation;
and (4) continuously stopping the thermal power generating unit i in the time period t.
6. The multi-target day-ahead optimization scheduling method of the multi-power-supply power system as claimed in claim 4, wherein the method comprises the following steps: the safety objective function is the confidence level that wind, water, and load shedding do not occur for the system as determined by the effective spare capacity of the system, as follows:
in the formula (I), the compound is shown in the specification,
for the total effective positive rotation of the system in the time period t as standby
The probability of the system being under-loaded,
for the total effective negative rotation of the system in the time period t to be standby
The probability of the system generating wind or water abandon.
7. The multi-target day-ahead optimization scheduling method of the multi-power-supply power system as claimed in claim 4, wherein the method comprises the following steps: the renewable energy consumption level objective function is represented by the sum of the abandoned wind electric quantity and the abandoned water electric quantity in the scheduling duration, and the more the abandoned wind and the abandoned water are, the weaker the consumption capability of the renewable energy is;
wherein the content of the first and second substances,
curtailment wind Power, N, for wind farm w occurring at time period tWIs the total number of the wind farm,
water power curtailment for hydropower station h occurring at time period t, NhTo the total number of hydro-power plants, Δ t is the duration of each time period.
8. The multi-target day-ahead optimization scheduling method of the multi-power-supply power system as claimed in claim 2, wherein: the constraint conditions comprise the self operation constraint conditions of the generator set, the operation constraint conditions of the system and the safety constraint conditions of the power network; the generator set operation constraints comprise thermal power unit operation constraints, hydroelectric power unit operation constraints, wind power unit operation constraints and pumped storage power station operation constraints; the power system level operation constraints comprise power balance constraints and standby capacity constraints; the power grid security constraints comprise network static security constraints and N-1 security check constraints.
9. The multi-target day-ahead optimization scheduling method of the multi-power-supply power system as claimed in claim 1, wherein the method comprises the following steps: in step 3, the solving method is as follows:
converting the multi-objective optimization model into a single-objective optimization model by adopting a linear weighting method, wherein the method comprises the following steps:
minF＝min(w1f1+w2f2+w3f3) (5)
in the formula, w1、w2、w3Is a weighting coefficient;
and solving the single-target optimization model by adopting a particle swarm optimization algorithm to obtain a day-ahead optimization scheduling strategy.
10. A multi-target day-ahead optimization scheduling device for a multi-power-supply power system is characterized in that: the method comprises the following steps:
the acquisition module is used for acquiring the system operation mode, the load prediction data and the renewable energy power generation prediction data within a set time range;
the modeling module is used for establishing a multi-target day-ahead optimization scheduling model of the multi-power-supply power system according to the system operation mode, the load prediction data and the renewable energy power generation prediction data;
the calculation module is used for solving the multi-target day-ahead optimization scheduling model of the multi-power-supply power system by adopting a particle swarm optimization algorithm;
and the output module is used for generating a day-ahead scheduling strategy according to the solving result.
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| CN111327079A (en) * | 2020-04-07 | 2020-06-23 | 广东电网有限责任公司电力科学研究院 | Power system with power flow router and scheduling method thereof |
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