CN108470233A

CN108470233A - A kind of the demand response capability assessment method and computing device of intelligent grid

Info

Publication number: CN108470233A
Application number: CN201810102553.6A
Authority: CN
Inventors: 曾博; 卫璇; 董厚琦; 冯家欢; 刘裕; 胡强; 曾鸣; 刘宗歧; 刘文霞
Original assignee: North China Electric Power University
Current assignee: North China Electric Power University
Priority date: 2018-02-01
Filing date: 2018-02-01
Publication date: 2018-08-31
Anticipated expiration: 2038-02-01
Also published as: CN108470233B

Abstract

Computing device the invention discloses a kind of demand response capability assessment method of intelligent grid and for executing this method, wherein this method include：The reliability model of generating set is established, which includes the output power function of generating set；Respond capacity model, user's participation model and the load for establishing demand response respectively rebound model, and the response demand function, participation function and load that these three models respectively include user rebound electric quantity function；According to the workload demand model of three kinds of model foundation demand responses, which includes the workload demand function of user；According to the confidence capacity model of the reliability model and the workload demand model foundation demand response, which includes the confidence capacity of demand response；The historical data of intelligent grid is obtained, and the confidence capacity model is solved according to the historical data, obtains the confidence capacity of the demand response of the intelligent grid.

Description

A kind of the demand response capability assessment method and computing device of intelligent grid

Technical field

The present invention relates to electricity power field, more particularly to the demand response capability assessment method and meter of a kind of intelligent grid Calculate equipment.

Background technology

Demand response (DR) utilizes Demand-side as a kind of emerging intelligent power grid technology, by economic or banking mechanism Flexibility provides the new tool of management electric system real time execution for utility company.Although the potential effect of demand response Beneficial highly significant, but be its influence to power supply reliability with one of utility company maximally related aspect, it is main because For in market with keen competition, regulatory agency usually applies mandatory limitation to the frequency of client interrupts/duration, and not Can reach requirement target may give cause serious punishment.However, demand response remedying scheme as one kind, it can be tight Loading demand is reduced in the case of urgency and provides capacity for power grid supports.This will be helpful to the load capacity of raising system, make public Utility company can realize the promise of reliability, without causing additional capacity extension.

It often assumes that total when the demand response participation of user is constant and is called during demand response project It is available.But actually user might have different consumption mode and preference, so utility company is difficult to understand The speciality of each client.Moreover, even if such information if can obtain the responsiveness of user can by it is various can not be pre- The factor known, for example, special event influence.Therefore, the demand response responding ability of user may not known very much, and can It can greatly deviate from predicted value.However it is considerably less about probabilistic research in the prior art, having usually only It is to consider the external uncertainty of some systems, this is obviously incomplete.Accordingly, it is desirable to provide a kind of more comprehensively more acurrate Demand response Capacity Evaluation Model and method.

Invention content

For this purpose, the present invention provides a kind of the demand response capability assessment method and computing device of intelligent grid, to try hard to solve Certainly or at least alleviate above there are the problem of.

According to an aspect of the present invention, a kind of demand response capability assessment method of intelligent grid is provided, suitable for counting It calculates and is executed in equipment, this method includes：The reliability model of generating set is established, uncertainty models include the defeated of generating set Go out power function；Respond capacity model, user's participation model and the load for establishing demand response respectively rebound model, can ring Answer capacity model, user's participation model and load rebound effect model respectively include can responding the response demand function of user, Participation function and load rebound electric quantity function；It rebounds model according to capacity model, user's participation model and load can be responded The workload demand model of demand response is established, workload demand model includes the workload demand function that can respond user；According to power generation The confidence capacity model of the reliability model of unit and the workload demand model foundation demand response of demand response, the confidence capacity Model includes the confidence capacity of demand response；And the historical data of intelligent grid is obtained, and according to the historical data to demand The confidence capacity model of response is solved, and the confidence capacity of the demand response of the intelligent grid is obtained.

Optionally, in demand response capability assessment method according to the present invention, generating set includes conventional power generation usage unit, The available output power P of conventional power generation usage unit_t ^cgFunction beWherein, C^cgIndicate the specified of conventional power generation usage unit Capacity, β_t ^cgIt is a 0-1 variable, indicates the machine performance of the routine generating set in time slot t, wherein when equipment works normally When β_t ^cgBe 1, it is on the contrary then be 0.

Optionally, in demand response capability assessment method according to the present invention, generating set includes development of renewable energy Motor group, the final output power P of renewable energy power generation unit_t ^rgFunction be：

Wherein,It is a 0-1 variable, indicates the machine performance of the renewable energy power generation unit in time slot t, wherein The β when equipment works normally_t ^cgBe 1, it is on the contrary then be 0；P_t ^rgpIndicate the available output power of renewable source of energy generation unit；C^rgTable Show the rated capacity of renewable energy power generation unit；v_tIt is the wind speed of period t；v_ci、v_ratAnd v_coIt is the incision wind of wind turbine respectively Speed, rated wind speed and cut-out wind speed,And σ_tIt is the average value and standard deviation of wind speed respectively；y_tIt is the time sequential value of period t.

Optionally, in demand response capability assessment method according to the present invention, the response demand function of user can be responded For：Wherein,It is estimation response quautities of the user k in period t,It is user k Response demand total capacity,Capacity can be responded by being 1 year, one month, one day and one hour maximum respectively Coefficient, I_k,tIt is the white noise for expressing load stochastic and dynamic characteristic in operational process.

Optionally, in demand response capability assessment method according to the present invention, the participation function that can respond user is：

Wherein, PL_k,tIt is participations of the user k in period t；RF_tAnd RI_tIt is the response that can respond user in period t respectively Frequency and response intensity；RF_k,tAnd RI_k,tIt is the response frequency and response intensity that can respond user k in period t respectively；K' is institute There is system user Ω_DIn any user；RF_k',τAnd RI_k',τIt is response frequencies and response of the system user k ＇ in period τ respectively Intensity；It is the load decrement in period τ；e_τIt is two state variables, takes 1 when the period τ demand response event occurring, no 0 is taken when generation；r_τIt is the duration；It is to indicate that user k is to the inconvenient sensitivity caused by demand response program Number；It is the weighting correlation coefficient for quantifying response frequency and response intensity in demand response program.

Optionally, in demand response capability assessment method according to the present invention,WithSuitable for using trapezoidal degree of membership Function describes：Wherein,WithIt isDistributed area in spy Fixed number value, at this point, the participation function that can respond user is：

Wherein,WithIt is for describingWithFor the estimated value of user k.

Optionally, in demand response capability assessment method according to the present invention, the load that can respond user rebounds electricity Function is：Wherein,Represent the electricity to rebound for being applied to period t' Amount,It is the load decrement in period t；It is the duration of load rebound process,WithRespectively represent slope With power compensation rate.

Optionally, in demand response capability assessment method according to the present invention, the workload demand function of user can be responded For：

Wherein,WithRespectively represent user k under normal circumstances and in emergency circumstances final Workload demand；Represent the load decrement needed in period t system；Load can not be responded in period t by representing user Amount.

Optionally, in demand response capability assessment method according to the present invention, the confidence capacity model of demand response withIndicate, wherein Y be model output value, vectorial X andThe probability variable and mould in smart electric grid system are indicated respectively Variable is pasted,

Optionally, in the demand response capability assessment method of intelligent grid according to the present invention, fuzzy variableWith person in servitude Category degree functionAt this timeProbability density function of equal value is：

Optionally, in demand response capability assessment method according to the present invention, the confidence capacity model of demand response with Expected loss of load EENS is Reliability Index.

Optionally, in demand response capability assessment method according to the present invention, the confidence capacity of demand response is with equivalent Fixed capacity EFC is indicated, at this point, the Reliability Index comprising demand response is： Reliability Index not comprising demand response is：Wherein, D represents system loading demand Time series, C^gIt is the gross generation in system, C^rlIt is the capacity of demand response resource, R is also to weigh system reliability Index, C^bmFor the reference capacity of generating set.

Optionally, in demand response capability assessment method according to the present invention, the confidence capacity of demand response with etc. The alternative power generation capacity EGCS of effect is indicated, at this point, the Reliability Index not comprising demand response is：Including the Reliability Index of demand response is：Wherein, C^agIndicate the generated energy replaced.

Optionally, in demand response capability assessment method according to the present invention, according to the historical data to the demand The process that the confidence capacity model of response is solved includes：Conventional power generation usage unit is generated according to the historical data that is inputted and can The time state sequence of renewable source of energy generation unit, and according to the output power of the time state sequence two kinds of generating sets of generation Curve；Calculate separately the reliability index EENS of system when comprising with not comprising demand response^drAnd EENS^base, wherein EENS^baseRepresent the reliability level of system in the basic case, EENS^drIt is caused by demand response participates in for quantization The raising for reliability of uniting；And by EENS^baseWith EENS^drIt is compared, according to the numerical value difference of the two, is calculated by using iteration Method in system equivalent fixed capacity or alternative power generation capacity be adjusted, and to adjustment result to two indices value carry out Update, until stop adjustment when meeting predetermined relationship between updated two indices value, equivalent fixed capacity at this time or Alternative power generation capacity is the confidence capacity of demand response.

Optionally, in demand response capability assessment method according to the present invention, predetermined relationship is | EENS^dr-EENS^base |/EENS^base≤ ζ, wherein ζ are threshold value.

Optionally, in demand response capability assessment method according to the present invention, to the confidence capacity model of demand response The process solved is combined using sequential Monte Carlo analogy method and optimal load flow method.

Optionally, further include step in the demand response capability assessment method of intelligent grid according to the present invention：It establishes The scheduling strategy model of demand response, scheduling strategy model include reliability driving scheduling strategy model or coordinated management scheduling plan Slightly model, scheduling strategy model includes object function and constraints；Scheduling Policy model is solved according to constraints, To determine the optimal scheduling planning of demand response.

Optionally, in demand response capability assessment method according to the present invention, reliability driving scheduling Policy model and the object function for coordinating and managing scheduling strategy model are respectively： Wherein, V^omSystem total load loss when being comprising response side driving；V^cmIt is Including system when response side drives always shuts down cost；Removal of load amounts of the expression user k in time t；r_tIt is when continuing Between；Represent the average unit cost of power breakdown during t；κ_kaIt is disturbing factor coefficient, for indicating users'comfort to electricity consumption Susceptibility.

According to a further aspect of the invention, provide a kind of computing device, including one or more processors, memory with And one or more programs, wherein one or more programs are stored in memory and are configured as by one or more processors It executes, one or more programs include the finger of the demand response capability assessment method for executing intelligent grid according to the present invention It enables.

According to a further aspect of the invention, a kind of computer-readable storage medium of the one or more programs of storage is also provided Matter, one or more programs include instruction, and instruction is when executed by a computing apparatus so that computing device executes according to the present invention The demand response capability assessment method of intelligent grid.

According to the technique and scheme of the present invention, it is proposed that a kind of to assess the new of intelligent grid (demand response) potential reliability value Method, this method are established in the conceptive of confidence capacity (CC).Different from existing work, the present invention can solve demand response Different types of uncertainty (i.e. probability and ambiguity) in project, and meter and physical factor and human factor are to demand response Influence.For this purpose, the present invention is using demand response ability in the process of running as the synthesis result of many aspects, i.e. user Load characteristic, participation and load recovery effect, and different models is proposed to indicate the effect of each part.Suitably to examine The randomness for considering demand response, introduces fuzzy theory, and establishes artificial related probabilistic under description imperfect information Fuzzy model.In addition, operation reserve also embodies the potential impact of demand response effectiveness in the present invention, pass through proposition Two customer satisfaction indexs quantify these influences.Based on Probabilistic Fuzzy transformation technology, involved is different types of not true It is qualitative to be standardized under identical frame and systematically solve.Then, using based on sequential Monte-Carlo simulation (SMCS) the demand response model of algorithm, proposition can be applied to confidence Capacity Assessment program, wherein considering two kinds of scheduling schemes Carry out Research Requirements response to run on its confidence capacity.This hair style in modified RTS systems by carrying out this method Test, the result obtained confirm its validity under real world conditions.

Description of the drawings

To the accomplishment of the foregoing and related purposes, certain illustrative sides are described herein in conjunction with following description and drawings Face, these aspects indicate the various modes that can put into practice principles disclosed herein, and all aspects and its equivalent aspect It is intended to fall in the range of theme claimed.Read following detailed description in conjunction with the accompanying drawings, the disclosure it is above-mentioned And other purposes, feature and advantage will be apparent.Throughout the disclosure, identical reference numeral generally refers to identical Component or element.

Fig. 1 shows the structure diagram of computing device 100 according to an embodiment of the invention；

Fig. 2 shows the demand response capability assessment methods 200 of intelligent grid according to an embodiment of the invention Flow chart；

Fig. 3 shows the basic framework schematic diagram of demand response workload demand model according to an embodiment of the present invention；

Fig. 4 shows according to an embodiment of the inventionWithTrapezoidal membership function；

Fig. 5 shows the confidence Capacity Assessment algorithm flow chart of demand response according to an embodiment of the present invention；

Fig. 6 and Fig. 7 respectively illustrates no demand response according to an embodiment of the invention and has when demand response System Reliability Evaluation Algorithm；And

Fig. 8 shows the demand indicated with EFC and EGCS under different loads flexibility according to an embodiment of the invention The confidence capacity of response.

Specific implementation mode

The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although showing the disclosure in attached drawing Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure Completely it is communicated to those skilled in the art.

Fig. 1 is the block diagram of Example Computing Device 100.In basic configuration 102, computing device 100, which typically comprises, is System memory 106 and one or more processor 104.Memory bus 108 can be used for storing in processor 104 and system Communication between device 106.

Depending on desired configuration, processor 104 can be any kind of processing, including but not limited to：Microprocessor (μ P), microcontroller (μ C), digital information processor (DSP) or any combination of them.Processor 104 may include such as The cache of one or more rank of on-chip cache 110 and second level cache 112 etc, processor core 114 and register 116.Exemplary processor core 114 may include arithmetic and logical unit (ALU), floating-point unit (FPU), Digital signal processing core (DSP core) or any combination of them.Exemplary Memory Controller 118 can be with processor 104 are used together, or in some implementations, and Memory Controller 118 can be an interior section of processor 104.

Depending on desired configuration, system storage 106 can be any type of memory, including but not limited to：Easily The property lost memory (RAM), nonvolatile memory (ROM, flash memory etc.) or any combination of them.System stores Device 106 may include operating system 120, one or more apply 122 and program data 124.In some embodiments, It may be arranged to be operated using program data 124 on an operating system using 122.Program data 124 includes instruction, in root In computing device 100 according to the present invention, program data 124 includes the demand response capability assessment method for executing intelligent grid 200 instruction.

Computing device 100 can also include contributing to from various interface equipments (for example, output equipment 142, Peripheral Interface 144 and communication equipment 146) to basic configuration 102 via the communication of bus/interface controller 130 interface bus 140.Example Output equipment 142 include graphics processing unit 148 and audio treatment unit 150.They can be configured as contribute to via One or more port A/V 152 is communicated with the various external equipments of such as display or loud speaker etc.Outside example If interface 144 may include serial interface controller 154 and parallel interface controller 156, they, which can be configured as, contributes to Via one or more port I/O 158 and such as input equipment (for example, keyboard, mouse, pen, voice-input device, touch Input equipment) or the external equipment of other peripheral hardwares (such as printer, scanner etc.) etc communicated.Exemplary communication is set Standby 146 may include network controller 160, can be arranged to convenient for via one or more communication port 164 and one The communication that other a or multiple computing devices 162 pass through network communication link.

Network communication link can be an example of communication media.Communication media can be usually presented as in such as carrier wave Or the computer-readable instruction in the modulated data signal of other transmission mechanisms etc, data structure, program module, and can To include any information delivery media." modulated data signal " can such signal, one in its data set or more It is a or it change can the mode of coding information in the signal carry out.As unrestricted example, communication media can be with Include the wire medium of such as cable network or private line network etc, and such as sound, radio frequency (RF), microwave, infrared (IR) the various wireless mediums or including other wireless mediums.Term computer-readable medium used herein may include Both storage medium and communication media.

Computing device 100 can be implemented as server, such as file server, database server, application program service Device and WEB server etc. can also be embodied as a part for portable (or mobile) electronic equipment of small size, these electronic equipments Can be such as cellular phone, personal digital assistant (PDA), personal media player device, wireless network browsing apparatus, individual Helmet, application specific equipment or may include any of the above function mixing apparatus.Computing device 100 can also be real It includes desktop computer and the personal computer of notebook computer configuration to be now.In some embodiments, computing device 100 It is configured as executing the demand response capability assessment method 200 of intelligent grid according to the present invention.

As it was noted above, the demand response responding ability of user may not known very much, and reason may be greatly deviated from By predicted value, thus lead to the false assessment to demand response capacity.Have in the prior art consider on a small quantity it is this probabilistic Research, but it is relevant uncertain usually by probability that is predetermined before assessment and determining with demand response in the prior art It is distributed to describe.In other words, existing demand response model primarily to the external uncertainty of processing and design, be Random response degree based on the future customer hypothesis unrelated with the control strategy that system operator uses and formulate.However, Under actual conditions, the participation of demand response can bring the return of user economically, it is also possible to the happiness for reducing them (is relaxed Appropriateness).In this way in actual implementation process, inappropriate scheduling strategy may result in response fatigue, and the side of demaning reduction Participation (disobey demand response calling).And the real response of user-responsiveness depends not only on its future outcomes sheet Body, but also influenced by power grid operation decision.Therefore, if not considering the uncertainty of this inherence in demand response And its relationship between the system decision-making, it is likely that the estimation of mistake can be carried out to the reliability value of demand response.However, so far Until, this problem is found and inquired into almost without any research.

For this purpose, the present invention proposes a kind of demand response capability assessment method of intelligent grid, it is based primarily upon a kind of new Demand response confidence capacity model, i.e. demand response modeling-probability-fuzzy frame holds for the confidence to demand response Amount is assessed.The concept of confidence capacity is designed and is applied to electric system offer ability to quantify generation assets 's.However, under the background of intelligent grid, due to flexibly loading as virtual generator unit, this definition is extended to How demand response scene influences the security of supply of system at different conditions to the flexibility of potential demand side.One As, the confidence capacity performance index of demand response generally includes payload capability (ELCC), equivalent fixed capacity (EFC), equivalent Conventional capacity (ECC) and equivalent alternative power generation capacity (EGCS), each index are suitable for different indexs.In view of the present invention The potentiality that demand response provides operation deposit are primarily upon, therefore select the two Measure Indexes of EFC and EGCS, also may be used certainly It is calculated with other two kinds of Measure Indexes.

According to one embodiment of present invention, the confidence capacity model of demand response is to be with expected loss of load EENS System reliability index, and in two kinds of measures, the computational methods of EENS are also different.In EFC methods, demand response Confidence capacity be defined as needing additional power generation unit capacity to be mounted when no demand response participates in, to realize and have demand Identical system reliability when response.In order to calculate the EFC values of demand response, need respectively to comprising with comprising demand response System carries out reliability assessment, and what is obtained includes the Reliability Index of demand responseFor：

Wherein, D represents the time series of system loading demand, C^gIt is the gross generation in system, C^rlIt is demand response money The capacity in source, R are also the index for weighing system reliability.

Then, when only (its reference capacity is C to generating set^bm) when existing but there is no demand response, the reliability of system IndexFor：

It should be noted that in EFC methods, base is considered completely reliable, it means that it has zero Forced outage rate.Base C^bmCapacity will be iterated adjustment, until the EENS of system reaches identical with there is demand response Reliability level, i.e.,Generated energy C needed at this time^bmIt is considered as the value of EFC, i.e. demand response Confidence capacity.

Different from EFC above-mentioned, EGCS quantifies the confidence capacity of demand response by the generating set in replacement system.Cause This, the confidence capacity of demand response is defined as the biography that can be substituted while keeping identical reliable level of supply under EGCS The capacity of system generating set.In practice, EGCS in order to obtain needs to first check for the system reliability not comprising demand response IndexIts formula is：

Then, consider the influence of demand response, and according to the descending of operation cost, reduce the capacity of generating set, calculate Obtain including the Reliability Index of demand responseIts formula is：

Wherein, C^agIndicate the generated energy replaced.C^agValue will constantly adjust, until systemReach no demand The case where responseThe EGCS values of demand response can be determined that C at this time^agFor final result.

Fig. 2 shows the streams of the demand response capability assessment method 200 of intelligent grid according to an embodiment of the invention Cheng Tu is suitable for executing in computing device (such as computing device 100 shown in FIG. 1).

As shown in Fig. 2, this method starts from step S210.In step S210, the reliability model of generating set is established, it should Uncertainty models include the output power function of generating set.

According to one embodiment, generating set includes conventional power generation usage unit, the reliability mould of conventional power generation usage unit (CGU) Type mainly consists of two parts：Fuel is supplied and mechanical availability, and wherein mechanical part is usually using two state Markov moulds Type describes, which indicates the normal and malfunction of the unit.For fuel supply, due to most of conventional generator Group depends on traditional energy (such as combustion gas or diesel oil), and it is reliable therefore no not true that primary energy is typically considered 100% It is qualitative.Based on above-mentioned model, for the available output power P of regular hour t conventional power generation usage unit_t ^cgFunction be：

Wherein, C^cgIndicate the rated capacity of conventional power generation usage unit, β_t ^cgIt is a 0-1 variable, indicates conventional in time slot t The machine performance of generating set, wherein the β when equipment works normally_t ^cgBe 1, it is on the contrary then be 0.In the present invention, β_t ^cgIt is to use The variable that random sampling based on long-term reliability data determines.

According to another embodiment, generating set includes renewable energy power generation unit, for simplicity, the present invention with For wind power generating set.The output power of wind power generating set depends primarily on the wind speed at scene.In practice, with the time Passage and the spatial coherence that changes over time of wind speed, random variation usually use auto regressive moving average time series models It indicates, the wind speed v of time period t_tIt can be expressed as：

Wherein,And σ_tIt is the average value and standard deviation of wind speed, y respectively_tIt is the time sequential value of period t.With wind-force shape State, the available output power P of renewable energy power generation unit (such as wind power generating set)_t ^rgpIt can be derived according to its operation characteristic Come：

Wherein, C^rgIndicate the rated capacity of renewable energy power generation unit；v_ci、v_ratAnd v_coIt is the incision wind of wind turbine respectively Speed, rated wind speed and cut-out wind speed.The final output power P of renewable energy power generation unit_t ^rgIt is the work(derived from operation characteristic Rate exports and mechanical availabilityIntegration obtain：

Wherein,Similar to β_t ^cgIt is a 0-1 variable, indicates the machinery of the renewable energy power generation unit in time slot t State, wherein the β when equipment works normally_t ^cgBe 1, it is on the contrary then be 0.

Then, in step S220, that establishes demand response respectively responds capacity model, user's participation model and negative Lotus is rebounded model, these three models respectively include can responding the response demand function of user, participation function and load and rebound electricity Flow function.

Modeling to load-side response is the premise for the confidence capacity that demand response is effectively estimated.In practical applications, it needs Ask the active volume of response associated with physical factor and human factor.From the point of view of physical angle, demand response capacity is actually Depend primarily on Demand-side electrical load uses power mode and part throttle characteristics.According to the flexibility of operation, the power equipment of user Can generally be divided to can respond load and can not respond load as two classes.The former refers to can be in the case where not sacrificing user's welfare The case where equipment uses is interrupted or postpones, and the use of the latter has flexibility at no time.In fact, due to user May there are different load compositions and electric energy use habit, therefore the demand response capacity and characteristic of different user may be very It is different in big degree, and have prodigious change at any time.Therefore, include and the relevant uncertainty of user demand in modeling Confidence capacity for demand response is effectively estimated is essential.

On the other hand, the degree of participation of party in request is another factor for influencing demand response active volume.As previously mentioned, Under open electricity market, user can freely decide whether to participate in demand response project according to the interests of oneself.In addition, right In non-straight control load, since demand response obtains on the basis of " voluntary ", user may take different action, need It asks in response project It is not necessary to always be in response to demand.Both of which can be brought additional to the active volume size of demand response It is uncertain.Although having been directed to the uncertain problem of demand response in existing research, party in request responds in these work Randomness be mostly used fixed probabilistic model greatly and indicate, wherein the behavior change with the time feature of user largely by It has ignored.But in long-term fail-safe analysis, since time span is longer, user is possible to be adjusted according to external condition dynamic Demand response strategy, to play the advantage of itself to the maximum extent.

It was verified that resident participates in the main excited target policy of degree of demand response project, inconvenience cost, religion Educate the influence of several factors such as degree；And in actual implementation, the wheel and deal essence of user will lead to the demand response of system The random variation of active volume.In addition, although the electric energy reduction of user can cause it some negative effects, but if using suitable When scheduling strategy, sustainable demand response may be implemented.The above result of study show the operation reserve of demand response for User determines that the latent capacity of demand response plays an important roll.However, probabilistic model single at present is not enough to include demand Respond this gradual and decision dependence of capacity.In addition, in real life, since utility company is generally difficult to obtain The partial data about individual consumer is obtained, so also needing to more advanced tool to simulate the demand caused by lacking information The randomness of response.

May be to influence with (LR) behavior of rebounding of the relevant load restoration of demand response project or load in addition to above-mentioned aspect Another factor of demand response characteristic.As an additional result of demand response, loading the presence rebounded may be significantly The demand of change system, to have a great impact to the reliability contribution of demand response.In practice, since user voluntarily determines Fixed load rebound process, therefore for each demand response event, load bounce mode should be that height is uncertain.Cause This, considers that this variation is necessary in the confidence Capacity Assessment of demand response.

In view of the above problems, the present invention proposes a kind of novel mixing probability-obscurity model building frame, the framework integration The external and inherent uncertainty present in the confidence capacity analysis of demand response.As shown in Fig. 3, the frame provided will Load characteristic, participation and the load that the active volume of demand response is considered as user rebound the synthesis result of the aspect of effect three, It can respond capacity model, user's participation model and load to establish and rebound model.Specifically, it is appreciated that enough need Ask side data that can be obtained in intelligent grid, the loading demand of client is considered as a stochastic variable, it is to use time mould What type indicated.On this basis, the influence of the aspect of people is further analyzed.Due to the imperfection of personal information, introduce Fuzzy set theory, and the fuzzy model that one is relied on based on decision is established to describe user to the random of demand response participation Variation.The output of above-mentioned model will be defined as to use the capacity of demand response in system in the single period in one day.Passing through will These results and load effect of rebounding are integrated, and final workload demand model can be obtained, after finally export is corrected The load curve of demand response.Final result of these data as carried model framework, will be for the confidence based on reliability Capacity Assessment program.

According to one embodiment of present invention, the response demand function that can respond user is：

Wherein,It is estimation response quautities of the user k in period t,It is the response demand total capacity of user k,Capacity coefficient, I can be responded by being 1 year, one month, one day and one hour maximum respectively_k,tIt is to use In the white noise for expressing load stochastic and dynamic characteristic in operational process.In general, formula (9) can be determined by carrying out load investigation In parameter：For various users (for example, business, industry, house etc.), hour consumption and the load curve of major electrical equipment are acquired Historical record.In intelligent grid, as terminal user is equipped with intelligent electric meter, utility company can obtain about each The adequate data of people's demand.The corresponding load curve obtained will be assessed further and will be filtered using statistical technique. It can estimate to be suitble to the average load of demand response horizontal in 1 year per hour in this way, and by these demand response curves Analysis, finally obtains the parameter of model.

According to another embodiment of the invention, in electricity market, the wish that client participates in demand response project is main Depending on expected revenus and since load reduction/transfer needs the balance between the corresponding non-comfort maintained.But in fact, by It is usually predefined by utility company in the incentive mechanism of demand response, therefore the degree of participation of demand response project is main It is driven by the uncomfortable factor of client.And the non-comfort cost of demand response participant depends primarily on their response frequency With responsiveness amount, the present invention indicates that calculation formula is as follows with response frequency (RF) and response intensity (RI)：

Wherein, RF_tAnd RI_tIt is the response frequency and response intensity that can respond user in period t respectively,It is in period τ Load decrement；e_τIt is two state variables, takes 1 when the period τ demand response event occurring, 0 is taken when not occurring；r_τIt is to continue Time.As can be seen that RF and RI have quantified respectively into period τ historic demand response events the frequency and average response water of client It is flat.Obviously, the two numerical value are smaller, and influence of the demand response to users'comfort is with regard to smaller, therefore individual may be more in the later stage It is ready that abiding by demand response dispatches.Since the correlation between demand response and its operation reserve is very strong, for inherent characteristic, Present invention assumes that between demand participation and response frequency/response intensity index, there are positive correlations.Therefore, period t can Response user participation (PL) function be：

Wherein, PL_k,tIt is participations of the user k in period t；RF_k,tAnd RI_k,tUser k can be responded respectively in period t Response frequency and response intensity；K' is all system user Ω_DIn any user；RF_k',τAnd RI_k',τIt is system user respectively Response frequencies and response intensity of the k ＇ in period τ；It is to indicate user k to the inconvenient sensitive journey caused by demand response program The coefficient of degree；It is the weighting correlation coefficient for quantifying response frequency and response intensity in demand response program.When user's When income is affected by response frequency,By the value that correspondence is larger；Otherwise, it should be applicable in smaller

Formula (12) defines user by synthesizing response frequency and response intensity result based on classical weighted sum method Participation.Response frequency and the value of response intensity are according to them in all system users (k' ∈ Ω_D) and time (τ ∈ 1:t- 1) the corresponding dominant record on is normalized, to ensure the consistency of index.According to formula (12), user's participation function is to close In the decreasing function of response frequency and response intensity index.Therefore, it is considered as the tradition of constant different from the participation of user Demand response model, the participation angle value proposedIt will change with the scheduling strategy of utility company, to make It is substantially the model changed over time to obtain the formula.According to related probabilistic property, variable in formula (12) can be with It is divided into two classes, you can observed quantity (includes mainly RF_k,tAnd RI_k,t) and unobservale quantity (weighting coefficient ω_kAnd ξ_k).Observable quantity Refer to those variables unknown in advance but that statistical method can be used suitably to be derived when there are enough data, this class variable exists It can be described, and will be determined according to the stochastic simulation under SMCS frames by probabilistic model when assessing confidence capacity.And it is unobservable Due to various reasons, the information measured is often very limited, therefore the statistical nature for directly acquiring these data is for quantitative change amount It is impossible.

For this purpose, the present invention models uncertain data using fuzzy variable, i.e., by using possibility distrabtion (PD, Or be membership function) indicate fuzzy variable.Under fuzzy frame, for each variableIts membership function mui_A(x) it will retouch Element x in the U of review domain belongs to the degree of fuzzy set A, and degree of membership is bigger, and x more belongs to A.In practical applications, according to corresponding The available degree of knowledge can indicate the uncertainty of Unobservable variable using different types of membership function.Specifically, The present invention describes stochastic variable with trapezoidal membership functionWithFig. 4 shows a signal of the membership function Figure, whereinWherein,WithIt is expressionDistributed area Special value.At this point, can respond participation (PL) function of user can be further represented as：

Wherein,WithIt is for describingWithFor the estimated value of user k.

According to still another embodiment of the invention, any load occurred during period t is reduced, total electricity It can be considered as the extra duty of other times section that amount, which is rebounded, these extra duties with certain lapse rate be added to period t it Period afterwards.At this point, the load that can respond user rebounds, electric quantity function is：

Wherein,The electricity to rebound for being applied to period t' is represented,It is the load decrement in period t； It is the duration of load rebound process,WithRespectively represent slope and power compensation rate.

As previously mentioned, in true demand response project, since user may restore theirs by different modes Cut down electricity, for system operator, the parameter in formula (14) Unobservable variable.Therefore, The present invention use similar to user's participation processing mode, using based on trapezoidal membership function come the load of analog subscriber The random variation of bounce mode.

Then, in step S230, what is responded according to demand responds capacity model, user's participation model and load bullet The workload demand model of model foundation demand response is returned, the workload demand model includes the workload demand letter that can respond user Number.The finally obtained workload demand model of institute after integrating three kinds of models in namely Fig. 4.

The output of comprehensive aforementioned three kinds of models, user is under normal circumstancesIn emergency circumstancesFinal is negative Lotus demand function can be expressed as follows：

Wherein,WithIt is the final workload demands of user k under normal circumstances and in emergency circumstances respectively；It is In the load decrement that period t system needs；Load can not be responded in period t by being user.By above-mentioned function it is found that When system is in normal condition, user's overall consumption electricity is the total of elastic load in the period and non-resilient workload demand With.However in case of emergency, customer demand is the original electrical energy demands amount of userSubtract the negative of period reduction Lotus amountIn addition the load amount of reboundingHere, in each demand response event user actual load decrement by with The participation at family and required demand response capacityProduct be calculated.Since user's participation reflects individual in demand The running participation wish of response project, the participation of user is bigger, bigger for the responsiveness of demand response signal.

In addition, having allowed also for demand both comprising stochastic variable or comprising fuzzy variable in formula (15) and (16) The external and inherent uncertainty of response.Therefore, workload demand model of the invention belongs to the fuzzy mould of a dynamic probability- Type.Due to introducing fuzzy theory, the present invention can efficiently solve the mould with demand response relevant contingency and awareness Paste property, to keep it more practical in practical applications.And need to depend critically upon historical data in the scheme of the prior art, because This cannot be used for processing to lack the uncertain variables of data and probabilistic statistical information being not intrinsic repeatable feelings Condition, such as the case where demand response.In addition, the model considers external and inherent uncertainty to demand response potentiality It influences, this enables demand response model of the invention as the pioneering work of the long term evolution and dependence of client's participation Make.Moreover, present invention incorporates the influences of demand response project and physics aspect and the aspect of people, therefore it can be in practical feelings Demand response more acurrate and comprehensive expression is provided under condition.

It should be noted that the model is based primarily upon it is assumed hereinafter that establishing：1) current confidence capacity research mainly from The angle of utility company, so being concentrated mainly on the demand characteristic of node level.In order to reduce complexity, present invention assumes that User on the same node has similar loading condition and power consumption custom, therefore can be equivalent to a user, bears Carry the polymerization for being characterized in individual demand.In practice, which can be used to represent a big user or one by region The response user set that load aggregation device is coordinated.2) demand response in the present invention refers to that non-straight control load, contingency drive Dynamic demand response project, rather than the plan based on price, such as Spot Price.Moreover, in order to avoid excessive complexity, it is false If all clients receive the Fee Schedule of nontraffic sensitive.3) the load structure fixation of customer does not develop at any time, so as to With in the confidence capacity of static base on-line analysis demand response, the changeability without considering user load characteristic.4) own Load all works under constant power factor.

Then, it in step S240, is built according to the workload demand model of the reliability model of generating set and demand response The confidence capacity model of vertical demand response, which includes the confidence capacity of demand response.Specifically, one it is general Demand response confidence capacity model can be expressed asWherein vector x andStudied intelligent grid is indicated respectively Probability variable in system and fuzzy variable.Specifically,About The Details: SUMMARY of these variables such as the following table 1.

Uncertainty in the smart electric grid system that table 1. is discussed

According to one embodiment of present invention, two kinds of uncertainties must be unified under identical appraisal framework, that is, united One at variable of the same race, the variable in such modelProbabilistic quantity is eventually converted by fuzzy variable, transformed probabilistic quantity can be straight Scoop out the assessment algorithm for confidence capacity.Standardized method, specifically, fuzzy variable usually can be usedWith degree of membership letter NumberAt this timeProbability density function of equal value is：

Then, in step s 250, the historical data of intelligent grid is obtained, and according to the historical data to the Demand-side The confidence capacity model of response is solved, and the confidence capacity of the Demand Side Response of the intelligent grid is obtained.

According to one embodiment of present invention, according to the historical data to the confidence capacity model of the Demand Side Response into Row solve process include：According to the historical data that is inputted generate conventional power generation usage unit and renewable energy power generation unit when Between status switch, and according to the time state sequence generate two kinds of generating sets output power curve；Calculate separately comprising The reliability index EENS of system when with not comprising Demand Side Response^drAnd EENS^base, wherein EENS^baseIt represents in basic condition The reliability level of lower system, EENS demand responses are used to quantify the system reliability caused by Demand Side Response participates in It improves；And by index EENS^baseWith index EENS^drIt is compared, according to the numerical value difference of the two, by using iterative algorithm To in system equivalent fixed capacity or alternative power generation capacity be adjusted, and to adjustment result to two indices value carry out more Newly, until stopping adjustment when meeting predetermined relationship between updated two indices value, equivalent fixed capacity EFC at this time or Alternative power generation capacity EGCS is the confidence capacity C C of Demand Side Response.Wherein, which can be | EENS^dr- EENS^base|/EENS^base≤ ζ, wherein ζ are threshold value, such as can be 2%, are certainly not limited to this.In addition, the historical data can be with It is the multinomial data such as grid information, element forced outage rate, RES models and the load data classified.

As it was noted above, more whether the confidence Capacity Assessment of demand response is actually established in assessment and there is demand to ring In seasonable system reliability, therefore sequential Monte Carlo simulation SMCS methods and optimal may be used in above-mentioned calculating solution procedure Trend OPF methods are combined.Wherein, SMCS methods are referred to by the random behavior of simulation system in chronological order to obtain reliability Mark, thus can effective pull-in time information, be very suitable for analysis related with the time.On the other hand, when being belonged to due to SMCS Sequence method, some information indexes, which are based especially on frequency or the information index of time, to be quantified by SMCS.

Fig. 5 shows the general step of the confidence capacity according to an embodiment of the invention responded based on SMCS potential demands Suddenly.Specifically, which obtains the Reliability Index value when no demand response participates in by running SMCS first EENS^base, which is the reliability level of system in the basic case.Then, demand response is included in and carries out SMCS again It calculates, to quantify the raising of the system reliability caused by demand response participates in, this will obtain new Reliability Index Value EENS^dr.Once completing above-mentioned steps, so that it may to be compared to the Reliability Index value result obtained, with Go out that whether there is or not reliability index when demand response (Fig. 6 and Fig. 7 respectively illustrate the computational methods of both indexs).According to them Numerical value difference, reference capacity (is calculated)/alternate capacity (being calculated for EGCS) for EFC and will be calculated by using iteration in system Method adjusts.EGCS is calculated, it is assumed that generating set is substituted according to its operating cost (arranging in descending order), is being adjusted every time After whole, check whether new system has reached preset precision by SMCS is carried out.When whether there is or not the two of demand response reliabilities Index value meets | EENS^dr-EENS^base|/EENS^baseWhen≤ζ (such as 2%), entire search, installed capacity at this time will be stopped EFC/ substitute generation amounts EGCS is considered as the confidence capacity of demand response.It should be noted that in the calculating process, this hair It is bright to be primarily upon the operation characteristic of demand response, namely the related uncertainty with power generation is only focused on, it may be influenced without considering The other factors of confidence capacity, for example, transmission line failure, the communications infrastructure safety and message delay etc..

Obviously, in the evaluation process of confidence capacity, how to determine that there are system reliability when demand response be crucial Sex chromosome mosaicism.In demand response project, as starting point, a Load flow calculation is repeated in each time t first, to check system Operating status.In practical situations, due to have a power failure may by network generation deficiency or be unsatisfactory for constraint (such as overload or owe Voltage) cause, therefore tidal current analysis is used in the evaluation process.If not finding fortuitous event, mean do not have currently There are generation load loss, the energy insufficient (ENS) of system that should be set as zero, otherwise means that the system is related to emergent feelings Condition.When this thing happens, some remedial measures will be taken as major corrections measure first.It is most common in intelligent grid Remedy scheme include capacitor switch, transformer tapping switching and network reconfigure.These remedy the optimal tune of resource Degree is typically to be determined according to prespecified strategy or by using optimisation technique.Every time trend point can be all carried out after adjustment Analysis, until system is released from or all remedial measures put into practice in violation of rules and regulations.Demand response as a last resort, only above-mentioned Remedy scheme prove it is insufficient in the case of can just start.In practice, in order to make full use of the ability of demand response, power grid operation Quotient analyzes generally according to OPF and formulates scheduling strategy.Therefore, it is necessary to the scheduling strategies to demand response to analyze, and then determine Best demand response scheduling strategy and the removal of load amount relative to each client.At this point it is possible to by will be required in time t The demand response capacity level wantedIt is multiplied by user's participation PL_k,tCarry out the actual load decrement of potential demand side.Based on this, The energy deficiency value ENS of system can be expressed as：

Wherein,Removal of load amounts of the expression user k in time t.After each demand response event, public affairs are used Formula (14) is incorporated to load and rebounds effect, and the load behavior of rebounding of user is according to the parameter in membership functionIt derives, and response frequency and response intensity index can be also updated according to respective formula.With The passage of simulation time, the EENS values of analogue system may finally pass through the record knot of the integration ENS in all simulation times Fruit and obtain.

According to one embodiment, invention also contemplates that influence of the scheduling strategy to dispatching efficiency is (in the prior art usually Only the optimal scheduling of demand response is determined from utility company or customer perspective).Specifically, it may include step：It establishes The scheduling strategy model of demand response, the scheduling strategy model include reliability driving scheduling strategy model (abbreviation RD models) or Scheduling strategy model (abbreviation CM models) is coordinated and managed, the scheduling strategy model includes object function and constraints；According to Constraints solves the scheduling strategy model, to determine that the optimal scheduling of Demand Side Response is planned.Wherein, in RD tune In degree strategy, it is assumed that utility company's deployment requirements response is only used for the reliability performance (mesh of RD models of maximization system Scalar functions also represent the intrinsic motivation that its service reliability improves in utility company)；And in CM scheduling strategies be then with Demand response operation in system reliability and customer satisfaction synthesis be target, that is, reduce to the greatest extent system it is total shut down at This, while minimum inconvenience is caused to demand response client.Based on this, the object function of RD models and CM models is respectively：

Wherein, V^omSystem total load loss when being comprising response side driving；V^cmSystem when being comprising response side driving It is total to shut down cost；Removal of load amounts of the expression user k in time t；r_tIt is the duration；Represent power breakdown during t Average unit cost ($/kWh)；L_k() is utility function, has quantified user and has demandd reduction during tExpection non-comfort Cost.Obviously,Value is bigger, at this time demand response to be scheduled to the non-comfort that user brings bigger, therefore to it Fitness value adds " punishment " of a bigger.For calculation formula (20), grid operator must be known by about each user Lk () concrete form, but these privacy informations can not always access in practical applications.It is asked to solve this Topic, the present invention commonly use the behavior pattern at family from the historical data middle school of user.Considered herein based on the approximation for speculating variableSuch as：

It namely uses and the demand response value needed for respective cycle tRelated linear function carrys out potential demand Respond the non-comfort of user.Further, according to disturbing factor coefficient κ_k,aIt is determined, wherein κ_k,aFor indicating user Susceptibility of the comfort level to electricity consumption.Assuming that the performance of user is continuous and is for each demand response event in time It is independent；So disturbing factor coefficient value can be estimated by the linear distribution lag model formed with experience：

WhereinIndicate that client disobeys the degree that demand response is dispatched in prior demand response events,λ is the average demand response cost of society；In addition, Bk () is a Standardization Operator, For its all constituents x to be normalized, the corresponding average value relative to all system users in addition to k. Therefore,Wherein Ω in K expressions system_DThe quantity of middle element.According to formula (22), The estimation of disturbing factor coefficient depends on its estimated value for the first period and in history the demand response track of user, it is larger not The demand response amount of obedienceIt will lead to the disturbing factor coefficient of bigger, and therefore cause to the higher non-comfort of user Estimation.In real world, since different time of the individual in one day has different life style and demand, so interference because A prime system number not instead of constant, will be different in system operation.In fact, due toAndIt can lead to It crosses historical data to obtain, so they are the external factors of model.On this basis, the object function of CM models can further table It is shown as：

According to one embodiment of present invention, the constraints of RD models and CM models is：

Wherein,WithIt is active and idle to be respectively that i-th of node conventional electric power generation unit is sent out；WithRespectively It is active and idle to be that i-th of node renewable energy power generation unit is sent out；Be respectively circuit ij line losses it is active and It is idle；WithIt is active and idle to be respectively that i-th of node load rebounds；P_ij,tAnd Q_ij,tIt is that circuit ij flows through respectively It is active and idle；G_ijAnd B_ijIt is the conductance and susceptance of circuit ij respectively；V_i,tAnd V_j,tIt is i-th respectively, the voltage amplitude of j node Value；δ_i,tAnd δ_j,tIt is i-th respectively, the voltage phase angle of j node；S_ij,maxIt is the apparent energy of circuit ij.

Constraints (24) and (25) respectively represent responding demand response and can not respond and needing for each client in system The scheduling level of response is asked to limit.Active/reactive power equilibrium and system load flow are considered in constraints (26) to (29) Traditional constraints.In addition, in order to ensure service quality, (30) and (31) enforce the limitation of feeder voltage deviation and current-carrying capacity. Finally, constraints (32) provides that the power factor of client during operation remains unchanged.Above-mentioned RD/CM models will determine system The optimal demand response plan (being used for flexible load) of client and cutting load (being used for inflexible load), i.e.,WithBase The load curve for being participated in by demand response and being modified can be derived in those results, and these new demand datas will It is used for the algorithm based on reliability of confidence capacity estimation.

Below by way of influence of the scheduling strategy to demand response confidence capacity is calculated in sample calculation analysis, it is based primarily upon IEEE-RTS systems are tested.The system installs 3405 megawatts of conventional power generation usage amount, 2850 megawatts of peak load altogether.In addition, false It is located in power generation combination and increases five 50 megawatts of wind power plant as the renewable energy power generation unit in system.Each wind-force Generating field is made of 25 wind power generating sets of 2MW with identical parameters, the mean free error time of the unit and Mean repair time is respectively 760 hours and 40 hours.In addition, further defining the chronological load data of system, wrap It includes load per hour and identifies that the user type on each busbar is distributed.In order to be fitted proposed demand response model, The membership function for showing each fuzzy variable in table 2 and accordingly establishing.

2 each parameter membership function of table

What load flexibility (FL) was defined as user responds capacityAccount for its total power consumption in time period tRatio, it is assumed that the FL of client increases to 50% from 0, step-length 5%, then can be responded under difference FL user with EFC and The confidence capacity (CC) that EGCS is indicated can be as shown in table 3 and Fig. 8.

With the confidence capacity of EFC and the EGCS demand response indicated under 3 difference FL of table

As can be seen that under the same terms, the confidence capacity of the demand response monotonic increase with the growth of load flexibility； But result tends to be saturated when the load flexibility in system reaches certain value, that is, the confidence capacity of demand response and use Family load flexibility is closely related.In other words, available to respond that load is more, demand response can be to the adequacy of supply Make the improvement of bigger.This is because larger FL means the load regulation ability for having bigger in terms of Demand-side；To subtract The risk of few system insufficient supply.However, with the raising of system reliability, since demand response cannot constantly promote to supply Sufficient (due to the physical limit of load), so marginal benefit is declined.

The confidence capability value of demand response under table 4.RD and CM scheduling strategy

Table 4 shows the confidence capability value of demand response under RD and CM scheduling strategies, as shown in table 4, under CM patterns Derived confidence capacity is always above the confidence capacity in the case of RD, and the gap between two kinds of results is with load flexibility Increase and increases.This shows in the demand response project that a user voluntarily participates in, the confidence capacity and system of demand response The migration efficiency that operator uses is closely related.Assuming that the load flexibility of system is respectively 20% and 50%, then can also be right The user's participation of two selected users accumulated in the case of loading reduction and RD and CM counts.The result shows that user Load reduction and participation exist it is negatively correlated；In RD, demand response may bring certain non-comfort to user, Influence the mean allocation of demand response；And the problem can be effectively relieved in CM schemes, after in view of recognizing man's activity, demand is rung It should distribute more uniformly.In addition, when the ratio that can respond load is relatively low, due to the limited potential of demand response, operation reserve It is not in apparent problem for system.But with the raising of ratio, the behavior of user will produce the income of demand response The influence of raw bigger, in this case, the effect of operation reserve can be showed clearly.

According to the technique and scheme of the present invention, it is proposed that a new confidence Capacity Evaluation Model, for estimating the following intelligence The confidence capacity of demand response in network system.Be can to introduce from party in request in place of the main innovation of the model by physics and The external and inherent uncertainty of demand response caused by human factor.In order to indicate the stochastic and dynamic of user response degree, carry Mixing Probabilistic Fuzzy model is gone out, there is defined two Satisfaction indexes (i.e. RF and RI), and can for quantifying demand response With property to the dependence of its operation reserve.Using Probabilistic Fuzzy transformation technology, by it is different types of it is related it is uncertain it is comprehensive at The same frame carries out confidence Capacity Assessment using the reliability algorithm that SMCS and OPF methods are combined.Simulation result also table It is bright, if the abundance of system can be greatlyd improve using proper demand response project, and sizable appearance is provided for system Amount is supported.Unlike conventional electric power generation unit, the confidence capacity of demand response not exclusively depends on the object of final power load Characteristic is managed, but also is influenced by other problems such as the client's consumption modes and demand response operation reserve that power grid uses.One As for, larger load flexibility or higher correlation between client response rate and system loading curve will lead to bigger Confidence capacity.Therefore, in practice, it is contemplated that the influence of client's non-comfort has demand response in demand response planning It is essential to imitate confidence capacity estimation.

A9, the method as described in A8, wherein the confidence capacity model of the demand response withIt indicates, Middle Y be model output value, vectorial X andThe probability variable in smart electric grid system and fuzzy variable are indicated respectively,

A10, the method as described in A9, wherein the fuzzy variableWith membership functionAt this timeOf equal value is general Rate density function is：

A11, the method as described in any one of A1-A10, wherein the confidence capacity model of the demand response is with electric power Insufficient desired value EENS is Reliability Index.

A12, the method as described in A11, wherein the confidence capacity of the demand response indicates with equivalent fixed capacity EFC, At this point, the Reliability Index comprising demand response is：Not comprising demand response Reliability Index is：Wherein, D represents the time series of system loading demand, C^gIt is to be Gross generation in system, C^rlIt is the capacity of demand response resource, R is also the index for weighing system reliability, C^bmFor generating set Reference capacity.

A13, the method as described in A12, wherein the confidence capacity of the demand response is with equivalent alternative power generation capacity EGCS is indicated, at this point, the Reliability Index not comprising demand response is：Including demand is rung The Reliability Index answered is：Wherein, C^agIndicate the generated energy replaced.

A14, the method as described in any one of A1-A13, wherein the demand response is set according to the historical data Believe that the process that capacity model is solved includes：Conventional power generation usage unit and regenerative resource are generated according to the historical data inputted The time state sequence of generating set, and according to the output power curve of the time state sequence two kinds of generating sets of generation；Point The reliability index EENS of system when comprising with not comprising demand response is not calculated^drAnd EENS^base, wherein EENS^baseIt represents The reliability level of system in the basic case, EENS^drFor quantifying the system reliability caused by demand response participates in It improves；By index EENS^baseWith index EENS^drIt is compared, according to the numerical value difference of the two, by using iterative algorithm to being Equivalent fixed capacity or alternative power generation capacity in system are adjusted, and are updated to two indices value to adjustment result, Until stopping adjustment, equivalent fixed capacity or alternative at this time when meeting predetermined relationship between updated two indices value Power generation capacity is the confidence capacity of demand response.

A15, the method as described in A14, wherein the predetermined relationship is | EENS^dr-EENS^base|/EENS^base≤ ζ, Middle ζ is threshold value.

A16, the method as described in A14, wherein according to the historical data to the confidence capacity model of the demand response into The process that row solves is combined using sequential Monte Carlo analogy method and optimal load flow method.

A17, the method as described in any one of A1-A16, wherein further include step：Establish the scheduling strategy of demand response Model, the scheduling strategy model include reliability driving scheduling strategy model or coordinated management scheduling strategy model, the tune It includes object function and constraints to spend Policy model；The scheduling strategy model is solved according to constraints, with true Determine the optimal scheduling planning of demand response.

A18, the method as described in A17, wherein the reliability driving scheduling strategy model and coordinated management scheduling strategy The object function of model is respectively： Wherein, V^omSystem total load loss when being comprising response side driving；V^cmSystem when being comprising response side driving always shuts down cost；Removal of load amounts of the expression user k in time t；r_tIt is the duration；Represent the average unit cost of power breakdown during t； κ_k,aIt is disturbing factor coefficient, for indicating susceptibility of the users'comfort to electricity consumption.

In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention Example can be put into practice without these specific details.In some instances, well known method, knot is not been shown in detail Structure and technology, so as not to obscure the understanding of this description.Similarly, it should be understood that in order to simplify the disclosure and help to understand One or more of each inventive aspect, in the above description of the exemplary embodiment of the present invention, of the invention is each Feature is grouped together into sometimes in single embodiment, figure or descriptions thereof.However, should not be by the method for the disclosure It is construed to reflect following intention：I.e. the claimed invention requires the feature than being expressly recited in each claim More features.More precisely, as reflected in the following claims, inventive aspect is less than disclosed above All features of single embodiment.Therefore, it then follows thus claims of specific implementation mode are expressly incorporated in the specific reality Mode is applied, wherein each claim itself is as a separate embodiment of the present invention.Those skilled in the art should manage It can be arranged in as depicted in this embodiment between the module or unit or group of equipment of the solution in example disclosed herein In equipment, or alternatively it can be positioned in one or more equipment different from the equipment in the example.Aforementioned exemplary In module can be combined into a module or be segmented into multiple submodule in addition.

Those skilled in the art, which are appreciated that, to carry out adaptively the module in the equipment in embodiment Change and they are arranged in the one or more equipment different from the embodiment.It can be the module or list in embodiment Member or group between be combined into one between module or unit or group, and can be divided into addition multiple submodule or subelement or Between subgroup.Other than such feature and/or at least some of process or unit exclude each other, it may be used any Combination is disclosed to all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so to appoint Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power Profit requires, abstract and attached drawing) disclosed in each feature can be by providing the alternative features of identical, equivalent or similar purpose come generation It replaces.In addition, it will be appreciated by those skilled in the art that although some embodiments described herein include included in other embodiments Certain features rather than other feature, but the combination of the feature of different embodiment means to be within the scope of the present invention And form different embodiments.For example, in the following claims, embodiment claimed it is one of arbitrary all It mode can use in any combination.In addition, be described as herein can be by department of computer science for some in the embodiment The combination for the method or method element that the processor of system or other devices by executing the function are implemented.Therefore, have and use The device for implementing this method or method element is formed in the processor for the necessary instruction for implementing the method or method element. In addition, device embodiment element described herein is the example of following device：The device is for implementing by order to implement the invention Purpose element performed by function.

Various technologies described herein are realized together in combination with hardware or software or combination thereof.To the present invention Method and apparatus or the process and apparatus of the present invention some aspects or part can take embedded tangible media, such as it is soft The form of program code (instructing) in disk, CD-ROM, hard disk drive or other arbitrary machine readable storage mediums, Wherein when program is loaded into the machine of such as computer etc, and is executed by the machine, the machine becomes to put into practice this hair Bright equipment.In the case where program code executes on programmable computers, computing device generally comprises processor, processor Readable storage medium (including volatile and non-volatile memory and or memory element), at least one input unit, and extremely A few output device.Wherein, memory is configured for storage program code；Processor is configured for according to the memory Instruction in the said program code of middle storage, the method for executing the present invention.

By way of example and not limitation, computer-readable medium includes computer storage media and communication media.It calculates Machine readable medium includes computer storage media and communication media.Computer storage media storage such as computer-readable instruction, The information such as data structure, program module or other data.Communication media is generally modulated with carrier wave or other transmission mechanisms etc. Data-signal processed embodies computer-readable instruction, data structure, program module or other data, and includes that any information passes Pass medium.Above any combination is also included within the scope of computer-readable medium.As used in this, Unless specifically stated, using ordinal number " first ", " second ", " third " etc. come describe plain objects be merely representative of be related to it is similar The different instances of object, and be not intended to imply the object that is described in this way must have the time it is upper, spatially, in terms of sequence Or given sequence in any other manner.

Although the embodiment according to limited quantity describes the present invention, above description, the art are benefited from It is interior it is clear for the skilled person that in the scope of the present invention thus described, it can be envisaged that other embodiments.In addition, it should be appreciated that The language that is used in this specification primarily to readable and introduction purpose and select, rather than in order to explain or limit Determine subject of the present invention and selects.Therefore, without departing from the scope and spirit of the appended claims, for this Many modifications and changes will be apparent from for the those of ordinary skill of technical field.For the scope of the present invention, to this The done disclosure of invention is illustrative and not restrictive, and it is intended that the scope of the present invention be defined by the claims appended hereto.

Claims

1. a kind of demand response capability assessment method of intelligent grid, suitable for being executed in computing device, this method includes：

The reliability model of generating set is established, the uncertainty models include the output power function of generating set；

Respond capacity model, user's participation model and the load for establishing demand response respectively rebound model, described to respond Capacity model, user's participation model and the load effect model that rebounds respectively include can responding the response demand function of user, ginseng It rebounds electric quantity function with degree function and load；

It is rebounded the workload demand of model foundation demand response according to capacity model, user's participation model and the load of responding Model, the workload demand model include the workload demand function that can respond user；

Held according to the confidence of the reliability model of the generating set and the workload demand model foundation demand response of demand response Model is measured, which includes the confidence capacity of demand response；And

The historical data of intelligent grid is obtained, and the confidence capacity model of the demand response is asked according to the historical data Solution, obtains the confidence capacity of the demand response of the intelligent grid.

2. the method for claim 1, wherein the generating set includes conventional power generation usage unit, the conventional generator The available output power P of group_t ^cgFunction be：

Wherein, C^cgIndicate the rated capacity of conventional power generation usage unit, β_t ^cgIt is a 0-1 variable, indicates the conventional power generation usage in time slot t The machine performance of unit, wherein the β when equipment works normally_t ^cgBe 1, it is on the contrary then be 0.

3. the method for claim 1, wherein the generating set includes renewable energy power generation unit, it is described can be again The final output power P of raw energy generator group_t ^rgFunction be：

Wherein,It is a 0-1 variable, the machine performance of the renewable energy power generation unit in time slot t is indicated, wherein when setting β when standby normal work_t ^cgBe 1, it is on the contrary then be 0；P_t ^rgpIndicate the available output power of renewable source of energy generation unit；C^rgExpression can The rated capacity of renewable source of energy generation unit；v_ci、v_ratAnd v_coIt is the incision wind speed of wind turbine, rated wind speed respectively and cuts out wind Speed, v_tIt is the wind speed of period t；And σ_tIt is the average value and standard deviation of wind speed respectively；y_tIt is the time sequential value of period t.

4. the method for claim 1, wherein the response demand function that user can be responded is：

Wherein,It is estimation response quautities of the user k in period t,It is the response demand total capacity of user k,Capacity coefficient, I can be responded by being 1 year, one month, one day and one hour maximum respectively_k,tIt is to use In the white noise for expressing load stochastic and dynamic characteristic in operational process.

5. the method for claim 1, wherein the participation function that user can be responded is：

Wherein, PL_k,tIt is participations of the user k in period t；RF_tAnd RI_tIt is the response frequency that can respond user in period t respectively And response intensity；RF_k,tAnd RI_k,tIt is the response frequency and response intensity that can respond user k in period t respectively；K' is all systems Unite user Ω_DIn any user；RF_k',τAnd RI_k',τIt is response frequencies and response intensity of the system user k ＇ in period τ respectively；It is the load decrement in period τ；e_τIt is two state variables, 1, when not occurring is taken when the period τ demand response event occurring Take 0；r_τIt is the duration；It is the coefficient for indicating user k to the inconvenient sensitivity caused by demand response program；It is Weighting correlation coefficient in demand response program for quantifying response frequency and response intensity.

6. method as claimed in claim 5, whereinWithSuitable for being described using trapezoidal membership function：Wherein,WithIt isDistributed area in special value, At this point, the participation function for responding user is：

Wherein,WithIt is for describingWithFor the estimated value of user k.

7. the load that can respond user described in the method for claim 1, wherein rebounds, electric quantity function is：

Wherein,The electricity to rebound for being applied to period t' is represented,It is the load decrement in period t；It is negative The duration of lotus rebound process,WithRespectively represent slope and power compensation rate.

8. the method as described in any one of claim 1-7, wherein the workload demand function for responding user is：

Wherein,WithRespectively represent the final workload demands of user k under normal circumstances and in emergency circumstances；It represents In the load decrement that period t system needs；Load can not be responded in period t by representing user.

9. a kind of computing device, including：

One or more processors；

Memory；And

One or more programs, wherein one or more of programs are stored in the memory and are configured as by described one A or multiple processors execute, and one or more of programs include for executing in the method according to claim 1-8 Either method instruction.

10. a kind of computer readable storage medium of the one or more programs of storage, one or more of programs include instruction, Described instruction is when executed by a computing apparatus so that the computing device executes in the method according to claim 1-8 Either method.