CN109859071A - The source lotus that virtual plant is provided multiple forms of energy to complement each other stores up Optimal Configuration Method - Google Patents
The source lotus that virtual plant is provided multiple forms of energy to complement each other stores up Optimal Configuration Method Download PDFInfo
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Abstract
The present invention relates to a kind of source lotuses that virtual plant is provided multiple forms of energy to complement each other to store up Optimal Configuration Method, according to prediction workload demand, inside sources place capacity, energy storage device capacity, establishes virtual plant operating cost model；According to the generation of cool and thermal power and energy-output ratio between internal distributed energy, internal distributed energy moving model is established；Then the operating cost model and distributed energy moving model for applying evolution particle swarm algorithm and virtual plant, to the carry out disposition optimization of distributed energy in virtual plant；To the carry out disposition optimization of distributed energy in virtual plant；Last basis predicts workload demand amount, the Spot Price of external electrical network, virtual plant source device operating parameter a few days ago, determines the minimum operating scheme of relative motion cost.The present invention can comprehensively, accurately, easily calculate the Optimization deployment scheme of distributed energy in virtual plant moving model, be virtual plant benefit and instruct the adjusting providing method of power grid.
Description
Technical field
The present invention relates to a kind of energy source configuration technology, in particular to a kind of source lotus that virtual plant is provided multiple forms of energy to complement each other storage optimization is matched
Set method.
Background technique
Virtual plant can by advanced Information and Communication Technology and software systems, realize distributed generation resource, energy-storage system, can
Control load, the polymerization of the electric car distributed energy and coordination optimization, using as a special power plant participate in electricity market and
The power supply coordinated management system of operation of power networks.
Modern electric application in, distributed energy using more and more extensive.Since distributed energy has randomness
And the features such as uncertainty, causing it to contribute, uneven, harmonic content is big, is difficult to coordinate to utilize.As distributed generation resource is passing
Permeation rate in power grid under unified central planning is continuously increased, and causes some adverse effects to electric system when generating electricity by way of merging two or more grid systems.Largely
Distributed generation resource access traditional power grid can make power grid output and input power mismatch, so that frequency and voltage magnitude be caused to fluctuate
Seriously, the stable operation for having threatened power grid strongly limits application of the distributed energy in modern power systems.
The use of wind energy, photovoltaic distributed power supply has timeliness, and the electric energy that weather condition difference obtains is different, and wind
Energy night is relatively stable, generating efficiency is higher, and photovoltaic can only generate electricity in the daytime.It is not known since wind-force, photovoltaic etc. have
Property, and the power quality generated poor is not able to satisfy real-time load demand.The load of external electrical network also has real-time, virtual electricity
When factory's internal load demand is high, external electrical network negative demand is higher；When virtual plant internal load demand is lower, external electrical network load
Demand is lower.
Due to load unbalanced supply-demand, causes higher abandonment, abandons optical phenomenon, cause energy utilization rate lower.And combustion gas
The equipment such as turbine can generate thermal energy while producing electricl energy, cannot be rationally using will cause a large amount of energy waste problem.
Summary of the invention
The problem of the present invention be directed to the storage of source lotus is distributed rationally in domestic and international virtual plant, it is more to propose a kind of virtual plant
Source lotus that can be complementary stores up Optimal Configuration Method, it is contemplated that the wind-solar-storage joint of electric energy is provided multiple forms of energy to complement each other, supply of cooling, heating and electrical powers is provided multiple forms of energy to complement each other
And Environmental costs problem, scheduling is optimized to source lotus storage in virtual plant using the particle swarm algorithm of evolution, by a few days ago
Prediction carries out device parameter setting.Obtain the source lotus storage allocation optimum scheme that virtual plant in virtual plant is provided multiple forms of energy to complement each other.
The technical solution of the present invention is as follows: a kind of source lotus that virtual plant is provided multiple forms of energy to complement each other stores up Optimal Configuration Method, specifically include
Following steps:
1) according to prediction workload demand, inside sources place capacity, energy storage device capacity, virtual plant operating cost mould is established
Type；
2) according to the generation of cool and thermal power and energy-output ratio between internal distributed energy, internal distributed energy fortune is established
Row model；
3) the operating cost model and distributed energy moving model for applying evolution particle swarm algorithm and virtual plant, to void
The carry out disposition optimization of distributed energy in quasi- power plant；
Be introduced into the storage of source lotus in evolution particle swarm algorithm to distribute rationally: evolution particle swarm algorithm will use per period electric power storage
As particle, virtual plant operating cost is minimised as objective function and finds target the amount of pond charge and discharge, when obtaining optimal solution
Particle numerical value determines the actual disposition amount of distributed energy inside per period virtual plant；
4) according to workload demand amount, the Spot Price of external electrical network, virtual plant source device operating parameter is predicted a few days ago, really
Determine the minimum operating scheme of relative motion cost, i.e., the source lotus that virtual plant is provided multiple forms of energy to complement each other stores up the optimal case of Optimized Operation.
Step 1) the virtual plant operating cost model, in the case where disregarding energy transfer losses, virtual plant fortune
Row cost CDhForWithThe sum of, unit is member；WhereinFor void
Quasi- power plant interacts expense with power grid；To buy natural gas expense；For battery aging cost；It is virtual
The operation expense of power plant；For the Environmental costs of virtual plant operation；Environmental costs are as follows:
M is pollutant kind；VejIt is the environmental improvement cost of jth item pollutant, unit is member/kg；DejIt is heat production unit
The jth item pollutant discharge amount of specific power, unit are kg/ (MWh)；VjIt is punishing for thermoelectricity unit discharge jth item pollutant
Penalize cost；The electrical power that the expression miniature gas turbine t period generates；The heat generated for t period gas turbine and boiler
Can and；
The objective function that virtual plant operating cost minimizes are as follows:
Wherein T be a few days ago dispatching cycle when number of segment, be 24.
The internal distributed energy moving model of the step 2), under the premise of disregarding energy loss, each moment is cold and hot
The demand of electricity is equal to the supply in virtual plant；Refrigeration source includes refrigerating machine, air-conditioning；Heating source includes boiler, combustion gas
Turbine；Energy source includes gas turbine, photovoltaic, wind energy；Public affairs are interacted by the source device of cool and thermal power and electricity storage, heat storage, power grid
The supply of cold and hot electric load is completed altogether.
The evolution particle swarm algorithm is introduced into selection, intersection and mutation operator in genetic algorithm, by traverse it is all can
It can be as a result, obtaining globally optimal solution.
The beneficial effects of the present invention are: the source lotus that virtual plant of the present invention is provided multiple forms of energy to complement each other stores up Optimal Configuration Method, can
Comprehensively, the Optimization deployment scheme of distributed energy in virtual plant moving model accurately, is easily calculated, is virtual plant benefit
Benefit is maximized and is instructed the adjusting providing method of power grid.
Detailed description of the invention
Fig. 1 is the method for the present invention virtual plant inside sources lotus storage and transportation row decision flow chart.
Specific embodiment
The source lotus that virtual plant is provided multiple forms of energy to complement each other stores up Optimal Configuration Method, specifically comprises the following steps:
1, according to prediction workload demand, inside sources place capacity, energy storage device capacity, virtual plant operating cost mould is established
Type；
In the case where disregarding energy transfer losses, C is enabledDhFor virtual plant operating cost, unit is member, and T is to adjust a few days ago
Spend period times number；Expense is interacted with power grid for virtual plant VPP, unit is member；To buy natural gas expense,
Unit is member；For battery aging cost, unit is member；For the operation expense of virtual plant, unit is member.For the Environmental costs of virtual plant operation, unit is member.Power grid interacts costAre as follows:
WhereinIt is t period virtual plant to power grid purchase electricity price, unit is member/(kWh)；When for t
For section virtual plant to power grid power purchase power, unit is (kWh)；It is t period virtual plant to power grid sale of electricity electricity price,
Unit is member/(kWh)；It is t period virtual plant to power grid sale of electricity power, unit is (kWh)；Δ t is fortune
The row time,
Fuel cost are as follows:
In formula:Gas Prices, member/m are bought for t period virtual plant3；The heat generated for gas turbine
Power；The thermal power generated for boiler；HNGFor heating value of natural gas；Δ t is runing time.
Accumulator cell charging and discharging aging cost:
In formulaFor the unit time aging cost of battery, identical element/number；For charge flag position；For the flag bit that discharges.
Operation expense are as follows:
In formula:The electrical power that the expression miniature gas turbine t period generates；RMT,rmIndicate miniature gas turbine operation dimension
Shield expense；The thermal power that the expression gas fired-boiler t period generates；RGB,rmIndicate gas fired-boiler operation and maintenance cost；Table
Show the thermic load power of virtual plant t period；ηheIndicate effectiveness of heat exchanger；Rhe,rmIndicate heat exchanger operation and maintenance cost；Indicate the input power of refrigerating machine t period；RTR,rmIndicate refrigerating machine operation and maintenance cost；Indicate the air-conditioning t period
Input electric power；RAC,rmIndicate operation of air conditioner maintenance cost；Indicate the predicted value of photovoltaic t period；RPV,rmIndicate photovoltaic
Battery maintenance expense；Indicate t period energy storage charge and discharge power；Indicate t period heat
Store up storage system power；RBS,rmIndicate battery operation and maintenance cost coefficient；RHS,rmIndicate heat storage operation and maintenance cost coefficient.
Environmental costs are as follows:
M is pollutant kind；VejIt is the environmental improvement cost of jth item pollutant, unit is member/kg；DejIt is heat production unit
The jth item pollutant discharge amount of specific power, unit are kg/ (MWh)；VjIt is punishing for thermoelectricity unit discharge jth item pollutant
Penalize cost.The electrical power that the expression miniature gas turbine t period generates；The heat generated for t period gas turbine and boiler
Can and.
The objective function that virtual plant operating cost minimizes are as follows:
Wherein T be a few days ago dispatching cycle when number of segment, be equal to 24 (24 hours one day).
2, according to the generation of cool and thermal power and energy-output ratio between internal distributed energy, internal distributed energy fortune is established
Row model；
When source lotus inside virtual plant stores up Optimized Operation, internal distributed energy moving model is disregarding energy loss
Under the premise of, the demand of each moment cool and thermal power is equal to the supply in virtual plant.Refrigeration source includes refrigerating machine, air-conditioning；
Heating source includes boiler, gas turbine；Energy source includes gas turbine, photovoltaic, wind energy.Pass through the source device and electricity of cool and thermal power
It stores up, heat is stored up, the supply of the cold and hot electric load of the public completion of interaction of power grid.
Cold power-balance constraint:
In formula: COPTR、COPACRespectively refrigerating machine, air conditioner refrigerating coefficient；QCFor t period refrigeration duty demand.
Heating power balance constraint:
In formulaIt is the thermal power that gas turbine generates；ηREIt is the efficiency of waste heat recycling；Be the t period heat it is negative
Lotus power；ηHEIt is the Heat transmission proportion of goods damageds.
Electrical power Constraints of Equilibrium:
WhereinFor the electric load of t period；The electric load generated for blower.
3, the particle swarm algorithm (E-PSO) of evolution is improved with genetic algorithm, particle swarm algorithm, using the grain of evolution
The moving model and distributed energy moving model of swarm optimization and virtual plant, the progress to distributed energy in virtual plant
Disposition optimization；
The storage of source lotus is introduced into evolution particle swarm algorithm to distribute rationally:
Evolution particle swarm algorithm will use the amount of per period accumulator cell charging and discharging as particle, most with final economic cost
Low is to find target.According to photovoltaic, blower, gas turbine, electricity storage, hot storage, boiler, air-conditioning, refrigerating machine model practical fortune
Row constraint search range, meets the optimal solution that cold and hot electric equilibrium in step 2 seeks economic cost, and population scale N takes 100；
The number of iterations takes 500；Search space dimension is 2；Studying factors c1=c2=1.5；Inertia weight w=0.6；It is adaptive to intersect generally
Rate takes 0.9；Mutation probability takes 0.5.And particle numerical value when obtaining optimal solution, determine the actual disposition of each model of per period
Amount.
Finally acquire the objective function in step 1:
Selection, intersection and mutation operator in genetic algorithm are added in particle swarm algorithm: in conventional particle group's algorithm
Operator global search performance it is relatively weak, the particle swarm algorithm of evolution is introduced into selection, intersection and variation in genetic algorithm
Operator obtains globally optimal solution, obtains the actual disposition amount and warp of each model of per period by traversing all possible outcomes
Ji minimum operation cost.
4, according to workload demand amount, the Spot Price of external electrical network, virtual plant source device operating parameter is predicted a few days ago, really
Determine the optimal scheduling scheme of relatively economical.
Distribute the virtual plant source lotus optimized using the particle swarm algorithm of evolution storage rationally middle photovoltaic, blower, combustion
The mechanical, electrical storage of turbine, hot storage, boiler, air-conditioning, refrigerating machine model use parameter, will be to source lotus storage configuration side in virtual plant
Output target of the total operating cost as the storage configuration of source lotus in case.
The source lotus storage Optimized Operation that the finally minimum operating scheme of selection relative motion cost, i.e. virtual plant are provided multiple forms of energy to complement each other
Optimal case.
Virtual plant inside sources lotus storage and transportation row decision flow chart as shown in Figure 1, a kind of virtual plant inside sources lotus storage and transportation row
Mode includes the following steps:
1, load prediction, source device prediction are carried out first.It is local cold that the cold and hot electrical load requirement of next day generally passes through analysis
Thermoelectricity historic demand amount combines next day weather forecasting situation to be predicted, since comfort level of the user for temperature is in a model
Within enclosing, and Temperature prediction is more accurate.Source device is predicted by equipment shop instructions and weather forecasting situation.
2, according to service condition, the thermal energy that the gas turbine and boiler of thermic load and prediction to prediction generate compares
Compared with.Gas turbine waste heat feeding refrigerating machine is freezed if the thermal energy that heat source generates is higher than thermic load, boiler delayed heat
It can be sent into heat storage and be saved, more new data；Enter if the demand that the thermal energy that heat source generates is not able to satisfy thermic load next
Step judgement.
3, the thermal energy of gas turbine, the boiler of the thermic load of prediction and the prediction thermal energy generated and heat storage release is carried out
Compare.The more new data if the thermal energy of thermal energy and heat storage release that heat source generates is higher than thermic load；If the thermal energy that heat source generates
And the thermal energy of heat storage release then carries out next step judgement lower than thermic load；
4, thermal energy, the hot thermal energy and light for storing up release that gas turbine, the boiler of the thermic load of prediction and prediction are generated
The electric heating that volt Fan Equipment generates is compared.If the thermal energy that heat source generates, the thermal energy of heat storage release, photovoltaic Fan Equipment generate
Electric heating be higher than thermic load then more new data；If the thermal energy that heat source generates, the thermal energy of heat storage release, photovoltaic Fan Equipment generate
Electric heating is then judged according to business type virtual plant CVPP from power grid power purchase or electricity storage electricity lower than thermic load.
5, it is compared according to dump energy in more new data and prediction electric load (including cooling load).If remaining electricity
Electric load can be greater than, sale of electricity energy is determined according to business type virtual plant or charged to energy storage；If it is negative that dump energy is less than electricity
Lotus determines purchase electric energy according to business type virtual plant or is discharged by energy storage.
It is distributed rationally by the source lotus storage provided multiple forms of energy to complement each other, electric energy is sold when power grid electricity price is higher, in power grid electricity price
Lower is to buy electric energy；Environmental costs are added, environmental pollution is effectively reduced；It is provided multiple forms of energy to complement each other by cool and thermal power and effectively improves energy benefit
With rate, by the way that compared with the virtual plant operation to be scheduled, operating cost of the present invention has significant decrease.
Claims (4)
1. a kind of source lotus that virtual plant is provided multiple forms of energy to complement each other stores up Optimal Configuration Method, which is characterized in that specifically comprise the following steps:
1) according to prediction workload demand, inside sources place capacity, energy storage device capacity, virtual plant operating cost model is established；
2) it according to the generation of cool and thermal power and energy-output ratio between internal distributed energy, establishes internal distributed energy and runs mould
Type；
3) the operating cost model and distributed energy moving model for applying evolution particle swarm algorithm and virtual plant, to virtual electricity
The carry out disposition optimization of distributed energy in factory；
Be introduced into the storage of source lotus in evolution particle swarm algorithm to distribute rationally: evolution particle swarm algorithm will use per period battery fill
As particle, virtual plant operating cost is minimised as objective function and finds target the amount of electric discharge, obtains particle when optimal solution
Numerical value determines the actual disposition amount of distributed energy inside per period virtual plant；
4) according to workload demand amount, the Spot Price of external electrical network, virtual plant source device operating parameter is predicted a few days ago, phase is determined
The optimal case for the source lotus storage Optimized Operation that the operating scheme minimum to operating cost, i.e. virtual plant are provided multiple forms of energy to complement each other.
2. the source lotus that virtual plant is provided multiple forms of energy to complement each other according to claim 1 stores up Optimal Configuration Method, which is characterized in that the step
Rapid 1) virtual plant operating cost model, in the case where disregarding energy transfer losses, virtual plant operating cost CDhForWithThe sum of, unit is member；WhereinIt is handed over for virtual plant and power grid
Mutual expense；To buy natural gas expense；For battery aging cost；For the operation and maintenance of virtual plant
Cost；For the Environmental costs of virtual plant operation；
Environmental costs are as follows:
M is pollutant kind；VejIt is the environmental improvement cost of jth item pollutant, unit is member/kg；DejIt is heat production unit unit
The jth item pollutant discharge amount of power output, unit are kg/ (MWh)；VjBe thermoelectricity unit discharge jth item pollutant punishment at
This；The electrical power that the expression miniature gas turbine t period generates；The thermal energy generated for t period gas turbine and boiler
With；
Wherein T be a few days ago dispatching cycle when number of segment, be 24.
3. the source lotus that virtual plant is provided multiple forms of energy to complement each other according to claim 1 stores up Optimal Configuration Method, which is characterized in that the step
Rapid 2) internal distributed energy moving model, under the premise of disregarding energy loss, the demand of each moment cool and thermal power is equal to
Supply in virtual plant；Refrigeration source includes refrigerating machine, air-conditioning；Heating source includes boiler, gas turbine；Energy source includes
Gas turbine, photovoltaic, wind energy；It is stored up by the source device and electricity storage, heat of cool and thermal power, the cold and hot electric load of the public completion of interaction of power grid
Supply.
4. virtual plant is provided multiple forms of energy to complement each other according to claim 1 source lotus stores up Optimal Configuration Method, which is characterized in that it is described into
Change particle swarm algorithm and be introduced into selection, intersection and mutation operator in genetic algorithm, by traversing all possible outcomes, obtains global
Optimal solution.
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| CN110322056A (en) * | 2019-06-19 | 2019-10-11 | 河海大学 | It is a kind of meter and central air conditioner system the random ADAPTIVE ROBUST Optimization Scheduling of virtual plant |
| CN111210079A (en) * | 2020-01-09 | 2020-05-29 | 华北电力大学 | Operation optimization method and system for distributed energy virtual power plant |
| CN111310973A (en) * | 2020-01-17 | 2020-06-19 | 清华大学 | Method for calculating adjustment cost of multifunctional virtual power plant |
| CN111382939A (en) * | 2020-03-06 | 2020-07-07 | 国网冀北电力有限公司 | Virtual power plant resource optimal configuration method, device and equipment |
| CN111536664A (en) * | 2020-05-09 | 2020-08-14 | 广州华跃电力工程设计有限公司 | Virtual power plant system of gas coupling central air conditioner |
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| CN112215433B (en) * | 2020-10-21 | 2024-05-07 | 国网冀北电力有限公司 | Virtual power plant day-ahead optimal scheduling method based on uncertainty of market electricity price |
| CN112419087A (en) * | 2020-11-20 | 2021-02-26 | 国网江苏省电力有限公司南京供电分公司 | Day-ahead optimal scheduling method for virtual power plant of aggregated comprehensive energy building |
| CN112419087B (en) * | 2020-11-20 | 2022-10-04 | 国网江苏省电力有限公司南京供电分公司 | Day-ahead optimal scheduling method for virtual power plant of aggregated comprehensive energy building |
| CN112766680A (en) * | 2021-01-08 | 2021-05-07 | 南京工程学院 | Virtual power plant controllable heat load scheduling method, system, equipment and electronic medium |
| CN112766680B (en) * | 2021-01-08 | 2024-02-09 | 南京工程学院 | Controllable thermal load scheduling method for virtual power plant |
| CN113570405A (en) * | 2021-07-01 | 2021-10-29 | 国网能源研究院有限公司 | Power generation and utilization cost modeling analysis method and device for self-contained power plant |
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