CN105958537B - Towards the energy conversion system and its optimal control method of energy internet - Google Patents
Towards the energy conversion system and its optimal control method of energy internet Download PDFInfo
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Abstract
The present invention relates to a kind of energy conversion system and its optimal control method towards energy internet, belong to energy source optimization technical field；Primarily to solving prior art energy conversion utilizes the problems such as not comprehensive；The system includes electric energy supply unit, thermal energy supply unit, energy conversion center and energy output unit, its control method is by calculating operating cost under single goal, the minimum value of pollution abatement costs, the initial position and initial velocity of random setting particle, and the speed of each particle is limited, the position and speed of particle are updated, trying to achieve makes the maximum membership degree value of intersection membership function, colony's extreme value of more new particle, the neighborhood solution currently solved by certain criterion generation, and some solutions are selected from the neighborhood solution of generation and are used as candidate solution, judge whether the candidate solution of generation meets aspiration criterion, the iterations of more new particle, whether the optimal solution and iterations that judgement is newly generated meet stopping criterion for iteration.
Description
Technical field
The invention belongs to energy source optimization technical field, and in particular to a kind of energy conversion system towards energy internet and
Its optimal control method.
Background technology
The energy is basis for the survival of mankind, in recent years, since excessive use of resource causes non-renewable energy resources
Amount of storage is extremely reduced, and in order to tackle energy crisis, various countries expert is directed to studying new energy technology.Due to wind energy, solar energy,
The regenerative resources such as tide energy have the advantages that renewable, clean environment firendly, pretend as main study subject, and renewable energy power generation
Randomness and intermittence it is bigger, the scattered access of distributed energy can cause electric network swim complex distribution changeable, not only sternly
The problems such as ghost image rings the quality of power supply and can cause the sensitivity decrease of system protection malfunction and action, so as to electricity
The stable operation of net produces threat.
In order to enable distributed energy efficiently to utilize and ensure power grid operation, it is necessary to solve distributed energy point
The problem of scattered access causes.A kind of new energy resource structure-energy internet comes into being, and energy internet leads on the basis of existing
Power Electronic Technique and information technology is crossed to combine renewable energy system and distributed energy storage system, relative to
It is a complete modular unit for bulk power grid, from the point of view of user, energy internet can meet user to electric energy
Basic demand.Energy internet is by an energy conversion system by different types of distributed energy with thermal energy and electric energy
Form shows, and solves the problems, such as that the scattered access of distributed energy causes.
The current prior art has an energy source router based on energy internet, such as by two kinds of electric energy of wind energy and solar energy
Energy input can obtain the summation of two kinds of electric energy energy, but this technology does not collect at the same time into energy conversion system in outlet
Into electric energy and thermal energy, its there are the problem of be that energy supply is unstable, energy supply is of low quality, energy conversion utilization is not comprehensive；As it can be seen that carry
The utilization rate of high resource, ensures load energy, and the load of user side is predicted, and makes energy conversion system outlet can be same
When obtain the energy such as electric energy and thermal energy, improve energy supply quality be particularly important.
The content of the invention
For disadvantages mentioned above, the present invention provides a kind of towards the energy conversion system of energy internet and its optimal control
Method, its electric energy is made of four power grid, wind energy, solar energy and CHP units (i.e. cogeneration unit) parts, by these four energy
Amount is input in energy conversion system, these four electric energy energy summations are obtained in outlet；Thermal energy is by heat supply network, CHP units, electric accumulation of heat
Formed with four parts of electric boiler heating system, the thermal energy obtained by the defeated converting system of energy is the summation of these four heat sources.
The present invention is solved the prior art and is individually energized for system using distributed generation resource, and energy supply is unstable, energizes the of low quality, energy
The problems such as conversion is not comprehensive.
To achieve these goals, present invention employs following technical scheme：
A kind of energy conversion system towards energy internet, including electric energy supply unit, thermal energy supply unit, the energy turn
Switching center9 and energy output unit；The energy conversion center includes inversion boosting unit one, the alternating current line of force one, alternating electromotive force
Line two, rectifier one, energy-storage battery unit, CHP units, electric thermal storage unit, electric boiler heating unit, heat supply network and control are single
Member；The electric energy supply unit includes photovoltaic generation unit, wind turbine power generation unit and power grid；The thermal energy supply unit includes combustion
Gas supply unit and heat supply network；The energy output unit includes DC load, AC load and thermic load；The energy-storage battery list
Member includes rectifier two, energy-storage battery, inversion boosting unit two and static transfer switch, the rectifier two and energy-storage battery phase
Even, the energy-storage battery is connected with inversion boosting unit two, and static state is connected between the rectifier two and inversion boosting unit two
Change-over switch, the CHP units include gas fired-boiler, heat exchanger, waste heat recovery valve, gas internal-combustion engine, electric compression refigerating machine, electricity
Switch board one, system water pump and pump power load demand, the gas fired-boiler are connected with heat exchanger, heat exchanger connection waste heat recovery
Valve, waste heat recovery valve are connected with gas internal-combustion engine and electric compression refigerating machine respectively, gas internal-combustion engine and electricity compression refigerating machine with
Electrical control cabinet one is connected, one welding system water pump of electrical control cabinet, and system water pump is connected with pump power load demand；The electric boiler supplies
Hot cell includes filter, water circulating pump, boilers heated electrically, hot water storage tank, expansion tank, electrical control cabinet two and water knockout drum, described
Expansion tank one end connects water treatment device, other end connection filter and water circulating pump, the filter and hot water storage tank
Between be connected to water knockout drum, the hot water storage tank is connected with boilers heated electrically, boilers heated electrically connection water circulating pump and electrical control cabinet
Two；The electricity thermal storage unit includes electrical control cabinet three, thermal storage electric boiler, hold over system and goes out heat control valve (HCV), the electrical control cabinet
Three are connected with thermal storage electric boiler, and thermal storage electric boiler connects hold over system, and hold over system connects out heat control valve (HCV)；The photovoltaic generator
Group and wind turbine power generation unit are connected with inversion boosting unit one, the inversion boosting unit one, rectifier one, rectifier two and electricity
Equal incoming transport power line one is netted, the rectifier one is connected with DC load, and the DC load is connected with energy-storage battery；Institute
State fuel gas transmission unit and be connected to combustion gas switch board, combustion gas switch board is connected with gas fired-boiler, inversion boosting unit two, power grid, electricity
Switch board one, electrical control cabinet two and electrical control cabinet three connect with the two-phase of the alternating current line of force, and it is negative that the alternating current line of force two connects exchange
Lotus, the electrical control cabinet two are connected with electrical control cabinet three, and the heat supply network is connected with heating network control valve, and the gas fired-boiler, change
Hot device, water knockout drum, go out heat control valve (HCV) and heating network control valve accesses heat supply network, and the heat supply network connects thermic load, the energy storage electricity
Pool unit, electric thermal storage unit, CHP units and electric boiler heating unit are connected with control unit.
The inversion boosting unit one and inversion boosting unit two include uncontrollable rectifier circuit, boost booster circuits and
AC conversion is direct current by full bridge inverter, the uncontrollable rectifier circuit, and the boost booster circuits are by uncontrollable rectifier
The direct current electric boost that circuit produces, the direct current for meeting inversion requirement or supply direct current are provided for follow-up full bridge inverter
Load uses；The DC conversion that the full bridge inverter produces boost booster circuits for can with power grid is in parallel exchanges
Electricity, or be converted into and directly feed the alternating current that AC load uses.
The energy-storage battery unit adjusts the electric energy towards the energy conversion system of energy internet by discharge and recharge, when being
The electric energy produced unite when supply exceed demand, energy-storage battery unit charges；When supply falls short of demand for system power supply, energy-storage battery unit
Discharge.
The CHP units, i.e. cogeneration unit, it is possible to provide electric energy and thermal energy needed for AC load and thermic load.
The electricity thermal storage unit, when supply exceed demand for the electric energy in system, can convert electrical energy into thermal energy and by conversion
Thermal energy is stored；When supply falls short of demand for the electric energy in system, releasable thermal energy.
The electric boiler heating unit, can convert electrical energy into thermal energy；And when unit operation power charge expense is relatively low, use
Electric boiler heating unit directly provides thermal energy to thermic load.
The present invention also provides a kind of energy conversion optimal control method towards energy internet, following step is specifically included
Suddenly：
Step 1：Need to establish multiple objective function according to optimum results, calculate the sons such as operating cost, pollution abatement costs
Minimum value of the object function under constraints, establishes the membership function on object function by membership function, solves
The minimum value of membership function, and then multi objective function optimization problem is converted into single goal nonlinear optimal problem；
Step 2：According to electric, thermic load predicted value in particle number and system is chosen, the initial of particle is set at random
Position and initial velocity, and the speed of each particle is limited, while the taboo list of sky is set；
Step 3：The position and speed of existing particle are updated, find the optimal value of target membership function；
Step 4：According to the operating cost and environment that system at this time is calculated by updating the position of obtained particle
Pollution abatement costs, then tries to achieve the maximum of intersection membership function by membership function, this solution is multiple objective function pair
The optimal solution answered；
Step 5：Optimal solution according to the multiple objective function that each particle obtains is contrasted is met the individual extreme value of condition, leads to
Cross the colony's extreme value for comparing current individual extreme value and current group extreme value more new particle；
Step 6：According to the current solution of generation, the neighborhood solution currently solved by certain criterion generation, and from the neighbour of generation
Some solutions are selected in the solution of domain and are used as candidate solution；
Step 7：Judge whether the candidate solution of generation meets aspiration criterion；If so, perform step 7.1；If it is not, then perform step
Rapid 7.2；
Step 7.1：Using the candidate solution for meeting aspiration criterion as current solution, replace to enter earliest with its corresponding object and prohibit
Avoid the object in table, update optimal solution, and return to step 6；
Step 7.2：Using candidate's optimum solution of non-taboo as current solution, replace to enter earliest with the corresponding object of the solution and prohibit
Avoid the object in table, perform step 8；
Step 8：Membership function is substituted into according to the solution for generating previous step, obtaining makes membership function take maximum membership degree
It is worth corresponding solution, the individual extreme value and colony's extreme value of more new particle, and the iterations of more new particle；
Step 9：Whether the optimal solution and iterations, both any one that judgement is newly generated meet iteration ends bar
Part；If so, perform step 10；If it is not, then return to step 3；
Step 10：Iteration ends, export multiple objective function optimal solution.
The step 1 comprises the following steps that：
Step 1.1：According to according to optimum results need establish multiple objective function：
Wherein, Cq(t) it is the operating cost of t moment system, Cf(t) it is the fuel cost of t moment system, CR, cost(t) it is t
The maintenance cost of moment system blower generating set and photovoltaic generation unit, Cs(t) for t moment system and power grid it is grid-connected when electricity
Measure tranaction costs, Ce(t) it is the pollution abatement costs of t moment system,The pollution produced for t moment by CHP units is punished
Expense,SO is produced for t momentxPenalty price,The penalty price of NO is produced for t moment,Produced for t moment
Raw COxPenalty price, pg(t) it is t moment system to power grid power purchase power, v (t) is that t moment is burnt the volume of natural gas, and τ is
System operation time；
Step 1.2：Uncontrollable energy output situation is predicted；Using the system load demand amount of synchronization in recent years as
Historical data, the predicted load of system at this time is obtained by load forecasting method；The wind turbine power generation that will be changed by weather conditions
Unit, the output situation of photovoltaic unit are predicted；Wherein, Unit commitment is：
(1) electrical power balances
eg(t)+ev(t)+ew(t)+ec(t)-eb(t)=eload(t) (3)
Wherein, eg(t) electricity provided for t moment power grid, ev(t) electricity, e are exported for t moment photovoltaicw(t) it is t moment
Wind turbine power generation unit exports electricity, ec(t) electricity, e are provided for t moment CHP unitsb(t) it is t moment energy-storage battery discharge and recharge,
eload(t) it is t moment system electric load amount；
(2) heating power balance
qc(t)+qb(t)+qh(t)+qe(t)=qloads(t) (4)
Wherein, qc(t) heat provided for t moment CHP units, qb(t) heat provided for t moment electric boiler, qh(t)
The heat provided for t moment heat supply network, qe(t) heat provided for t moment electricity thermal storage unit, qloads(t) it is t moment research object
Thermic load amount；
(3) energy-storage battery constrains
Wherein, pomaxFor energy-storage battery maximum discharge power, pominFor energy-storage battery minimum discharge power, piminFor energy storage
Battery minimum charge power, pimaxFor energy-storage battery maximum charge power；pb(t) battery is in discharge condition, p during ＞ 0b(t) ＜
Battery is in charged state when 0；
(4) CHP units constraint
Wherein, xc(t) it is the actual electric load rate of CHP units,x cFor the minimum electric load rate of CHP units,It is mono- for CHP
The maximum electric load rate of member；
(5) heat supply constraint
Heating load, heat supply network, electric boiler heating unit and the e lectric-store heating to supply heat unit of CHP units need to change with time
And change, then the operating cost of unit interval CHP units is：
The operating cost of unit interval heat supply network is：
Cr=C Δs t (9)
Unit interval electric boiler heating unit operating cost：
Wherein, Ch(t) it is unit time CHP units operating cost, Cn1For Gas Prices, P2For t moment CHP units electricity
Power, η1For CHP unit electric work rate coefficients, L is natural gas low heat value, CrFor unit time heating network operation expense, CbFor unit when
Between electric boiler heating unit operating cost, C supplies level Waste Heat Price, C for heat supply networkgrid(t) it is t moment electricity price, PeSupplied for t moment electric boiler
Thermal power, η2Turn thermal transition efficiency for t moment electricity, Δ t is simulation time section；
Step 1.3：Choose operating cost (the i.e. C in addition to electric thermal storage unit in the unit intervalh、Cb、Cr) less in three
As main supplying heat source；
Step 1.4：Membership function on object function is established by membership function,
Wherein, μ (Fi(t)) it is object function Fi(t) membership function, Fi(t) it is i-th of object function, δiFor i-th
A receptible flexible angle value of object function, FiminFor the minimum function value of i-th of object function, wherein i=1,2, t=1,
2 ..., 24；
Step 1.5：The minimum value of membership function is solved, it is non-linear that multiple objective function optimal problem is converted into single goal
Optimize optimal problem, define maximum satisfaction λ：
λ=max min { μ (F1(t)), μ (F2(t))} (12)
Maximum in the minimum value that membership function intersection is obtained is carried as the optimal solution of multiple objective function for system
For energy scheduling strategy.
In the step 2, to being limited to for the speed V of particle：
- 15 ＜ V ＜ 20 (13)
Wherein, V is the speed for representing particle；
In the step 3, the calculating process that speed and position to particle are updated is as follows：
Wherein, VidFor the speed of particle,For the speed of kth time iteration particle i,For (k+1) secondary iteration particle i
Speed, ω is inertia weight, c1And c2For Studying factors, r1And r2For random factor,For kth time iteration average optimal pole
Value,For colony's extreme value of population,For the position of kth time iteration particle i,For the position of (k+1) secondary iteration particle i
Put, ωstartFor initial inertia weight, ωendFor iteration to maximum times when inertia weight；Wherein, d=1,2....D, i=
1,2 ..., n, N be current iteration number, n is population scale, NmaxFor maximum iteration, PndFor kth time iteration particle n's
Extreme value；
It is as follows to the maximum value calculation process of intersection membership function in the step 4：
The maximum satisfaction λ of definition,
λ=maxmin { μ (F1(t)), μ (F2(t))} (18)
Optimal solution using the maximum obtained as multiple objective function, energy scheduling strategy is provided for system；
In the step 5, the method for finding individual extreme value and colony's extreme value is：
The position of particle, obtains the multiple target letter of current particle when obtaining making the membership function be minimized according to step 4
Numerical value, solves the individual extreme value and colony's extreme value of membership function, brings the position of each particle into membership function and asks for being subordinate to
The value of function is spent, compares the membership function value between particle, chooses the maximum of membership function as individual extreme value；Ought
The individual extreme value that preceding iterations obtains and colony's extreme value that iteration obtains before, which are compared, chooses conduct larger in both
Colony's extreme value.
The energy conversion system towards energy internet of the present invention is turned with optimal control method by introducing a kind of energy
System is changed, strengthens the contact between different energy sources, for meeting electricity, the heat demand of user, distributed energy is connected with power grid
Feed system after the output of system is integrated, reduces the scattered access of the distributed energy shadow changeable to electric network swim complex distribution
Ring, improve the sensitivity of the quality increase system protection of power supply, ensure the stable operation of power grid；By in systems using taboo
Particle swarm optimization algorithm, reduces the time of system optimization, improves system accuracy, add the reliability of optimization, ensure be
Economic benefit, social benefit are optimal in system operational process.
Brief description of the drawings
Fig. 1 is the module connection figure of the embodiment of the present invention；
Fig. 2 is the structure diagram of the embodiment of the present invention；
Fig. 3 is the energy-storage battery cellular construction figure of the embodiment of the present invention；
Fig. 4 is the CHP cellular construction figures of the embodiment of the present invention；
Fig. 5 is the electric boiler heating unit structure chart of the embodiment of the present invention；
Fig. 6 is the electric thermal storage unit structure chart of the embodiment of the present invention；
Fig. 7 is the optimal control method flow chart of the embodiment of the present invention；
Wherein：1st, photovoltaic generation unit；2nd, wind turbine power generation unit；3rd, inversion boosting unit one；4th, rectifier one；5th, energy storage
Battery unit；6th, CHP units；7th, electric boiler heating unit；8th, electric thermal storage unit；9th, power grid；10th, fuel gas transmission unit；11st, fire
Gas switch board；12nd, heating network control valve；13rd, heat supply network；14th, heating network control valve；51st, rectifier two；52nd, energy-storage battery；53rd, inversion
Boosting unit two；54th, static transfer switch；61st, gas fired-boiler；62nd, heat exchanger；63rd, waste heat recovery valve；64th, gas internal-combustion engine；
65th, electric compression refigerating machine；66th, electrical control cabinet one；67th, system water pump；71st, filter；72nd, water circulating pump；73rd, boilers heated electrically；
74th, hot water storage tank；75th, electrical control cabinet two；76th, water knockout drum；81st, electrical control cabinet three；82nd, thermal storage electric boiler；83rd, hold over system；
84th, heat control valve (HCV) is gone out
Embodiment
Embodiments of the present invention is described in detail below in conjunction with the accompanying drawings.
As shown in Figure 1, towards the energy conversion system of energy internet, including electric energy supply unit, thermal energy supply unit,
Energy conversion center and energy output unit；The energy conversion center includes inversion boosting unit one, the alternating current line of force one, hands over
The galvanic electricity line of force two, rectifier one, energy-storage battery unit, CHP units, electric thermal storage unit, electric boiler heating unit, heat supply network and control
Unit processed.
(in order to make the figure clear, eliminate control unit) as shown in Figure 2, electric energy supply unit includes photovoltaic generation unit
1st, wind turbine power generation unit 2 and power grid 9；Thermal energy supply unit includes fuel gas transmission unit 10 and heat supply network 13；Energy output unit bag
Include DC load, AC load and thermic load.
As shown in figure 3, energy-storage battery unit 5 includes rectifier 2 51, energy-storage battery 52, inversion boosting unit 2 53 and quiet
State change-over switch 54, rectifier 2 51 are connected with energy-storage battery 52, and energy-storage battery 52 is connected with inversion boosting unit 2 53, rectification
Static transfer switch 54 is connected between device 2 51 and inversion boosting unit 2 53.Energy-storage battery unit 5 is used to adjust supply and demand injustice
Weighing apparatus problem, as stand-by unit, can also improve the schedulability of the distributed generation unit of system, the distribution for non-scheduling
Formula generator unit, energy-storage battery unit 5 can improve the schedulability of unit, and realization is incorporated into the power networks with power grid.Energy-storage battery
Unit adjusts the electric energy of system by discharge and recharge；When supply exceed demand for the electric energy that system produces, energy-storage battery unit charges,
Unnecessary electric energy is stored in the form of chemical energy；When supply falls short of demand for system power supply, energy-storage battery unit discharges,
It is system power supply that chemical energy is re-converted into electric energy.
Inversion unit boosting unit 1 and inversion boosting unit 2 53 include uncontrollable rectifier circuit, boost booster circuits
And full bridge inverter, be used for realization distributed generation resource directly feed load using and it is grid-connected with power grid, uncontrollable rectifier is main
For being direct current by AC conversion, the influence of frequency change is reduced；Boost booster circuits are used to produce in uncontrollable rectifier circuit
Raw direct current electric boost, the direct current for meeting inversion requirement is provided for follow-up full bridge inverter；Wherein, inversion boosting unit
Full bridge inverter in one is used for can be with the friendship of power grid parallel connection by being converted into by the direct current that boost booster circuits produce
Galvanic electricity, the direct current that the full bridge inverter in inversion boosting unit two is used to produce boost booster circuits is by being converted into
Directly feed the alternating current that AC load uses.
As shown in figure 4, CHP units 6, i.e. cogeneration unit, it is possible to provide electric energy needed for AC load and thermic load and
Thermal energy.CHP units 6 include gas fired-boiler 61, heat exchanger 62, waste heat recovery valve 63, gas internal-combustion engine 64, electric compression refigerating machine
65th, electrical control cabinet 1, system water pump 67 and pump power load demand, gas fired-boiler 61 are connected with heat exchanger 62, heat exchanger 62
Waste heat recovery valve 63 is connected, waste heat recovery valve 63 is connected with gas internal-combustion engine 64 and electric compression refigerating machine 65 respectively, combustion gas internal combustion
Machine 64 and electric compression refigerating machine 65 are connected with electrical control cabinet 1, one 66 welding system water pump 67 of electrical control cabinet, system water pump 67
It is connected with pump power load demand.CHP units 6 produce a certain proportion of electric energy and thermal energy according to the fuel of offer, there is provided load
Required electric energy and thermal energy；CHP units 6 are additionally operable to serve as stand-by station, appropriate according to the power generation situation of generator unit in system
Adjustment CHP units energy supply situation, increase the stability of systemic-function；Generate electricity for peakload, be distributed according to day part
Formula generator unit output situation and load condition, expense and other schedulable supply units are energized by contrasting CHP units
Functional expense adjusts distributed energy supply unit output situation, improves the utilization rate of the energy.
As shown in figure 5, electric boiler heating unit 7 includes filter 71, water circulating pump 72, boilers heated electrically 73, hot water storage tank
74th, expansion tank, electrical control cabinet 2 75 and water knockout drum 76, expansion tank one end connection water treatment device, the other end connected
Filter 71 and water circulating pump 72, are connected to water knockout drum 76, hot water storage tank 74 and boilers heated electrically between filter 71 and hot water storage tank 74
73 connections, boilers heated electrically 73 connect water circulating pump 72 and electrical control cabinet 2 75.Electric boiler heating unit 7 can convert electrical energy into heat
Energy；And when unit operation power charge expense is relatively low, directly thermal energy is provided to thermic load using electric boiler heating unit 7.Electric boiler
Heating unit 7 is used to convert electrical energy into thermal energy, heat supply network heating unit, electric thermal storage unit in comparison system, electric boiler heat supply
Three kinds of heat-supplying modes of unit, it is direct using electric boiler heating unit when the unit operating cost of electric boiler heating unit is relatively low
Thermal energy is provided to thermic load, reduces the consumption to non-renewable energy resources in loss and the CHP units in electric heat-accumulating process.
As shown in fig. 6, electric thermal storage unit 8 includes electrical control cabinet 3 81, thermal storage electric boiler 82, hold over system 83 and goes out thermal control
Valve 84 processed, electrical control cabinet 3 81 are connected with thermal storage electric boiler 82, and thermal storage electric boiler 82 connects hold over system 83, and hold over system 83 connects
Go out heat control valve (HCV) 84.When supply exceed demand for the electric energy in system, electric thermal storage unit 8 can convert electrical energy into thermal energy and by conversion
Thermal energy is stored；When supply falls short of demand for the electric energy in system, releasable thermal energy, and by the thermal energy storage of conversion for system it
Need；Electric thermal storage unit is used to adjust unbalanced supply-demand problem, and the supply and demand requirement in system can store unnecessary electricity and protect
The utilization rate of system power is demonstrate,proved, the stability that thermal energy ensures system energy supply can also be discharged according to demand, at the same time, moreover it is possible to reduction pair
The pollution of environment, electric thermal storage unit is mainly used for storing electric energy when distributed generation resource produces unnecessary electric energy and power grid low ebb, by this
Part electric energy is converted into thermal energy, avoids and directly provides thermal energy by combustion of fossil fuels, and then reduces the discharge capacity of pollutant
And then environmental protection.
Photovoltaic generation unit 1 and wind turbine power generation unit 2 are connected with inversion boosting unit 1, inversion boosting unit 1, whole
Stream device 1, rectifier 2 51 and the equal incoming transport power line one of power grid 9, rectifier 1 are connected with DC load, DC load
It is connected with energy-storage battery 52；Fuel gas transmission unit 10 is connected to combustion gas switch board 11, and combustion gas switch board 11 is connected with gas fired-boiler 61,
Inversion boosting unit 2 53, power grid 9, electrical control cabinet 1, electrical control cabinet 2 62 and electrical control cabinet 3 81 with the alternating current line of force
Two are connected, and the alternating current line of force two connects AC load, and electrical control cabinet 2 62 is connected with electrical control cabinet 3 81, heat supply network 13 and heat supply network control
Valve 14 processed is connected, and gas fired-boiler 61, heat exchanger 62, water knockout drum 76, go out heat control valve (HCV) 84 and heating network control valve 14 accesses heating power
Net, heat supply network connect thermic load, energy-storage battery unit 5, electric thermal storage unit 8, CHP units 6, electric boiler heating unit 7 with control
Unit is connected.
As shown in fig. 7, the present invention also provides a kind of energy conversion optimal control method towards energy internet, specific bag
Include following steps：
Step 1：Need to establish multiple objective function according to optimum results, calculate the sons such as operating cost, pollution abatement costs
Minimum value of the object function under constraints, establishes the membership function on object function by membership function, solves
The minimum value of membership function, and then multiple objective function optimal problem is converted into single goal nonlinear optimization optimal problem；Its
Comprise the following steps that：
Step 1.1：According to according to optimum results need establish multiple objective function：
Wherein, Cq(t) it is the operating cost of t moment system, Cf(t) it is the fuel cost of t moment system, CR, cost(t) it is t
The maintenance cost of moment system blower generating set and photovoltaic generation unit, Cs(t) for t moment system and power grid it is grid-connected when electricity
Measure tranaction costs, Ce(t) it is the pollution abatement costs of t moment system,The pollution produced for t moment by CHP units is punished
Expense,SO is produced for t momentxPenalty price,The penalty price of NO is produced for t moment,Produced for t moment
Raw COxPenalty price, pg(t) it is t moment system to power grid power purchase power, v (t) is that t moment is burnt the volume of natural gas, and τ is
System operation time；
Step 1.2：Uncontrollable energy output situation is predicted；Using the system load demand amount of synchronization in recent years as
Historical data, the predicted load of system at this time is obtained by load forecasting method；The wind turbine power generation that will be changed by weather conditions
Unit, the output situation of photovoltaic unit are predicted；Wherein, Unit commitment is：
(1) electrical power balances
eg(t)+ev(t)+ew(t)+ec(t)-eb(t)=eload(t) (3)
Wherein, eg(t) electricity provided for t moment power grid, ev(t) electricity, e are exported for t moment photovoltaicw(t) it is t moment
Wind turbine power generation unit exports electricity, ec(t) electricity, e are provided for t moment CHP unitsb(t) it is t moment energy-storage battery discharge and recharge,
eload(t) it is t moment system electric load amount；
(2) heating power balance
qc(t)+qb(t)+qh(t)+qe(t)=qloads(t) (4)
Wherein, qc(t) heat provided for t moment CHP units, qb(t) heat provided for t moment electric boiler, qh(t)
The heat provided for t moment heat supply network, qe(t) heat provided for t moment electricity thermal storage unit, qloads(t) it is t moment research object
Thermic load amount；
(3) energy-storage battery constrains
Wherein, pomaxFor energy-storage battery maximum discharge power, pominFor energy-storage battery minimum discharge power, piminFor energy storage
Battery minimum charge power, pimaxFor energy-storage battery maximum charge power；pb(t) battery is in discharge condition, p during ＞ 0b(t) ＜
Battery is in charged state when 0.
(4) CHP units constraint
Wherein, xc(t) it is the actual electric load rate of CHP units,x cFor CHP unit minimum electric load rates,For CHP units
Maximum electric load rate.
(5) heat supply constraint
The heating load of heat supply network, electric boiler heating unit and CHP units needs to change with time, then the unit interval
The operating cost of CHP units is：
The operating cost of unit interval heat supply network is：
Cr=C Δs t (9)
Unit interval electric boiler heating unit operating cost：
Wherein, Ch(t) it is unit time CHP units operating cost, Cn1For Gas Prices, P2For t moment CHP units electricity
Power, η1For CHP unit electric work rate coefficients, L is natural gas low heat value, CrFor unit time heating network operation expense, CbFor unit when
Between electric boiler heating unit operating cost, C supplies level Waste Heat Price for heat supply network, and Δ t is simulation time section.
Step 1.3：Choose operating cost (the i.e. C in addition to electric thermal storage unit in the unit intervalh、Cb、Cr) less in three
As main supplying heat source.
Step 1.4：Membership function on object function is established by membership function,
In formula, μ (Fi(t)) it is object function Fi(t) membership function, Fi(t) it is i-th of object function, δiFor i-th
A receptible flexible angle value of object function, FiminFor the minimum function value of i-th of object function, wherein i=1,2, t=1,
2 ..., 24.
Step 1.5：The minimum value of membership function is solved, it is non-linear that multiple objective function optimal problem is converted into single goal
Optimize optimal problem, define maximum satisfaction λ：
λ=maxmin { μ (F1(t)), μ (F2(t))} (12)
Maximum in the minimum value that membership function intersection is obtained is carried as the optimal solution of multiple objective function for system
For energy scheduling strategy.
Step 2：According to electric, thermic load predicted value in particle number and system is chosen, the initial of particle is set at random
Position and initial velocity, and the speed of each particle is limited, while empty taboo list is set to be subsequently inputted into needs
The particle of taboo.In the step, to being limited to for the speed V of particle：
- 15 ＜ V ＜ 20 (13)
In formula, V is the speed for representing particle.
Step 3：The position and speed of existing particle are updated, find the optimal value of target membership function；To grain
The calculating process that the speed and position of son are updated is as follows：
Wherein, VidFor the speed of particle,For the speed of kth time iteration particle i,For (k+1) secondary iteration particle i
Speed, ω is inertia weight, c1And c2For Studying factors, r1And r2For random factor,For kth time iteration average optimal pole
Value,For colony's extreme value of population,For the position of kth time iteration particle i,For the position of (k+1) secondary iteration particle i
Put, ωstartFor initial inertia weight, ωendFor iteration to maximum times when inertia weight；Wherein, d=1,2....D, i=
1,2 ..., n, N be current iteration number, n is population scale, NmaxFor maximum iteration, PndFor kth time iteration particle n's
Extreme value.
Step 4：According to the operating cost and environment that system at this time is calculated by updating the position of obtained particle
Pollution abatement costs, then tries to achieve the maximum membership degree value for making intersection membership function by membership function, this solution is more mesh
The corresponding optimal solution of scalar functions；It is as follows to the maximum value calculation process of intersection membership function：
The maximum satisfaction λ of definition,
λ=maxmin { μ (C1(t)), μ (C2(t))} (18)
Optimal solution using the maximum obtained as multiple objective function, energy scheduling strategy is provided for system.
Step 5：Optimal solution according to the multiple objective function that each particle obtains is contrasted is met the individual extreme value of condition, leads to
Cross the colony's extreme value for comparing current individual extreme value and current group extreme value more new particle；Find the side of individual extreme value and colony's extreme value
Method is：The position of particle, obtains the multiple objective function of current particle when obtaining making the membership function be minimized according to step 4
Value, solves the individual extreme value and colony's extreme value of membership function, brings the position of each particle into membership function and ask for degree of membership
The value of function, compares the membership function value between particle, chooses the maximum of membership function as individual extreme value；Will be current
The individual extreme value that iterations obtains and colony's extreme value that iteration obtains before, which are compared, chooses conduct group larger in both
Body extreme value.
Step 6：According to the current solution of generation, the neighborhood solution currently solved by certain criterion generation, and from the neighbour of generation
Some solutions are selected in the solution of domain and are used as candidate solution.
Step 7：Judge whether the candidate solution of generation meets aspiration criterion；If so, perform step 7.1；If it is not, then perform step
Rapid 7.2；
Step 7.1：Using the candidate solution for meeting aspiration criterion as current solution, replace to enter earliest with its corresponding object and prohibit
Avoid the object in table, update optimal solution, and return to step 6；
Step 7.2：Using candidate's optimum solution of non-taboo as current solution, replace to enter earliest with the corresponding object of the solution and prohibit
Avoid the object in table, perform step 8；
Step 8：Membership function is substituted into according to the solution for generating previous step, obtaining makes membership function take maximum membership degree
It is worth corresponding solution, the individual extreme value and colony's extreme value of more new particle, and the iterations of more new particle.
Step 9：Whether the optimal solution and iterations, both any one that judgement is newly generated meet iteration ends bar
Part；If so, perform step 10；If it is not, then return to step 3；
Step 10：Iteration ends, export multiple objective function optimal solution.
The carrying out practically process of the present invention towards the energy conversion system of energy internet is as follows：
Photovoltaic generation unit and wind turbine power generation unit in electric energy supply unit send certain electricity, pass through inversion liter
Pressure unit one by the electric energy that photovoltaic generation unit and wind turbine power generation unit are sent be converted into can be grid-connected with power grid alternating current, will
Alternating current incoming transport power line one after photovoltaic generation unit, wind turbine power generation unit and power grid conversion；There is provided with reference to the present invention
The result of calculation that draws of optimal control method by corresponding electric energy in the alternating current line of force one by being supplied after one rectification of rectifier
DC load；Another part electric energy of the alternating current line of force one by after one rectification of rectifier according to optimal control method to storage battery
Charge, the electric energy in storage battery can directly feed DC load, through two inversion of inversion boosting unit according to optimal control method
After be connected in the alternating current line of force two the alternating current line of force one powered or supplied to AC load and then is powered to DC load；Electricity
Electric energy in net is transferred to alternating current wires one to DC load and storage battery power supply according to optimal control method or is transferred to
The alternating current line of force two；Fuel gas transmission unit is controlled to be conveyed to gas fired-boiler and gas internal-combustion engine according to optimal control method
Combustion gas in gas fired-boiler produces heat energy utilization heat exchanger progress thermal energy exchange by burning, and thermal energy is connected in heat supply network,
The combustion gas being conveyed in gas internal-combustion engine is produced electricl energy by burning and thermal energy, its electric energy incoming transport power line two produced,
The thermal energy of generation supplies heat supply network by waste heat recovery valve；The alternating current line of force two provides electric energy for AC load；Controlled according to optimization
Method processed is powered for electric boiler, and boilers heated electrically converts electrical energy into thermal energy and then transfers thermal energy to current using water circulating pump,
The temperature of water is improved, is flowed by current in electric boiler heating unit and passes thermal energy；Will according to optimal control method
Electric energy in the alternating current line of force two is supplied to electric thermal storage unit, and thermal storage unit converts electrical energy into thermal energy, and is stored, according to
The electric thermal storage unit of thermal energy control of required offer discharges heat, and then is connected in heat supply network；According to optimal control method control
Heating net provides energy, and the energy that heat supply network provides is also connected in heat supply network, by CHP units, electric boiler heating unit, electric accumulation of heat
The centralized heat energy that unit and heat supply network provide is got up, the thermic load in feed system.
Although the foregoing describing the embodiment of the present invention, those skilled in the art in the art should manage
Solution, these are merely illustrative of, and various changes or modifications can be made to these embodiments, without departing from the principle of the present invention
And essence.The scope of the present invention is defined by the claims.
Claims (9)
1. a kind of energy conversion system towards energy internet, including electric energy supply unit, thermal energy supply unit and energy are defeated
Go out unit, it is characterised in that：Energy conversion center is further included, the energy conversion center includes inversion boosting unit one, exchange
Power line one, the alternating current line of force two, rectifier one, energy-storage battery unit, CHP units, electric thermal storage unit, electric boiler heat supply list
Member, heat supply network and control unit；The electric energy supply unit includes photovoltaic generation unit, wind turbine power generation unit and power grid；It is described
Thermal energy supply unit includes fuel gas transmission unit and heat supply network；The energy output unit includes DC load, AC load and heat
Load；The energy-storage battery unit includes rectifier two, energy-storage battery, inversion boosting unit two and static transfer switch, described
Rectifier two is connected with energy-storage battery, and the energy-storage battery is connected with inversion boosting unit two, the rectifier two and inversion liter
Static transfer switch is connected between pressure unit two, the CHP units are including in gas fired-boiler, heat exchanger, waste heat recovery valve, combustion gas
Combustion engine, electric compression refigerating machine, electrical control cabinet one, system water pump and pump power load demand, the gas fired-boiler and heat exchanger phase
Even, heat exchanger connection waste heat recovery valve, waste heat recovery valve are connected with gas internal-combustion engine and electric compression refigerating machine respectively, combustion gas internal combustion
Machine and electric compression refigerating machine are connected with electrical control cabinet one, one welding system water pump of electrical control cabinet, system water pump and pump power load
Demand is connected；The electric boiler heating unit includes filter, water circulating pump, boilers heated electrically, hot water storage tank, expansion tank, electricity
Switch board two and water knockout drum, described expansion tank one end connect water treatment device, other end connection filter and water circulating pump,
Water knockout drum is connected between the filter and hot water storage tank, the hot water storage tank is connected with boilers heated electrically, and the boilers heated electrically connects
Connect water circulating pump and electrical control cabinet two；The electricity thermal storage unit includes electrical control cabinet three, thermal storage electric boiler, hold over system and goes out heat
Control valve, the electrical control cabinet three are connected with thermal storage electric boiler, and thermal storage electric boiler connects hold over system, and hold over system connects out thermal control
Valve processed；The photovoltaic generation unit and wind turbine power generation unit are connected with inversion boosting unit one, the inversion boosting unit one, whole
Stream device one, rectifier two and the equal incoming transport power line one of power grid, the rectifier one are connected with DC load, and the direct current is born
Lotus is connected with energy-storage battery；The fuel gas transmission unit is connected to combustion gas switch board, and combustion gas switch board is connected with gas fired-boiler, inversion
Boosting unit two, power grid, electrical control cabinet one, electrical control cabinet two and electrical control cabinet three connect with the two-phase of the alternating current line of force, the friendship
The galvanic electricity line of force two connects AC load, and the electrical control cabinet two is connected with electrical control cabinet three, the heat supply network and heating network control valve phase
Even, the gas fired-boiler, heat exchanger, water knockout drum, go out heat control valve (HCV) and heating network control valve accesses heat supply network, the heat supply network connects
Thermic load, the energy-storage battery unit, electric thermal storage unit, CHP units and electric boiler heating unit are connected with control unit.
2. as claimed in claim 1 towards the energy conversion system of energy internet, it is characterised in that：The inversion boosting list
Member one and inversion boosting unit two include uncontrollable rectifier circuit, boost booster circuits and full bridge inverter, it is described do not control it is whole
AC conversion is direct current by current circuit, and the direct current electric boost that the boost booster circuits produce uncontrollable rectifier circuit, is
Follow-up full bridge inverter provides the direct current for meeting inversion requirement or supply DC load uses；The full-bridge inverting electricity
The DC conversion that road produces boost booster circuits is alternating current that can be in parallel with power grid, or is converted into and directly feeds exchange
The alternating current that load uses.
3. as claimed in claim 1 towards the energy conversion system of energy internet, it is characterised in that：The energy-storage battery list
Electric energy of the member by discharge and recharge adjustment towards the energy conversion system of energy internet, when the electric energy that system produces, supply exceed demand
When, energy-storage battery unit charges；When supply falls short of demand for system power supply, energy-storage battery unit discharges.
4. as claimed in claim 1 towards the energy conversion system of energy internet, it is characterised in that：The CHP units, i.e.,
Cogeneration unit, it is possible to provide electric energy and thermal energy needed for AC load and thermic load.
5. as claimed in claim 1 towards the energy conversion system of energy internet, it is characterised in that：The electricity accumulation of heat list
Member, when supply exceed demand for the electric energy in system, can convert electrical energy into thermal energy and be stored the thermal energy of conversion；When in system
Electric energy when supply falls short of demand, releasable thermal energy.
6. as claimed in claim 1 towards the energy conversion system of energy internet, it is characterised in that：The electric boiler heat supply
Unit, can convert electrical energy into thermal energy；And when unit operation power charge expense is relatively low, using electric boiler heating unit directly to heat
Load provides thermal energy.
7. the optimization control of the energy conversion system towards energy internet as described in any claim in claim 1 to 6
Method processed, it is characterised in that specifically include following steps：
Step 1：Need to establish multiple objective function according to optimum results, calculate the sub-goals such as operating cost, pollution abatement costs
Minimum value of the function under constraints, establishes the membership function on object function, solution is subordinate to by membership function
Functional minimum value is spent, and then multi objective function optimization problem is converted into single goal nonlinear optimal problem；
Step 2：According to electric, thermic load predicted value in particle number and system is chosen, the initial position of particle is set at random
And initial velocity, and the speed of each particle is limited, while the taboo list of sky is set；
Step 3：The position and speed of existing particle are updated, find the optimal value of target membership function；
Step 4：The operating cost of system and environmental pollution at this time are calculated according to the position by updating obtained particle
Control expense, then tries to achieve the maximum of intersection membership function by membership function, this solution is corresponding for multiple objective function
Optimal solution；
Step 5：Optimal solution according to the multiple objective function that each particle obtains is contrasted is met the individual extreme value of condition, passes through ratio
Compared with current individual extreme value and colony's extreme value of current group extreme value more new particle；
Step 6：According to the current solution of generation, the neighborhood solution currently solved by certain criterion generation, and from the neighborhood solution of generation
In select it is some solution be used as candidate solution；
Step 7：Judge whether the candidate solution of generation meets aspiration criterion；If so, perform step 7.1；If it is not, then perform step
7.2；
Step 7.1：Using the candidate solution for meeting aspiration criterion as current solution, replaced with its corresponding object and enter taboo list earliest
In object, update optimal solution, and return to step 6；
Step 7.2：Using candidate's optimum solution of non-taboo as current solution, replaced with the corresponding object of the solution and enter taboo list earliest
In object, perform step 8；
Step 8：Membership function is substituted into according to the solution for generating previous step, obtaining makes membership function take maximum membership degree value pair
The individual extreme value and colony's extreme value of the solution answered, more new particle, and the iterations of more new particle；
Step 9：Whether the optimal solution and iterations, both any one that judgement is newly generated meet stopping criterion for iteration；
If so, perform step 10；If it is not, then return to step 3；
Step 10：Iteration ends, export multiple objective function optimal solution.
8. optimal control method as claimed in claim 7, it is characterised in that the step 1 comprises the following steps that：
Step 1.1：According to according to optimum results need establish multiple objective function：
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Wherein, Cq(t) it is the operating cost of t moment the system, Cf(t) it is the fuel cost of t moment the system, CR, cost(t) it is t
The maintenance cost of wind turbine generating set and photovoltaic generation unit in moment the system, Cs(t) it is grid-connected for t moment the system and power grid
When electricity tranaction costs, Ce(t) it is the pollution abatement costs of t moment system,The pollution produced for t moment by CHP units
Rejection penalty,SO is produced for t momentxPenalty price,The penalty price of NO is produced for t moment,For t when
Carve and produce COxPenalty price, pg(t) it is t moment the system to power grid power purchase power, v (t) is that t moment is burnt the body of natural gas
Product, τ is system operation time；
Step 1.2：Uncontrollable energy output situation is predicted；Using the system load demand amount of synchronization in recent years as history
Data, the predicted load of system at this time is obtained by load forecasting method；By the wind turbine power generation unit changed by weather conditions,
The output situation of photovoltaic unit is predicted；Wherein, Unit commitment is：
(1) electrical power balances
eg(t)+ev(t)+ew(t)+ec(t)-eb(t)=eload(t) (3)
Wherein, eg(t) electricity provided for t moment power grid, ev(t) electricity, e are exported for t moment photovoltaicw(t) it is t moment wind turbine
Generating set exports electricity, ec(t) electricity, e are provided for t moment CHP unitsb(t) it is t moment energy-storage battery discharge and recharge, eload
(t) it is t moment system electric load amount；
(2) heating power balance
qc(t)+qb(t)+qh(t)+qe(t)=qloads(t) (4)
Wherein, qc(t) heat provided for t moment CHP units, qb(t) heat provided for t moment electric boiler, qh(t) when being t
Carve the heat that heat supply network provides, qe(t) heat provided for t moment electricity thermal storage unit, qloads(t) it is negative for t moment research object heat
Lotus amount；
(3) energy-storage battery constrains
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<mi>max</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, pomaxFor energy-storage battery maximum discharge power, pominFor energy-storage battery minimum discharge power, piminFor energy-storage battery
Minimum charge power, pimaxFor energy-storage battery maximum charge power；pb(t) battery is in discharge condition, p during ＞ 0b(t) during ＜ 0
Battery is in charged state；
(4) CHP units constraint
<mrow>
<msub>
<munder>
<mi>x</mi>
<mo>&OverBar;</mo>
</munder>
<mi>c</mi>
</msub>
<mo>&le;</mo>
<msub>
<mi>x</mi>
<mi>c</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>c</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>e</mi>
<mi>c</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>p</mi>
<mo>&OverBar;</mo>
</mover>
<mi>c</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>x</mi>
<mi>c</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<mi>&tau;</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<munder>
<mi>x</mi>
<mo>&OverBar;</mo>
</munder>
<mi>c</mi>
</msub>
<mo>&le;</mo>
<msub>
<mi>x</mi>
<mi>c</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>c</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mi>e</mi>
<mi>l</mi>
<mi>s</mi>
<mi>e</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, xc(t) it is the actual electric load rate of CHP units,x cFor the minimum electric load rate of CHP units,For CHP units most
Heavy electric power load rate；
(5) heat supply constraint
Heating load, heat supply network, electric boiler heating unit and the e lectric-store heating to supply heat unit of CHP units need to change with time and become
Change, then the operating cost of unit interval CHP units is：
<mrow>
<msub>
<mi>C</mi>
<mi>h</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>n</mi>
<mn>1</mn>
</mrow>
</msub>
<mo>.</mo>
<mfrac>
<mrow>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
<mo>&CenterDot;</mo>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<mrow>
<msub>
<mi>&eta;</mi>
<mn>1</mn>
</msub>
<mo>&CenterDot;</mo>
<mi>L</mi>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
The operating cost of unit interval heat supply network is：
Cr=C Δs t (9)
Unit interval electric boiler heating unit operating cost：
<mrow>
<msub>
<mi>C</mi>
<mi>b</mi>
</msub>
<mo>=</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>.</mo>
<mfrac>
<mrow>
<msub>
<mi>P</mi>
<mi>e</mi>
</msub>
<mo>.</mo>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&eta;</mi>
<mn>2</mn>
</msub>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Ch(t) it is unit time CHP units operating cost, Cn1For Gas Prices, P2For t moment CHP unit electrical power,
η1For CHP unit electric work rate coefficients, L is natural gas low heat value, CrFor unit time heating network operation expense, CbFor unit time electricity
Boiler heat supplying unit operating cost, C supply level Waste Heat Price, C for heat supply networkgrid(t) it is t moment electricity price, PeHot merit is supplied for t moment electric boiler
Rate, η2Turn thermal transition efficiency for t moment electricity, Δ t is simulation time section；
Step 1.3：Choose operating cost (the i.e. C in addition to electric thermal storage unit in the unit intervalh、Cb、Cr) less conduct in three
Main supplying heat source；
Step 1.4：Membership function on object function is established by membership function,
<mrow>
<mi>&mu;</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>F</mi>
<mi>i</mi>
</msub>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>F</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo><</mo>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&delta;</mi>
<mi>i</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>F</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>&delta;</mi>
<mi>i</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
</mrow>
</mfrac>
</mtd>
<mtd>
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>F</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&delta;</mi>
<mi>i</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>F</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>></mo>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&delta;</mi>
<mi>i</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, μ (Fi(t)) it is object function Fi(t) membership function, Fi(t) it is i-th of object function, δiFor i-th of target
The receptible flexible angle value of function, FiminFor the minimum function value of i-th of object function, wherein i=1,2, t=1,2 ...,
24；
Step 1.5：The minimum value of membership function is solved, multiple objective function optimal problem is converted into single goal nonlinear optimization
Optimal problem, defines maximum satisfaction λ：
λ=maxmin { μ (F1(t)), μ (F2(t))} (12)
Maximum in the minimum value that membership function intersection is obtained provides energy as the optimal solution of multiple objective function for system
Source scheduling strategy.
9. optimal control method as claimed in claim 7, it is characterised in that in the step 2, to the limit of the speed V of particle
It is made as：
- 15 ＜ V ＜ 20 (13)
Wherein, V is the speed for representing particle；
In the step 3, the calculating process that speed and position to particle are updated is as follows：
<mrow>
<msubsup>
<mi>V</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>&omega;V</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>+</mo>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<msub>
<mi>r</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>m</mi>
<mi>e</mi>
<mi>a</mi>
<mi>n</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<msub>
<mi>r</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>14</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>V</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>15</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mi>&omega;</mi>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>&omega;</mi>
<mrow>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>-</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>&omega;</mi>
<mrow>
<mi>s</mi>
<mi>t</mi>
<mi>a</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>&omega;</mi>
<mrow>
<mi>e</mi>
<mi>n</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<mi>N</mi>
<msub>
<mi>N</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>16</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>P</mi>
<mrow>
<mi>m</mi>
<mi>e</mi>
<mi>a</mi>
<mi>n</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mn>1</mn>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<msubsup>
<mo>+</mo>
<mrow>
<mn>2</mn>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>+</mo>
<mn>...</mn>
<msubsup>
<mo>+</mo>
<mrow>
<mi>n</mi>
<mi>d</mi>
</mrow>
<mi>k</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>/</mo>
<mi>n</mi>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>17</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, VidFor the speed of particle,For the speed of kth time iteration particle i,For the speed of (k+1) secondary iteration particle i
Degree, ω are inertia weight, c1And c2For Studying factors, r1And r2For random factor,For kth time iteration average optimal extreme value,For colony's extreme value of population,For the position of kth time iteration particle i,For the position of (k+1) secondary iteration particle i,
ωstartFor initial inertia weight, ωendFor iteration to maximum times when inertia weight；Wherein, d=1,2....D, i=1,
2 ..., n, N be current iteration number, n is population scale, NmaxFor maximum iteration, PndFor the pole of kth time iteration particle n
Value；
It is as follows to the maximum value calculation process of intersection membership function in the step 4：
The maximum satisfaction λ of definition,
λ=maxmin { μ (F1(t)), μ (F2(t))} (18)
Optimal solution using the maximum obtained as multiple objective function, energy scheduling strategy is provided for system；
In the step 5, the method for finding individual extreme value and colony's extreme value is：
The position of particle, obtains the multiple objective function value of current particle when obtaining making the membership function be minimized according to step 4,
The individual extreme value and colony's extreme value of membership function are solved, the position of each particle is brought into membership function and asks for membership function
Value, compare the membership function value between particle, choose the maximum of membership function as individual extreme value；By current iteration
The individual extreme value that number obtains and colony's extreme value that iteration obtains before, which are compared, chooses conduct colony pole larger in both
Value.
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- 2016-06-08 CN CN201610403261.7A patent/CN105958537B/en active Active
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| Title |
|---|
| 能源互联网动态协调优化控制体系构建;孙秋野等;《中国电机工程学报》;20150720;第35卷(第14期);第3569-3576页 * |
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