CN109687518B - Optimized scheduling method for household micro-grid system - Google Patents

Optimized scheduling method for household micro-grid system Download PDF

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CN109687518B
CN109687518B CN201811642474.0A CN201811642474A CN109687518B CN 109687518 B CN109687518 B CN 109687518B CN 201811642474 A CN201811642474 A CN 201811642474A CN 109687518 B CN109687518 B CN 109687518B
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CN109687518A (en
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周磊
董学育
朱建忠
葛鹏
张亚亚
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Nanjing Institute of Technology
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    • H02J3/383—
    • H02J3/386—
    • H02J3/387—
    • 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
    • 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
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Abstract

The invention discloses an optimal scheduling method for a household micro-grid system, belongs to the aspect of micro-grid optimal scheduling, and mainly aims to solve the problems that a large power grid in rural areas is far in power transmission line and large in power loss. The large power grid and the household micro-power grid are used for supplying power, heating and cooling in a coordinated mode, and the aims of minimum total cost and minimum pollution are achieved. The general idea is as follows: the solar energy wind-powered solar energy water heater is characterized in that a day is divided into a valley time period, a normal time period and a peak time period, each time period is divided into two parts with equal time length, the first half part of each time period is powered by wind energy and solar energy preferentially, and if the power is insufficient, a designated standby power supply is used for powering. And for the second half time, fuzzy self-adaptive particle swarm is adopted for optimal distribution according to the energy supply and load conditions in the first half time, so that an optimal solution is obtained. The design combines real-time scheduling and predictive scheduling to achieve the goals of least cost and least pollution.

Description

Optimized scheduling method for household micro-grid system
Technical Field
The invention relates to the field of micro-grids, in particular to an optimized dispatching method for a household micro-grid system.
Background
At present, the optimal scheduling of the micro-grid is realized by adopting a prediction and summary method, the output conditions of a fan and a solar cell in one day can be predicted by predicting the wind speed and the solar illumination in the next day, the load conditions in one day can be predicted by summarizing and summarizing the previous load, and therefore the scheduling is realized by adopting an intelligent algorithm, and the optimal scheme with the lowest economic and pollution costs is achieved. However, the prior art generally has the following disadvantages:
1. uncertainty exists in the prediction of illumination and wind speed, so errors can occur in the output condition on the basis, and scheduling is likely to cause that the load requirements cannot be completely met; for the load prediction, the prediction is inaccurate, the power supply is insufficient, the electric equipment cannot operate, and the generated redundant energy is wasted.
2. At present, a household distributed power supply mainly comprises a solar battery, and the type of the distributed power supply is single, so that the supplied electric quantity is small, and the power supply is unstable.
Disclosure of Invention
Aiming at the defects, the invention provides an energy management optimization scheme of a microgrid suitable for different time periods, wherein one day is divided into a valley time period, a normal time period and a peak time period, each time period is divided into two parts with equal time length, the first half part of each time period is powered by preferentially utilizing wind energy and solar energy, and if the power supply is insufficient, the designated standby power supply is used for powering. And for the second half time, fuzzy self-adaptive particle swarm is adopted for optimal distribution according to the energy supply and load conditions in the first half time, so that an optimal solution is obtained.
An optimized scheduling method for a household micro-grid system comprises the following steps:
the method comprises the following steps: the electricity price of 24h of a day is divided into valley periods: 21: 00-07: 00; ordinary time period: 07: 00-10: 00, 15: 00-18: 00; peak period: 10: 00-15: 00, 18: 00-21: 00;
step two: for the valley period: every five hours of the valley period was taken as a unit; in the first five hours of the valley period, the electric load is supplied with power by preferentially utilizing the electric quantity actually generated by solar energy and wind energy, cold and heat loads are supplied after the electric load is met, and a large power grid is used as a replacement energy source; if the output of the large power grid is required to meet the requirements of all loads in the first five hours, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm in the last five hours, and distributing the output of the gas turbine, the large power grid and the fuel cell so that the total output is the sum of the output of the large power grid in the first five hours and the energy capable of fully charging the storage battery; if the large power grid does not need to output power in the first five hours, all redundant energy is fed back to the storage battery in the valley period, the energy is fed back to the large power grid if the storage battery is full, and the large power grid is used for charging if the storage battery is not full;
for the usual period: 07: 00-10: 00; taking each half hour of the ordinary time period as a unit; in the first half hour of the ordinary time period, the electricity load is preferentially supplied with electricity by preferentially utilizing the electricity quantity actually generated by the solar energy and the wind energy, and the fuel cell is used as a substitute energy source; if the output of the fuel cell is required to meet the requirements of all loads within the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the next half hour, and distributing the output of the gas turbine, the large power grid and the fuel cell so that the total output is the sum of the output of the fuel cell within the previous half hour; if the fuel cell does not need to output power in the previous half hour, the redundant electric quantity is fed back to the large power grid in a usual time period;
for the peak period: 10: 00-15: 00; every two and a half hours of the peak period are taken as a unit; in the first two half hours of the peak time period, the electric load is preferentially supplied with power by preferentially utilizing the electric quantity actually generated by the solar energy and the wind energy, and the storage battery is used as a substitute energy; if the output of the storage battery is required to meet the requirements of all loads within the first two half hours, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the last two half hours, and distributing the output of the gas turbine, the large power grid, the fuel cell and the storage battery, so that the total output is the sum of the output of the storage battery within the first two half hours; if the storage battery does not need to output power within the first two half hours, the redundant electric quantity is fed back to the large power grid within the ordinary time period;
for the usual period: 15: 00-18: 00; taking each half hour of the ordinary time period as a unit; in the first half hour of the ordinary time period, the electricity load is preferentially supplied with electricity by preferentially utilizing the electricity quantity actually generated by the solar energy and the wind energy, and the fuel cell is used as a substitute energy source; if the output of the fuel cell is required to meet the requirements of all loads within the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the next half hour, and distributing the output time and the output size of the gas turbine, the large power grid and the fuel cell, wherein if the storage battery is not fully charged, the total output size of the optimal scheme is the sum of the output of the fuel cell within the previous half hour and the energy capable of fully charging the storage battery; if the fuel cell does not need to output power within the previous half hour, the redundant electric quantity is fed back to the storage battery within the ordinary time period, the storage battery is not fully charged, the large power grid is used for supplementing the redundant electric quantity, and the electric quantity is fed back to the large power grid if the storage battery is fully charged;
for the peak period: 18: 00-21: 00; taking each half hour of the peak period as a unit; in the first half hour of the peak period, the electricity load is preferentially supplied with electricity by preferentially utilizing the electricity actually generated by the solar energy and the wind energy, and the storage battery is used as a substitute energy source; if the output of the storage battery is required to meet the requirements of all loads within the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the next half hour, and distributing the output of the gas turbine, the large power grid, the fuel cell and the storage battery so that the total output is the sum of the output of the storage battery within the previous two half hours; if the storage battery does not need to output power in the previous half hour, the redundant electric quantity is fed back to the large power grid in a usual time period.
For the valley period, when the first five hours preferentially utilize solar energy and wind energy to generate electricity, if the electricity generated by the two is sufficient, the output voltage and the frequency are relatively stable, and when the amplitude of the output voltage exceeds +/-5 percent or the frequency fluctuates and exceeds +/-0.5 HZ, the large power grid is automatically switched to supply power;
for the peak time period, in the first half period, when the storage battery is used as a solar energy and wind energy alternative energy source, whether the storage battery can be used for charging is determined by the fluctuation of the output voltage and the frequency, and when the amplitude of the output voltage exceeds +/-5% or the fluctuation of the frequency exceeds +/-0.5 HZ, the storage battery is automatically switched to supply power;
for the ordinary time, in the first half period, when the fuel cell is used as a substitute energy source for solar energy and wind energy, whether the fuel cell is used for supplying power is still determined by the fluctuation of the output voltage and the frequency, and when the amplitude of the output voltage exceeds +/-5% or the frequency fluctuates and exceeds +/-0.5 HZ, the fuel cell is automatically switched to supply power.
The method for selecting the optimal scheduling scheme by adopting the fuzzy self-adaptive particle swarm algorithm specifically comprises the following steps:
(1) calculating the total cost of the operation of the micro-grid: including the fuel cost required by the power generation unit, the start-stop cost of the equipment and the cost of exchanging electric energy with the large power grid; the equation is as follows:
Figure GDA0002003759740000031
wherein, BGi(t) and Bsj(t) is the unit fuel cost of the distributed power supply and the energy storage equipment in the microgrid at the moment t, and the unit is gamma/(kWh), SGiAnd SsjThe start-stop cost of the distributed power supply and the energy storage equipment in the microgrid, BGrid(t) is the time of use price, PGrid(t) is the active power exchanged by the micro-grid and the large grid; in the equation, X is a state variable which comprises the active power of the power generation and energy storage equipment and the starting and stopping state of the power generation and energy storage equipment, the running state of the power generation and energy storage equipment is set to be 1, and the stopping state is set to be 0;
X＝[Pg,Ug]1×2nT；
Pg＝[PG,Ps]；
n＝Ns+Ng+1；
Ug＝{ui}1×n∈{0,1}；
where N is the total number of devices, NsIs the number of energy storage devices, NgIs the number of power generation equipment;
PGand PsIs the active power, U, of all power generating equipment and energy storage equipmentgThe state quantity of all the power generation equipment and the energy storage equipment in running or stopping is obtained; t represents the total operating time;
(2) the pollution emission target is as follows: comprising the emission of gas comprising CO from each power generation unit2、SO2And NO2：
Figure GDA0002003759740000041
Wherein E isGi(t)、Esj(t) and EGrid(t) the pollution discharge amount of power generation equipment, energy storage equipment and a large power grid in the microgrid is kg/(kW.h);
EGi(t)＝CO2Gi(t)+SO2Gi(t)+NO2Gi(t)；
EGrid(t)＝CO2Grid(t)+SO2Grid(t)+NO2Grid(t)；
ESj(t)＝CO2Sj(t)+SO2Sj(t)+NO2Sj(t)；
(3) constraint conditions are as follows:
power balance:
Figure GDA0002003759740000042
Figure GDA0002003759740000043
wherein, Pload(t) is the total electrical load at time t, Qload(t) is the total cold and heat load at time t;
active power constraints:
PGi,min(t)≤PGi(t)≤PGi,max(t)；
PSj,min(t)≤PSj(t)≤PSj,max(t)；
PGrid,min(t)≤PGrid(t)≤PGrid,max(t)；
wherein, PGi,min(t) and PGi,max(t) minimum active power and maximum active power of the power generation device i at time t, respectively; pSj,min(t) and PSj,max(t) the minimum active power and the maximum active power of the energy storage device j at the moment t, respectively; pGrid,min(t) and PGrid,max(t) the minimum active power and the maximum active power of the large power grid at the moment t respectively;
charging and discharging limitation of energy storage equipment:
Wees(t)＝(1-σ)Wees(t-1)+[Pcharge(t)ηch-Pdischarge(t)/ηdis]Δt；
Wees.min(t)≤Wees(t)≤Wees.max(t)；
Pcharge(t)≤Pcharge.max(t)；
Pdischarge(t)≤Pdischarge.max(t)；
wherein, Wees(t) is the electrical energy storage capacity over a period of time t; sigma is the self-discharge rate of the electric energy storage; pcharge(t)、Pdischarge(t) and ηch、ηdisThe power and efficiency of charging and discharging of the electrical energy storage at the moment t are respectively; wees.min(t) and Wees.max(t) is the minimum and maximum capacity of the electrical energy storage at time t; pcharge.max(t)、Pdischarge.max(t) is the maximum charging power and the minimum charging power of the electrical energy storage at the moment t;
(4) solving: converting the pollutant emission target into an economic function according to the unit treatment cost of three kinds of pollutant gases:
Figure GDA0002003759740000051
SGi(t)＝1.55CO2Gi(t)+3.57SO2Gi(t)+5.88NO2Gi(t)
Ssj(t)＝1.55CO2sj(t)+3.57SO2sj(t)+5.88NO2sj(t)
SGrid(t)＝1.55CO2Grid(t)+3.57SO2Grid(t)+5.88NO2Grid(t)
wherein λ is1And λ2Weighting coefficients are selected according to the attention degree of the individual to the target;
solving by adopting a particle swarm algorithm:
Vi (K+1)＝ω×Vi (K)+C1×rand1(.)×(Pbest,i-Xi (K))+C2×rand2(.)×(Gbest-Xi (K))；
Xi (K+1)＝Xi (K)+Vi (K+1)；
wherein, Vi (K+1)Refers to an updated speed variable, Xi (K+1)Refers to an updated location variable, rand1(.) and rand2(.) is then [0, 1]]Random number in between, C1And C2Two learning factors, ω is a weight coefficient, Pbest,iAnd GbestRespectively a local optimal solution and a global optimal solution of the particle i;
the inertial weight is selected as
Figure GDA0002003759740000061
Wherein the inertia weight ωstart＝0.9，ωend0.4, k is the current iteration number, TmaxSet to 300 for the maximum number of iterations;
improving C with a fuzzy adaptive mechanism1And C2The method specifically comprises the following steps:
the objective function of each particle is ranked using a fuzzy membership function:
Figure GDA0002003759740000062
wherein:
Figure GDA0002003759740000063
to C1、C2The specific definition is as follows:
Figure GDA0002003759740000071
Figure GDA0002003759740000072
Figure GDA0002003759740000073
or:
Figure GDA0002003759740000074
Figure GDA0002003759740000075
Figure GDA0002003759740000076
f1 min、
Figure GDA0002003759740000078
is the minimum of the first objective function and the minimum of the second objective function, f1 max、
Figure GDA0002003759740000079
Is the maximum of the first objective function and the maximum of the second objective function;
Figure GDA0002003759740000077
representing the first and second objective function values of particle i at the kth iteration; determining C from membership functions1And C2The relationship (2) of (c).
Has the advantages that: the invention combines real-time scheduling and predictive scheduling to realize the aims of least cost and least pollution.
Drawings
Fig. 1 is a block diagram of the connection between a microgrid power supply end and a user end.
Fig. 2 is a flow chart of the operation of the microgrid according to the present invention.
Detailed Description
The invention is further elucidated with reference to the drawings and the embodiments.
As shown in fig. 1 and 2, the present invention is directed to a household micro-grid system in rural areas, and mainly includes three loads: 1. common electrical loads including electric lamps, mobile phones, etc.; 2. cold load, mainly air conditioning refrigeration; 3. the heat load is mainly supplied by the water heater. The energy supply device of the household micro-grid comprises: 1. a fan; 2. a solar cell; 3. a cold, hot and electricity triple-supply gas turbine; 4. a combined heat and power fuel cell; 5. and (4) a storage battery. In addition, the system is connected with a large power grid and is in a grid-connected state.
The electricity price of 24h in one day is divided into a valley period (21: 00-07: 00), a normal period (07: 00-10: 00, 15: 00-18: 00) and a peak period (10: 00-15: 00, 18: 00-21: 00).
The system scheme is as follows:
1. valley time period (21: 00-07: 00)
Every five hours of the valley period was taken as a unit. In the first five hours of the valley period, the electric load is supplied with power by preferentially utilizing the electric quantity actually generated by solar energy and wind energy, cold and heat loads are supplied after the electric load is met, and a large power grid is used as a replacement energy source. Since the time intervals are five hours and all are in the valley period, the load requirements, wind energy and solar work trend are considered to be the same for the first and second five hours. If the large power grid output is required to meet the requirements of all loads in the first five hours, an optimal scheduling scheme is selected in the last five hours by adopting a fuzzy self-adaptive particle swarm algorithm, and the output of the gas turbine, the large power grid and the fuel cell is distributed, so that the total output is the sum of the output of the large power grid in the first five hours and the energy capable of fully charging the storage battery. If the large grid does not need to deliver power during the first five hours, all of the excess energy is fed back to the battery during the valley time. And if the storage battery is full, feeding energy back to the large power grid, and if the storage battery is not full, charging by using the large power grid.
2. Ordinary time period (07: 00-10: 00)
Every half hour of the ordinary period is taken as a unit. The electric load is preferentially powered by the electricity actually generated by the solar energy and the wind energy in the first half hour of the ordinary time period (07: 00-10: 00), and the fuel cell is used as a replacement energy source. Because the time interval is one and a half hours, the load requirement and the trend of the wind energy and the solar energy are considered to be the same. And if the output of the fuel cell is required to meet the requirements of all loads within the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the next half hour, and distributing the output of the gas turbine, the large power grid and the fuel cell so that the total output is the sum of the output of the fuel cell within the previous half hour. If the fuel cell does not need to output power in the previous half hour, the surplus electric quantity is fed back to the large power grid in the usual time period.
3. Peak time period (10: 00-15: 00)
Every two and a half hours of the peak period is taken as a unit. In the first two half hours of the peak time period (10: 00-15: 00), the electric load is preferentially supplied with power by preferentially utilizing the electric quantity actually generated by solar energy and wind energy, and the storage battery is used as a substitute energy source. Because the time interval is two and a half hours, the load requirements, wind energy and solar energy of the two and a half hours before and after are considered to be the same. And if the output of the storage battery is required to meet the requirements of all loads in the first two half hours, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm in the last two half hours, and distributing the output of the gas turbine, the large power grid, the fuel cell and the storage battery so that the total output is the sum of the output of the storage battery in the first two half hours. If the storage battery does not need to output power in the first two half hours, the redundant electric quantity is fed back to the large power grid in a usual time period.
4. Usual time period (15: 00-18: 00)
Every half hour of the ordinary period is taken as a unit. The electricity load is preferentially powered by the electricity generated by solar energy and wind energy in the first half hour of the ordinary time period (15: 00-18: 00), and the fuel cell is used as a replacement energy source. Because the time interval is one and a half hours, the load requirement and the trend of the wind energy and the solar energy are considered to be the same. If the output of the fuel cell is required to meet the requirements of all loads in the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm in the next half hour, and allocating the output time and the output magnitude of the gas turbine, the large power grid and the fuel cell, wherein if the storage battery is not fully charged, the total output magnitude of the optimal scheme is the sum of the output of the fuel cell in the previous half hour and the energy capable of fully charging the storage battery. If the fuel cell does not need to output power within the previous half hour, the surplus electric quantity is fed back to the storage battery within a usual time period, the storage battery is not fully charged, the storage battery is supplemented by a large power grid, and the electric quantity is fed back to the large power grid when the storage battery is fully charged. It should be ensured that the battery remains fully charged after this period.
5. Peak time period (18: 00-21: 00)
Each half hour of the peak period is taken as one unit. In the first half hour of the peak period (18: 00-21: 00), the electricity load is preferentially powered by the electricity generated by solar energy and wind energy, and the storage battery is used as a substitute energy source. Because the time interval is one and a half hours, the load requirement and the trend of the wind energy and the solar energy are considered to be the same. And if the output of the storage battery is required to meet the requirements of all loads in the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm in the next half hour, and distributing the output of the gas turbine, the large power grid, the fuel cell and the storage battery so that the total output is the sum of the output of the storage battery in the previous two half hours. If the storage battery does not need to output power in the previous half hour, redundant electric quantity is fed back to the large power grid in a usual time period.
The method comprises the following specific steps:
1. switching of energy during the first half of the period
For the valley period, when the first five hours preferentially utilize solar energy and wind energy to generate electricity, if the electricity generated by the two is sufficient, the output voltage and the frequency are relatively stable, and when the amplitude of the output voltage exceeds +/-5 percent or the frequency fluctuates and exceeds +/-0.5 HZ, the large power grid is automatically switched to supply power.
For the peak period, in the first half period, when the storage battery is used as a solar energy and wind energy alternative energy source, whether the storage battery can be used for charging is determined by the fluctuation of the output voltage and the frequency, and when the amplitude of the output voltage exceeds +/-5% or the frequency fluctuates and exceeds +/-0.5 HZ, the storage battery is automatically switched to supply power.
For the ordinary time, in the first half period, when the fuel cell is used as a substitute energy source for solar energy and wind energy, whether the fuel cell is used for supplying power is still determined by the fluctuation of the output voltage and the frequency, and when the amplitude of the output voltage exceeds +/-5% or the frequency fluctuates and exceeds +/-0.5 HZ, the fuel cell is automatically switched to supply power.
2. Target function and constraint condition for seeking optimal scheme
[1] Total cost of microgrid operation: including the fuel costs required for the power generation unit, the start-stop costs for the plant and the costs for exchanging electrical energy with the large grid. The equation is as follows
Figure GDA0002003759740000101
BGi(t) and Bsj(t) is the unit fuel cost of the distributed power supply and the energy storage equipment in the microgrid at the moment t, and the unit is gamma/(kWh), SGiAnd SsjThe start-stop cost of the distributed power supply and the energy storage equipment in the microgrid, BGrid(t) is the time of use price, PGridAnd (t) the active power exchanged between the micro-grid and the large grid. In this equation, X is a state variable including both the active power of the power generation and energy storage device and the start-stop state thereof, the operating state of the power generation and energy storage device is set to 1, and the stop state is set to 0.
X＝[Pg,Ug]1×2nT Pg＝[PG,Ps] n＝Ns+Ng+1
Ug＝{ui}1×n∈{0,1}
N is the total number of devices (including the large grid), NsIs the number of energy storage devices, NgIs the number of power generation devices.
PGAnd PsIs the active power, U, of all power generation equipment (including large power grids) and energy storage equipmentgIt is the state quantity that all power generation equipment (including the large power grid) and energy storage equipment are running or stopped. T denotes the time of the total operation,
[2]the pollution emission target is as follows: mainly comprising the emission gas treatment cost of each power generation unit, and the emission gas comprises CO2、SO2And NO2。
Figure GDA0002003759740000111
EGi(t)、Esj(t) and EGrid(t) the pollution emission of power generation equipment, energy storage equipment and a large power grid in the micro power grid respectively, the unit is kg/(kW.h),
EGi(t)＝CO2Gi(t)+SO2Gi(t)+NO2Gi(t) EGrid(t)＝CO2Grid(t)+SO2Grid(t)+NO2Grid(t)
ESj(t)＝CO2Sj(t)+SO2Sj(t)+NO2Sj(t)
[3] constraint conditions
1. The power balance is that the primary constraint condition of the microgrid is to meet the electric quantity requirement of the load, namely, the electric energy among the power generation unit, the energy storage unit and the large power grid in the microgrid can enable the load to obtain sufficient electric energy.
Figure GDA0002003759740000112
Figure GDA0002003759740000113
Pload(t) is the total electrical load at time t, Qload(t) is the total cold and heat load at time t.
2. The active power is restricted, in actual life, power generation equipment, energy storage equipment and a large power grid are required to be within a specified active power range.
PGi,min(t)≤PGi(t)≤PGi,max(t) PSj,min(t)≤PSj(t)≤PSj,max(t)
PGrid,min(t)≤PGrid(t)≤PGrid,max(t)
PGi,min(t) and PGi,max(t) is the minimum active power and the maximum active power of the power plant i at time t, respectively.
PSj,min(t) and PSj,maxAnd (t) is the minimum active power and the maximum active power of the energy storage device j at the moment t respectively.
PGrid,min(t) and PGrid,maxAnd (t) respectively the minimum active power and the maximum active power of the large power grid at the moment t.
3. Charging and discharging limitation of energy storage device
Wees(t)＝(1-σ)Wees(t-1)+[Pcharge(t)ηch-Pdischarge(t)/ηdis]Δt
Wees.min(t)≤Wees(t)≤Wees.max(t)
Pcharge(t)≤Pcharge.max(t) Pdischarge(t)≤Pdischarge.max(t)
Wees(t) is the electrical energy storage capacity over a period of time t; sigma is the electrical energy storage self-discharge rate; p ischarge(t)、Pdischarge(t) and ηch、ηdisThe power and efficiency of charging and discharging of the electrical energy storage at the moment t are respectively; wees.min(t) and Wees.max(t) is the minimum and maximum capacity of the electrical energy storage at time t; pcharge.max(t)、Pdischarge.maxAnd (t) is the maximum charging power and the minimum charging power of the electric energy storage at the time t.
[3] Solving for
For the solution of the multi-objective problem, the multi-objective is generally multi-objective into a single objective by using weighting coefficients, so that a plurality of objectives must be made into the same dimension, and therefore, the objective 2 must be made into an economic function. In this design, the unit treating cost of three pollution gases is as follows
Kind of polluting gas Treatment expense (Yuan/Kg)
CO2 1.55
SO2 3.57
NO2 5.88
Figure GDA0002003759740000121
SGi(t)＝1.55CO2Gi(t)+3.57SO2Gi(t)+5.88NO2Gi(t)
Ssj(t)＝1.55CO2sj(t)+3.57SO2sj(t)+5.88NO2sj(t)
SGrid(t)＝1.55CO2Grid(t)+3.57SO2Grid(t)+5.88NO2Grid(t)
λ1And λ2The weighting coefficients are selected according to the degree of importance of the individual to the target.
3. Particle swarm algorithm
Each particle in the particle swarm algorithm represents a potential solution to the problem, and each particle corresponds to a fitness value determined by a fitness function. The speed of the particles determines the moving direction and distance of the particles, and the speed is dynamically adjusted according to the moving experiences of the particles and other particles, so that the optimization of the individual in a solvable space is realized.
In each iteration, the particle updates its own velocity and position, i.e. the velocity and position, through the individual extremum and the population extremum
Vi (K+1)＝ω×Vi (K)+C1×rand1(.)×(Pbest,i-Xi (K))+C2×rand2(.)×(Gbest-Xi (K))
Xi (K+1)＝Xi (K)+Vi (K+1)
Vi (K+1)Refers to an updated speed variable, Xi (K+1)Refers to an updated location variable, rand1(.) and rand2(.) is then [0, 1]]Random number in between, C1And C2Two learning factors, ω is a weight coefficient, Pbest,iAnd GbestRespectively, a local optimal solution and a global optimal solution for particle i.
The algorithm can keep stronger global search capability by larger inertial weight in the initial iteration stage, and the algorithm can carry out more accurate local search by smaller weight coefficient in the later iteration stage. The inertial weight is therefore chosen in this algorithm as
Figure GDA0002003759740000131
Inertial weight ωstart＝0.9，ωend0.4, k is the current iteration number, TmaxFor the maximum number of iterations, 300 is set in this design.
For a generic particle swarm algorithm, C1And C2Is usually a constant, C1Is a representation of the perception of the particle on itself, C2This is to show the recognition of the whole particle. And the fuzzy adaptive mechanism is adopted in the design to improve C1And C2. The specific method is as follows
(1) The design uses a fuzzy membership function to rank the objective function of each particle.
Figure GDA0002003759740000132
Figure GDA0002003759740000133
(2) To C1、C2The specific definition is as follows:
Figure GDA0002003759740000141
Figure GDA0002003759740000142
Figure GDA0002003759740000143
elsif
Figure GDA0002003759740000144
Figure GDA0002003759740000145
Figure GDA0002003759740000146
f1 min、
Figure GDA0002003759740000147
is the minimum of the first objective function and the minimum of the second objective function, f1 max、
Figure GDA0002003759740000148
Is the maximum of the first objective function and the maximum of the second objective function.
Figure GDA0002003759740000149
The first and second objective function values for particle i at the kth iteration are shown. Determining C from membership functions1And C2The relationship (2) of (c). When the membership function value is small, the global search capability needs to be enhanced, and the difference between the particles and the optimal particles is large, so that the cognition on the whole particles is large, and the cognition on the particles is small. And when the membership function value is larger, the local searching capability needs to be enhanced, and the self cognition is smaller. C is to be1、C2And combining with the distance between the particles, and continuously adjusting self cognition and overall cognition through the judgment of a fuzzy function, so that the particles tend to be optimal particles.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the foregoing embodiments, and various equivalent changes (such as number, shape, position, etc.) may be made to the technical solution of the present invention within the technical spirit of the present invention, and the equivalents are protected by the present invention.

Claims (3)

1. An optimized scheduling method of a family micro-grid system is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: the electricity price of 24h of a day is divided into valley periods: 21: 00-07: 00; the ordinary time period is as follows: 07: 00-10: 00, 15: 00-18: 00; peak period: 10: 00-15: 00, 18: 00-21: 00;
step two: for the valley period: every five hours of the valley period was taken as a unit; in the first five hours of the valley period, the electric load is supplied with power by preferentially utilizing the electric quantity actually generated by solar energy and wind energy, cold and heat loads are supplied after the electric load is met, and a large power grid is used as a replacement energy source; if the output of the large power grid is required to meet the requirements of all loads in the first five hours, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm in the last five hours, and distributing the output of the gas turbine, the large power grid and the fuel cell so that the total output is the sum of the output of the large power grid in the first five hours and the energy capable of fully charging the storage battery; if the large power grid does not need to output power in the first five hours, all redundant energy is fed back to the storage battery in the valley period, the energy is fed back to the large power grid if the storage battery is full, and the large power grid is used for charging if the storage battery is not full;
for the usual period: 07: 00-10: 00; taking each half hour of the ordinary time period as a unit; the electric load is preferentially powered by utilizing the electric quantity actually generated by the solar energy and the wind energy in the first half hour of the ordinary time period, and the fuel cell is used as a substitute energy source; if the output of the fuel cell is required to meet the requirements of all loads within the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the next half hour, and distributing the output of the gas turbine, the large power grid and the fuel cell so that the total output is the sum of the output of the fuel cell within the previous half hour; if the fuel cell does not need to output power in the previous half hour, the redundant electric quantity is fed back to the large power grid in a usual time period;
for the peak period: 10: 00-15: 00; every two and a half hours of the peak period are taken as a unit; in the first two half hours of the peak period, the electricity load is preferentially supplied with electricity by preferentially utilizing the electricity actually generated by solar energy and wind energy, and the storage battery is used as a replacement energy source; if the output of the storage battery is required to meet the requirements of all loads within the first two half hours, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the last two half hours, and distributing the output of the gas turbine, the large power grid, the fuel cell and the storage battery, so that the total output is the sum of the output of the storage battery within the first two half hours; if the storage battery does not need to output power within the first two half hours, the redundant electric quantity is fed back to the large power grid within the ordinary time period;
for the usual period: 15: 00-18: 00; taking each half hour of the ordinary time period as a unit; in the first half hour of the ordinary time period, the electricity load is preferentially supplied with electricity by preferentially utilizing the electricity quantity actually generated by the solar energy and the wind energy, and the fuel cell is used as a substitute energy source; if the output of the fuel cell is required to meet the requirements of all loads within the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the next half hour, and distributing the output time and the output size of the gas turbine, the large power grid and the fuel cell, wherein if the storage battery is not fully charged, the total output size of the optimal scheme is the sum of the output of the fuel cell within the previous half hour and the energy capable of fully charging the storage battery; if the fuel cell does not need to output power within the previous half hour, the redundant electric quantity is fed back to the storage battery within the ordinary time period, the storage battery is not fully charged, the large power grid is used for supplementing the redundant electric quantity, and the electric quantity is fed back to the large power grid if the storage battery is fully charged;
for the peak period: 18: 00-21: 00; taking each half hour of the peak period as a unit; in the first half hour of the peak period, the electricity load is preferentially supplied with electricity by preferentially utilizing the electricity actually generated by the solar energy and the wind energy, and the storage battery is used as a substitute energy source; if the output of the storage battery is required to meet the requirements of all loads within the previous half hour, selecting an optimal scheduling scheme by adopting a fuzzy self-adaptive particle swarm algorithm within the next half hour, and distributing the output of the gas turbine, the large power grid, the fuel cell and the storage battery so that the total output is the sum of the output of the storage battery within the previous two half hours; if the storage battery does not need to output power in the previous half hour, the redundant electric quantity is fed back to the large power grid in a usual time period.
2. The optimal scheduling method of the home microgrid system as claimed in claim 1, characterized in that: for the valley period, when the first five hours preferentially utilize solar energy and wind energy to generate electricity, if the electricity generated by the two is sufficient, the output voltage and the frequency are relatively stable, and when the amplitude of the output voltage exceeds +/-5 percent or the frequency fluctuates and exceeds +/-0.5 HZ, the large power grid is automatically switched to supply power;
for the peak time period, in the first half period, when the storage battery is used as a solar energy and wind energy alternative energy source, whether the storage battery can be used for charging is determined by the fluctuation of the output voltage and the frequency, and when the amplitude of the output voltage exceeds +/-5% or the fluctuation of the frequency exceeds +/-0.5 HZ, the storage battery is automatically switched to supply power;
for the ordinary time, in the first half period, when the fuel cell is used as a substitute energy source for solar energy and wind energy, whether the fuel cell is used for supplying power is still determined by the fluctuation of the output voltage and the frequency, and when the amplitude of the output voltage exceeds +/-5% or the frequency fluctuates and exceeds +/-0.5 HZ, the fuel cell is automatically switched to supply power.
3. The optimal scheduling method of the home microgrid system as claimed in claim 1, characterized in that: the method for selecting the optimal scheduling scheme by adopting the fuzzy self-adaptive particle swarm algorithm specifically comprises the following steps:
(1) calculating the total cost of the operation of the micro-grid: including the fuel cost required by the power generation unit, the start-stop cost of the equipment and the cost of exchanging electric energy with the large power grid; the equation is as follows:
Figure FDA0001931410310000031
wherein, BGi(t) and Bsj(t) is the unit fuel cost of the distributed power supply and the energy storage equipment in the microgrid at the moment t, and the unit is gamma/(kWh), SGiAnd SsjThe start-stop cost of the distributed power supply and the energy storage equipment in the microgrid, BGrid(t) is the time of use price, PGrid(t) is the active power exchanged by the micro-grid and the large grid; in the equation, X is a state variable which comprises the active power of the power generation and energy storage equipment and the starting and stopping state of the power generation and energy storage equipment, the running state of the power generation and energy storage equipment is set to be 1, and the stopping state is set to be 0;
X＝[Pg,Ug]1×2nT；
Pg＝[PG,Ps]；
n＝Ns+Ng+1；
Ug＝{ui}1×n∈{0,1}；
where N is the total number of devices, NsIs the number of energy storage devices, NgIs the number of power generation equipment;
PGand PsIs the active power, U, of all power generating equipment and energy storage equipmentgThe state quantity of the running or stopping of all the power generation equipment and the energy storage equipment is obtained; t represents the total operating time;
(2) the pollution emission target is as follows: comprises the gas discharged from each power generation unit, the discharged gas containing CO2、SO2And NO2：
Figure FDA0001931410310000032
Wherein E isGi(t)、Esj(t) and EGrid(t) the pollution emission of power generation equipment, energy storage equipment and a large power grid in the microgrid is kg/(kW & h);
EGi(t)＝CO2Gi(t)+SO2Gi(t)+NO2Gi(t)；
EGrid(t)＝CO2Grid(t)+SO2Grid(t)+NO2Grid(t)；
ESj(t)＝CO2Sj(t)+SO2Sj(t)+NO2Sj(t)；
(3) constraint conditions are as follows:
power balance:
Figure FDA0001931410310000041
Figure FDA0001931410310000042
wherein, Pload(t) is the total electrical load at time t, Qload(t) is the total cold and heat load at time t;
active power constraints:
PGi,min(t)≤PGi(t)≤PGi,max(t)；
PSj,min(t)≤PSj(t)≤PSj,max(t)；
PGrid,min(t)≤PGrid(t)≤PGrid,max(t)；
wherein, PGi,min(t) and PGi,max(t) minimum active power and maximum active power of the power generation device i at time t, respectively; p isSj,min(t) and PSj,max(t) the minimum active power and the maximum active power of the energy storage device j at the moment t, respectively; pGrid,min(t) and PGrid,max(t) the minimum active power and the maximum active power of the large power grid at the moment t respectively;
charging and discharging limitation of energy storage equipment:
Wees(t)＝(1-σ)Wees(t-1)+[Pcharge(t)ηch-Pdischarge(t)/ηdis]Δt；
Wees.min(t)≤Wees(t)≤Wees.max(t)；
Pcharge(t)≤Pcharge.max(t)；
Pdischarge(t)≤Pdischarge.max(t)；
wherein, Wees(t) is the electrical energy storage capacity over a period of time t; sigma is the electrical energy storage self-discharge rate; pcharge(t)、Pdischarge(t) and ηch、ηdisThe power and efficiency of charging and discharging of the electrical energy storage at the moment t are respectively; wees.min(t) and Wees.max(t) is the minimum and maximum capacity of the electrical energy storage at time t; pcharge.max(t)、Pdischarge.max(t) is the maximum charging power and the minimum charging power of the electrical energy storage at the time t;
(4) solving: converting the pollutant emission target into an economic function according to the unit treatment cost of three kinds of pollutant gases:
Figure FDA0001931410310000051
SGi(t)＝1.55CO2Gi(t)+3.57SO2Gi(t)+5.88NO2Gi(t)
Ssj(t)＝1.55CO2sj(t)+3.57SO2sj(t)+5.88NO2sj(t)
SGrid(t)＝1.55CO2Grid(t)+3.57SO2Grid(t)+5.88NO2Grid(t)
wherein λ is1And λ2Weighting coefficients are respectively selected according to the attention degree of the individual to the target;
solving by adopting a particle swarm algorithm:
Vi (K+1)＝ω×Vi (K)+C1×rand1(.)×(Pbest,i-Xi (K))+C2×rand2(.)×(Gbest-Xi (K))；
Xi (K+1)＝Xi (K)+Vi (K+1)；
wherein, Vi (K+1)Refers to an updated speed variable, Xi (K+1)Refers to an updated location variable, rand1(.) and rand2(.) is [0, 1]]Random number in between, C1And C2Two learning factors, ω is a weight coefficient, Pbest,iAnd GbestRespectively a local optimal solution and a global optimal solution of the particle i;
the inertial weight is selected as
Figure FDA0001931410310000052
Wherein the inertia weight ωstart＝0.9，ωend0.4, k is the current iteration number, TmaxSet to 300 for the maximum number of iterations;
improving C with a fuzzy adaptive mechanism1And C2The method specifically comprises the following steps:
the objective function of each particle is ranked using a fuzzy membership function:
Figure FDA0001931410310000053
wherein:
Figure FDA0001931410310000061
to C1、C2The specific definition is as follows:
Figure FDA0001931410310000062
Figure FDA0001931410310000063
Figure FDA0001931410310000064
or:
Figure FDA0001931410310000065
Figure FDA0001931410310000066
Figure FDA0001931410310000067
f1 min、
Figure FDA0001931410310000068
is the minimum of the first objective function and the minimum of the second objective function, f1 max、
Figure FDA0001931410310000069
Is the maximum of the first objective function and the maximum of the second objective function;
Figure FDA00019314103100000610
representing the first and second objective function values of particle i at the kth iteration; determining C from a membership function1And C2The relationship (c) in (c).
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