CN112909932A - Optimization method and terminal of peak-shaving type virtual power plant - Google Patents

Optimization method and terminal of peak-shaving type virtual power plant Download PDF

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CN112909932A
CN112909932A CN202110132428.1A CN202110132428A CN112909932A CN 112909932 A CN112909932 A CN 112909932A CN 202110132428 A CN202110132428 A CN 202110132428A CN 112909932 A CN112909932 A CN 112909932A
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power
thermal power
period
electric heating
generating unit
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CN112909932B (en
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王宏
田石刚
魏晓强
张轶平
刘扬
祁峰
徐洪涛
李兵
贾清泉
王珺
孙玲玲
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State Grid Corp of China SGCC
Yanshan University
State Grid Heilongjiang Electric Power Co Ltd
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State Grid Corp of China SGCC
Yanshan University
State Grid Heilongjiang Electric Power Co Ltd
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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
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • 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
    • 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]
    • 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
    • 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
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention is suitable for the technical field of electric power, and provides an optimization method of a peak shaving type virtual power plant, which comprises the following steps: constructing a target function by taking the minimum wind power delivery cost of the regional power grid in a preset time period as a target; constructing a constraint condition of an objective function; the constraint conditions comprise virtual power plant power balance constraint, thermal power balance constraint, electric heating equipment heat storage constraint and thermal power unit operation constraint; solving the objective function to obtain virtual power plant regulation power, thermal power unit output power and wind power output power corresponding to the minimum wind power delivery cost; and optimizing the peak-adjusting type virtual power plant according to the regulation and control power of the virtual power plant, the output power of the thermal power generating unit and the wind power output power. The method can solve the problems that the peak regulation capability of the existing peak regulation type virtual power plant is not flexible enough and the peak regulation cost is high.

Description

Optimization method and terminal of peak-shaving type virtual power plant
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to an optimization method and a terminal of a peak shaving type virtual power plant.
Background
With the construction and production of a plurality of new energy transmission channels in China, the power delivery capacity of the wind power enrichment area is further improved, so that the area becomes a typical wind power delivery type regional power grid.
However, wind power has the characteristics of randomness and intermittence, so that the peak-valley difference of the outgoing power of the regional power grid is large, the fluctuation is strong, and the risk is brought to the safe and stable operation of the receiving-end power grid. At present, the peak regulation is carried out on a regional power grid by utilizing a virtual power plant technology, and the method is a better solution. However, the inventor of the present application finds that the existing peak-shaving type virtual power plant does not consider the influence of the electric heating load, the peak shaving capability is not flexible enough, and the peak shaving cost is high.
Disclosure of Invention
In view of this, the embodiment of the invention provides an optimization method and a terminal for a peak shaving type virtual power plant, so as to solve the problems that the existing peak shaving type virtual power plant is not flexible in peak shaving capability and high in peak shaving cost.
The first aspect of the embodiment of the invention provides an optimization method for a peak shaving type virtual power plant, which comprises the following steps:
constructing a target function by taking the minimum wind power delivery cost of the regional power grid in a preset time period as a target;
constructing a constraint condition of an objective function; the constraint conditions comprise virtual power plant power balance constraint, thermal power balance constraint, electric heating equipment heat storage constraint and thermal power unit operation constraint;
solving the objective function to obtain virtual power plant regulation power, thermal power unit output power and wind power output power corresponding to the minimum wind power delivery cost;
and optimizing the peak-adjusting type virtual power plant according to the regulation and control power of the virtual power plant, the output power of the thermal power generating unit and the wind power output power.
A second aspect of an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the optimization method for a peak shaver type virtual power plant as described above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the method, the target function is constructed by taking the minimum wind power delivery cost of the regional power grid in a preset time period as a target, the target function is solved by combining the power balance constraint of the virtual power plant, the thermal power balance constraint, the heat storage capacity constraint of the electric heating equipment and the operation constraint of the thermal power unit, and the virtual power plant regulation power, the thermal power unit output power and the wind power output power corresponding to the minimum wind power delivery cost can be obtained to optimize the peak-regulation type virtual power plant, so that the peak regulation cost is effectively reduced, the peak regulation capability of the electric heating load participation system is fully exerted, and the method has the advantages of flexibility in regulation and control. The method can solve the problems that the peak regulation capability of the existing peak regulation type virtual power plant is not flexible enough and the peak regulation cost is high.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an implementation of an optimization method of a peak shaving type virtual power plant provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a daily prediction curve of wind power and load provided by an embodiment of the invention;
FIG. 3 is a schematic diagram of an electric heating load power curve provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of a thermal power depth peak shaving regulation result provided by an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
A first aspect of an embodiment of the present invention provides an optimization method for a peak shaving type virtual power plant, as shown in fig. 1, the method specifically includes the following steps:
and S101, constructing an objective function by taking the minimum wind power delivery cost of the regional power grid in a preset time period as a target.
Optionally, as a specific implementation manner of the optimization method for the peak shaving type virtual power plant provided by the embodiment of the present invention, the constructing of the objective function with the minimum wind power delivery cost of the regional power grid in a preset time period as a target includes:
Figure BDA0002925851200000031
wherein C (t) is the wind power delivery cost of the power grid in the t period region, C1(t) polymerization cost of electric heating of virtual power plant at t period, C2.j(t) Peak shaving cost, N, of the jth thermal power generating Unit in the t time periodGIs the number of thermal power generating units in a virtual power plant, C3And (t) is the wind curtailment penalty cost of the t period.
Optionally, as a specific implementation manner of the optimization method for the peak shaving type virtual power plant provided by the embodiment of the present invention, the calculation method for the electric heating aggregation cost of the virtual power plant in the t period includes:
Figure BDA0002925851200000032
in the formula, cEWThe unit electric heating installation capacity subsidy cost for the electric heating users, N is the number of the electric heating users, SEW.iCapacity is installed for the electric heating of the ith electric heating user.
Optionally, as a specific implementation manner of the optimization method for the peak shaving type virtual power plant provided by the embodiment of the present invention, the method for calculating the peak shaving cost of the jth thermal power generating unit in the t period is as follows:
Figure BDA0002925851200000033
in the formula, CP.j(t) is the coal consumption cost of the jth thermal power generating unit in the t period,Cq.j(t) the deep peak shaving loss cost generated by the jth thermal power generating unit in the t period, Cw.j(t) electric quantity loss cost generated by jth thermal power generating unit in t period, Cr.j(t) the oil charging cost P generated by the jth thermal power generating unit in the t time periodG.j(t) is the output power P of the jth thermal power generating unit in the t periodG.a.jMinimum output power P when oil is put into the jth thermal power generating unitG.b.jIs the minimum output power P when the jth thermal power generating unit is not charged with oilG.min.jIs the minimum output power, P, of the jth thermal power generating unitG.max.jAnd the maximum output power of the jth thermal power generating unit.
In the examples of the present invention, CP.j(t)、Cq.j(t)、Cw.j(t)、Cr.j(t)、PG.j(t) can be calculated by the following formulas, respectively:
Figure BDA0002925851200000041
in the formula, aj、bjAnd cjAnd the coal consumption coefficient is the coal consumption coefficient of the jth thermal power generating unit.
Cq.j(t)＝ζCG.U/2Nf[PG.j(t)]
In the formula, zeta is the influence coefficient of the operation of the thermal power generating unit, CG.UPurchase cost of thermal power generating units, Nf[PG.j(t)]And the loss cycle number of the rotor of the jth thermal power generating unit in the t period is shown.
Cw.j(t)＝kGΔPW(t)
In the formula, kGFor the power unit pole-marking on-line electricity price, delta PWAnd (t) wind power grid-connected electric quantity increased due to thermal power deep peak shaving.
Cr.j(t)＝coilQoil
In the formula, coilFor unit cost of oil charge, QoilThe fuel consumption of the thermal power generating unit during deep peak regulation and stable combustion is obtained.
Figure BDA0002925851200000042
In the formula, PG.n.j(t)、PG.e.j(t) and PG.u.jAnd (t) the output power of the jth thermal power unit in a normal operation state, a no-oil-feeding peak-shaving state and an oil-feeding peak-shaving state respectively.
Optionally, as a specific implementation manner of the optimization method for the peak shaving type virtual power plant provided by the embodiment of the present invention, the calculation method of the wind curtailment cost in the t period is as follows:
Figure BDA0002925851200000051
in the formula, kWaIn order to make the wind abandon penalty factor,
Figure BDA0002925851200000052
predicting wind power output, P, for the day ahead of time tWAnd (t) is the wind power output power in the period of t.
In the embodiment of the invention, the wind power output power in the time period t can be calculated according to the wind speed, and the specific method comprises the following steps:
Figure BDA0002925851200000053
in the formula, is PW,NRated power of a single fan, v (t) is wind speed in t period, vcFor cutting into the wind speed, vfTo cut out wind speed, vNIs the rated wind speed.
S102, constructing a constraint condition of an objective function; the constraint conditions comprise virtual power plant power balance constraint, thermal power balance constraint, electric heating equipment heat storage constraint and thermal power unit operation constraint.
Optionally, as a specific implementation manner of the peak shaving type virtual power plant optimization method provided by the embodiment of the present invention, the power balance constraint of the virtual power plant is as follows:
Figure BDA0002925851200000054
in the formula, PL(t) load power of regional power grid in t period, PS(t) delivery power of regional grid in time period t, PVPP(t) virtual power plant regulation and control power P at t time periodG.j(t) is the output power P of the jth thermal power generating unit in the t periodW(t) wind power output, N, for a period of tGThe number of thermal power generating units in the virtual power plant is T, and the dispatching period of the power grid is T.
In the embodiment of the invention, the regulated power P of the virtual power plant is regulated and controlled in the t periodVPP(t) needs to be defined, specifically:
(1) firstly, the electric heating load power before the price excitation of the ith electric heating user is obtained
Figure BDA0002925851200000055
And determining the electric heating load power P of the ith electric heating user in the t time periodEW.i(t)：
Figure BDA0002925851200000056
In the formula,. DELTA.PEW.iAnd (T) is the electric heating load power variation from the T' time period to the T time period of the ith user, and T is the power grid dispatching cycle.
(2) Electric heating load power P according to t time period of ith electric heating userEW.i(t) determining the regulated power P of the virtual power plant in the t periodVPP(t)：
Figure BDA0002925851200000061
In the formula, N is the number of electric heating users.
Optionally, as a specific implementation manner of the optimization method for the peak shaver type virtual power plant provided by the embodiment of the present invention, the thermal power balance constraint is as follows:
Figure BDA0002925851200000062
in the formula, Qh.i(t) the house heat load demand, η, of the ith electric heating user in the period of tWIs the heat storage loss rate etaCFor the heat dissipation efficiency of electric heating, PE.i(t) electric heating power of ith electric heating user in t time period, PEW.i(t) electric heating load power, Q, of the ith electric heating user in the t periodW.iAnd (T) is the electric heating heat storage capacity of the ith heating electric user in the T period, and T is the power grid dispatching cycle.
In the embodiment of the invention, the electric heating power P of the ith electric heating user in the t periodE.i(t) electric heating heat storage quantity QW.i(t) can be calculated by the following formula:
PE.i(t)＝PEW.i(t)ηE
Figure BDA0002925851200000063
Figure BDA0002925851200000064
in the formula:
Figure BDA0002925851200000065
for the heat storage power of the ith electric heating user in the period t,
Figure BDA0002925851200000066
the heat release power, eta, of the ith electric heating user in the period tWThe loss rate of the stored heat is high,
Figure BDA0002925851200000067
is the heat release loss rate, etaEThe efficiency ratio is shown.
Optionally, as a specific implementation manner of the optimization method for the peak shaving type virtual power plant provided by the embodiment of the present invention, the heat storage constraint of the electric heating device is as follows:
QW.min≤QW.i(t)≤QW.max
in the formula, QW.i(t) the electric heating heat storage capacity of the ith heating electric user in the t period, QW.minMinimum heat storage capacity, Q, for electric heating equipmentW.maxThe maximum heat storage capacity of the electric heating equipment.
Optionally, as a specific implementation manner of the optimization method for the peak shaving type virtual power plant provided by the embodiment of the present invention, the operation constraints of the thermal power unit include a limit of power output constraint of the thermal power unit, a deep peak shaving output constraint of the thermal power unit, and a climbing constraint of the thermal power unit. Wherein the content of the first and second substances,
the output limit constraint of the thermal power generating unit is as follows:
PG.min.j≤PG.j(t)≤PG.max.j
in the formula, PG.j(t) is the output power P of the jth thermal power generating unit in the t periodG.min.jIs the minimum output power, P, of the jth thermal power generating unitG.max.jAnd the maximum output power of the jth thermal power generating unit.
The deep peak regulation output constraint of the thermal power generating unit is as follows:
PG.j(t)≥PG.b.j
in the formula, PG.b.jAnd the minimum output power is the minimum output power of the jth thermal power generating unit when the oil is not put into the thermal power generating unit.
The climbing restraint of the thermal power generating unit is as follows:
γd.j≤PG.j(t+1)-PG.j(t)≤γu.j
in the formula, gammad.jIs the maximum downward climbing speed, gamma, of the jth thermal power generating unitu.jAnd the maximum upward climbing speed of the jth thermal power generating unit.
And S103, solving the objective function to obtain virtual power plant regulation power, thermal power unit output power and wind power output power corresponding to the minimum wind power delivery cost.
In the embodiment of the present invention, a particle swarm algorithm may be adopted to solve, and the specific process is as follows:
(1) setting the time interval of two time intervals as 1h, and setting a scheduling period T as 8760;
(2) electric heating polymerization cost C of selected t-period virtual power plant1(t) peak regulation cost C of jth thermal power generating unit in t time period2.jWind curtailment penalty cost C of (t) and t periods3(t) as particles, the initial value of which is set to 0;
(3) setting the initial iteration number k as 1 and the maximum iteration number kmax＝500；
(4) According to step S101C1(t)、C2.j(t)、C3(t) calculating and limiting the numerical value of each particle so as to satisfy the constraint condition;
(5) update the velocity and position of the particle:
vk+1＝ω×vk+c1×r1×(pbestk-xk)+c2×r2×(gbestk-xk)
xk+1＝xk+vk+1
Figure BDA0002925851200000081
where k is the number of iterations, xkRepresenting the spatial position of the particle at the number of iterations k, vkRepresenting the speed of the particles when the iteration number is k, c1 and c2 are learning factors, the value of r is between 0 and 41、r2Is a random number uniformly distributed between (0, 1), omega is an inertia factor, pbestkFor the particle's own optimal solution at the kth iteration, gbestkThe global optimal solution of the particles in the k iteration is obtained;
(6) if k is>kmaxOutputting the calculation results of the virtual power plant regulation power, the thermal power unit output power and the wind power output power corresponding to the minimum wind power delivery cost; otherwise, let k be k +1, return to step (4).
And S104, optimizing the peak-adjusting virtual power plant according to the virtual power plant regulation power, the thermal power unit output power and the wind power output power.
Illustratively, the optimization method of the peak shaving type virtual power plant provided by the application is subjected to example analysis by taking a regional power grid in a certain area as an example.
The regional power grid comprises 6 thermal power generating units, relevant parameters of the thermal power generating units are shown in table 1, the installed wind power capacity is 500MW, and a typical winter day prediction curve of wind power and load prediction is shown in fig. 2. Selecting a thermal power generating unit with the capacity of 200MW as a peak shaving unit, wherein the compensation electrovalence of each stage of deep peak shaving is as follows: the compensation price in the non-oil-throwing stage is 200 yuan/MWh, and the compensation price in the oil-throwing stage is 500 yuan/MWh; the unit polymerization cost of the virtual power plant is 150 yuan/MWh.
TABLE 1 thermal power generating unit-related parameters
Figure BDA0002925851200000082
Figure BDA0002925851200000091
The power of the electric heating equipment in the region is aggregated into 3 typical electric heating user types, and specific parameters are shown in table 2.
TABLE 2 typical electric heating user parameters
Figure BDA0002925851200000092
Wind power, thermal power and load power of 8760 hours all year round are taken as basic data, a certain typical day in winter is extracted for analysis, the optimization method of the peak-shaving virtual power plant is adopted for solving, the optimal configuration result of the electric heating of the peak-shaving virtual power plant considering the wind power agreement delivery is obtained, as shown in table 3, and the operation cost of the virtual power plant under the configuration is shown in table 4.
TABLE 3 virtual plant electric heating optimization configuration results
Figure BDA0002925851200000093
TABLE 4 virtual plant operating cost results
Figure BDA0002925851200000094
As can be seen from tables 3 and 4, excessive electric heating configured in the virtual power plant increases the polymerization cost of the electric heating, and the overall economy is reduced; too little configuration will increase the thermal power depth peak shaving and wind abandonment costs. The electric heating polymerization cost, the thermal power deep peak regulation cost and the wind abandoning cost are comprehensively considered, and when the electric heating configuration capacity is 100MW, the economy of the peak regulation type virtual power plant for the wind power protocol delivery is optimal.
The electric heating load power curve after optimization by the peak shaving type virtual power plant optimization method is shown in fig. 3, and the thermal power deep peak shaving regulation result is shown in fig. 4.
As can be seen from fig. 3 and 4, after the electric heating load power is adjusted, the electric heating load power is transferred from the peak time to the valley time of the electric power, the peak-valley difference of the load curve is obviously reduced, and the improvement of the wind power delivery stability is facilitated. In 1-8 h, the regional power grid load is small, the wind power output is large, firstly, the electric heating is excited to store heat at the moment, the load power is improved, secondly, the output is reduced by deep peak regulation through the participation of a thermal power generating unit, and the surplus wind power is sent out by a digestion protocol. At 9-18 h, the regional power grid load is large and the wind power output is small, so the heat stored at night is released by electric heating at the moment, normal electricity is used for heating when the heat is still insufficient, the load power is reduced as much as possible, and then the thermal power unit participates in conventional peak regulation to reduce the output so as to absorb the surplus wind power after the agreement is sent out. In 19-24 hours, the regional power grid load is small, the wind power output is large, firstly, the electric heating is excited to store heat in advance while heating is performed normally, the load power is improved, secondly, the output is reduced by deep peak regulation through the participation of a thermal power generating unit, and the complementary effect is realized with surplus wind power. The result can show that the optimization method of the peak shaving type virtual power plant can effectively adjust the electric heating load power, improve the wind power consumption stability and reduce the wind abandoning rate.
According to the method, the target function is constructed by taking the minimum wind power delivery cost of the regional power grid in the preset time period as a target, the target function is solved by combining the power balance constraint, the thermal power balance constraint, the heat storage constraint of the electric heating equipment and the operation constraint of the thermal power unit, and the virtual power plant regulation and control power, the thermal power unit output power and the wind power output power corresponding to the minimum wind power delivery cost can be obtained to optimize the peak-regulation type virtual power plant, so that the peak regulation cost is effectively reduced, the peak regulation capability of the electric heating load participation system is fully exerted, the wind power consumption capability of the regional power grid and the stability of the wind power delivery power are improved, and the method has the advantages of flexibility, regulation and control. The method can solve the problems that the peak regulation capability of the existing peak regulation type virtual power plant is not flexible enough and the peak regulation cost is high.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present invention. As shown in fig. 5, the terminal 5 of this embodiment includes: a processor 50, a memory 51 and a computer program 52 stored in the memory 51 and executable on the processor 50. The processor 50, when executing the computer program 52, implements the steps in the above described embodiments of the optimization method of the peak shaver type virtual power plant, such as the steps S101 to S104 shown in fig. 1.
Illustratively, the computer program 52 may be divided into one or more modules/units, which are stored in the memory 51 and executed by the processor 50 to carry out the invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 52 in the terminal 5. For example, the computer program 52 may be divided into an objective function constructing module, a constraint constructing module, a solving module, and an optimizing module (module in the virtual device), and each module has the following specific functions:
and the objective function construction module is used for constructing an objective function by taking the minimum wind power delivery cost of the regional power grid in a preset time period as an objective.
The constraint condition construction module is used for constructing a constraint condition of the objective function; the constraint conditions comprise virtual power plant power balance constraint, thermal power balance constraint, electric heating equipment heat storage constraint and thermal power unit operation constraint.
And the solving module is used for solving the objective function to obtain the virtual power plant regulation power, the thermal power unit output power and the wind power output power corresponding to the minimum wind power delivery cost.
And the optimization module is used for optimizing the peak-regulating virtual power plant according to the virtual power plant regulating power, the thermal power generating unit output power and the wind power output power.
The terminal 5 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal may include, but is not limited to, a processor 50, a memory 51. It will be appreciated by those skilled in the art that fig. 5 is only an example of a terminal 5 and does not constitute a limitation of the terminal 5, and that it may comprise more or less components than those shown, or some components may be combined, or different components, e.g. the terminal may further comprise input output devices, network access devices, buses, etc.
The Processor 50 may be a Central Processing Unit (CPU), 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, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 51 may be an internal storage unit of the terminal 5, such as a hard disk or a memory of the terminal 5. The memory 51 may also be an external storage device of the terminal 5, such as a plug-in hard disk provided on the terminal 5, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 51 may also include both an internal storage unit of the terminal 5 and an external storage device. The memory 51 is used for storing computer programs and other programs and data required by the terminal. The memory 51 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims (10)

1. A method for optimizing a peak shaver type virtual power plant is characterized by comprising the following steps:
constructing a target function by taking the minimum wind power delivery cost of the regional power grid in a preset time period as a target;
constructing a constraint condition of the objective function; the constraint conditions comprise virtual power plant power balance constraint, thermal power balance constraint, electric heating equipment heat storage constraint and thermal power unit operation constraint;
solving the objective function to obtain virtual power plant regulation power, thermal power unit output power and wind power output power corresponding to the minimum wind power delivery cost;
and optimizing the peak-adjusting type virtual power plant according to the virtual power plant regulation and control power, the thermal power generating unit output power and the wind power output power.
2. The optimization method of the peak shaver type virtual power plant according to claim 1, wherein the constructing of the objective function with the minimum wind power delivery cost of the regional power grid in a preset time period as a target comprises:
Figure FDA0002925851190000011
wherein C (t) is the wind power delivery cost of the power grid in the t period region, C1(t) polymerization cost of electric heating of virtual power plant at t period, C2.j(t) Peak shaving cost, N, of the jth thermal power generating Unit in the t time periodGIs the number of thermal power generating units in a virtual power plant, C3And (t) is the wind curtailment penalty cost of the t period.
3. The optimization method of the peak shaving type virtual power plant according to claim 2, wherein the calculation method of the electric heating polymerization cost of the t-period virtual power plant comprises the following steps:
Figure FDA0002925851190000012
in the formula, cEWThe unit electric heating installation capacity subsidy cost for the electric heating users, N is the number of the electric heating users, SEW.iCapacity is installed for the electric heating of the ith electric heating user.
4. The optimization method of the peak shaving type virtual power plant according to claim 2, wherein the calculation method of the peak shaving cost of the jth thermal power generating unit in the t period is as follows:
Figure FDA0002925851190000021
in the formula, CP.j(t) the coal consumption cost of the jth thermal power generating unit in the t period, Cq.j(t) the deep peak shaving loss cost generated by the jth thermal power generating unit in the t period, Cw.j(t) electric quantity loss cost generated by jth thermal power generating unit in t period, Cr.j(t) the oil charging cost P generated by the jth thermal power generating unit in the t time periodG.j(t) is the output power P of the jth thermal power generating unit in the t periodG.a.jMinimum output power P when oil is put into the jth thermal power generating unitG.b.jIs the minimum output power P when the jth thermal power generating unit is not charged with oilG.min.jIs the minimum output power, P, of the jth thermal power generating unitG.max.jAnd the maximum output power of the jth thermal power generating unit.
5. The optimization method of the peak shaving type virtual power plant according to claim 2, wherein the wind curtailment penalty cost in the period t is calculated by:
Figure FDA0002925851190000022
in the formula, kWaIn order to make the wind abandon penalty factor,
Figure FDA0002925851190000023
predicting wind power output, P, for the day ahead of time tWAnd (t) is the wind power output power in the period of t.
6. The optimization method of a peaker virtual plant as claimed in claim 1, wherein the virtual plant power balance constraints are:
Figure FDA0002925851190000024
in the formula, PL(t) load power of regional power grid in t period, PS(t) delivery power of regional grid in time period t, PVPP(t) virtual power plant regulation and control power P at t time periodG.j(t) is the output power P of the jth thermal power generating unit in the t periodW(t) wind power output, N, for a period of tGThe number of thermal power generating units in the virtual power plant is T, and the dispatching period of the power grid is T.
7. The optimization method of a peaker virtual plant, according to claim 1, characterized in that said thermal power balance constraint is:
Figure FDA0002925851190000031
in the formula, Qh.i(t) the house heat load demand, η, of the ith electric heating user in the period of tWIs the heat storage loss rate etaCFor the heat dissipation efficiency of electric heating, PE.i(t) electric heating power of ith electric heating user in t time period, PEW.i(t) electric heating load power, Q, of the ith electric heating user in the t periodW.iAnd (T) is the electric heating heat storage capacity of the ith heating electric user in the T period, and T is the power grid dispatching cycle.
8. The optimization method of the peak shaving type virtual power plant according to claim 1, wherein the heat storage capacity constraint of the electric heating equipment is as follows:
QW.min≤QW.i(t)≤QW.max
in the formula, QW.i(t) the electric heating heat storage capacity of the ith heating electric user in the t period, QW.minMinimum heat storage capacity, Q, for electric heating equipmentW.maxThe maximum heat storage capacity of the electric heating equipment.
9. The optimization method for the peaker virtual power plant according to any one of claims 1 to 8, wherein the thermal power unit operation constraints comprise a thermal power unit output limit constraint, a thermal power unit depth peaker output constraint, and a thermal power unit ramp constraint; wherein the content of the first and second substances,
the output limit constraint of the thermal power generating unit is as follows:
PG.min.j≤PG.j(t)≤PG.max.j
in the formula, PG.j(t) is the output power P of the jth thermal power generating unit in the t periodG.min.jIs the minimum output power, P, of the jth thermal power generating unitG.max.jIs the largest of the jth thermal power generating unitOutputting power;
the deep peak regulation output constraint of the thermal power generating unit is as follows:
PG.j(t)≥PG.b.j
in the formula, PG.b.jThe minimum output power is the minimum output power of the jth thermal power generating unit when oil is not put into the jth thermal power generating unit;
the climbing restraint of the thermal power generating unit is as follows:
γd.j≤PG.j(t+1)-PG.j(t)≤γu.j
in the formula, gammad.jIs the maximum downward climbing speed, gamma, of the jth thermal power generating unitu.jAnd the maximum upward climbing speed of the jth thermal power generating unit.
10. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 9 when executing the computer program.
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