CN110098623B - Prosumer unit control method based on intelligent load - Google Patents

Prosumer unit control method based on intelligent load Download PDF

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CN110098623B
CN110098623B CN201910354010.8A CN201910354010A CN110098623B CN 110098623 B CN110098623 B CN 110098623B CN 201910354010 A CN201910354010 A CN 201910354010A CN 110098623 B CN110098623 B CN 110098623B
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power
load
prosumer
unit
intelligent
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CN110098623A (en
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王颖
马刚
王加澍
郑梅
许洁
吴薛红
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Nanjing Zhijingrong New Energy Technology Co.,Ltd.
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Nanjing Normal University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • 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
    • H02J3/383—
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • 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
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

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  • Power Engineering (AREA)
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Abstract

The invention discloses a Prosumer unit control method based on intelligent load, wherein the Prosumer unit comprises a family key load, a family photovoltaic and an electric automobile family charging pile which are connected in parallel on a bus, and the method comprises the following steps: (1) selecting a plurality of non-critical loads capable of bearing preset voltage deviation, and connecting each non-critical load in series with a power spring to form an intelligent load which is connected to a bus in parallel; (2) constructing a power model of the Prosumer unit; (3) monitoring the variation of voltage amplitude and phase on a bus, and issuing a power regulation instruction for regulating active power and reactive power to a power spring in an intelligent load; (4) constructing an objective function with constraint conditions of real-time power balance of the Prosumer unit and an optimization target of minimum daily load fluctuation of the power of the Prosumer unit, and solving by adopting an NSGA-II algorithm so as to decompose a power regulation instruction issued to an intelligent load; (5) the non-critical load of the smart load executes the resolved power adjustment instruction. The invention solves the problems of uncontrollable power of a Prosumer unit and large daily load fluctuation.

Description

Prosumer unit control method based on intelligent load
Technical Field
The invention relates to a power system control technology, in particular to a Prosumer unit control method based on an intelligent load.
Background
With the development of new renewable energy and the continuous reduction of reserves of traditional energy sources such as coal, the fuel structure of the world is changed greatly. The household photovoltaic has the advantages of low cost of land, less power transmission loss, low power transmission and transformation investment and the like. In the future, China will further promote the grid connection of distributed photovoltaic power generation and promote the roof photovoltaic engineering of residents. Meanwhile, as one of new energy representatives, new energy automobiles become the trend of the future automobile industry due to the characteristics of cleanness, environmental protection and the like, and the research on the electric automobile charging technology also becomes a hot spot at home and abroad.
With the popularization of household photovoltaic and electric vehicles, household electricity is no longer a traditional simple electric energy consumer, but also an electric energy producer, and such users are defined as prosumers. The occurrence of Prosumer changes the traditional power utilization structure, and factors such as power quality and the like can influence the stable operation of a power grid when the residual power is connected to the grid. Therefore, it is necessary to research a reasonable power load matching manner inside the Prosumer to reduce the adverse effect of the distributed energy and improve the power consumption economy of the Prosumer system. The current research on the family Prosumer needs further discussion on the following two aspects:
1. at present, the condition that a household photovoltaic and an electric automobile are simultaneously connected into a household is not fully considered in modeling of a Prosumer unit, and most of existing researches adopt an energy storage unit to participate in energy management, so that the user economy is not high, and the effect of reducing load fluctuation in the household unit cannot be achieved. In addition, the problem of power circulation in the Prosumer is not solved, and the active/reactive circulation cannot be effectively controlled.
2. At present, the research on the household photovoltaic power generation grid-connected technology is deeper, but domestic household photovoltaic adopts a mode of all internet access at present, and the principle of local consumption cannot be comprehensively considered. In addition, the problem that the household photovoltaic supply is larger than the demand is solved by adopting a mode that an energy storage device is matched with the household photovoltaic inverter in a reactive power regulation mode, the cost is higher, and the photovoltaic utilization rate is lower.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a Prosumer unit control method based on intelligent load, which can flexibly control the power of a Prosumer unit and minimize the load fluctuation.
The technical scheme is as follows: the invention discloses a Prosurer unit control method based on intelligent load, wherein the applied Prosurer units comprise household key load, household photovoltaic and electric automobile household charging piles which are connected in parallel on a bus, and the method comprises the following steps:
(1) selecting a plurality of non-critical loads capable of bearing preset voltage deviation, and connecting each non-critical load in series with a power spring to form an intelligent load which is connected to a bus in parallel;
(2) constructing a power model of the Prosumer unit;
(3) transmitting an adjustment active power and reactive power adjustment instruction to a power spring in the intelligent load according to the requirement;
(4) constructing an objective function with constraint conditions of real-time power balance of the Prosumer unit and an optimization target of minimum daily load fluctuation of the power of the Prosumer unit, and solving by adopting an NSGA-II algorithm so as to decompose a power regulation instruction issued to an intelligent load;
(5) the non-critical load of the smart load executes the resolved power adjustment instruction.
Further, the power model constructed in the step (2) is specifically:
PP＝PH+PEV+PES-PPV
in the formula, PESFor the intelligent load power, PPIs Prosumer unit power, PHFor a household critical load, PEVCharging the electric vehicle household with a load, PPVThe photovoltaic power generation power is used for the users.
Further, the charging load of the electric automobile family based on the trip chain is modeled by a Monte Carlo simulation method, the key load of the family is modeled by a Weibull probability model, and the photovoltaic power generation power of the family is modeled by a three-point method practical model.
Further, the active and reactive control method for the intelligent load in the step (3) specifically comprises the following steps:
in need of reducing Pp(t), increasing QpWhen (t), V is adjusted according to the requirementESMagnitude and theta to [0 DEG, 90 DEG ]]The intelligent load equivalently sends out active power and absorbs idle power to realize Pp(t) reduction and Qp(t) is increased;
when P needs to be increasedp(t), increasing QpWhen (t), V is adjusted according to the requirementESMagnitude and theta to [90 DEG, 180 DEG ]]The intelligent load equivalently consumes the active power and the reactive power to realize Pp(t) elevation and Qp(t) is increased;
in need ofIncrease Pp(t), lowering QpWhen (t), V is adjusted according to the requirementESMagnitude and theta to [180 DEG, 270 DEG ]]The intelligent load equivalently consumes the power and sends out the idle power to realize Pp(t) elevation and Qp(t) decrease;
in need of reducing Pp(t), lowering QpWhen (t), V is adjusted according to the requirementESMagnitude and theta to [270 DEG, 360 DEG ]]The intelligent load equivalently sends out active power and reactive power to realize Pp(t) reduction and Qp(t) is decreased.
Wherein, Pp(t) represents the real-time active power of the Prosumer unit; qp(t) represents the real-time reactive power of the Prosumer unit; vESRepresents the output voltage amplitude of the power spring ES; theta represents the output voltage V of the power spring ESESAnd an output current IESThe phase difference of (1).
Further, the step (4) specifically comprises:
(4.1) constructing an objective function with the constraint conditions of real-time power balance of the Prosumer unit and the optimization target of minimum daily load fluctuation of the Prosumer unit power:
Figure GDA0002545081790000021
Figure GDA0002545081790000031
in the formula, Pp(t) represents the real-time load power of the Prosumer unit at time t,
Figure GDA0002545081790000032
represents the average daily load power of the Prosumer unit in one day, N represents the division into N time segments in one day, and deltaPES_max、ΔPES_minThe upper limit and the lower limit of the active power regulation capacity, delta Q, of the intelligent loadES_max、ΔQES_minUpper and lower limits of reactive power regulation capability for smart loads, PHFor a household critical load, PEVCharging the electric vehicle household with a load, PPVFor the homeUsing photovoltaic power generation, PESFor intelligent load power, the form (t) represents the power at the time t, Δ P, of the corresponding loadES(t) is an intelligent load real-time active power adjustment instruction;
and (4.2) solving the objective function by adopting an NSGA-II algorithm, thereby decomposing the power regulation instruction issued to the intelligent load.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: according to the invention, the power spring and the non-critical load in the Prosumer unit form the intelligent load, the non-critical load voltage and the output voltage of the power spring are controlled according to the bus requirement, the intelligent load sends out/absorbs corresponding active/reactive power, the problem of uncontrollable power of the Prosumer unit is solved, and the active/reactive power of the Prosumer load can be flexibly and continuously adjusted and controlled. On the basis of the Prosumer unit active/reactive control method, the NSGA-II algorithm is adopted, the problem of large daily load fluctuation of the Prosumer unit is solved, and the daily load fluctuation minimization control of the Prosumer unit is realized. Based on the above, the invention has positive effects on improving the permeability of renewable energy sources in the power system and improving the reliability and stability of power supply.
Drawings
FIG. 1 is a schematic flow diagram of one embodiment of the present invention;
FIG. 2 is a diagram of the hardware configuration of the present invention;
FIG. 3 is a schematic diagram of a smart load-based Prosumer unit power composition model in accordance with the present invention;
FIG. 4 is a flowchart of the method for predicting the household charging load of the electric vehicle based on the Monte Carlo simulation method according to the present invention;
FIG. 5 is a phasor diagram of the intelligent load four-quadrant control method according to the present invention; comprises four subgraphs of (a), (b), (c) and (d);
FIG. 6 is a flowchart of a Prosumer unit daily load fluctuation minimization control method according to the present invention.
Detailed Description
The embodiment provides a method for controlling a Prosumer unit based on a smart load, as shown in fig. 1, including:
(1) a plurality of non-critical loads capable of bearing preset voltage deviation are selected, and each non-critical load is connected with a power spring in series to form an intelligent load which is connected to a bus in parallel.
The specific hardware composition is shown in fig. 2, the Prosumer unit comprises a household key load, a household photovoltaic charging pile, an electric automobile household charging pile and the like which are connected in parallel on a bus, the non-key load specifically refers to a household load such as a household water heater and a lighting system, and the power spring comprises a first-stage converter A and a second-stage converter B.
(2) And constructing a power model of the Prosumer unit.
The constructed power model is shown in fig. 3, and specifically includes:
PP＝PH+PEV+PES-PPV(1)
in the formula, PESFor the intelligent load power, PPIs Prosumer unit power, PHFor a household critical load, PEVCharging the electric vehicle household with a load, PPVThe photovoltaic power generation power is used for the users; . In the power model, the consumed power is positive and the generated power is negative. If P isPThe value is positive, which indicates that the power consumption in the Prosumer unit is greater than the power generated by the household photovoltaic, and the power distribution network is required to supply power to the Prosumer unit; if P isPA negative value of (a) indicates that the power produced by the photovoltaic is greater than the power consumed by the Prosumer unit, which can then supply power to the distribution grid.
For the family key load PHAnd modeling by using a Weibull probability density function. The weibull function estimates hourly, daily, weekly, and yearly data categories using maximum likelihood, resulting in a total of 24 × 7 × 12 to 2016 distributions. The probability density function X of the weibull random variables is defined as:
Figure GDA0002545081790000041
where k >0 is referred to as a shape parameter and λ >0 is referred to as a scale parameter. The integral of the weibull random variable W is of the form:
Figure GDA0002545081790000042
in particular, for the electric automobile household charging load PEVBy starting charging time t for the electric automobilestartAnd predicting the household charging load of the electric automobile by modeling the daily driving mileage s. Wherein, the electric automobile starts to charge for time tstartThe approximation fits a normal distribution with the probability density function as follows:
Figure GDA0002545081790000043
wherein musMu is the expectation of the probability density functions＝17.6；σsIs the standard deviation, σs＝3.4。
The daily mileage s is basically in accordance with the normal distribution, and the probability density function is as follows:
Figure GDA0002545081790000051
for charging load PEVThe charging load of the electric vehicle within one day is predicted by the monte carlo simulation method, as shown in fig. 4.
Photovoltaic power generation P for a userPVA three-point method practical model is adopted to predict local solar radiation firstly, and then a photovoltaic output model is obtained through sunlight intensity conversion efficiency. Because the instantaneous radiation intensity of the two points of the solar rise and the solar fall is zero, the instantaneous radiation intensity can be defined as the intersection point of a parabola and a real number axis, and a parabola can be obtained through the three points as long as the peak value of solar irradiance is known, and the parabola is a practical model of solar radiation. Specifically, let parabola y be ax2+ bx + c, and two points of sunrise and sunset be (x)1,0)、(x2,0). Assuming the peak of the solar intensity is 12 points in time, the vertex (x) of the parabola is corresponding tomax，ymax). When these three points are known, the corresponding model can be obtained from equation (6):
Figure GDA0002545081790000052
the two points of the sun rise and the sun fall can be obtained by a calculation formula of the sun altitude angle:
sinα＝sinηsin+cosηcossinω (7)
wherein alpha is the solar altitude; eta is the local geographical latitude, the south latitude takes a negative value, and the north latitude takes a positive value; solar declination angle 23.45 ° (360 ° (284+ N)/365), N being the number of days from denier; ω is the solar hour angle at that time. When the altitude of the sun is 0 ° at sunrise and sunset, α in (9) is made 0 °, and the obtained product is obtained
cosωa＝-tanηtan (8)
Daily rise angle omegar＝-ωa(ii) a Sunset time angle omegas＝ωaThe solar hour angle corresponds to one hour every 15 °, so that the sunrise and sunset time can be obtained. The peak value is solved through an ASHRAE model, and the expression is as follows:
I＝(C+sinα)Aexp(-B/sinα) (9)
Ib＝Aexp(-B/sinα) (10)
Id＝CIb(11)
wherein A is the apparent solar radiation at zero atmospheric mass; b is the extinction coefficient of the atmosphere; c is a scattering radiation coefficient; i is the total solar radiation value on the horizontal plane; i isbThe value of the direct radiation of the horizontal plane is; i isdIs the horizontal plane scattered radiation value.
(3) And issuing active and reactive power regulation instructions for regulating the intelligent load to a power spring in the intelligent load according to the requirements.
Specifically, a phasor diagram for the Prosumer unit intelligent load active/reactive control is shown in fig. 5. Wherein, VSIs the magnitude of the power supply voltage; vSLRepresenting a voltage magnitude of the smart load; vNCL' and ISL' non-critical load voltage and current before the power spring is started, respectively;
Figure GDA0002545081790000061
is a non-critical load impedance angle; theta is the phase of the modulation signal of the converter (i.e. V lead I)SLThe phase angle of (d); v can be decomposed intoSLCoaxial component VPAnd with ISLPerpendicular component VQWherein V isPCorresponding to the component of V which plays a role in controlling the load successfully; vQCorresponding to the component of V that acts as a load reactive control. The active and reactive control method for the intelligent load specifically comprises the following steps:
in need of reducing Pp(t), increasing QpWhen (t), V is adjusted according to the requirementESMagnitude and theta to [0 DEG, 90 DEG ]]The intelligent load equivalently sends out active power and absorbs idle power to realize Pp(t) reduction and Qp(t) is increased;
when P needs to be increasedp(t), increasing QpWhen (t), V is adjusted according to the requirementESMagnitude and theta to [90 DEG, 180 DEG ]]The intelligent load equivalently consumes the active power and the reactive power to realize Pp(t) elevation and Qp(t) is increased;
when P needs to be increasedp(t), lowering QpWhen (t), V is adjusted according to the requirementESMagnitude and theta to [180 DEG, 270 DEG ]]The intelligent load equivalently consumes the power and sends out the idle power to realize Pp(t) elevation and Qp(t) decrease;
in need of reducing Pp(t), lowering QpWhen (t), V is adjusted according to the requirementESMagnitude and theta to [270 DEG, 360 DEG ]]The intelligent load equivalently sends out active power and reactive power to realize Pp(t) reduction and Qp(t) is decreased.
Wherein, Pp(t) represents the real-time active power of the Prosumer unit; qpAnd (t) represents the real-time reactive power of the Prosumer unit. Calculating V to be adjusted according to the formula (12) and the formula (13)ESAnd theta is adjusted to emit required active or reactive power or to absorb the required change of active or reactive power.
Figure GDA0002545081790000062
Figure GDA0002545081790000063
Wherein, Δ PES(t)、ΔQES(t) real-time active/reactive power adjustment instructions for the intelligent load are respectively provided; zNCLIs the resistance value of NCL.
When active and reactive power control is realized, the first-stage converter A is responsible for converting alternating current of a bus into stable direct current voltage and is controlled by using a traditional unit power factor voltage-stabilizing control loop; the second-stage converter B is responsible for sending out a specified alternating voltage, and the amplitude and the phase of the alternating voltage are changed, so that the active power and the reactive power of the intelligent load can be effectively controlled, and the active power/the reactive power of the Prosumer unit can be controlled.
(4) And constructing an objective function with the constraint conditions of real-time power balance of the Prosumer unit and the optimization target of minimum daily load fluctuation of the Prosumer unit, and solving by adopting an NSGA-II algorithm so as to decompose a power regulation instruction issued to the intelligent load.
The method specifically comprises the following steps:
(4.1) constructing an objective function with the constraint conditions of real-time power balance of the Prosumer unit and the optimization target of minimum daily load fluctuation of the Prosumer unit power:
Figure GDA0002545081790000071
Figure GDA0002545081790000072
in the formula, Pp(t) represents the real-time load power of the Prosumer unit at time t,
Figure GDA0002545081790000073
represents the average daily load power of the Prosumer unit in one day, N represents the division into N time segments in one day, and deltaPES_max、ΔPES_minActive power for smart loadsUpper and lower limits of regulation ability, Δ QES_max、ΔQES_minUpper and lower limits of reactive power regulation capability for smart loads, PHFor a household critical load, PEVCharging the electric vehicle household with a load, PPVFor photovoltaic power generation, PESFor intelligent load power, the form (t) represents the power at the time t, Δ P, of the corresponding loadES(t) an intelligent load real-time active power adjustment instruction;
and (4.2) solving the objective function by adopting an NSGA-II algorithm, thereby decomposing the power regulation instruction issued to the intelligent load.
As shown in fig. 6, the solving method specifically includes:
① random generation of an initial population P0The population size is N;
secondly, calculating the current population and solving each objective function value;
③ fast non-dominated sorting by calculating the dominated number n of each individual ppAnd the set S of solutions governed by the individualpThese two parameters.
④ calculating the degree of congestion nd: sorting the individuals of the grade according to an objective function f, and recording the fmaxMaximum value of individual objective function values f, fminIs the minimum value of the individual objective function values f; congestion degree 1 for two sorted boundariesdAnd NdSetting the value to be infinity. Calculating nd：
nd＝nd+(f(t+1)-f(t-1))/(fmax-fmin) (16)
Where f (i +1) is the value of the objective function one bit after the individual's ranking.
⑤ sorting by Elite Retention strategy by first sorting the parent population CtAnd progeny population DtSynthetic population Rt(ii) a From population R according to the following rulestGenerating a new parent population C t+1① placing the whole population into the parent population C according to the Pareto grade from low to hight+1Until a certain layer of individuals in the layer can not be all put into the parent population Ct+1② the individual layers are arranged according to the crowdedness degree,sequentially putting into parent population Ct+1Until the parent population Ct+1And (6) filling.
Sixthly, simulating binary crossing:
Figure GDA0002545081790000081
wherein:
Figure GDA0002545081790000082
variation of the polynomial:
x1j(t)＝x1j(t)+Δj(19)
wherein:
Figure GDA0002545081790000083
and 0. ltoreq. uj≤1。
Selecting operation by using a binary system tournament method: A. randomly selecting k (k < N) individuals from the N individuals, wherein the value of k is small, the efficiency is high (the running time is saved), but the value is not small, and is generally N/2 (rounded); B. selecting the individual with the best fitness value from the fitness values of the individuals to enter a next generation population; C. step A, B is repeated until new N individuals are obtained.
(5) The non-critical load of the smart load executes the resolved power adjustment instruction.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (2)

1. A Prosumer unit control method based on intelligent load is disclosed, wherein the Prosumer unit comprises a family key load, a family photovoltaic and electric automobile family charging pile which are connected in parallel on a bus, and the method is characterized by comprising the following steps:
(1) selecting a plurality of non-critical loads capable of bearing preset voltage deviation, and connecting each non-critical load in series with a power spring to form an intelligent load which is connected to a bus in parallel;
(2) constructing a power model of the Prosumer unit:
PP＝PH+PEV+PES-PPV
in the formula, PESFor the intelligent load power, PPIs Prosumer unit power, PHFor a household critical load, PEVCharging the electric vehicle household with a load, PPVThe photovoltaic power generation power is used for the users;
(3) the method comprises the following steps of issuing a power regulation instruction for regulating active power and reactive power to a power spring in an intelligent load according to requirements, and specifically comprises the following steps:
in need of reducing Ppp(t), increasing QppWhen (t), V is adjusted according to the requirementESMagnitude and theta to [0 DEG, 90 DEG ]]The intelligent load equivalently sends out active power and absorbs idle power to realize Ppp(t) reduction and Qpp(t) is increased;
when P needs to be increasedpp(t), increasing QppWhen (t), V is adjusted according to the requirementESMagnitude and theta to [90 DEG, 180 DEG ]]The intelligent load equivalently consumes the active power and the reactive power to realize Ppp(t) elevation and Qpp(t) is increased;
when P needs to be increasedpp(t), lowering QppWhen (t), V is adjusted according to the requirementESMagnitude and theta to [180 DEG, 270 DEG ]]The intelligent load equivalently consumes the power and sends out the idle power to realize Ppp(t) elevation and Qpp(t) decrease;
in need of reducing Ppp(t), lowering QppWhen (t), V is adjusted according to the requirementESMagnitude and theta to [270 DEG, 360 DEG ]]The intelligent load equivalently sends out active power and reactive power to realize Ppp(t) reduction and Qpp(t) decrease;
wherein, Ppp(t) represents the total real-time active power of the non-smart load in the Prosumer unit; qpp(t) Total real-time reactive Power representing the non-Smart load in the Prosumer UnitThe non-intelligent load PP comprises a family key load, an electric automobile family charging load and household photovoltaic power generation;
4) constructing an objective function with constraint conditions of real-time power balance of the Prosumer unit and an optimization target of minimum daily load fluctuation of the power of the Prosumer unit, and solving by adopting an NSGA-II algorithm so as to decompose a power regulation instruction issued to an intelligent load; wherein the objective function is:
Figure FDA0002547427520000011
Figure FDA0002547427520000012
in the formula, Pp(t) represents the real-time load power of the Prosumer unit at time t,
Figure FDA0002547427520000013
represents the average daily load power of the Prosumer unit in one day, N represents the division into N time segments in one day, and deltaPES_max、ΔPES_minThe upper limit and the lower limit of the active power regulation capacity, delta Q, of the intelligent loadES_max、ΔQES_minThe upper limit and the lower limit of the reactive power regulation capacity of the intelligent load are represented as (t) which corresponds to the power of the load at the t moment, and delta PES(t)、ΔQES(t) real-time active and reactive power adjustment instructions for the intelligent load are respectively provided;
(5) the non-critical load of the smart load executes the resolved power adjustment instruction.
2. The smart load-based Prosumer unit control method of claim 1, characterized in that: the method comprises the steps of modeling household charging loads of the electric automobile based on a trip chain by a Monte Carlo simulation method, modeling household key loads by a Weibull probability model, and modeling household photovoltaic power generation power by a three-point method practical model.
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