CN107403256A - One kind considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response - Google Patents

One kind considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response Download PDF

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CN107403256A
CN107403256A CN201710528343.9A CN201710528343A CN107403256A CN 107403256 A CN107403256 A CN 107403256A CN 201710528343 A CN201710528343 A CN 201710528343A CN 107403256 A CN107403256 A CN 107403256A
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李姚旺
苗世洪
李超
韩佶
李力行
刘君瑶
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Huazhong University of Science and Technology
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Abstract

The invention discloses one kind to consider the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response, comprises the following steps：(1) demand response amount triangle fuzzy model under the tou power price a few days ago based on load self-elasticity coefficient is established；(2) tou power price Optimized model a few days ago is established；(3) the tou power price Optimized model based on triangle fuzzy model and a few days ago, establishes and considers the probabilistic energy-storage system Optimal Allocation Model of demand response；(4) the fuzzy optimistic value expression in energy-storage system Optimal Allocation Model, fuzzy pessimistic value expression and Fuzzy Chance Constraint are respectively converted into its clear equivalent form of value and solved, obtain parallel networking type photovoltaic microgrid battery energy storage allocation plan.The method of the present invention eliminates complicated program process and the longer optimal time when being solved using analogy method, operation demand response technology reduces the demand that photovoltaic microgrid configures scale to energy-storage system, and the probabilistic influence of demand response is considered, more meet the demand of real system.

Description

One kind considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response
Technical field
The invention belongs to energy-storage system to distribute technical field rationally, considers that demand response is not true more particularly, to one kind Qualitatively photovoltaic microgrid battery energy storage collocation method.
Background technology
With the landing and propulsion of " photovoltaic help-the-poor policy ", the distributed photovoltaic power generation cause in China has obtained quick hair Open up, the general layout of high permeability distributed photovoltaic access power distribution network has been formed in subregion, being contributed due to photovoltaic has interval Property and fluctuation, it is large-scale distributed grid-connected electric network security and economy to be brought challenges.Large-scale distributed Under the conditions of grid-connected, relative to direct grid-connected scheme, economy and conjunction are had more using parallel networking type photovoltaic micro-capacitance sensor access scheme Rationality, it is an effective means for tackling large-scale distributed grid-connected problem.Energy-storage system is in photovoltaic micro One of important component, there is the effects such as lifting photovoltaic consumption rate, the quality of power supply, micro-capacitance sensor operation income.However, due to storage The specific investment cost construction cost of energy system is expensive, and how reasonable disposition energy-storage system will be lifting micro-capacitance sensor performance driving economy, peace The key of full property and environment friendly, is current urgent problem to be solved.
The research of photovoltaic microgrid battery energy storage configuration strategy at this stage, most individually consideration photovoltaic and energy-storage system are matched somebody with somebody Close, from stabilize distributed power source go out fluctuation, peak load shifting and lifting system economy etc. propose energy storage device optimization match somebody with somebody Strategy is put, the energy-storage system under less research photovoltaic, energy storage and demand response mechanism cooperate distributes strategy rationally, and lacks Research to considering the uncertain photovoltaic microgrid battery energy storage collocation method of demand response.Demand response is not considered, can cause to match somebody with somebody The energy storage put is excessive, causes the wasting of resources；Demand response is considered, but does not consider the uncertainty of demand response, can be caused again The result of energy storage configuration is excessively optimistic, causes energy storage configuration scale deficiency.
The content of the invention
For the disadvantages described above or Improvement requirement of prior art, the invention provides a kind of method, it is intended that with Demand response technology reduces configuration needs of the photovoltaic microgrid to energy storage device, and considers demand response uncertainty to photovoltaic microgrid Energy-storage system configures the influence of scale, energy-storage system configuration scale is more conformed to actual demand, thus solves to contain demand response The photovoltaic microgrid energy-storage system of mechanism distributes technical problem rationally.
In order to achieve the above object, the invention provides one kind to consider the probabilistic photovoltaic microgrid battery storage of demand response Energy collocation method, comprises the following steps：
(1) it is as follows to establish demand response amount triangle fuzzy model under the tou power price a few days ago based on load self-elasticity coefficient：
In formula,
The load responding rate triangular fuzzy variable of-the t periods, t=1,2 ..., 24；
λΔq1,t、λΔq2,t、λΔq3,t- be the t periods load responding rate fuzzy variable degree of membership parameter, and λΔq1,t≤ λΔq2,t≤λΔq3,t；
εtt- load self-elasticity coefficient；
ktt- demand response amount worst error horizontal proportion coefficient, its value and εttPositive correlation；
λΔc,tThe electricity price rate of change of-the t periods；
ktt|εttλΔc,t|-load responding amount worst error is horizontal；
The total load response quautity triangular fuzzy variable of-the t periods；
Δqload1,t、Δqload2,t、Δqload3,t- be the t periods load responding amount fuzzy variable degree of membership parameter, And Δ qload1,t≤Δqload2,t≤Δqload3,t；
(2) based on step (1)The object function for establishing tou power price Optimized model a few days ago is as follows：
In formula,
PPV,tFor the photovoltaic output size of t periods；
PL,tFor the payload of t periods；
Tou power price unit is segmented duration before Δ t- days；
E () is that Triangular Fuzzy Number desired value calculates operator；
Wherein, the minimum value of formula (3) is the optimal solution of tou power price Optimized model a few days ago；
(3) optimal solution of the Optimized model of tou power price a few days ago based on step (2), establish and consider that demand response is uncertain Energy-storage system Optimal Allocation Model object function it is as follows：
In formula,
The optimistic value of-micro-capacitance sensor sale of electricity income
C girdinThe pessimistic value of-micro-capacitance sensor purchases strategies；
GPVsub- photovoltaic subsidizes income；
CBESSThe investment of-energy-storage system and operation expense；
Sup { }-take upper bound operator；
Inf { }-remove boundary's operator；
Cr { }-confidence level expression formula；
R- aleatory variables；
α-optimistic value confidence level；
β-pessimistic value confidence level；
The triangular fuzzy variable of the sale of electricity electricity of-the t period micro-capacitance sensors；
The triangular fuzzy variable of the power purchase electricity of-the t period micro-capacitance sensors；
cPVsub- distributed photovoltaic power generation unit is subsidized；
IBESSThe unit price of-monomer energy-storage battery；
L- energy-storage system service lives；
nBESSThe quantity of-monomer energy-storage battery；
γ-discount rate；
mBESS- monomer operating cost accounts for the ratio of cost of investment；
(4) feasible solution for making formula (7) value maximum is solved, is matched somebody with somebody as considering that the probabilistic energy-storage system of demand response optimizes The optimal solution of model is put, that is, obtains corresponding photovoltaic microgrid battery energy storage allocation plan.
Further, in step (1), load self-elasticity coefficient εttFor：
In formula,
λΔc,tThe electricity price rate of change of-the t periods；
λΔq,tThe load responding rate of-the t periods.
Further, in step (2), a few days ago tou power price Optimized model include based on formula (1), (2) establish it is following about Beam condition：
Electricity tariff constraint：
λΔc,min≤λΔc,t≤λΔc,max (4)
In formula,
λΔc,tThe electricity price rate of change of-the t periods；
λΔc,minElectricity price maximum up-regulation ratio under-demand response mechanism；
λΔc,maxElectricity price maximum downward ratio under-demand response mechanism；
Power mode satisfaction constrains：
In formula,
sway- power mode satisfaction lower limit；
qload,tThe load of-the t periods；
Electric cost expenditure satisfaction constrains：
In formula, scost- electric cost expenditure satisfaction lower limit；
The feasible zone of tou power price Optimized model is determined based on formula (4)~(6), then travels through the feasible zone, finding makes formula (3) the minimum feasible solution of value, the optimal solution as tou power price Optimized model.
Further, in step (3), the probabilistic energy-storage system Optimal Allocation Model of demand response is considered, in addition to The following constraints that the optimal solution of the Optimized model of tou power price a few days ago based on formula (1), (2) and step (2) is established：
Power-balance constraint：
In formula,
PBESS,tThe output of-the t period energy-storage systems；
Pload0,tInitial load in-the t period photovoltaic micros；
Energy-storage battery state-of-charge restriction：
In formula,
SOCtThe state-of-charge of-the t period energy-storage systems；
SOCmax- maximum state-of-charge；
SOCmin- minimum state-of-charge；
ηBESS- energy-storage battery efficiency for charge-discharge；
QBESS- battery maximum monomer capacity；
Micro-capacitance sensor send power limit Fuzzy Chance Constraint to power distribution network：
In formula,
Pgridout,max- micro-capacitance sensor allows the peak power sent to power distribution network；
- micro-capacitance sensor send the confidence level of power limit to power distribution network；
Discharge and recharge Constraints of Equilibrium：
SOCs0=SOCsT (12)
In formula,
SOCs0The initial state-of-charge of-energy-storage system in typical day；
SOCsTThe state-of-charge of last the period of-energy-storage system in typical day.
Further, in step (4), also comprise the following steps：
By in step (3)Expression formula,C girdinExpression formula and formula (11) be respectively converted into its clear shape of equal value Formula is as follows：
The clear equivalent form of value of expression formula is as follows：
In formula,
α-optimistic value confidence level；
qgirdout1,t、qgirdout2,t- it is t period sale of electricity electricity fuzzy variable degree of membership parameters, and qgirdout1,t≤ qgirdout2,t；
C girdinThe clear equivalent form of value of expression formula is as follows：
In formula,
β-pessimistic value confidence level；
qgirdin2,t、qgirdin3,t- it is t period power purchase electricity fuzzy variable degree of membership parameters, and qgirdin2,t≤ qgirdin3,t；
The clear equivalent form of value of formula (11) is as follows：
In formula,
- micro-capacitance sensor send the confidence level of power limit to power distribution network；
qgridout2,t、qgridout3,t- it is t period sale of electricity electricity fuzzy variable degree of membership parameters, and qgridout2,t≤ qgridout3,t；
The feasible zone of energy-storage system Optimal Allocation Model is determined based on formula (9), (10), (12)~(15), then traversal should Feasible zone, it is determined that making the maximum feasible solution of formula (7) value, mould is distributed rationally as the consideration probabilistic energy-storage system of demand response The optimal solution of type, that is, obtain corresponding photovoltaic microgrid battery energy storage allocation plan.
In general, by the contemplated above technical scheme of the present invention compared with prior art, have below beneficial to effect Fruit：
1st, demand response amount triangle fuzzy model under the tou power price a few days ago based on load self-elasticity coefficient, the model are established Demand response amount desired value and load own elasticity number can be reflected on the probabilistic influence of demand response amount；
2nd, the tou power price Optimized model based on demand response fuzzy model and a few days ago, establish and consider that demand response is uncertain Energy-storage system Optimal Allocation Model, the model can operation demand response technology reduce photovoltaic microgrid to energy-storage system configure advise The demand of mould, and the probabilistic influence of demand response is considered, more meet the demand of real system；
3rd, it can be utilized and be converted to based on the energy-storage system Optimal Allocation Model that demand response amount triangle fuzzy model is established Clear method of equal value is solved, when eliminating complicated program process and the longer optimizing when being solved using analogy method Between, it is simpler quick.
Brief description of the drawings
Fig. 1 is the photovoltaic microgrid structure chart of the mechanism containing demand response；
Fig. 2 is the horizontal change mechanism schematic diagram of load responding rate worst error；
Fig. 3 is typical daylight volt output and payload；
Fig. 4 is that the present invention is directed to main flow schematic diagram；
Fig. 5 is that tou power price implements afterload size and typical daily load size comparison diagram a few days ago.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and It is not used in the restriction present invention.As long as in addition, technical characteristic involved in each embodiment of invention described below Conflict can is not formed each other to be mutually combined.
Before the formal method for introducing the present invention, first introduce in the preferred embodiment of the present invention that some used can be straight Obtain the parameter taken, and specific value in a preferred embodiment of the invention.Specifically, typical daylight volt output number is chosen According to as shown in Figure 1, 2, energy-storage battery parameter is as shown in table 1 with load, electricity price information is as shown in table 2, the electrical network parameter such as institute of table 3 Show, load and demand response information are as shown in table 4；
The energy-storage battery parameter of table 1
Parameter name Numerical value Unit
Battery maximum monomer capacity QBESS 50 A·h
Maximum state-of-charge SOCmax 0.95 -
Minimum state-of-charge SOCmin 0.45 -
Monomer maximum discharge power PBESSdisc,max 42 W
Monomer maximum charge power PBESSc,max 21 W
Efficiency for charge-discharge ηBESS 85 %
Monomer initial outlay cost IBESS 70 Member/
Monomer operating cost accounts for cost of investment ratio mBESS 0.3 %
Energy-storage battery service life l 7 Year
The electricity price information of table 2
Parameter name Numerical value Unit
Micro-capacitance sensor is to power distribution network sale of electricity electricity price csell 0.485 Member/degree
Micro-capacitance sensor is from power distribution network purchase electricity price cbuy 0.620 Member/degree
Photovoltaic generation often spends subsidy electricity price cPVsub 1.000 Member/degree
The electrical network parameter of table 3
The load of table 4 and demand response information
Fig. 4 is refer to, considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response for the present invention is a kind of Broad flow diagram, method of the invention comprises the following steps：
(1) demand response amount triangle fuzzy model under the tou power price a few days ago based on load self-elasticity coefficient is established：
In formula,For the load responding rate triangular fuzzy variable of t periods, the present invention uses tou power price a few days ago, because T=1,2 in this present invention ..., 24；
λΔq1,t、λΔq2,tAnd λΔq3,tFor the load responding rate fuzzy variable degree of membership parameter of t periods, λΔq1,t≤λΔq2,t ≤λΔq3,t；
εttRepresent load self-elasticity coefficient；
kttRepresent demand response amount worst error horizontal proportion coefficient, its value and load self-elasticity coefficient absolute value into positive Close；
λΔc,tRepresent the electricity price rate of change of t periods；
ktt|εttλΔc,t| represent that load responding amount worst error is horizontal, its change mechanism schematic diagram is as shown in figure 3, in figure The self-elasticity coefficient absolute value of load 1 is more than the self-elasticity coefficient absolute value of load 2；As historical data deficiency, kttIt can not measure When obtaining, load self-elasticity coefficient absolute value can be contrasted, estimates demand response amount worst error horizontal proportion coefficient；
For the total load response quautity triangular fuzzy variable of t periods；
Δqload1,t, Δ qload2,t, Δ qload3,tFor the load responding amount fuzzy variable degree of membership parameter of t periods, Δ qload1,t≤Δqload2,t≤Δqload3,t。
(2) tou power price Optimized model a few days ago, including the constraint of object function, electricity tariff constraint, power mode satisfaction are established Constrained with electric cost expenditure satisfaction；
1) tou power price Optimized model object function a few days ago is established：
In formula, PPV,tFor the photovoltaic output size of t periods；
PL,tFor the payload of t periods；
Δ t represents the duration of tou power price unit segmentation a few days ago；
E () is that Triangular Fuzzy Number desired value calculates operator；
2) electricity tariff constraint is established：
λΔc,min≤λΔc,t≤λΔc,max (4)
In formula, λΔc,tRepresent the electricity price rate of change of t periods；λΔc,minAnd λΔc,maxRepresent respectively under demand response mechanism Electricity price maximum up-regulation ratio and maximum downward ratio.
3) constraint of power mode satisfaction is established：
Wherein, swayRepresent power mode satisfaction lower limit；qload,tRepresent the load of t periods.
4) constraint of electric cost expenditure satisfaction is established：
Wherein, scostRepresent electric cost expenditure satisfaction lower limit；
The feasible zone of tou power price Optimized model is determined based on formula (4)~(6), then travels through the feasible zone, finding makes formula (3) the minimum feasible solution of value, the optimal solution as tou power price Optimized model.
(3) optimal solution of tou power price Optimized model based on demand response amount triangle fuzzy model and a few days ago, establish and consider The probabilistic energy-storage system Optimal Allocation Model of demand response, including：Object function, power-balance constraint, energy-storage battery lotus Electricity condition restriction, energy-storage battery output restriction, micro-capacitance sensor send power limit Fuzzy Chance Constraint to power distribution network, filled Discharge capacity Constraints of Equilibrium；
1) establish and consider the probabilistic energy-storage system Optimal Allocation Model object function of demand response：
In formula,WithC girdinThe compassion of the optimistic value and micro-capacitance sensor purchases strategies of micro-capacitance sensor sale of electricity income is represented respectively Sight value, its implication refers to respectively, under the conditions of confidence degree, less than sale of electricity income fuzzy variable maximum sale of electricity income and be more than The minimum purchases strategies of purchases strategies fuzzy variable；
GPVsubRepresent photovoltaic subsidy income；
CBESSRepresent investment and the operation expense of energy-storage system；
Sup { } and inf { } is respectively to take the upper bound and remove boundary's operator；
Cr { } is confidence level expression formula；
R is aleatory variable；
α and β is respectively optimistic value confidence level and pessimistic value confidence level；
WithThe sale of electricity electricity and power purchase electricity triangular fuzzy variable of t period micro-capacitance sensors are represented respectively；
cPVsubRepresent the subsidy of distributed photovoltaic power generation unit；
IBESSRepresent the unit price of monomer energy-storage battery；
L represents energy-storage system service life；
nBESSRepresent the quantity of monomer energy-storage battery；
γ represents discount rate；
mBESSRepresent that monomer operating cost accounts for the ratio of cost of investment.
2) power-balance constraint：
In formula,
PBESS,tRepresent the output of t period energy-storage systems；
Pload0,tRepresent the initial load in t period photovoltaic micros.
3) energy-storage battery state-of-charge restriction：
In formula,
SOCtRepresent the state-of-charge of t period energy-storage systems；
SOCmaxAnd SOCminMaximum state-of-charge and minimum state-of-charge are represented respectively；
ηBESSRepresent energy-storage battery efficiency for charge-discharge；
QBESSRepresent battery maximum monomer capacity.
4) micro-capacitance sensor send power limit Fuzzy Chance Constraint to power distribution network：
In formula,
Pgridout,maxRepresent that micro-capacitance sensor allows the peak power sent to power distribution network；
Represent that micro-capacitance sensor send the confidence level of power limit to power distribution network.
5) discharge and recharge Constraints of Equilibrium：
SOCs0=SOCsT (12)
In formula, SOCs0And SOCsTWhen representing initial state-of-charge of the energy-storage system in typical day respectively with last The state-of-charge of section.
(4) by step (3)Expression formula,C girdinExpression formula and formula (11) to be respectively converted into its clear The equivalent form of value：
1) optimistic value of sale of electricity incomeThe clear equivalent form of value of expression formula：
In formula, qgirdout1,tAnd qgirdout2,tRepresent t period sale of electricity electricity fuzzy variable degree of membership parameters.
2) pessimistic value of purchases strategiesC girdinThe clear equivalent form of value of expression formula：
Wherein, qgirdin2,tAnd qgirdin3,tRepresent t period power purchase electricity fuzzy variable degree of membership parameters.
3) micro-capacitance sensor send the clear equivalent form of value of power limit Fuzzy Chance Constraint to power distribution network
In formula, qgridout3,tRepresent t period sale of electricity electricity fuzzy variable degree of membership parameters.
The feasible zone of energy-storage system Optimal Allocation Model is determined based on formula (9), (10), (12)~(15), then traversal should Feasible zone, the feasible solution for making formula (7) value maximum is found, as the optimal solution of energy-storage system Optimal Allocation Model, i.e. grid type light Lie prostrate microgrid battery energy storage allocation plan.
Generally speaking, the present invention considers the uncertainty of demand response under tou power price a few days ago, establishes based on load The demand response amount triangle fuzzy model of self-elasticity coefficient.With customer charge and the minimum target of photovoltaic output total variances, establish Tou power price Optimized model a few days ago.Based on demand response fuzzy model and a few days ago tou power price Optimized model, establishes consideration The probabilistic energy-storage system Optimal Allocation Model of demand response.By fuzzy optimistic value expression, obscure pessimistic value expression and Fuzzy Chance Constraint is respectively converted into its clear equivalent form of value, and the Optimized model after conversion is asked using optimization software Solution, obtain parallel networking type photovoltaic microgrid battery energy storage allocation plan.
Below, the present embodiment is provided with 4 kinds of scenes to analyze effectiveness of the invention：
Scene 1：The parallel networking type photovoltaic micro-capacitance sensor operation result before energy-storage system is configured to calculate；
Scene 2：The photovoltaic microgrid energy-storage system for disregarding demand response is distributed rationally；
Scene 3：Consider demand response but disregard it and respond probabilistic photovoltaic microgrid energy-storage system to distribute rationally；
Scene 4：Consider demand response and consider that it responds probabilistic photovoltaic microgrid energy-storage system and distributed rationally.
The system loading after tou power price a few days ago is formulated in scene 3 and scene 4 as shown in figure 5, the system under 4 kinds of scenes is excellent It is as shown in table 5 to change result.
System optimization result under the different scenes of table 5
Parameter name Scene 1 Scene 2 Scene 3 Scene 4
Battery quantity/ - 29048 18029 21324
Whole year abandons light quantity/(MW.h) 556.63 0 0 0
Sale of electricity income/ten thousand yuan 28.37 28.37 26.20 21.34
Purchases strategies/ten thousand yuan 117.85 78.19 66.42 67.44
Photovoltaic subsidizes/ten thousand yuan 109.82 134.40 134.40 134.40
Energy-storage system cost/ten thousand yuan - 58.74 36.46 43.12
Net benefits/ten thousand yuan 20.34 25.84 57.72 45.18
The result of calculation of the Scene 1 of contrast table 2 and scene 2 can obtain, after the method configuration energy-storage system of the present invention, light No longer occur abandoning optical phenomenon in volt micro-capacitance sensor, abandon light quantity and reduce 556.63MW.h, and energy storage device is that the benefit that system is brought is big In the cost of its own, system net profit increases by 5.5 ten thousand yuan.It illustrates that configuration energy-storage system can lift the warp of photovoltaic micro Ji property and photovoltaic consumption rate.
The result of calculation of the Scene 2 of contrast table 2, scene 3 and scene 4 can obtain, and after introducing demand response, whether consider The response uncertainty of demand response, energy-storage system cost and system purchases strategies, which have, largely to be declined.Consider response Before and after uncertainty, system net profit adds 31.88 ten thousand yuan and 19.34 ten thousand yuan respectively, and battery configuration quantity is reduced respectively 11019 and 7724.This explanation this method effectively can reduce photovoltaic microgrid to energy-storage system with demand response technology Configure scale demand.
In addition, before compared to price type DR uncertainties are considered, after considering that response is uncertain, energy-storage system configuration scale increases Add, purchases strategies increase, sale of electricity income declines, and system net profit declines 12.54 ten thousand yuan, battery configuration quantity increase by 3295 It is individual.It illustrates after considering demand response uncertainty that demand response is still with reduction energy-storage system configuration scale and lifting system The ability of system performance driving economy, but the ability has weakened, and when this explanation does not consider demand response uncertainty, energy-storage system is matched somebody with somebody It is excessively optimistic to put the optimum results of scale, considers that the probabilistic energy-storage system configuration scale of demand response more conforms to system Actual demand.
The result of above scene shows that method of the invention can effectively lift micro-capacitance sensor economy and photovoltaic consumption Rate, and can effectively embody the influence that demand response uncertainty configures scale to energy-storage system.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to The limitation present invention, all any modification, equivalent and improvement made within the spirit and principles of the invention etc., all should be included Within protection scope of the present invention.

Claims (5)

1. one kind considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response, it is characterised in that this method bag Containing the following steps：
(1) it is as follows to establish demand response amount triangle fuzzy model under the tou power price a few days ago based on load self-elasticity coefficient：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>&amp;lambda;</mi> <mo>~</mo> </mover> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>1</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>2</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>3</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>1</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;epsiv;</mi> <mrow> <mi>t</mi> <mi>t</mi> </mrow> </msub> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>k</mi> <mrow> <mi>t</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>|</mo> <mrow> <msub> <mi>&amp;epsiv;</mi> <mrow> <mi>t</mi> <mi>t</mi> </mrow> </msub> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> <mo>|</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>2</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;epsiv;</mi> <mrow> <mi>t</mi> <mi>t</mi> </mrow> </msub> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>3</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;epsiv;</mi> <mrow> <mi>t</mi> <mi>t</mi> </mrow> </msub> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>k</mi> <mrow> <mi>t</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>|</mo> <mrow> <msub> <mi>&amp;epsiv;</mi> <mrow> <mi>t</mi> <mi>t</mi> </mrow> </msub> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> <mo>|</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>&amp;Delta;</mi> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>&amp;Delta;q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mn>1</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;Delta;q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mn>2</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;Delta;q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mn>3</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;Delta;q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mn>1</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>1</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <msub> <mi>q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;Delta;q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mn>2</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>2</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <msub> <mi>q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;Delta;q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mn>3</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mn>3</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <msub> <mi>q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
In formula,
The load responding rate triangular fuzzy variable of-the t periods, t=1,2 ..., 24；
λΔq1,t、λΔq2,t、λΔq3,t- be the t periods load responding rate fuzzy variable degree of membership parameter, and λΔq1,t≤λΔq2,t ≤λΔq3,t；
εtt- load self-elasticity coefficient；
ktt- demand response amount worst error horizontal proportion coefficient, its value and εttPositive correlation；
λΔc,tThe electricity price rate of change of-the t periods；
ktt|εttλΔc,t|-load responding amount worst error is horizontal；
The total load response quautity triangular fuzzy variable of-the t periods；
Δqload1,t、Δqload2,t、Δqload3,t- be the t periods load responding amount fuzzy variable degree of membership parameter, and Δ qload1,t≤Δqload2,t≤Δqload3,t；
(2) based on step (1)The object function for establishing tou power price Optimized model a few days ago is as follows：
<mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <mo>|</mo> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>L</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>-</mo> <mi>E</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>&amp;Delta;</mi> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> <mrow> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>|</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
In formula,
PPV,tFor the photovoltaic output size of t periods；
PL,tFor the payload of t periods；
Tou power price unit is segmented duration before Δ t- days；
E () is that Triangular Fuzzy Number desired value calculates operator；
Wherein, the minimum value of formula (3) is the optimal solution of tou power price Optimized model a few days ago；
(3) optimal solution of the Optimized model of tou power price a few days ago based on step (2), establish and consider the probabilistic storage of demand response Energy system optimization allocation models object function is as follows：
<mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <msub> <mover> <mi>G</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>G</mi> <mrow> <mi>P</mi> <mi>V</mi> <mi>s</mi> <mi>u</mi> <mi>b</mi> </mrow> </msub> <mo>-</mo> <msub> <munder> <mi>C</mi> <mo>&amp;OverBar;</mo> </munder> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>C</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mover> <mi>G</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mi>sup</mi> <mo>{</mo> <mi>r</mi> <mo>|</mo> <mi>C</mi> <mi>r</mi> <mo>{</mo> <msub> <mi>Rc</mi> <mrow> <mi>s</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>&amp;GreaterEqual;</mo> <mi>r</mi> <mo>}</mo> <mo>&amp;GreaterEqual;</mo> <mi>&amp;alpha;</mi> <mo>}</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <munder> <mi>C</mi> <mo>&amp;OverBar;</mo> </munder> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mi>inf</mi> <mo>{</mo> <mi>r</mi> <mo>|</mo> <mi>C</mi> <mi>r</mi> <mo>{</mo> <msub> <mi>Rc</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>y</mi> </mrow> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>r</mi> <mo>}</mo> <mo>&amp;GreaterEqual;</mo> <mi>&amp;beta;</mi> <mo>}</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>G</mi> <mrow> <mi>P</mi> <mi>V</mi> <mi>s</mi> <mi>u</mi> <mi>b</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>Rc</mi> <mrow> <mi>P</mi> <mi>V</mi> <mi>s</mi> <mi>u</mi> <mi>b</mi> </mrow> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>C</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>n</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> </msub> <mfrac> <mrow> <mi>&amp;gamma;</mi> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>&amp;gamma;</mi> <mo>)</mo> </mrow> <mi>l</mi> </msup> </mrow> <mrow> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>&amp;gamma;</mi> <mo>)</mo> </mrow> <mi>l</mi> </msup> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <msub> <mi>m</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow>
In formula,
The optimistic value of-micro-capacitance sensor sale of electricity income
C girdinThe pessimistic value of-micro-capacitance sensor purchases strategies；
GPVsub- photovoltaic subsidizes income；
CBESSThe investment of-energy-storage system and operation expense；
Sup { }-take upper bound operator；
Inf { }-remove boundary's operator；
Cr { }-confidence level expression formula；
R- aleatory variables；
α-optimistic value confidence level；
β-pessimistic value confidence level；
The triangular fuzzy variable of the sale of electricity electricity of-the t period micro-capacitance sensors；
The triangular fuzzy variable of the power purchase electricity of-the t period micro-capacitance sensors；
cPVsub- distributed photovoltaic power generation unit is subsidized；
IBESSThe unit price of-monomer energy-storage battery；
L- energy-storage system service lives；
nBESSThe quantity of-monomer energy-storage battery；
γ-discount rate；
mBESS- monomer operating cost accounts for the ratio of cost of investment；
(4) feasible solution for making formula (7) value maximum is solved, mould is distributed rationally as the consideration probabilistic energy-storage system of demand response The optimal solution of type, that is, obtain corresponding photovoltaic microgrid battery energy storage allocation plan.
2. according to the method for claim 1, it is characterised in that in step (1), load self-elasticity coefficient εttFor：
<mrow> <msub> <mi>&amp;epsiv;</mi> <mrow> <mi>t</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>q</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mfrac> </mrow>
In formula,
λΔc,tThe electricity price rate of change of-the t periods；
λΔq,tThe load responding rate of-the t periods.
3. according to the method for claim 2, it is characterised in that in step (2), tou power price Optimized model includes base a few days ago In the following constraints that formula (1), (2) are established：
Electricity tariff constraint：
λΔc,min≤λΔc,t≤λΔc,max (4)
In formula,
λΔc,tThe electricity price rate of change of-the t periods；
λΔc,minElectricity price maximum up-regulation ratio under-demand response mechanism；
λΔc,maxElectricity price maximum downward ratio under-demand response mechanism；
Power mode satisfaction constrains：
<mrow> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <mo>|</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <mi>E</mi> <mrow> <mo>(</mo> <mi>&amp;Delta;</mi> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>|</mo> </mrow> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <msub> <mi>q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mfrac> <mo>&amp;GreaterEqual;</mo> <msub> <mi>s</mi> <mrow> <mi>w</mi> <mi>a</mi> <mi>y</mi> </mrow> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
In formula,
sway- power mode satisfaction lower limit；
qload,tThe load of-the t periods；
Electric cost expenditure satisfaction constrains：
<mrow> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <mo>|</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <mi>E</mi> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>&amp;Delta;</mi> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;Delta;</mi> <mi>c</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>&amp;rsqb;</mo> <mo>|</mo> </mrow> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <msub> <mi>q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </mfrac> <mo>&amp;GreaterEqual;</mo> <msub> <mi>s</mi> <mrow> <mi>cos</mi> <mi>t</mi> </mrow> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
In formula, scost- electric cost expenditure satisfaction lower limit；
The feasible zone of tou power price Optimized model is determined based on formula (4)~(6), then travels through the feasible zone, finding makes formula (3) value Minimum feasible solution, the optimal solution as tou power price Optimized model.
4. according to the method for claim 3, it is characterised in that in step (3), consider the probabilistic energy storage of demand response System optimization allocation models, in addition to the optimal solution of the Optimized model of tou power price a few days ago based on formula (1), (2) and step (2) are built Vertical following constraints：
Power-balance constraint：
<mrow> <msub> <mi>P</mi> <mrow> <mi>P</mi> <mi>V</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>P</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <mfrac> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mrow> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow> </mfrac> <mo>=</mo> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mn>0</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <mfrac> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mrow> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow> </mfrac> <mo>+</mo> <mfrac> <mrow> <mi>&amp;Delta;</mi> <msub> <mover> <mi>q</mi> <mo>~</mo> </mover> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> <mrow> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
In formula,
PBESS,tThe output of-the t period energy-storage systems；
Pload0,tInitial load in-the t period photovoltaic micros；
Energy-storage battery state-of-charge restriction：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>SOC</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>SOC</mi> <mi>t</mi> </msub> <mo>&amp;le;</mo> <msub> <mi>SOC</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>SOC</mi> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>SOC</mi> <mi>t</mi> </msub> <mo>-</mo> <mfrac> <mrow> <msub> <mi>&amp;eta;</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> </msub> <msub> <mi>P</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mi>&amp;Delta;</mi> <mi>t</mi> </mrow> <mrow> <msub> <mi>n</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> </msub> <msub> <mi>Q</mi> <mrow> <mi>B</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> </msub> </mrow> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow> 3
In formula,
SOCtThe state-of-charge of-the t period energy-storage systems；
SOCmax- maximum state-of-charge；
SOCmin- minimum state-of-charge；
ηBESS- energy-storage battery efficiency for charge-discharge；
QBESS- battery maximum monomer capacity；
Micro-capacitance sensor send power limit Fuzzy Chance Constraint to power distribution network：
In formula,
Pgridout,max- micro-capacitance sensor allows the peak power sent to power distribution network；
- micro-capacitance sensor send the confidence level of power limit to power distribution network；
Discharge and recharge Constraints of Equilibrium：
SOCs0=SOCsT (12)
In formula,
SOCs0The initial state-of-charge of-energy-storage system in typical day；
SOCsTThe state-of-charge of last the period of-energy-storage system in typical day.
5. according to the method for claim 4, it is characterised in that in step (4), also comprise the following steps：
By in step (3)Expression formula,C girdinExpression formula and formula (11) be respectively converted into its clear equivalent form of value such as Under：
The clear equivalent form of value of expression formula is as follows：
<mrow> <msub> <mi>G</mi> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>2</mn> <mi>&amp;alpha;</mi> <mo>)</mo> </mrow> <msub> <mi>c</mi> <mrow> <mi>s</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <msub> <mi>q</mi> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> <mn>2</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>2</mn> <mi>&amp;alpha;</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <msub> <mi>c</mi> <mrow> <mi>s</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>24</mn> </munderover> <msub> <mi>q</mi> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>o</mi> <mi>u</mi> <mi>t</mi> <mn>1</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow>
In formula,
α-optimistic value confidence level；
qgirdout1,t、qgirdout2,t- it is t period sale of electricity electricity fuzzy variable degree of membership parameters, and qgirdout1,t≤ qgirdout2,t；
C girdinThe clear equivalent form of value of expression formula is as follows：
<mrow> <msub> <munder> <mi>C</mi> <mo>&amp;OverBar;</mo> </munder> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>-</mo> <mn>2</mn> <mi>&amp;beta;</mi> <mo>)</mo> </mrow> <msub> <mi>c</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>y</mi> </mrow> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>q</mi> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> <mn>2</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>2</mn> <mi>&amp;beta;</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <msub> <mi>c</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>y</mi> </mrow> </msub> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>q</mi> <mrow> <mi>g</mi> <mi>i</mi> <mi>r</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> <mn>3</mn> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>14</mn> <mo>)</mo> </mrow> </mrow>
In formula,
β-pessimistic value confidence level；
qgirdin2,t、qgirdin3,t- it is t period power purchase electricity fuzzy variable degree of membership parameters, and qgirdin2,t≤qgirdin3,t；
The clear equivalent form of value of formula (11) is as follows：
In formula,
- micro-capacitance sensor send the confidence level of power limit to power distribution network；
qgridout2,t、qgridout3,t- it is t period sale of electricity electricity fuzzy variable degree of membership parameters, and qgridout2,t≤ qgridout3,t；
The feasible zone of energy-storage system Optimal Allocation Model is determined based on formula (9), (10), (12)~(15), it is feasible then to travel through this Domain, it is determined that making the maximum feasible solution of formula (7) value, as the consideration probabilistic energy-storage system Optimal Allocation Model of demand response Optimal solution, that is, obtain corresponding photovoltaic microgrid battery energy storage allocation plan.
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CN110380405A (en) * 2019-07-04 2019-10-25 上海交通大学 Consider that demand response cooperates with optimization micro-capacitance sensor operation method with energy storage
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