CN104104116A - Design method for photovoltaic microgrid supply-demand control system containing distributed energy sources - Google Patents

Design method for photovoltaic microgrid supply-demand control system containing distributed energy sources Download PDF

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CN104104116A
CN104104116A CN201410309233.XA CN201410309233A CN104104116A CN 104104116 A CN104104116 A CN 104104116A CN 201410309233 A CN201410309233 A CN 201410309233A CN 104104116 A CN104104116 A CN 104104116A
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micro
photovoltaic
power
electrical network
energy
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CN201410309233.XA
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CN104104116B (en
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刘士荣
郑凌蔚
吴舜裕
竺健
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杭州电子科技大学
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    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/50Photovoltaic [PV] energy
    • Y02E10/56Power conversion systems, e.g. maximum power point trackers

Abstract

The invention discloses a design method for a photovoltaic microgrid supply-demand control system containing distributed energy sources, aiming at improving system stability, scheduling response capability and economy. A 24-hour operation plan of a photovoltaic microgrid is calculated by predicting photovoltaic and local load power values and after fully considering the running cost of distributed power supplies, during running, the operation plan is revised in real time by the supply-demand system according to the latest photovoltaic and load prediction results, finally, the operation plan and grid-connected point power serves as input of the control system, and the output power of each distributed power supply is controlled. By the design method, the operation plan can be adjusted in real time, supply-demand stability in the system and response to high grid are effectively guaranteed, and meanwhile, maximum running benefit of the photovoltaic microgrid is achieved

Description

A kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies
Technical field
The present invention relates to new forms of energy electric power and micro-electric power network technique field.Refer more particularly to a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies.
Background technology
The end of the year in 2012, China's wind power generation increment has exceeded coal, charcoal energy output, and generating total amount has surmounted nuclear energy power generation amount first.Under the background slowing down in whole world generating total amount, the ratio of regenerative resource in whole world generating total amount rises to 4.7%.China exceeded Germany in 2012 become second largest renewable energy power generation state, is only second to the U.S..In regenerative resource, photovoltaic generation, wind power generation are subject to weather effect larger, and the fluctuation of its power stage causes in the time of practical application, there is certain limitation.Access the impact on large electrical network in order effectively to reduce regenerative resource; propose in the world to integrate micro power network (being called for short micro-electrical network) by distributed power source, energy storage device, energy converter, controlled and uncontrollable load, protective device and supervisory control system; micro-electrical network is a small-sized electric system of being transported to of regionality; be to realize the autonomous system from master control, protection and management, can eliminate the impact of distributed power source on public electric wire net.Micro-electrical network has two kinds of operational modes: be incorporated into the power networks and islet operation.
In micro-electrical network, distributed power source has regenerative resource, normal power supplies, and wherein regenerative resource has: wind power generation, solar power generation, normal power supplies has: miniature gas turbine, diesel engine generator.Micro-electrical network combines a large amount of distributed power generation unit and distributed energy storage unit according to the actual requirements with certain capacity ratio, this locality load is powered.
According to the definition of CERTS, micro-electrical network be one can be autonomous unit, can realize the planned or unplanned seamless switching between island mode and grid-connected pattern.Each micro-electrical network combines local regenerative resource or other distributed energy generator units and local load or energy storage device.Form relatively independent supplied for electronic system.To reduce or to avoid the system impact on user when fault on a large scale.Micro-electrical network is made as a whole and outer net and is contacted, and both can be incorporated into the power networks with outer net and supply the local electric energy needing from outer net.Also can be in the time of large electric network fault.Ensure the power supply of local important load by the independent operating of the micro-electrical network of rational allocation.For large electrical network, micro-electrical network can be considered as a controllable.It can be according to large dispatching of power netwoks instruction, carries out Power Exchange by PCC site and large electrical network.Numerous micro-electrical network cooperative cooperatings, has improved the electrical stability that supplies in large electrical network.
Desirable micro-electrical network will possess following these features:
(1) can adapt to generator unit distributed, intermittently, the various characteristics such as schedulable;
(2) EMS that can authorized user realize in micro-electrical network and intelligent building interconnects, and makes user can manage its energy and uses and reduce its energy consumption;
(3) plug-and-play feature is the changeable feature for suitable operational mode of micro-electrical network, i.e. grid-connected or isolated island; Micro-electrical network provides voltage and frequency protection and has safety the ability that is synchronized to again large electrical network in the time of islet operation;
(4), under island mode, in micro-electrical network, all loads are provided by distributed power generation unit and share;
(5) micro-electrical network has the thermoelectric device that utilizes used heat, and used heat is the byproduct of cogeneration of heat and power generating, recycles used heat in the mode of freezing or heat.
(6) micro-electrical network can be served various loads, comprises residential block, office building, and industry park, shopping centre, campus, for user provides the needed quality of power supply;
(7), when emergency and large grid power blackout cause electricity shortage, provide a good solution;
(8) can bear the attack from external physical and network, alleviate the harm that electric power system is subject to, and keep the flexibility of electric power system;
(9) there is self-healing property.For fear of having a power failure or alleviating interruption duration and power quality problem, micro-electrical network function of must predicting and make an immediate response.
(10) there is the abundant market competitiveness.When micro-electrical network enters energy market, because its real time information, low transaction cost are applicable to any user; When micro-electrical network is in optimized operation with reduce when maintenance cost, can realize asset optimization, continue to optimize assets by monitoring and obtain better investment returns rate.
Above-mentioned micro-electrical network feature has proposed high requirement to microgrid energy management control ability.In order to realize reliability, economy and the responding ability to large dispatching of power netwoks of powering in micro-electrical network, microgrid energy management and control system thereof have become one of research emphasis of current micro-electric power network technique.
Summary of the invention
The object of the invention is the photovoltaic micro-grid system for photovoltaic generating system, synchronous generator system (miniature gas generating, fuel gas generation, diesel generation), energy-storage system, super capacitor system and local load composition, according to the characteristic of regenerative resource and load in the micro-electrical network of photovoltaic, provide a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies.Ensure the long-term equilibrium of supply and demand and the stability thereof of micro-electrical network when grid-connected and the islet operation by formulating rationally effectively synchronous generator, storage battery operational plan.
The present invention is by the following technical solutions:
Step 1: the micro-electric network data of photovoltaic detects, obtains:
(1) weather information of obtaining and storing the micro-electrical network of photovoltaic region by atmospherium in supervisory control system, wherein having for supply and demand system: temperature, radiation, relative humidity, wind speed, mean temperature, weather pattern, sun set/raise time.
(2) obtain weather information on the same day by Public meteorology net: temperature, radiation, relative humidity, wind speed, mean temperature, weather pattern, sun set/raise time.
(3) obtain local load real-time measurement values storage.
(4) obtain the energy output of the photovoltaic generating system of the micro-electrical network of photovoltaic.
(5) charging and discharging state and the dump energy of detection energy-storage system.
Step 2: energy output prediction in photovoltaic generating system effectual time:
(1) extract historical meteorological data and the photovoltaic power generation quantity data in micro-grid monitoring system, and according to weather pattern, meteorological data and corresponding photovoltaic power generation quantity data are divided into three types; Weather pattern is divided into fine day, cloudy and rainy day, cloudy turn to fine.
(2), according to different weather patterns, meteorological data and corresponding photovoltaic power generation quantity data, taking t hour as step-length, are put into neural network model and carried out learning training.Obtain three kinds of neural network models under different weather type.Wherein, the input variable of neural net has: temperature, radiation, relative humidity, wind speed, temperature on average, weather pattern, sun set/raise time.
(3) obtain prediction day climatic data from Public meteorology station, the neural network model of putting into corresponding weather pattern carries out photovoltaic power generation quantity prediction.Obtain the prediction photovoltaic generation discharge curve of T hour day, step-length is t hour.
Step 3: local load effectual time power prediction
(1) extract historical data in micro-grid monitoring system: temperature, weather pattern, historical load amount.
(2) using temperature, temperature on average, weather pattern, day type as input, historical date load curve is as output, and step-length is set to t hour, and neural net is carried out to learning training.Obtain T hourly load forecasting neural network model; Day type is divided into: working day and holiday.
(3) obtain prediction daily temperature, temperature on average, weather pattern from Public meteorology station.
(4) will predict that daily temperature, temperature on average, weather pattern, a day type input as neural net, step-length is set to t hour, obtains a prediction day T hourly load forecasting curve.
Step 4: operational plan is formulated
(1), according to micro-electrical network internal loading level, determine energy-storage system output constant P bat_lim.
(2) determine synchronous generator cost of electricity-generating C gewith energy-storage units cost of electricity-generating C bat.
(3) input data, constraints, target function are put into linear programming model, obtain synchronous generator and energy-storage units operational plan power is respectively: P ge_plan, P bat_plan.Wherein:
Input variable: photovoltaic power generation quantity, load level, tou power price, synchronous generator cost of electricity-generating, energy-storage units cost of electricity-generating, Environmental costs, distributed power source initial state information that day part is corresponding;
Constraints: energy-storage units output constant P bat=± | P bat_lim|, energy-storage units dump energy Q bat> q%, micro-electrical network absorbed power and distributed energy power sum equal load power; Q% is the energy-storage units dump energy percentage of setting;
Target: economic benefit maximum in micro-electrical network.
Step 5: photovoltaic power generation quantity and the correction of load level prediction curve
(1) from Public meteorology net, obtain up-to-date temperature, radiation, relative humidity, wind speed, temperature on average, weather pattern data.
(2) the up-to-date weather information in " step 5-(1) " is input to the neural net that step 2 obtains, obtains after current time the photovoltaic power generation quantity predicted value of T hour.
(3) by the weather information in step 5-(1) and the same day known load curve put into the neural net of step 3, obtain after current time the load prediction value of T hour.
Step 6: operational plan correction
(1) predict photovoltaic power generation quantity and load level prediction according to the T of step 2 and step 3 hour, obtain the demand power P of i period id1.
(2) according to step 5, the i period demand power P under the up-to-date weather condition of basis id2, 0 < i≤T.
(3) establishing operational plan correction threshold is δ, computation requirement power error value Δ P i: Δ P=P id1-P id2.If | Δ P i|≤δ, does not carry out operational plan correction.If | Δ P i| > δ, the photovoltaic power generation quantity step 5 being obtained and load prediction value are as input, and repeating step four obtains the unit operational plan of the each distributed power generation of revised micro-electrical network.
Step 7: the tracking control of trend
Micro-electric network swim is followed the tracks of to control and has been realized the response of micro-electrical network to large dispatching of power netwoks.This control structure is made up of two trend control modules: permanent power flow control module and real-time energy balance control module.
(1) obtain the scheduling performance number of large electrical network to micro-electrical network, control system to and site set value of the power: P pcc_plan.
(2) detect micro-electrical network site power flow value: P pcc.
(3) energy balance control in real time: to P pcc_plan, P gscarry out integral operation, obtain charge value W pcc_plan, W gs; W pcc_plan: the specified electric quantity that micro-electrical network is carried to large electrical network under operational plan; W gs: micro-electrical network reality is carried electric weight to large electrical network.Solve W pcc_planwith W gsdifference obtain Δ W, put into real-time energy balance control module using Δ W as input variable and obtain the controlling value P to each distributed power source c2.
(4) by the output valve of real-time energy balance control module site plan power P pcc_plan, synchronous generator and storage battery operational plan power is as the input value of permanent power control module, obtains the total power signal P to distributed energy c1.That is:
P c1=P pcc_plan+P c2
(5) by P c1as input, by the PI controller of each distributed power source, obtain the power control signal of synchronous generator, storage battery, super capacitor.
Beneficial effect:
1, the formulation flexibility of operational plan of the present invention is high, can revise in real time operational plan according to actual photovoltaic power generation quantity, the load power of moving the same day, improves the micro-operation of power networks economy of photovoltaic.
2, the real-time energy balance control module in the present invention has ensured the consistency of the exchange of electric weight between the micro-electrical network of photovoltaic and power distribution network and Expected energy.Permanent power control module has ensured the stability of the grid-connected point output power of the micro-electrical network of photovoltaic.
3, in the present invention, control system structure, in the time that the micro-electrical network of photovoltaic contains different distributions formula generator unit, still can effectively realize supply and demand stability and the responding ability to large electrical network in system.
Brief description of the drawings:
Fig. 1 is the micro-electric network composition of typical photovoltaic;
Fig. 2 is supply and demand control system flow chart;
Fig. 3 is constant power control structure;
Fig. 4 is real-time energy balance control structure;
Fig. 5 is the power control structure after permanent power control is combined with energy balance control in real time.
Embodiment
Specifically set forth specific implementation of the present invention for what formed by photovoltaic generation, synchronous unit, energy-storage system, super capacitor system containing many distributed energies photovoltaic micro-grid system.As shown in Figure 1, the micro-electrical network supply and demand of photovoltaic control system as shown in Figure 2 for micro-grid equipment configuration of considering and structure thereof.Specifically under implementation step institute.
Step 1: micro-electric network data detects, obtains:
(1) weather information of obtaining and storing the micro-electrical network of photovoltaic region by atmospherium in supervisory control system, wherein having for supply and demand system: temperature, radiation, relative humidity, wind speed, mean temperature, weather pattern, sun set/raise time.
(2) obtain weather information on the same day by Public meteorology net: temperature, radiation, relative humidity, wind speed, mean temperature, weather pattern, sun set/raise time.
(3) obtain local load real-time measurement values storage.
(4) obtain the energy output of the photovoltaic generating system of the micro-electrical network of photovoltaic.
(5) charging and discharging state and the dump energy of detection energy-storage system.
Step 2: energy output prediction in photovoltaic generation unit effectual time:
(1) extract historical meteorological data and the photovoltaic power generation quantity data in micro-grid monitoring system, and according to weather pattern, meteorological data and corresponding photovoltaic power generation quantity data are divided into three types; Weather pattern is divided into fine day, cloudy and rainy day, cloudy turn to fine.
(2), according to different weather patterns, meteorological data and corresponding photovoltaic power generation quantity data, taking 1 hour as step-length, are put into neural network model and carried out learning training.Obtain three kinds of neural network models under different weather type.Wherein, the input variable of neural net has: temperature, radiation, relative humidity, wind speed, temperature on average, weather pattern, sun set/raise time.
(3) obtain prediction day climatic data from Public meteorology station, the neural network model of putting into corresponding weather pattern carries out photovoltaic power generation quantity prediction.Obtain the prediction photovoltaic generation discharge curve Pi of 24 hours days pv, step-length is 1 hour.I represents the period, 1≤i≤24.
Step 3: local load effectual time power prediction
(1) extract historical data in micro-grid monitoring system: temperature, weather pattern, historical load amount.
(2) using temperature, temperature on average, weather pattern, day type as input, historical date load curve is as output, and step-length is set to 1 hour, and neural net is carried out to learning training.Obtain 24 hourly load forecasting neural network models; Day type is divided into: working day and holiday.
(3) obtain prediction daily temperature, temperature on average, weather pattern from Public meteorology station.
(4) will predict that daily temperature, temperature on average, weather pattern, a day type input as neural net, step-length is set to 1 hour, obtains prediction day 24 hourly load forecasting curve P iload.
Step 4: operational plan is formulated:
(1) as load level P iload≤ 80 o'clock, set | P bat_lim|=30kW; 80 < P iload≤ 120 o'clock, set | P bat_lim|=40kW, P iloadwhen > 120, set | P bat_lim|=50kW.
(2) determine synchronous generator cost of electricity-generating C ge(unit/kWh) and energy-storage units cost of electricity-generating C bat(unit/kWh).
A.C ge(unit/kWh) calculation process:
In the time that synchronous generator is diesel engine generator, its cost of electricity-generating C dgcalculation process is as follows, C dg=C ge: 1) fuel consumption is calculated
F=F 0P genrate+F 1P idg
P genrate, P idgbe respectively rated power and the operational plan power output of diesel engine generator; F 0, F 1for diesel engine generator consumption curve intercept coefficient.
2)C fuel=C price×F
C fuel: diesel engine generator fuel cost, C price: diesel oil unit price
3) diesel engine generator total power production cost C dg:
C dg=C om+C fuel+C po
C po: generator disposal of pollutants fine.
C om: generator maintenance cost.
B. in the time that energy-storage units is storage battery, C bat(unit/kWh) calculation process is as follows:
Cycle: storage battery theory discharges and recharges number of times.
The present invention gets cycle=1250, C bat=0.528.
(3) input data, constraints, target function are put into linear programming model, obtain synchronous generator and energy-storage units operational plan power is respectively: P ge_plan, P bat_plan.Wherein:
Input variable: photovoltaic power generation quantity, load level, electricity price, diesel engine generator cost of electricity-generating, energy-storage units cost of electricity-generating, Environmental costs, distributed power source initial state information that day part is corresponding
Constraints: energy-storage units output constant P bat=± | P bat_lim|, energy-storage units dump energy Q bat> 40%, micro-electrical network absorbed power and distributed energy power sum equal load power.
Target: economic benefit maximum in micro-electrical network, that is:
min{C microgrid}=min{C ge+C bat+C grid_buy-C grid_sell}
C grid_buy: electrical network is bought electric cost;
C grid_sell: electrical network is sold electric gained.
Step 5: photovoltaic power generation quantity and the correction of load level prediction curve
(1) from Public meteorology net, obtain up-to-date temperature, temperature on average, weather pattern data.
(2) the up-to-date weather information in step 5-(1) is put into the neural net that step 2 obtains, obtain after current time the photovoltaic power generation quantity predicted value of 24 hours.
(3) by the weather information in step 5-(1) and the same day known load curve put into the neural net of step 3, obtain after current time the load prediction value of 24 hours.
Step 6: operational plan correction
(1), according to the photovoltaic power generation quantity of step 2 and step 3 and load level prediction, obtain the demand power P of i period d1.
(2) according to step 5, the i period demand power P under the up-to-date weather condition of basis d2.
(3) establishing operational plan correction threshold is δ=20kW, computation requirement power error value Δ P i: Δ P=P id1-P id2.If | Δ P i|≤δ, does not carry out operational plan correction.If | Δ P i| > δ, the photovoltaic power generation quantity step 5 being obtained and load prediction value are as input, and repeating step four obtains revised micro-operation of power networks plan.
Step 7: the tracking control of trend
Micro-electric network swim is followed the tracks of to control and has been realized the response of micro-electrical network to large dispatching of power netwoks.This control structure is made up of two trend control modules: permanent power flow control module and real-time energy balance control module.
(1) obtain the scheduling performance number of large electrical network to micro-electrical network, control system to and site set value of the power: P pcc_plan.
(2) detect micro-electrical network site power flow value: P pcc.
(3) energy balance control in real time as shown in Figure 3: to P pcc_plan, P gscarry out integral operation, obtain charge value W pcc_plan, W gs.W pcc_plan: the specified electric quantity that micro-electrical network is carried to large electrical network under operational plan; W gs: micro-electrical network reality is carried electric weight to large electrical network.Solve W pcc_planwith W gsdifference obtain Δ W, put into controller using Δ W as input variable and obtain the controlling value P of real-time energy balance control module to distributed power source c2.
(4) permanent power control module structure as shown in Figure 4.In the present invention, by the output valve of real-time energy balance control module site plan power P pcc_plan, synchronous generator and energy-storage units operational plan power is as the input value of permanent power control module, obtains the total power signal P to distributed energy c1.That is:
P c1=P pcc_plan+P c2
(5) by P c1as input, by the PI controller of each distributed power source, obtain the power control signal of synchronous generator, energy-storage units, super capacitor.Master control structure after real-time energy balance control module is combined with permanent power control module as shown in Figure 5.

Claims (8)

1. containing the micro-electrical network supply and demand of a photovoltaic Control System Design method for many distributed energies, it is characterized in that:
Step 1: the micro-electric network data of photovoltaic detects, obtains;
Step 2: energy output prediction in photovoltaic generating system effectual time;
Step 3: local load effectual time power prediction;
Step 4: operational plan is formulated;
Step 5: photovoltaic power generation quantity and the correction of load level prediction curve
Step 6: operational plan correction;
Step 7: the tracking control of trend.
2. a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies according to claim 1, is characterized in that, the micro-electric network data of described photovoltaic detects, obtain and comprise the following steps:
(1) weather information of obtaining and storing the micro-electrical network of photovoltaic region by atmospherium in supervisory control system, wherein having for supply and demand system: temperature, radiation, relative humidity, wind speed, mean temperature, weather pattern, sun set/raise time;
(2) obtain weather information on the same day by Public meteorology net: temperature, radiation, relative humidity, wind speed, mean temperature, weather pattern, sun set/raise time;
(3) obtain local load real-time measurement values storage;
(4) obtain the energy output of the photovoltaic generating system of the micro-electrical network of photovoltaic;
(5) charging and discharging state and the dump energy of detection energy-storage system.
3. a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies according to claim 1, is characterized in that, in described photovoltaic generating system effectual time, energy output prediction comprises the following steps:
(1) extract historical meteorological data and the photovoltaic power generation quantity data in micro-grid monitoring system, and according to weather pattern, meteorological data and corresponding photovoltaic power generation quantity data are divided into three types; Weather pattern is divided into fine day, cloudy and rainy day, cloudy turn to fine;
(2), according to different weather patterns, meteorological data and corresponding photovoltaic power generation quantity data, taking t hour as step-length, are put into neural network model and carried out learning training; Obtain three kinds of neural network models under different weather type; Wherein, the input variable of neural net has: temperature, radiation, relative humidity, wind speed, temperature on average, weather pattern, sun set/raise time;
(3) obtain prediction day climatic data from Public meteorology station, the neural network model of putting into corresponding weather pattern carries out photovoltaic power generation quantity prediction; Obtain the prediction photovoltaic generation discharge curve of T hour day, step-length is t hour.
4. a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies according to claim 1, is characterized in that, described this locality load effectual time power prediction comprises the following steps:
(1) extract historical data in micro-grid monitoring system: temperature, weather pattern, historical load amount;
(2) using temperature, temperature on average, weather pattern, day type as input, historical date load curve is as output, and step-length is set to t hour, and neural net is carried out to learning training; Obtain T hourly load forecasting neural network model; Day type is divided into: working day and holiday;
(3) obtain prediction daily temperature, temperature on average, weather pattern from Public meteorology station;
(4) will predict that daily temperature, temperature on average, weather pattern, a day type input as neural net, step-length is set to t hour, obtains a prediction day T hourly load forecasting curve.
5. a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies according to claim 1, is characterized in that, described operational plan is formulated and comprised the following steps:
(1), according to micro-electrical network internal loading level, determine energy-storage system output constant P bat_lim;
(2) determine synchronous generator cost of electricity-generating C gewith energy-storage units cost of electricity-generating C bat;
(3) input data, constraints, target function are put into linear programming model, obtain synchronous generator and energy-storage units operational plan power is respectively: P ge_plan, P bat_plan; Wherein:
Input variable: photovoltaic power generation quantity, load level, tou power price, synchronous generator cost of electricity-generating, energy-storage units cost of electricity-generating, Environmental costs, distributed power source initial state information that day part is corresponding;
Constraints: energy-storage units output constant P bat=± | P bat_lim|, energy-storage units dump energy Q bat> q%, micro-electrical network absorbed power and distributed energy power sum equal load power; Q% is the energy-storage units dump energy percentage of setting;
Target: economic benefit maximum in micro-electrical network.
6. a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies according to claim 1, is characterized in that, described photovoltaic power generation quantity and the correction of load level prediction curve comprise the following steps:
(1) from Public meteorology net, obtain up-to-date temperature, radiation, relative humidity, wind speed, temperature on average, weather pattern data;
(2) the up-to-date weather information in " step 5-(1) " is input to the neural net that step 2 obtains, obtains after current time the photovoltaic power generation quantity predicted value of T hour;
(3) by the weather information in step 5-(1) and the same day known load curve put into the neural net of step 3, obtain after current time the load prediction value of T hour.
7. a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies according to claim 1, is characterized in that, described operational plan correction comprises the following steps:
(1) predict photovoltaic power generation quantity and load level prediction according to the T of step 2 and step 3 hour, obtain the demand power P of i period id1;
(2) according to step 5, the i period demand power P under the up-to-date weather condition of basis id2, 0 < i≤T;
(3) establishing operational plan correction threshold is δ, computation requirement power error value Δ P i: Δ P=P id1-P id2; If | Δ P i|≤δ, does not carry out operational plan correction; If | Δ P i| > δ, the photovoltaic power generation quantity step 5 being obtained and load prediction value are as input, and repeating step four obtains the unit operational plan of the each distributed power generation of revised micro-electrical network.
8. a kind of micro-electrical network supply and demand of photovoltaic Control System Design method containing many distributed energies according to claim 1, is characterized in that, the tracking control of described trend comprises the following steps:
Micro-electric network swim is followed the tracks of to control and has been realized the response of micro-electrical network to large dispatching of power netwoks; This control structure is made up of two trend control modules: permanent power flow control module and real-time energy balance control module;
(1) obtain the scheduling performance number of large electrical network to micro-electrical network, control system to and site set value of the power: P pcc_plan;
(2) detect micro-electrical network site power flow value: P pcc;
(3) energy balance control in real time: to P pcc_plan, P gscarry out integral operation, obtain charge value W pcc_plan, W gs; W pcc_plan: the specified electric quantity that micro-electrical network is carried to large electrical network under operational plan; W gs: micro-electrical network reality is carried electric weight to large electrical network; Solve W pcc_planwith W gsdifference obtain Δ W, put into real-time energy balance control module using Δ W as input variable and obtain the controlling value P to each distributed power source c2;
(4) by the output valve of real-time energy balance control module site plan power P pcc_plan, synchronous generator and storage battery operational plan power is as the input value of permanent power control module, obtains the total power signal P to distributed energy c1; That is:
P c1=P pcc_plan+P c2
(5) by P c1as input, by the PI controller of each distributed power source, obtain the power control signal of synchronous generator, storage battery, super capacitor.
CN201410309233.XA 2014-07-01 2014-07-01 A kind of photovoltaic micro supply/demand control system design method containing many distributed energies CN104104116B (en)

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Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104268806A (en) * 2014-11-03 2015-01-07 四川慧盈科技有限责任公司 Micro grid power monitoring system
CN104600731A (en) * 2015-02-06 2015-05-06 山东理工大学 Energy storage system control method of optical storage system for peak shifting
CN105490267A (en) * 2015-12-28 2016-04-13 易事特集团股份有限公司 Micro grid energy management system and energy management method
CN105610201A (en) * 2016-02-29 2016-05-25 国家电网公司 Photovoltaic distributed type power supply day-ahead output optimization method
CN105703467A (en) * 2016-03-25 2016-06-22 中能易电新能源技术有限公司 Method and apparatus for charging electric vehicle by photovoltaic grid-connected system
CN105958479A (en) * 2016-05-24 2016-09-21 广东电网有限责任公司电力科学研究院 Energy management optimizing method of microgrid including sodium-sulfur cells
CN106249714A (en) * 2016-08-23 2016-12-21 华电电力科学研究院 A kind of distributed energy remote monitoring and managing system and method
CN106296446A (en) * 2016-07-29 2017-01-04 国家电网公司 A kind of power supply safety management system and method
CN106374501A (en) * 2016-11-17 2017-02-01 新智能源系统控制有限责任公司 Micro grid system for balancing power supplied by micro power sources and power consumed by loads
CN106529747A (en) * 2017-01-04 2017-03-22 成都四方伟业软件股份有限公司 Power load predicting method and system based on large data
CN106960252A (en) * 2017-03-08 2017-07-18 深圳市景程信息科技有限公司 Methods of electric load forecasting based on long Memory Neural Networks in short-term
CN107017625A (en) * 2017-04-28 2017-08-04 北京天诚同创电气有限公司 The method and apparatus that energy dynamics for independent micro-capacitance sensor are dispatched
CN107181280A (en) * 2017-06-15 2017-09-19 上海泛智能源装备有限公司 A kind of photovoltaic consumption system
CN108075471A (en) * 2017-12-27 2018-05-25 国电南瑞科技股份有限公司 Multi-objective constrained optimization dispatching of power netwoks strategy based on the output prediction of randomness power supply
CN109767033A (en) * 2018-12-25 2019-05-17 深圳供电局有限公司 Dispatching method, device, computer equipment and the storage medium of photovoltaic electric
CN110071576A (en) * 2019-04-23 2019-07-30 国核电力规划设计研究院有限公司 Region distribution system and method
CN111193293A (en) * 2019-12-31 2020-05-22 国网北京市电力公司 Power distribution network coordinated multi-main-body scheduling processing method and device

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004147445A (en) * 2002-10-25 2004-05-20 Hitachi Ltd Distributed power supply system and its control method
CN102521670A (en) * 2011-11-18 2012-06-27 中国电力科学研究院 Power generation output power prediction method based on meteorological elements for photovoltaic power station
CN102684199A (en) * 2012-06-05 2012-09-19 国电南瑞科技股份有限公司 Multiple time scale control method of exchange power of microgrid and power distribution network
CN103218673A (en) * 2013-03-27 2013-07-24 河海大学 Method for predicating short-period output power of photovoltaic power generation based on BP (Back Propagation) neural network
CN103400204A (en) * 2013-07-26 2013-11-20 华南理工大学 Forecasting method for solar photovoltaic electricity generation amount based on SVM (support vector machine) - Markov combination method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004147445A (en) * 2002-10-25 2004-05-20 Hitachi Ltd Distributed power supply system and its control method
CN102521670A (en) * 2011-11-18 2012-06-27 中国电力科学研究院 Power generation output power prediction method based on meteorological elements for photovoltaic power station
CN102684199A (en) * 2012-06-05 2012-09-19 国电南瑞科技股份有限公司 Multiple time scale control method of exchange power of microgrid and power distribution network
CN103218673A (en) * 2013-03-27 2013-07-24 河海大学 Method for predicating short-period output power of photovoltaic power generation based on BP (Back Propagation) neural network
CN103400204A (en) * 2013-07-26 2013-11-20 华南理工大学 Forecasting method for solar photovoltaic electricity generation amount based on SVM (support vector machine) - Markov combination method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
郑凌蔚等: ""基于非线性扩散粒子群算法的光伏微网并网点恒定潮流控制"", 《电网技术》 *
马溪原等: ""采用改进细菌觅食算法的风/光/储混合微电网电源优化配置"", 《中国电机工程学报》 *

Cited By (20)

* Cited by examiner, † Cited by third party
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CN104600731A (en) * 2015-02-06 2015-05-06 山东理工大学 Energy storage system control method of optical storage system for peak shifting
CN105490267A (en) * 2015-12-28 2016-04-13 易事特集团股份有限公司 Micro grid energy management system and energy management method
CN105610201A (en) * 2016-02-29 2016-05-25 国家电网公司 Photovoltaic distributed type power supply day-ahead output optimization method
CN105703467A (en) * 2016-03-25 2016-06-22 中能易电新能源技术有限公司 Method and apparatus for charging electric vehicle by photovoltaic grid-connected system
CN105958479A (en) * 2016-05-24 2016-09-21 广东电网有限责任公司电力科学研究院 Energy management optimizing method of microgrid including sodium-sulfur cells
CN106296446A (en) * 2016-07-29 2017-01-04 国家电网公司 A kind of power supply safety management system and method
CN106249714A (en) * 2016-08-23 2016-12-21 华电电力科学研究院 A kind of distributed energy remote monitoring and managing system and method
CN106374501A (en) * 2016-11-17 2017-02-01 新智能源系统控制有限责任公司 Micro grid system for balancing power supplied by micro power sources and power consumed by loads
CN106374501B (en) * 2016-11-17 2019-05-24 新智能源系统控制有限责任公司 A kind of micro-grid system for realizing micro battery power supply and load coulomb balance
CN106529747A (en) * 2017-01-04 2017-03-22 成都四方伟业软件股份有限公司 Power load predicting method and system based on large data
WO2018161722A1 (en) * 2017-03-08 2018-09-13 深圳市景程信息科技有限公司 Power load forecasting method based on long short-term memory neural network
CN106960252A (en) * 2017-03-08 2017-07-18 深圳市景程信息科技有限公司 Methods of electric load forecasting based on long Memory Neural Networks in short-term
CN107017625A (en) * 2017-04-28 2017-08-04 北京天诚同创电气有限公司 The method and apparatus that energy dynamics for independent micro-capacitance sensor are dispatched
CN107017625B (en) * 2017-04-28 2019-12-06 北京天诚同创电气有限公司 Method and apparatus for dynamic energy scheduling for independent micro-grids
CN107181280A (en) * 2017-06-15 2017-09-19 上海泛智能源装备有限公司 A kind of photovoltaic consumption system
CN108075471A (en) * 2017-12-27 2018-05-25 国电南瑞科技股份有限公司 Multi-objective constrained optimization dispatching of power netwoks strategy based on the output prediction of randomness power supply
CN109767033A (en) * 2018-12-25 2019-05-17 深圳供电局有限公司 Dispatching method, device, computer equipment and the storage medium of photovoltaic electric
CN110071576A (en) * 2019-04-23 2019-07-30 国核电力规划设计研究院有限公司 Region distribution system and method
CN111193293A (en) * 2019-12-31 2020-05-22 国网北京市电力公司 Power distribution network coordinated multi-main-body scheduling processing method and device

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