CN104021429A - Management method of intelligent power grid demand side - Google Patents

Management method of intelligent power grid demand side Download PDF

Info

Publication number
CN104021429A
CN104021429A CN201410261127.9A CN201410261127A CN104021429A CN 104021429 A CN104021429 A CN 104021429A CN 201410261127 A CN201410261127 A CN 201410261127A CN 104021429 A CN104021429 A CN 104021429A
Authority
CN
China
Prior art keywords
amp
demand
energy
side
user
Prior art date
Application number
CN201410261127.9A
Other languages
Chinese (zh)
Inventor
刘南杰
董文杰
李大鹏
赵海涛
吴军民
张刚
黄在朝
黄辉
喻强
于海
张增华
邓辉
吴鹏
王向群
Original Assignee
国家电网公司
中国电力科学研究院
国网上海市电力公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 国家电网公司, 中国电力科学研究院, 国网上海市电力公司 filed Critical 国家电网公司
Priority to CN201410261127.9A priority Critical patent/CN104021429A/en
Publication of CN104021429A publication Critical patent/CN104021429A/en

Links

Abstract

The invention provides a management method of an intelligent power grid demand side. The method comprises the steps that (1) modeling is carried out based on a demand side user; (2) renewable energy sources are optimized in advance; and (3) the game theory is used for solving an accumulation expenditure function of the demand side and a corresponding Nash equilibrium point in an optimization mode. According to the method, renewable energy sources are used in distributed generation, the mode that the renewable energy sources and non-renewable energy resources are used for generating energy resources at the same time is used, energy source transmission cost and production cost are lowered, and the problems that in a traditional power grid, environmental pollution is severe, and demand side user expenditure is high are solved. In addition, from the point of sustainable development, using of the renewable energy sources is beneficial for sustainability of ecology, society, economy and science and technology, the using rate of natural resources is improved, and harmony between man and nature is promoted.

Description

A kind of management method of intelligent grid Demand-side

Technical field

The present invention relates to a kind of intelligent grid management method, specifically relate to a kind of management method of intelligent grid Demand-side.

Background technology

The service that intelligent grid provides to user, have advantages of safe, quality is high and business efficiency is high.The development of intelligent grid has also promoted the development of low-carbon energy industry greatly, guarantees the sustainable use of regenerative resource, adapts with the requirement in current low-carbon environment-friendly border.The Advanced Mechanism that so-called intelligent grid dsm means to encourage Demand-side user to play an active part in the network optimization improves the process of temporal mode and demand amplitude.

The main part that intelligent grid development relates to has the concepts such as dsm (DSM:Demand-Side Management), distributed energy generating (DG:Distributed energy Generation), distributed energy storage (DS:Distributed energy Storage), when can dispatch DG and DS include in electric power networks Demand-side and simultaneously implement innovation dsm method time, the challenge that can bring in connection with regenerative resource is down to and is minimized.In fact, the Integrated using of DG, DS, DSM concept not only can be made to the energy supply variation of whole electrical network, but also improve as much as possible Demand-side user's energy use efficiency.Dsm provides management to the energy in electrical network, and this is an important step in following intelligent grid.

The DSM method that the existing management for Demand-side proposes does not generally comprise the management of regenerative resource at interior Demand-side.Existing these dsm methods are normally to use on the basis of the energy based on user, realize minimizing of traditional electrical network generated energy, and the accumulation spending that Demand-side user bears minimizes, but these methods are only applicable in specific traditional electrical network.

Summary of the invention

For the deficiencies in the prior art, the invention provides a kind of management method of intelligent grid Demand-side, the present invention is based on the basis of traditional electrical network DSM, in DG, introduce renewable energy power generation, meet the requirement of current green electrical network, realized the reproducible utilization of the energy, saved resource.Normally using based on user on the basis of the energy, Non-synergic Game Theory and Nash Equilibrium Theory are applied in the Demand-side user of intelligent grid, the method can reduce production cost and the transmission cost of the energy, is applicable to domestic consumer, belongs to the innovation of intelligent grid aspect.The method is applied to regenerative resource and non-renewable energy resources the distributed power generation of Demand-side simultaneously, reduces the generated energy of electrical network, thereby solution environmental pollution is serious and Demand-side user accumulates the higher problem of spending.

The object of the invention is to adopt following technical characterictic to realize:

A management method for intelligent grid Demand-side, its improvements are, described method comprises

(1) based on Demand-side user modeling;

(2) optimize in advance regenerative resource;

(3) with the accumulation spending function of game theory Optimization Solution Demand-side and the Nash Equilibrium point of correspondence.

Preferably, described Demand-side user includes the energy-consuming user of DG and/or DS equipment.

Preferably, described Demand-side user comprises the passive user that basic power source consumes, and participates in optimizing process, and improves the active user of self energy demand according to the energy resource consumption in the unit interval.

Preferably, described step (1) comprises

Single Demand-side user n in the load of h time, unit source moment is:

l n ( h ) = e n ( h ) n ∈ P e n ( h ) - g n ( h ) - g R ( h ) + s n ( h ) n ∈ N - - - ( 1 )

The all users of Demand-side are in the total unit source load of time h moment:

L ( h ) = Σ m ∈ P e n ( h ) + Σ n ∈ N l n ( h ) - - - ( 2 )

Adopt secondary electrical network cost function:

C h(L(h))=K hL 2(h)????(3)

Demand-side user n at total accumulation spending function of analysis phase is:

f n = Σ h = 1 H ( C h ( L ( h ) ) l n ( h ) / L ( h ) + W n ( g n ( h ) ) ) = Σ h = 1 H ( K h L ( h ) ( e n ( h ) - g n ( h ) - g R ( h ) + s n ( h ) ) + W n ( g n ( h ) ) ) - - - ( 4 )

Wherein, e n(h) be that user n is in h time, energy resource consumption unit interval in moment;

G n(h) for can dispatch the turnout of the energy at h time, energy unit interval in moment;

G r(h) be the turnout of the non-scheduling energy at h time, energy unit interval in moment;

S n(h) be the memory space of energy storage equipment at h time, energy unit interval in moment;

K hfor the electrical network coefficient in time h moment;

L n(h) be the unit source load of user n in the time h moment;

L (h) is that user n is in the total unit source load of time h moment;

C h(L (h)) is electrical network cost function;

W n(g n(h)) be the production cost function of electrical network;

F nfor user n is at total accumulation spending function of analysis phase.

Preferably, described step (2) comprises by optimization method in advance and determines the energy mean value that regenerative resource produces every day.

Further, described step (2) comprises by optimization method in advance, estimates the energy mean value that regenerative resource produces every day, is designated as g 0, substitution formula of reduction (4):

f n = Σ h = 1 H ( C h ( L ( h ) ) l ( h ) / L ( h ) + W n ( g n ( h ) ) ) = Σ h = 1 H ( K h ( Σ n ∈ P e n ( h ) + Σ n ∈ N ( e n ( h ) - g n ( h ) - g 0 + s n ( h ) ) ) ( e n ( h ) - g n ( h ) - g 0 + s n ( h ) ) + W n ( g n ( h ) ) ) - - - ( 5 )

Ω g n = { g n ∈ R + H : g n ≤ g n ( max ) 1 H , 1 H T g n ≤ Σ h = 1 H g n ( max ) } Ω s n = s n ∈ R + 2 H : Δ β , n s n ≤ s n ( max ) 1 H , - q n ( 0 ) b n ≤ An Δ β , n s n ≤ c n 1 H - q n ( 0 ) b n , ( 1 - α n H ) q n ( 0 ) - ϵ n ≤ a n T Δ β , n s n ≤ ( 1 - α n H ) q n ( 0 ) + ϵ n - - - ( 6 )

Wherein, Δ β , n = ( β n + 1 H - β n - 1 H ) ;

A n, a n, b nfor H dimension matrix, [ A n ] i , j = α n ( i - j ) , [ a n ] i = α n ( H - i ) , [ b n ] i = α n i ;

for dispatching energy production person's strategy set;

for the strategy set of distributed energy memory device.

Preferably, described step (3) comprises the accumulation spending function that uses non-cooperative game opinion method and Nash Equilibrium Theory analysis and solution Demand-side, obtains single Demand-side user's Optimal. strategies set

Further, described step (3) comprises by selecting optimum tactful g nand s (h) n(h), make the cost function f of self nminimum; At this definition strategy set matrix be:

x n(h)=(g n(h),s n(h)) T????(7)

Under set of strategies formula (6) constraint condition, take off the group that establishes an equation:

▿ x n ( h 1 ) x n ( h 2 ) 2 f ( x n , 1 - n ) = 2 K h δδ T + δ g δ g T W n ′ ′ ( δ g T x n ( h ) ) ▿ x n ( h 1 ) x n ( h 2 ) 2 f ( x n , 1 - n ) = 0 3 , h 1 = h 2

Wherein: f n ( x n , 1 - n ) = Σ h = 1 H K h ( l - n ( h ) + e n ( h ) + δ T x n ( h ) ) × ( e n ( h ) + δ T x n ( h ) ) + Σ h = 1 H W n ( δ g T x n ( h ) )

δ=(-1,1,-1) Tδ g=(1,0,0) T

Required solution is Nash Equilibrium point, corresponding user's Optimal. strategies set when (5) formula obtains minimum value

Compared with the prior art, beneficial effect of the present invention is:

The present invention is applied to regenerative resource in distributed power generation, adopt regenerative resource and non-renewable energy resources to produce the mode of the energy simultaneously, reduce transmission cost and the production cost of the energy, solved in traditional electrical network the serious and high problem of Demand-side user effort of environmental pollution.

In addition, from angle of sustainable development, the use of regenerative resource is conducive to ecology, society, economy and scientific and technological continuation, improves the utilization factor of natural resources, enhances harmony between man and nature.

Brief description of the drawings

Fig. 1 is the management connection diagram of a kind of intelligent grid Demand-side provided by the invention.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.

In intelligent grid, Demand-side user is commonly referred to as the energy-consuming user who is equipped with DG and/or DS equipment.Analyze by the accumulation spending to single Demand-side user in intelligent grid, regenerative resource is adopted to pre-front optimizing process, use Non-synergic Game Theory and Nash Equilibrium Theory, solve Demand-side user and accumulate spending function f nminimum value and corresponding Nash Equilibrium point.

(1) the intelligent grid model proposing based on Demand-side user

In intelligent grid, Demand-side user is divided into active user (active users) and the large class of passive user (passive users) two by we.Conventionally, passive user refers to basic energy resource consumption user, the same with traditional Demand-side user.Active user refers to participation optimizing process, and improves the user of self energy demand according to the energy resource consumption in the unit interval.The present invention will introduce active user.For convenience of discussing, at this, active user is designated as to N; Passive user is designated as P.(seeing accompanying drawing one)

As shown in Figure 1, household electrical appliance typically refer to basic energy resource consumption person, belong to passive user's scope, its unit interval energy resource consumption e n(h) represent e n(h) be constant definite value.DG can provide the energy for Demand-side user, and it can be divided into two parts: (1) can dispatch energy production person G (Dispatchable energy producers); (2) non-scheduling energy production person (Nondispatchable energy producers), the energy that its unit interval energy can produce is used respectively g nand g (h) r(h) represent, we define g n(h) institute likely value is strategy set dS energy supply deficiency (energy more than needed) in the situation that for user provides the energy (carrying out energy storage), its unit interval energy storage s n(h) represent, we define its institute likely value be strategy set the efficiency for charge-discharge of definition DS is respectively with conventionally have defining its electric leakage speed α is the minimizing situation of passing in time energy level, if use q n(h) represent the charge level when time, h finished, in the time of the time (h+1), DS charge level reduces to α nq n(h) corresponding energy level time.From accompanying drawing one, single Demand-side user n in the unit source load at time h place is:

l n ( h ) = e n ( h ) n ∈ P e n ( h ) - g n ( h ) - g R ( h ) + s n ( h ) n ∈ N - - - ( 1 )

Therefore, all users of Demand-side are in the total unit source load in time h place:

L ( h ) = Σ m ∈ P e n ( h ) + Σ n ∈ N l n ( h ) - - - ( 2 )

The production cost function that we define respectively electrical network is W n(g n(h)), electrical network cost function is C h(L (h)).W n(g n(h)) represent to produce g n(h) production cost of energy requirement.Work as g n(h), there is W at=0 o'clock n(0)=0.C hwhen (L (h)) is illustrated in time h, by energy supply lateral root according to total determined fixed price of load L (h).Wherein K hit is electrical network coefficient.L n(h) when > 0, C h(L (h)) (l n(h) L (h)) represent that from electrical network, buying load by user n is l n(h) expense that need to bear time.L n(h), when < 0, represent that user n is l to electrical network traffic load n(h) income time.In intelligent grid, we extensively adopt secondary electrical network cost function:

C h(L(h))=K hL 2(h)????(3)

Therefore, Demand-side user n at total accumulation spending function of analysis phase is:

f n = &Sigma; h = 1 H ( C h ( L ( h ) ) l n ( h ) / L ( h ) + W n ( g n ( h ) ) ) = &Sigma; h = 1 H ( K h L ( h ) ( e n ( h ) - g n ( h ) - g R ( h ) + s n ( h ) ) + W n ( g n ( h ) ) ) - - - ( 4 )

(2) cost function of system after optimization method before adopting in advance

Because regenerative resource has uncertainty, the amount of the energy that produces is closely bound up with instrument and equipment, the external environment etc. using, so g r(h) be complete uncertain variable.But we can, by optimization method before pre-, estimate the energy mean value of regenerative resource generation every day, are designated as g 0, substitution formula of reduction (4):

f n = &Sigma; h = 1 H ( C h ( L ( h ) ) l ( h ) / L ( h ) + W n ( g n ( h ) ) ) = &Sigma; h = 1 H ( K h ( &Sigma; n &Element; P e n ( h ) + &Sigma; n &Element; N ( e n ( h ) - g n ( h ) - g 0 + s n ( h ) ) ) ( e n ( h ) - g n ( h ) - g 0 + s n ( h ) ) + W n ( g n ( h ) ) ) - - - ( 5 )

&Omega; g n = { g n &Element; R + H : g n &le; g n ( max ) 1 H , 1 H T g n &le; &Sigma; h = 1 H g n ( max ) } &Omega; s n = s n &Element; R + 2 H : &Delta; &beta; , n s n &le; s n ( max ) 1 H , - q n ( 0 ) b n &le; An &Delta; &beta; , n s n &le; c n 1 H - q n ( 0 ) b n , ( 1 - &alpha; n H ) q n ( 0 ) - &epsiv; n &le; a n T &Delta; &beta; , n s n &le; ( 1 - &alpha; n H ) q n ( 0 ) + &epsiv; n - - - ( 6 )

Wherein, &Delta; &beta; , n = ( &beta; n + 1 H - &beta; n - 1 H ) ;

A n, a n, b nall H dimension matrix, [ A n ] i , j = &alpha; n ( i - j ) , [ a n ] i = &alpha; n ( H - i ) , [ b n ] i = &alpha; n i

Like this, the accumulation of Demand-side spending function f njust only and g nand s (h) n(h) two variablees are relevant.Each symbol used herein and annotation see attached list one.

(3) game theory optimization

What in the present invention, adopt is Non-cooperative, and in this betting model, the each active user n ∈ N of Demand-side is participant.In the case of given total energy load, the tactful g of user by selecting optimum nand s (h) n(h), make the cost function f of self nminimum.At this definition strategy set matrix be:

x n(h)=(g n(h),s n(h)) T。(7)

Under set of strategies formula (6) constraint condition, take off the group that establishes an equation:

&dtri; x n ( h 1 ) x n ( h 2 ) 2 f ( x n , 1 - n ) = 2 K h &delta;&delta; T + &delta; g &delta; g T W n &prime; &prime; ( &delta; g T x n ( h ) ) &dtri; x n ( h 1 ) x n ( h 2 ) 2 f ( x n , 1 - n ) = 0 3 , h 1 = h 2

Wherein: f n ( x n , 1 - n ) = &Sigma; h = 1 H K h ( l - n ( h ) + e n ( h ) + &delta; T x n ( h ) ) &times; ( e n ( h ) + &delta; T x n ( h ) ) + &Sigma; h = 1 H W n ( &delta; g T x n ( h ) )

δ=(-1,1,-1) Tδ g=(1,0,0) T

Required solution is Nash Equilibrium point, corresponding user's Optimal. strategies set when (5) formula obtains minimum value

Symbol used and annotation herein:

Subordinate list 1

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit; those of ordinary skill in the field still can modify or be equal to replacement the specific embodiment of the present invention with reference to above-described embodiment; these do not depart from any amendment of spirit and scope of the invention or are equal to replacement, within the claim protection domain of the present invention all awaiting the reply in application.

Claims (8)

1. a management method for intelligent grid Demand-side, is characterized in that, described method comprises
(1) based on Demand-side user modeling;
(2) optimize in advance regenerative resource;
(3) with the accumulation spending function of game theory Optimization Solution Demand-side and the Nash Equilibrium point of correspondence.
2. the management method of a kind of intelligent grid Demand-side as claimed in claim 1, is characterized in that, described Demand-side user includes the energy-consuming user of DG and/or DS equipment.
3. the management method of a kind of intelligent grid Demand-side as claimed in claim 1, it is characterized in that, described Demand-side user comprises the passive user that basic power source consumes, and participates in optimizing process, and improves the active user of self energy demand according to the energy resource consumption in the unit interval.
4. the management method of a kind of intelligent grid Demand-side as claimed in claim 1, is characterized in that, described step (1) comprises
Single Demand-side user n in the load of h time, unit source moment is:
l n ( h ) = e n ( h ) n &Element; P e n ( h ) - g n ( h ) - g R ( h ) + s n ( h ) n &Element; N - - - ( 1 )
The all users of Demand-side are in the total unit source load of time h moment:
L ( h ) = &Sigma; m &Element; P e n ( h ) + &Sigma; n &Element; N l n ( h ) - - - ( 2 )
Adopt secondary electrical network cost function:
C h(L(h))=K hL 2(h)????(3)
Demand-side user n at total accumulation spending function of analysis phase is:
f n = &Sigma; h = 1 H ( C h ( L ( h ) ) l n ( h ) / L ( h ) + W n ( g n ( h ) ) ) = &Sigma; h = 1 H ( K h L ( h ) ( e n ( h ) - g n ( h ) - g R ( h ) + s n ( h ) ) + W n ( g n ( h ) ) ) - - - ( 4 )
Wherein, e n(h) be that user n is in h time, energy resource consumption unit interval in moment;
G n(h) for can dispatch the turnout of the energy at h time, energy unit interval in moment;
G r(h) be the turnout of the non-scheduling energy at h time, energy unit interval in moment;
S n(h) be the memory space of energy storage equipment at h time, energy unit interval in moment;
K hfor the electrical network coefficient in time h moment;
L n(h) be the unit source load of user n in the time h moment;
L (h) is that user n is in the total unit source load of time h moment;
C h(L (h)) is electrical network cost function;
W n(g n(h)) be the production cost function of electrical network;
F nfor user n is at total accumulation spending function of analysis phase.
5. the management method of a kind of intelligent grid Demand-side as claimed in claim 1, is characterized in that, described step (2) comprises by optimization method in advance determines the energy mean value that regenerative resource produces every day.
6. the management method of a kind of intelligent grid Demand-side as claimed in claim 5, is characterized in that, described step (2) comprises by optimization method in advance, estimates the energy mean value that regenerative resource produces every day, is designated as g 0, substitution formula of reduction (4):
f n = &Sigma; h = 1 H ( C h ( L ( h ) ) l ( h ) / L ( h ) + W n ( g n ( h ) ) ) = &Sigma; h = 1 H ( K h ( &Sigma; n &Element; P e n ( h ) + &Sigma; n &Element; N ( e n ( h ) - g n ( h ) - g 0 + s n ( h ) ) ) ( e n ( h ) - g n ( h ) - g 0 + s n ( h ) ) + W n ( g n ( h ) ) ) - - - ( 5 )
&Omega; g n = { g n &Element; R + H : g n &le; g n ( max ) 1 H , 1 H T g n &le; &Sigma; h = 1 H g n ( max ) } &Omega; s n = s n &Element; R + 2 H : &Delta; &beta; , n s n &le; s n ( max ) 1 H , - q n ( 0 ) b n &le; An &Delta; &beta; , n s n &le; c n 1 H - q n ( 0 ) b n , ( 1 - &alpha; n H ) q n ( 0 ) - &epsiv; n &le; a n T &Delta; &beta; , n s n &le; ( 1 - &alpha; n H ) q n ( 0 ) + &epsiv; n - - - ( 6 )
Wherein, &Delta; &beta; , n = ( &beta; n + 1 H - &beta; n - 1 H ) ;
A n, a n, b nfor H dimension matrix, [ A n ] i , j = &alpha; n ( i - j ) , [ a n ] i = &alpha; n ( H - i ) , [ b n ] i = &alpha; n i ;
for dispatching energy production person's strategy set;
for the strategy set of distributed energy memory device.
7. the management method of a kind of intelligent grid Demand-side as claimed in claim 1, it is characterized in that, described step (3) comprises the accumulation spending function that uses non-cooperative game opinion method and Nash Equilibrium Theory analysis and solution Demand-side, obtains single Demand-side user's Optimal. strategies set
8. the management method of a kind of intelligent grid Demand-side as claimed in claim 7, is characterized in that, described step (3) comprises by selecting optimum tactful g nand s (h) n(h), make the cost function f of self nminimum; At this definition strategy set matrix be:
x n(h)=(g n(h),s n(h)) T????(7)
Under set of strategies formula (6) constraint condition, take off the group that establishes an equation:
&dtri; x n ( h 1 ) x n ( h 2 ) 2 f ( x n , 1 - n ) = 2 K h &delta;&delta; T + &delta; g &delta; g T W n &prime; &prime; ( &delta; g T x n ( h ) ) &dtri; x n ( h 1 ) x n ( h 2 ) 2 f ( x n , 1 - n ) = 0 3 , h 1 = h 2
Wherein: f n ( x n , 1 - n ) = &Sigma; h = 1 H K h ( l - n ( h ) + e n ( h ) + &delta; T x n ( h ) ) &times; ( e n ( h ) + &delta; T x n ( h ) ) + &Sigma; h = 1 H W n ( &delta; g T x n ( h ) )
δ=(-1,1,-1) Tδ g=(1,0,0) T
Required solution is Nash Equilibrium point, corresponding user's Optimal. strategies set when (5) formula obtains minimum value
CN201410261127.9A 2014-06-12 2014-06-12 Management method of intelligent power grid demand side CN104021429A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201410261127.9A CN104021429A (en) 2014-06-12 2014-06-12 Management method of intelligent power grid demand side

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201410261127.9A CN104021429A (en) 2014-06-12 2014-06-12 Management method of intelligent power grid demand side

Publications (1)

Publication Number Publication Date
CN104021429A true CN104021429A (en) 2014-09-03

Family

ID=51438169

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201410261127.9A CN104021429A (en) 2014-06-12 2014-06-12 Management method of intelligent power grid demand side

Country Status (1)

Country Link
CN (1) CN104021429A (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102420428A (en) * 2011-12-19 2012-04-18 中国电力科学研究院 Method and system for managing microgrid energy
US20130245849A1 (en) * 2012-03-14 2013-09-19 Accenture Global Services Limited Customer-centric demand side management for utlities

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102420428A (en) * 2011-12-19 2012-04-18 中国电力科学研究院 Method and system for managing microgrid energy
US20130245849A1 (en) * 2012-03-14 2013-09-19 Accenture Global Services Limited Customer-centric demand side management for utlities

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ITALO ATZENI 等: "Demand-side management via distributed energy generation and storage optimization", 《IEEE TRANSACTIONS ON SMART GRID》 *
王帆: "智能电网需求侧管理配套政策建议及评价机制研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *

Similar Documents

Publication Publication Date Title
Hawkes et al. Modelling high level system design and unit commitment for a microgrid
Molderink et al. Domestic energy management methodology for optimizing efficiency in smart grids
Tushar et al. Three-party energy management with distributed energy resources in smart grid
Zakariazadeh et al. Multi-objective scheduling of electric vehicles in smart distribution system
Papathanassiou et al. Power limitations and energy yield evaluation for wind farms operating in island systems
Rae et al. Energy autonomy in sustainable communities—A review of key issues
Liu et al. Vehicle-to-grid control for supplementary frequency regulation considering charging demands
Wu et al. Demand side management for wind power integration in microgrid using dynamic potential game theory
Salom et al. Analysis of load match and grid interaction indicators in net zero energy buildings with simulated and monitored data
Weitemeyer et al. Integration of Renewable Energy Sources in future power systems: The role of storage
Loisel et al. Large-scale deployment of electric vehicles in Germany by 2030: An analysis of grid-to-vehicle and vehicle-to-grid concepts
Muratori et al. Role of residential demand response in modern electricity markets
Guo et al. Decentralized coordination of energy utilization for residential households in the smart grid
Khodaei Provisional microgrids
Shackley et al. A conceptual framework for exploring transitions to decarbonised energy systems in the United Kingdom
Franco et al. Strategies for optimal penetration of intermittent renewables in complex energy systems based on techno-operational objectives
Salinas et al. Dynamic energy management for the smart grid with distributed energy resources
Liang et al. Stochastic information management in smart grid
Miceli Energy management and smart grids
CN102751728B (en) Energy management method for isolated network running mode in micro network based on load interruption model
Borba et al. Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil
Yu et al. Balancing power demand through EV mobility in vehicle-to-grid mobile energy networks
Collins et al. Real and reactive power control of distributed PV inverters for overvoltage prevention and increased renewable generation hosting capacity
Alejandro et al. Combined environmental and economic dispatch of smart grids using distributed model predictive control
Zou et al. Evaluating the contribution of energy storages to support large-scale renewable generation in joint energy and ancillary service markets

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20140903