CN114937990A - Method, system, device and storage medium for determining reserve capacity of power system - Google Patents
Method, system, device and storage medium for determining reserve capacity of power system Download PDFInfo
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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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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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
- H02J3/381—Dispersed generators
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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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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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
- 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]
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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- 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
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- 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
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention belongs to the field of power automation, and discloses a method, a system, equipment and a storage medium for determining reserve capacity of a power system, wherein the method comprises the following steps: acquiring new energy output prediction data, thermal power unit output data and load prediction data of a power system; according to the new energy output prediction data, the thermal power generating unit output data and the load prediction data of the power system, solving a preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction to obtain the power system reserve capacity considering the uncertainty of the new energy output prediction; and outputting the reserve capacity of the power system considering the uncertainty of the new energy output prediction. The influence of the uncertainty of the output prediction of the new energy on the reserve capacity of the power system is fully considered, the consistency of the calculated reserve capacity of the power system and the actual reserve capacity of the power system is effectively improved, the phenomena of wind and light abandoning are effectively reduced, the consumption capacity of clean energy is improved, and the operation cost of the power system is reduced.
Description
Technical Field
The invention belongs to the field of power automation, and relates to a method, a system, equipment and a storage medium for determining reserve capacity of a power system.
Background
The operation scheduling of the power system is crucial to the safe, reliable and economic operation of the power system. In the scheduling process of the power system, more available power generation capacity than the predicted load must be configured to ensure that the power system can keep balance between power generation and load in both a normal state and a disturbance state, the extra capacity is called spare capacity, the spare capacity is an important resource for ensuring the reliability of the power system, and optimizing the spare configuration is a key for considering both the operation reliability and the economy of the power system.
However, with the continuous improvement of the permeability of new energy, the power generation uncertainty of the power system is increased, a large number of conventional generator sets are replaced, so that the spare capacity of the system is insufficient, and with the increasingly serious phenomena of wind and light abandonment, the influence of the uncertainty of the new energy on the power system is increased, the existing spare capacity determination method is continuously adopted, so that the spare capacity is easily insufficient or more, and the reliable and economic operation of the power system is further influenced.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned disadvantages of the prior art and providing a method, system, device and storage medium for determining a reserve capacity of a power system.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
in a first aspect of the present invention, a method for determining a reserve capacity of a power system includes:
acquiring new energy output prediction data, thermal power unit output data and load prediction data of a power system;
according to the new energy output prediction data, the thermal power generating unit output data and the load prediction data of the power system, solving a preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction to obtain the power system reserve capacity considering the uncertainty of the new energy output prediction;
and outputting the reserve capacity of the power system considering the uncertainty of the new energy output prediction.
Optionally, the model for determining the reserve capacity of the power system in consideration of the uncertainty of the new energy output prediction takes the minimum total operating cost of the power system as an optimization target, and takes a power balance constraint, a unit operating constraint, an energy storage constraint, a demand response constraint and a line power flow constraint as constraint conditions.
Optionally, the objective function of the power system reserve capacity determination model considering the uncertainty of the new energy output prediction is as follows:
wherein, F g (t) is the operating cost of the thermal power generating unit; f ess (t) energy storage cost; f w (t) wind power operating cost; f pv (t) photovoltaic operating costs; f dr (t) demand response operating costs; u is equal to U w +U pv ：
Wherein p is w (t) is the uncertain power of the wind power in a time period t;
the power prediction value of the wind power in the time period t is obtained;
the fluctuation amplitude of the power deviation power predicted value of the wind power in the time period t is obtained; p is a radical of pv (t) is the uncertain power of the photovoltaic during time period t;
the power prediction value of the photovoltaic in the time period t is obtained;
the fluctuation amplitude of the power deviation power predicted value of the photovoltaic at the time t is obtained; w, P and T are respectively the collection of wind power, photovoltaic and scheduling time periods, and represent the number of elements in the collection; delta. for the preparation of a coating w (t) is the uncertainty of wind power in time period t, gamma w For wind power uncertainty, δ pv (t) is the uncertainty of the photovoltaic in time period t, Γ pv Is the photovoltaic uncertainty.
Optionally, the unit operation constraint includes a unit power upper and lower limit constraint, a unit reserve capacity upper and lower limit constraint, a climbing constraint, a start-stop state constraint and a start-stop time constraint.
Optionally, when the preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction is solved, the start-stop time constraint is linearized by using a large M method.
Optionally, the power balance constraint is:
wherein p is g (t) is the power of the thermal power generating unit, p d (t) discharge power for stored energy, p c (t) charging power for energy storage, p load (t) predicted power for load, p dr (t) is the demand response power.
Optionally, when the preset electric power system reserve capacity determination model considering the uncertainty of the new energy output prediction is solved, an upward fluctuation maximum value and a downward fluctuation maximum value of the new energy output are obtained, and according to the upward fluctuation maximum value and the downward fluctuation maximum value of the new energy output, the power balance constraint is converted into a dual constraint through a dual theory.
In a second aspect of the present invention, a power system reserve capacity determination system includes:
the data acquisition module is used for acquiring new energy output prediction data, thermal power unit output data and load prediction data of the power system;
the model solving module is used for solving a preset electric power system reserve capacity determination model considering the uncertainty of the output prediction of the new energy according to the new energy output prediction data, the thermal power generating unit output data and the load prediction data of the electric power system to obtain the electric power system reserve capacity considering the uncertainty of the output prediction of the new energy;
and the data output module is used for outputting the reserve capacity of the power system considering the uncertainty of the new energy output prediction.
In a third aspect of the present invention, a computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the power system reserve capacity determination method when executing the computer program.
In a fourth aspect of the present invention, a computer-readable storage medium stores a computer program which, when executed by a processor, implements the steps of the above-described power system reserve capacity determination method.
Compared with the prior art, the invention has the following beneficial effects:
the method for determining the reserve capacity of the power system obtains the reserve capacity of the power system considering the uncertainty of the new energy output prediction by utilizing the new energy output prediction data, the thermal power unit output data and the load prediction data of the power system and by utilizing a preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction, fully considers the influence of the uncertainty of the new energy output prediction on the reserve capacity of the power system, fully utilizes the reserve capacity resources of a generator set, energy storage, demand response and the like in the power system, further effectively improves the consistency of the calculated reserve capacity of the power system and the actual reserve capacity of the power system, further ensures the reliable and economic operation of the power system, effectively reduces the occurrence of the phenomena of wind and light abandon and improves the capacity of clean energy, the operation cost is reduced.
Drawings
FIG. 1 is a flow chart of a method for determining a reserve capacity of a power system according to an embodiment of the invention;
FIG. 2 is a system wiring diagram of an IEEE RTS-79 test system according to an embodiment of the present invention;
FIG. 3 is a graph of predicted output of a wind turbine generator according to an embodiment of the present invention;
FIG. 4 is a graph of a predicted output of a photovoltaic unit according to an embodiment of the present invention;
fig. 5 is a diagram illustrating a simulation result of the reserve capacity of the power system in consideration of uncertainty of the new energy output prediction according to the embodiment of the present invention;
FIG. 6 is a diagram illustrating a simulation result of reserve capacity of an electrical power system without considering uncertainty of new energy contribution prediction according to an embodiment of the present invention;
fig. 7 is a block diagram of a power system spare capacity determination system according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As introduced in the background art, as the new energy permeability is continuously improved, the power generation uncertainty of the power system increases, a large number of conventional generator sets are replaced, so that the spare capacity of the system is insufficient, and as the phenomena of wind abandon and light abandon become more serious, the influence of the new energy uncertainty on the power system is increased, and the existing spare capacity determination method is easy to cause the spare capacity to be insufficient or more, so that the reliable and economic operation of the power system is influenced.
In order to improve the above problem, an embodiment of the present invention provides a method for determining a reserve capacity of a power system, including: acquiring new energy output prediction data, thermal power unit output data and load prediction data of a power system; according to the new energy output prediction data, the thermal power generating unit output data and the load prediction data of the power system, solving a preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction to obtain the power system reserve capacity considering the uncertainty of the new energy output prediction; and outputting the reserve capacity of the power system in consideration of the uncertainty of the new energy output prediction. The influence of the uncertainty of the output prediction of the new energy on the reserve capacity of the power system is fully considered, the consistency of the calculated reserve capacity of the power system and the actual reserve capacity of the power system is effectively improved, the phenomena of wind and light abandoning are effectively reduced, the consumption capacity of clean energy is improved, and the operation cost of the power system is reduced. The above scheme is explained in detail below.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, in an embodiment of the present invention, a method for determining a reserve capacity of a power system is provided, which aims at minimizing a total cost, utilizes reserve capacity resources such as a generator set, energy storage, demand response, and the like in the power system, considers uncertainty of new energy output, and establishes a double-layer robust reserve capacity optimization decision model based on uncertainty, that is, a reserve capacity determination model of the power system.
Specifically, the method for determining the reserve capacity of the power system comprises the following steps:
s1: and acquiring new energy output prediction data, thermal power unit output data and load prediction data of the power system.
S2: and solving a preset electric power system reserve capacity determination model considering the uncertainty of the output prediction of the new energy according to the new energy output prediction data, the output data of the thermal power generating unit and the load prediction data of the electric power system to obtain the electric power system reserve capacity considering the uncertainty of the output prediction of the new energy.
S3: and outputting the reserve capacity of the power system considering the uncertainty of the new energy output prediction.
Optionally, the new energy output prediction data, the thermal power generating unit output data and the load prediction data of the power system may be obtained directly from a scheduling system of the power system.
Specifically, the method for determining the reserve capacity of the power system obtains the reserve capacity of the power system considering the uncertainty of the new energy output prediction by utilizing the new energy output prediction data, the thermal power generating unit output data and the load prediction data of the power system and by utilizing a preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction, fully considers the influence of the uncertainty of the new energy output prediction on the reserve capacity of the power system, fully utilizes the reserve capacity resources of a generator set, energy storage, demand response and the like in the power system, further effectively improves the consistency of the calculated reserve capacity of the power system and the actual reserve capacity of the power system, further ensures the reliable and economic operation of the power system, and effectively reduces the phenomena of wind and light abandon, the consumption capacity of clean energy is improved, and the operation cost is reduced.
In one possible implementation, the power system reserve capacity determination model considering the uncertainty of the new energy output prediction takes the minimum total operating cost of the power system as an optimization target and takes power balance constraints, unit operating constraints, energy storage constraints, demand response constraints and line flow constraints as constraints.
Optionally, the objective function of the power system reserve capacity determination model considering the uncertainty of the new energy output prediction is as follows:
wherein, F g (t) is the operating cost of the thermal power generating unit; f ess (t) energy storage cost; f w (t) wind power operating cost; f pv (t) photovoltaic operating costs; f dr (t) demand response operating costs; u is equal to U w +U pv ：
Wherein p is w (t) is the uncertain power of the wind power in a time period t;
the power prediction value of the wind power in the time period t is obtained;
the fluctuation amplitude of the power deviation power predicted value of the wind power in the time period t is obtained; p is a radical of pv (t) is the uncertain power of the photovoltaic during time period t;
the power prediction value of the photovoltaic in the time period t is obtained;
the fluctuation amplitude of the power deviation power predicted value of the photovoltaic at the time t is obtained; w, P and T are respectively the collection of wind power, photovoltaic and scheduling time period, and represent the number of elements in the collection; delta w (t) is the uncertainty of wind power in time period t, gamma w For wind power uncertainty, δ pv (t) is the uncertainty of the photovoltaic over time period t, Γ pv Is the photovoltaic uncertainty.
Specifically, in order to fully consider the influence of the uncertainty of the new energy output prediction on the reserve capacity of the power system, the uncertainty of the new energy output prediction is modeled first. Taking wind power as an example, in order to depict the influence of wind power uncertainty, various possible occurrence conditions of wind power output are placed in a bounded set in advance, the set is defined as a wind power uncertain set, and the optimal strategy of standby optimization has adaptability to random scenes in the uncertain set. If the standby strategy can adapt to the condition with the most serious fluctuation in the set, the standby strategy also has adaptability to other fluctuation conditions in the set. The box type uncertain set is widely applied due to the advantages of strong operability, easy acquisition of fluctuation boundary information and the like. However, the backup optimization strategy is too conservative due to the fact that the box type uncertain set has strong robustness, and in actual engineering, according to the central limit theorem, the probability that all wind power plants reach the situation with the most serious fluctuation at the same moment is very low. Therefore, the uncertainty of the wind power output is described by adopting a polyhedron uncertain set model. The method specifically comprises the following steps:
step 1-1, constructing a new energy output prediction interval model, wherein the prediction interval is as follows:
wherein p is w (t) is the uncertain power of the wind power in the time period t;
the predicted value of the wind power in the time period t is obtained;
the fluctuation amplitude of the wind power deviation predicted value at the time t is obtained; p is a radical of formula pv (t) is the uncertain power of the photovoltaic during time period t;
the predicted value of the photovoltaic power in the time period t is obtained;
the fluctuation amplitude of the output deviation predicted value of the photovoltaic at the time t is obtained; w, P and T are the collection of wind farm, photovoltaic and scheduling period, respectively, the number of elements in the collection.
Step 1-2, constructing a new energy output prediction uncertainty model. Taking wind power as an example, the method specifically comprises the following steps:
wherein, delta w (t) representing the uncertainty degree of the wind power in a time period t; gamma-shaped w For wind power uncertaintyDegree of definite, when gamma w And when the output is 0, the actual output of the wind power is the same as the predicted value, and the model becomes a deterministic model. Wind power uncertainty gamma w The total deviation amount of the wind power uncertain power value and the predicted value is limited essentially. With gamma w The value is continuously increased, the more serious wind power random fluctuation condition can be considered by the standby strategy, at the moment, due to the increase of the standby configuration requirement, more surplus needs to be reserved for standby of the unit, the operation efficiency of the unit is reduced, and the standby power generation cost is increased.
Similarly, a photovoltaic new energy output prediction uncertainty model is established, and the method specifically comprises the following steps:
wherein, delta pv (t) characterizing the uncertainty of the photovoltaic over time period t; gamma-shaped pv For photovoltaic uncertainty, when Γ pv When the output is 0, the actual output of the photovoltaic is the same as the predicted value, and the model becomes a deterministic model. With gamma pv The value is continuously increased, and more serious photovoltaic random fluctuation conditions can be considered by the standby strategy.
Furthermore, a power system reserve capacity determination model considering the uncertainty of the new energy output prediction is constructed based on the uncertainty model of the new energy output prediction, and the specific steps are as follows:
step 2-1, constructing a power system reserve capacity determination model objective function by taking the minimum total cost as a target:
the objective function is a two-layer model, which means that the scene with the lowest cost in all scenes of all new energy uncertainty sets is searched as the target of the final optimization. Wherein:
wherein alpha is 1 、α 2 And alpha 3 Are all fuel cost coefficients; p is a radical of formula g (t) is the unit output; i all right angle g (t) the unit is in a starting and stopping state, is a variable of 0-1, is in a starting state when the value is 1, and is in a stopping state when the value is 0;
and
respectively a startup cost coefficient and a shutdown cost coefficient; u. of g (t) is the starting action state of the unit at the time t, is a variable of 0-1, when the value is 1, the action of starting the unit at the time t is shown, otherwise, the action is 0; v. of g (t) is the shutdown action state of the unit at the time t, is a variable of 0-1, when the value is 1, the action of shutdown of the unit occurs at the time t, otherwise, the action is 0;
and
an upper standby cost coefficient and a lower standby cost coefficient of the unit are respectively;
and
the upper spare capacity and the lower spare capacity of the unit are respectively.
F ess (t)＝c d p d (t)+c c p c (t)
Wherein, c d And c c Respectively representing the discharge cost coefficient and the charge cost coefficient of the stored energy; p is a radical of formula d (t) and p c And (t) respectively representing the discharge power and the charging power of the stored energy.
F w (t)＝c w p w (t)
Wherein, c w The wind power running cost coefficient; p is a radical of w And (t) wind power output. Optionally, because wind power belongs to clean energy, in order to encourage wind power integration and improve wind power consumption, the value is usually zero.
F pv (t)＝c pv p pv (t)
Wherein, c pv Is a photovoltaic operating cost coefficient; p is a radical of pv (t) is the photovoltaic contribution. Alternatively, since photovoltaic is a clean energy source, its value is usually zero to encourage photovoltaic grid connection.
F dr (t)＝c dr p dr (t)
Wherein, c dr A demand response cost factor; p is a radical of dr (t) is the demand response power.
And 2-2, analyzing the reserve capacity of the power system to determine constraint conditions to be considered in the model optimization process, wherein the constraint conditions comprise power balance constraint, unit operation constraint, energy storage constraint, demand response constraint and line power flow constraint.
The power balance constraint means that in the system operation process, the system meets power balance at any time, and the input and output powers are equal at any time, and the specific constraint is as follows: p is a radical of formula g (t)+p w (t)+p pv (t)+p d (t)-p c (t)＝p load (t)-p dr (t) wherein p load (t) predicting power for the load. Since the actual contribution of the new energy in the power balance constraint takes into account the uncertainty of the contribution uncertainty, the uncertainty consideration will have an impact on the result compared to a box model that takes into account the uncertainty of the contribution of the new energy.
The unit operation constraint means that the unit is mainly constrained by inequalities such as upper and lower power limit constraint, climbing constraint and the like in the operation process. System fortuneThe unit power in the traveling process needs to meet the unit power upper and lower limit constraints:
wherein the content of the first and second substances,
and
respectively the upper limit and the lower limit of the unit power. The reserve capacity of the unit needs to meet the constraint of the upper limit and the lower limit of the reserve capacity of the unit:
wherein the content of the first and second substances,
the upper limit of the spare upper capacity of the unit;
and the upper limit of the spare capacity under the unit. The unit operation in-process needs to satisfy the climbing restraint:
wherein the content of the first and second substances,
and
respectively an upward climbing rate and a downward climbing rate. The start-stop state of the unit needs to satisfy the start-stop state constraint:
the unit operation process needs to satisfy the start-stop time constraint:
wherein, t on And t off Respectively representing the start-up time and the stop time of the unit, T on And T off Respectively representing the upper limit of the allowable starting time and the upper limit of the allowable stopping time of the unit.
And energy storage constraint, namely the charging and discharging power of each time step meets the upper and lower limit constraint at the time required in the operation process of the energy storage system, and the residual capacity of the energy storage at the time t is related to the time t-1. Specifically, the energy storage capacity needs to satisfy the energy storage capacity upper and lower limit constraints:
wherein, E ess (t) is the energy storage capacity;
and E ess And (t) is respectively an upper energy storage capacity limit and a lower energy storage capacity limit. The computational expression of the energy storage capacity is as follows:
wherein eta is c And η d Respectively charge efficiency and discharge efficiency. The energy storage charging and discharging power needs to meet the energy storage charging and discharging power upper and lower limit constraints:
wherein the content of the first and second substances,
and
respectively, a charging power upper limit and a discharging power upper limit; mu.s c The charging state of the stored energy is a variable of 0-1, if the value is 1, the energy storage unit is charged, otherwise, the value is 0; mu.s d The discharge state of the stored energy is a 0-1 variable, if the value is 1, the energy storage unit is discharging, otherwise, the value is 0. Mu.s c And mu d The relationship of (1) is: mu.s c +μ d ≤1。
And (3) demand response constraint, namely in the system operation process, the demand response needs to meet the demand response upper limit constraint:
wherein the content of the first and second substances,
is the upper limit of demand response.
And (3) line power flow constraint:
wherein, F l Is the upper limit of the line power flow; pi il 、π wl 、π pl 、π sl And pi ql Are power transfer distribution coefficients.
Through the steps, the establishment of the power system reserve capacity determination model considering the uncertainty of the new energy output prediction is realized, but in the practical application process, due to the fact that nonlinear terms exist in the start-stop state constraint and the start-stop time constraint of the power system reserve capacity determination model, the model is difficult to solve directly. Therefore, in this embodiment, when a preset electric power system reserve capacity determination model considering uncertainty of new energy output prediction is solved, the large M method is adopted to perform linearization on the start-stop time constraint. The method specifically comprises the following steps:
first, the parameter form of the large M method is set:
y＝a·x a∈{0,1}x∈[0,M]
0≤x≤M
0≤y≤x·M
x+M(a-1)≤y≤x-M(a-1)
wherein a is a variable from 0 to 1, x is a continuous variable, y is a function value, and M is a positive number as large as possible.
And carrying out linear processing on the start-stop state constraint and the start-stop time constraint of the unit by using the large M method.
Order:
and (3) processing by using a large M method, wherein the start-stop state constraint and the start-stop time constraint can be converted into:
x a1 (t)-x a2 (t)-T on (i g (t-1)-i g (t))≥0
x b1 (t)-x b2 (t)-T off (i g (t)-i g (t-1))≥0
0≤x a1 (t)≤M·i g (t-1)
0≤x a2 (t)≤M·i g (t)
0≤x b1 (t)≤M·i g (t)
0≤x b2 (t)≤M·i g (t-1)
t on (t-1)+M(i g (t-1)-1)≤x a1 (t)≤t on (t-1)
-M(i g (t-1)-1)
t on (t-1)+M(i g (t)-1)≤x a2 (t)≤t on (t-1)
-M(i g (t)-1)
tt off (t-1)+M(i g (t)-1)≤x b1 (t)≤t off (t-1)
-M(i g (t)-1)
t off (t-1)+M(i g (t-1)-1)≤x b2 (t)≤t off (t-1)
-M(i g (t-1)-1)
meanwhile, the electric power system spare capacity determination model is a double-layer max-min robust model and is difficult to solve, so that the maximum value of upward fluctuation and the maximum value of downward fluctuation of new energy output can be obtained optionally, the worst condition processing is carried out on the constraint containing the new energy output through a dual theory according to the maximum value of the upward fluctuation and the maximum value of the downward fluctuation of the new energy output, and the double-layer max-min robust model is converted into a single-layer max model.
Specifically, since the new energy output is difficult to predict accurately, the new energy output may be expressed in the following form:
first, the wind power output is expressed in the form:
the photovoltaic contribution is then expressed in the form:
thus, the power balance constraint may be expressed in the form:
further conversion to the following form:
then, the constraint containing the new energy output is processed in the worst case, wherein the worst case is the case when the new energy output fluctuates upwards or downwards maximally respectively. The method comprises the following specific steps: when the worst condition is that the new energy output fluctuates upwards to the maximum, the sum of the output of each unit is greater than the required total load, namely:
wherein the content of the first and second substances,
and
respectively representing the upper limit of the fan output allowable error and the upper limit of the photovoltaic output allowable error, and being a decision variable larger than zero; Δ p w (t) and Δ p pv (t) respectively represents the lower limit of the fan output allowable error and the lower limit of the photovoltaic output allowable error, and is a decision variable less than zero.
With new decision variable mu 1 (t)And mu 2 (t) replacement of the optimization variable p w (t) and p pv (t), obtaining:
introducing a dual variable processing robustness problem, and converting into dual constraint:
similarly, when the worst condition is that the output of the renewable energy fluctuates maximally downwards, the output sum of each unit is less than the required total load:
with new decision variable mu 3 (t) and μ 4 (t) Replacing the optimization variable p w (t) and p pv (t), obtaining:
introducing a dual variable processing robustness problem, and converting into dual constraint:
through the linearization processing and conversion based on the dual theory, the electric power system reserve capacity determination model is finally converted into a mixed integer programming model, and can be solved through a CPLEX solver and other similar mixed integer programming model solvers, so that the electric power system reserve capacity considering the uncertainty of the new energy output prediction is obtained.
In one possible implementation, the power system reserve capacity determination method is verified using an IEEE RTS-79 test system, which contains 24 nodes, 38 lines, and 32 gensets, and the system wiring diagram is shown in fig. 2. Wind power and photovoltaic sets with the installed capacity of 1000MW are respectively connected to nodes 14 and 17, and the predicted output is respectively shown in the graph 3 and the graph 4; the startup and shutdown cost of the unit is 0.014$/kWh, and the upper and lower standby costs are 1.5 $/MW; the demand response cost is 10 $/MW.
In this embodiment, the uncertainty of the new energy output uncertainty is 0.9, and the final simulation result of the power system reserve capacity determination model considering the uncertainty of the new energy output prediction is shown in fig. 5, and the final cost is $ 26614. If the uncertainty of the new energy output prediction is not considered, the new energy output is only considered as one interval, and the box model of the uncertainty of the new energy output is considered, the specific simulation result is shown in fig. 6, and the total cost is 29678 dollars, so that the method can effectively reduce the cost.
It can be known through comparison that if only the interval model of new energy output is considered, because the established model is the robust optimization model, the result of the robust model is the optimal result under the condition of meeting all the operating conditions, but because the probability of certain operating conditions is very small, if the optimization result of the model is required to meet all the scenes, the final optimization result inevitably has the condition of too high conservation, and from the result, the too high conservation can improve the final total operating cost. After the uncertainty of the output prediction of the new energy is considered, the uncertainty essentially limits the total deviation amount of the actual output and the predicted output of the new energy, and the uncertainty of the output of the new energy neglects some scenes with small occurrence probability, so that the conservatism of the optimization result of the robust model is reduced, the total cost of the model is further reduced, and the balance of the robustness and the economy of the system is ensured.
The scenario in which the demand response is considered and the scenario in which the demand response is not considered are set as scenario 1 and scenario 2, respectively, which are used in this embodiment, and the result pair is shown in table 1.
Table 1 comparative tables for consideration and non-consideration of demand response results
| Scene | Total cost ($) |
| Scene 1 | 26614 |
| Scene 2 | 29301 |
In general, when the demand-side response standby is considered, the total cost is reduced, because the access of the demand-side response further increases the total standby capacity of the system, and the unit does not need to leave more margin as the standby in the operation process. The increase of the total spare capacity relieves the spare pressure of the unit, improves the operation efficiency of the unit, reduces the total power generation cost of the system, and therefore improves the flexibility and the economical efficiency of the system operation.
Referring to table 2, a comparison of results for different uncertainty values is shown.
TABLE 2 COMPARATIVE TABLE FOR CONCENTRATION AND UNCENTRATION OF DEMAND RESPONSE RESULTS
Therefore, as the uncertainty value of the new energy output prediction is increased, the final total cost of the system is also increased. This is because the uncertainty of the new energy contribution prediction essentially limits the total deviation of the actual value of the new energy uncertainty power from the predicted value. When the uncertainty is 0, the actual output power of the new energy is equal to the predicted output power, the model becomes a deterministic model, and with the continuous increase of the uncertainty value, some more serious random fluctuation conditions of the output of the new energy can be considered by a standby strategy of the system, and at the moment, due to the enhancement of the fluctuation of the output of the new energy, the standby configuration requirement of the system can also be increased. An increase in reserve capacity can result in a reduction in unit operating efficiency, increasing the reserve power generation cost of the system, and ultimately, an increase in overall cost.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details not disclosed in the device embodiments, reference is made to the method embodiments of the invention.
Referring to fig. 7, in a further embodiment of the present invention, a power system spare capacity determining system is provided, which can be used to implement the above power system spare capacity determining method. The system comprises a data acquisition module, a power system and a power system, wherein the data acquisition module is used for acquiring new energy output prediction data, thermal power unit output data and load prediction data of the power system; the model solving module is used for solving a preset electric power system reserve capacity determining model considering the uncertainty of the new energy output prediction according to the new energy output prediction data, the thermal power generating unit output data and the load prediction data of the electric power system to obtain the electric power system reserve capacity considering the uncertainty of the new energy output prediction; and the data output module is used for outputting the reserve capacity of the power system in consideration of the uncertainty of the new energy output prediction.
In one possible implementation, the power system reserve capacity determination model considering the uncertainty of the new energy output prediction takes the minimum total operating cost of the power system as an optimization target and takes power balance constraints, unit operating constraints, energy storage constraints, demand response constraints and line flow constraints as constraints.
In one possible embodiment, the objective function of the power system reserve capacity determination model considering the uncertainty of the new energy contribution prediction is as follows:
wherein, F g (t) is the operating cost of the thermal power generating unit; f ess (t) is the cost of energy storage; f w (t) wind power operating cost; f pv (t) photovoltaic operating costs; f dr (t) demand response operating costs; u is equal to U w +U pv ：
Wherein p is w (t) is the uncertain power of the wind power in the time period t;
the power prediction value of the wind power in the time period t is obtained;
the fluctuation amplitude of the power deviation power predicted value of the wind power in the time period t is obtained; p is a radical of pv (t) is the uncertain power of the photovoltaic during time period t;
the power prediction value of the photovoltaic in the time period t is obtained;
the fluctuation amplitude of the power deviation power predicted value of the photovoltaic at the time t is obtained; w, P and T are respectively the collection of wind power, photovoltaic and scheduling time period, and represent the number of elements in the collection; delta w (t) is the uncertainty of wind power in time period t, gamma w For uncertainty of wind power, delta pv (t) is the uncertainty of the photovoltaic over time period t, Γ pv Is the photovoltaic uncertainty.
In a possible implementation manner, the unit operation constraints include unit power upper and lower limit constraints, unit reserve capacity upper and lower limit constraints, climbing constraints, start-stop state constraints, and start-stop time constraints.
In a possible implementation manner, when the preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction is solved, the start-stop time constraint is linearized by using a large M method.
In one possible embodiment, the power balance constraint is:
wherein p is g (t) is the power of the thermal power generating unit, p d (t) discharge power for stored energy, p c (t) charging power for energy storage, p load (t) predicted power for load, p dr (t) is the demand response power.
In a possible implementation manner, when the preset electric power system reserve capacity determination model considering the uncertainty of the new energy output prediction is solved, the upward fluctuation maximum value and the downward fluctuation maximum value of the new energy output are obtained, and the power balance constraint is converted into the dual constraint through the dual theory according to the upward fluctuation maximum value and the downward fluctuation maximum value of the new energy output.
All relevant contents of each step related to the embodiment of the method for determining the reserve capacity of the power system may be introduced to the functional description of the functional module corresponding to the system for determining the reserve capacity of the power system in the embodiment of the present invention, and are not described herein again.
The division of the modules in the embodiments of the present invention is schematic, and only one logical function division is provided, and in actual implementation, there may be another division manner, and in addition, each functional module in each embodiment of the present invention may be integrated in one processor, or may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor according to the embodiment of the invention can be used for the operation of the power system spare capacity determination method.
In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium may be loaded and executed by a processor to perform the corresponding steps of the method for determining a reserve capacity of a power system in the above-described embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 power system reserve capacity determination method, comprising:
acquiring new energy output prediction data, thermal power unit output data and load prediction data of a power system;
according to the new energy output prediction data of the power system, the thermal power generating unit output data and the load prediction data, solving a preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction to obtain the power system reserve capacity considering the uncertainty of the new energy output prediction;
and outputting the reserve capacity of the power system considering the uncertainty of the new energy output prediction.
2. The method of claim 1, wherein the model for determining reserve capacity of the power system considering uncertainty of the new energy output prediction is optimized for minimizing total cost of operation of the power system, and constrained by a power balance constraint, a unit operation constraint, an energy storage constraint, a demand response constraint, and a line flow constraint.
3. The power system reserve capacity determination method of claim 2, wherein the objective function of the power system reserve capacity determination model that accounts for new energy contribution prediction uncertainty is:
wherein, F g (t) is the operating cost of the thermal power generating unit; f ess (t) energy storage cost; f w (t) wind power operating cost; f pv (t) photovoltaic operating costs; f dr (t) demand response operating costs; u is equal to U w +U pv ：
Wherein p is w (t) is the uncertain power of the wind power in the time period t;
the power prediction value of the wind power in the time period t is obtained;
the fluctuation amplitude of the power deviation power predicted value of the wind power in a time period t is obtained; p is a radical of pv (t) is the uncertainty power of the photovoltaic at time period t;
a power predicted value of the photovoltaic power in a time period t;
the fluctuation amplitude of the power deviation power predicted value of the photovoltaic at the time t is obtained; w, P and T are respectively the collection of wind power, photovoltaic and scheduling time periods, and represent the number of elements in the collection; delta. for the preparation of a coating w (t) is the uncertainty of wind power in time period t, gamma w For uncertainty of wind power, delta pv (t) is the uncertainty of the photovoltaic over time period t, Γ pv Is the photovoltaic uncertainty.
4. The method for determining the reserve capacity of the electric power system according to claim 3, wherein the unit operation constraints comprise unit power upper and lower limit constraints, unit reserve capacity upper and lower limit constraints, ramp constraints, start-stop state constraints and start-stop time constraints.
5. The method for determining the reserve capacity of the power system according to claim 4, wherein a large M method is adopted to linearize the start-stop time constraint when solving the preset power system reserve capacity determination model considering the uncertainty of the new energy output prediction.
6. The power system reserve capacity determination method of claim 3, wherein the power balance constraint is:
wherein p is g (t) is the power of the thermal power generating unit, p d (t) discharge power for stored energy, p c (t) charging power for energy storage, p load (t) predicted power for load, p dr (t) is the demand response power.
7. The method according to claim 6, wherein the solving of the predetermined power system reserve capacity determination model taking into account uncertainty of the new energy output prediction obtains an upward fluctuation maximum value and a downward fluctuation maximum value of the new energy output, and the power balance constraint is converted into a dual constraint by a dual theory according to the upward fluctuation maximum value and the downward fluctuation maximum value of the new energy output.
8. A power system reserve capacity determination system, comprising:
the data acquisition module is used for acquiring new energy output prediction data, thermal power unit output data and load prediction data of the power system;
the model solving module is used for solving a preset electric power system reserve capacity determination model considering the uncertainty of the output prediction of the new energy according to the new energy output prediction data, the thermal power generating unit output data and the load prediction data of the electric power system to obtain the electric power system reserve capacity considering the uncertainty of the output prediction of the new energy;
and the data output module is used for outputting the reserve capacity of the power system considering the uncertainty of the new energy output prediction.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor when executing the computer program realizes the steps of the power system spare capacity determination method according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the power system backup capacity determination method according to any one of claims 1 to 7.
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