CN108470233B

CN108470233B - Demand response capability assessment method and computing device for smart power grid

Info

Publication number: CN108470233B
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: 2020-05-15
Anticipated expiration: 2038-02-01
Also published as: CN108470233A

Abstract

The invention discloses a demand response capability assessment method of a smart grid and a computing device for executing the method, wherein the method comprises the following steps: establishing a reliability model of the generator set, wherein the reliability model comprises an output power function of the generator set; respectively establishing a responsive capacity model, a user participation model and a load rebound model of demand response, wherein the three models respectively comprise a response demand function, a participation function and a load rebound electric quantity function of a user; establishing a load demand model of demand response according to the three models, wherein the load demand model comprises a load demand function of a user; establishing a confidence capacity model of the demand response according to the reliability model and the load demand model, wherein the confidence capacity model comprises the confidence capacity of the demand response; and acquiring historical data of the intelligent power grid, and solving the confidence capacity model according to the historical data to obtain the confidence capacity of the demand response of the intelligent power grid.

Description

Demand response capability assessment method and computing device for smart power grid

Technical Field

The invention relates to the field of energy and power, in particular to a demand response capacity assessment method and computing equipment for a smart grid.

Background

Demand Response (DR), an emerging smart grid technology, provides utilities with a new means of managing the real-time operation of power systems by exploiting demand-side flexibility through economic or financial mechanisms. Although the potential benefits of demand response are significant, one of the most relevant aspects to utility companies is its impact on power supply reliability, primarily because in a competitive market, regulatory agencies often impose mandatory limits on the frequency/duration of customer outages, while failure to meet required goals may impose severe penalties. However, demand response as a remedy may reduce load demand and provide capacity support for the grid in case of emergency. This will help to increase the load capacity of the system, enabling the utility company to achieve a commitment of reliability without incurring additional capacity expansion.

It is generally assumed that the user's demand response engagement is constant and always available when invoked during a demand response project. However, in practice, users may have different consumption patterns and preferences, so it is difficult for the utility company to know the traits of each customer. Moreover, even if such information is available, the user's response rate may be affected by various unpredictable factors, such as particular events. Thus, the demand response capability of the user may be very uncertain and may deviate significantly from the predicted value. However, the prior art has been very limited to the study of this uncertainty, and it is obviously not comprehensive to consider some of the external uncertainty of the system, if any. Therefore, there is a need to provide a more comprehensive and accurate demand response capacity assessment model and method.

Disclosure of Invention

To this end, the present invention provides a demand response capability assessment method and computing device of a smart grid in an attempt to solve or at least alleviate the above-presented problems.

According to an aspect of the present invention, there is provided a demand response capability assessment method of a smart grid, adapted to be executed in a computing device, the method comprising: establishing a reliability model of the generator set, wherein the uncertainty model comprises an output power function of the generator set; respectively establishing a response capacity model, a user participation model and a load rebound model of demand response, wherein the response capacity model, the user participation model and the load rebound effect model respectively comprise a response demand function, a participation function and a load rebound electric quantity function of a response user; establishing a load demand model of demand response according to the responsive capacity model, the user participation model and the load rebound model, wherein the load demand model comprises a load demand function of the responsive user; establishing a confidence capacity model of demand response according to a reliability model of the generator set and a load demand model of the demand response, wherein the confidence capacity model comprises the confidence capacity of the demand response; and acquiring historical data of the intelligent power grid, and solving the confidence capacity model of the demand response according to the historical data to obtain the confidence capacity of the demand response of the intelligent power grid.

Optionally, in the demand response capability assessment method according to the invention, the generator set comprises a conventional generator set, the available output power P of which is_t ^cgA function of

Wherein, C^cgIndicating the rated capacity of a conventional generator set, β_t ^cgIs a variable 0-1, representing the mechanical state of the conventional generator set at time slot t, wherein β is used when the device is operating normally_t ^cgIs 1, otherwise is 0.

Optionally, in the demand response capability evaluation method according to the present invention, the generator set includes a renewable energy generator set, and a final output power P of the renewable energy generator set_t ^rgThe function of (d) is:

wherein the content of the first and second substances,

is a variable 0-1 representing the mechanical state of the renewable energy generator set at time slot t, wherein β is used when the device is operating normally_t ^cg1, otherwise 0; p_t ^rgpRepresenting the available output power of the renewable energy generator set; c^rgRepresenting the rated capacity of the renewable energy generator set; v. of_tIs the wind speed for time period t; v. of_ci、v_ratAnd v_coRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan,

and σ_tMean and standard deviation of wind speed, respectively; y is_tIs the time series value of the epoch t.

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

wherein the content of the first and second substances,

is the estimated response of user k in time period t,

is the total capacity of the response demand of user k,

maximum responsive capacity factor, I, of one year, one month, one day and one hour, respectively_k,tIs white noise used to express the random dynamics of the load during operation.

Optionally, in the demand response capability evaluation method according to the present invention, the engagement function of the responsive users is:

wherein PL_k,tIs the engagement of user k during time period t; RF (radio frequency)_tAnd RI_tRespectively responding to the response frequency and the response strength of the user in the time period t; RF (radio frequency)_k,tAnd RI_k,tRespectively responding to the response frequency and the response strength of the user k in the time period t; k' is all system users Ω_DAny of the users in (1); RF (radio frequency)_k',τAnd RI_k',τRespectively, the response frequency and the response intensity of the system user k' in the time period tau;

is the load reduction over time period τ; e.g. of the type_τIs two state variables, and takes 1 when the demand response event occurs in the time period tau and takes 0 when the demand response event does not occur; r is_τIs the duration;

is a coefficient representing the degree of sensitivity of user k to the inconvenience caused by the demand response program;

is a weight correlation coefficient used in the demand response program to quantify the response frequency and the response strength.

Alternatively, in the demand response capability evaluation method according to the invention,

and

it is suitable to use a trapezoidal membership function to describe:

wherein the content of the first and second substances,

and

is that

In the distribution interval, the responsive user engagement function is:

wherein the content of the first and second substances,

and

is used for the description

And

an estimate for user k.

Optionally, in the demand response capability assessment method according to the present invention, the function of the rebounding capacity in response to the load of the user is:

wherein the content of the first and second substances,

representing the amount of power applied to the bounce at time t',

is the load reduction over time period t;

is the duration of the load rebound process,

and

representing slope and power compensation rate, respectively.

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

wherein the content of the first and second substances,

and

respectively representing the final load requirements of the user k under the normal condition and the emergency condition;

represents the amount of load reduction required by the system during time period t;

representing the amount of unresponsive load for the user over time period t.

Optionally, in the demand response capability evaluation method according to the present invention, the confidence capacity model of the demand response is calculated by using the confidence capacity model

Where Y is the model output value, vector X and

respectively representing probability variables and fuzzy variables in the smart grid system,

optionally, in the demand response capability evaluation method of the smart grid according to the invention, the fuzzy variable is used

Having a function of degree of membership

At this time

The equivalent probability density function is:

optionally, in the demand response capability evaluation method according to the present invention, the confidence capacity model of the demand response takes the power shortage expected value EENS as the system reliability index.

Optionally, in the demand response capability evaluation method according to the present invention, the confidence capacity of the demand response is expressed by equivalent fixed capacity EFC, and at this time, the system reliability index including the demand response is:

the system reliability index that does not contain a demand response is:

wherein D represents the time series of the system load demands, C^gIs the total power generation in the system, C^rlIs the capacity of demand response resources, R is also an index for measuring system reliability, C^bmIs the reference capacity of the generator set.

Optionally, in the demand response capability evaluation method according to the present invention, the confidence capacity of the demand response is expressed by an equivalent alternative power generation capacity EGCS, and at this time, the system reliability index not including the demand response is:

the system reliability indexes including the demand response are:

wherein, C^agIndicating alternative power generation.

Optionally, in the demand response capability evaluation method according to the present invention, the solving the confidence capacity model of the demand response according to the historical data includes: generating time state sequences of a conventional generator set and a renewable energy generator set according to the input historical data, and generating output power curves of the two generator sets according to the time state sequences; calculating the reliability index EENS of the system with and without the requirement response respectively^drAnd EENS^baseWherein EENS^baseRepresenting the reliability level of the system in the basic case, EENS^drFor quantifying the increase in system reliability due to demand response participation; and EENS^baseAnd EENS^drAnd comparing, adjusting the equivalent fixed capacity or the replaceable generating capacity in the system by using an iterative algorithm according to the numerical difference of the two, updating the two index values according to the adjustment result until the two updated index values meet the preset relationship, and stopping adjustment, wherein the equivalent fixed capacity or the replaceable generating capacity is the confidence capacity of the demand response.

Alternatively, in the demand response capability evaluation method according to the present invention, the predetermined relationship is | EENS^dr-EENS^base|/EENS^baseζ, where ζ is the threshold.

Optionally, in the demand response capability assessment method according to the present invention, a sequential monte carlo simulation method and an optimal power flow method are used in the process of solving the confidence capacity model of the demand response.

Optionally, in the demand response capability evaluation method of the smart grid according to the present invention, the method further includes: establishing a scheduling strategy model of demand response, wherein the scheduling strategy model comprises a reliability driving scheduling strategy model or a coordination management scheduling strategy model, and the scheduling strategy model comprises a target function and a constraint condition; and solving the scheduling strategy model according to the constraint conditions to determine the optimal scheduling plan of the demand response.

Optionally, in the method for evaluating demand response capability according to the present invention, the objective functions of the reliability-driven scheduling policy model and the coordinated management scheduling policy model are respectively:

wherein, V^omIs the total load loss of the system including the response side driving; v^cmIs the total system down cost including response side drive;

representing the load shedding amount of the user k at the time t; r is_tIs the duration;

represents the average cost of power interruption during t; kappa_kaIs an interference factor coefficient used to represent the sensitivity of user comfort to power usage.

According to yet another aspect of the present invention, there is provided a computing device comprising one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the demand response capability assessment method of a smart grid according to the present invention.

According to yet another aspect of the present invention, there is also provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a demand response capability assessment method of a smart grid according to the present invention.

According to the technical scheme of the invention, a novel method for evaluating the potential reliability value of the smart grid (demand response) is provided, and the method is established on the concept of Confidence Capacity (CC). Unlike prior work, the present invention can account for different types of uncertainties (i.e., probabilistic and fuzzy) in demand response projects, and account for the effects of physical and human factors on demand response. To this end, the present invention takes the ability of demand response during operation as a composite result of multiple aspects, namely, the load characteristics, engagement and load recovery effects of the user, and proposes different models to represent the effect of each component. In order to properly consider the randomness of the demand response, a fuzzy theory is introduced, and a fuzzy model describing the artificial correlation uncertainty under incomplete information is established. In addition, the potential impact of operating strategies on demand response utility is also embodied in the present invention, which is quantified by presenting two customer satisfaction indicators. Based on probabilistic fuzzy transformation techniques, the different types of uncertainties involved can be standardized and solved systematically under the same framework. The proposed demand response model may then be applied to a confidence capacity estimator using a Sequential Monte Carlo Simulation (SMCS) based algorithm, in which two scheduling schemes are considered to study the operation of the demand response on its confidence capacity. The results obtained by testing this method on a modified RTS system confirm its effectiveness under realistic conditions.

Drawings

To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.

FIG. 1 shows a block diagram of a computing device 100, according to an embodiment of the invention;

FIG. 2 illustrates a flow diagram of a demand response capability assessment method 200 for a smart grid according to one embodiment of the present invention;

FIG. 3 illustrates a basic framework diagram of a demand responsive load demand model, according to one embodiment of the present invention;

FIG. 4 shows an embodiment in accordance with the invention

And

a trapezoidal membership function of;

FIG. 5 illustrates a flowchart of a confidence capacity assessment algorithm for demand response, according to one embodiment of the present invention;

FIGS. 6 and 7 illustrate a system reliability assessment algorithm with and without demand response, respectively, in accordance with one embodiment of the present invention; and

FIG. 8 illustrates confidence capacities of demand responses expressed in EFC and EGCS for different load flexibilities according to one embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

Fig. 1 is a block diagram of an example computing device 100. In a basic configuration 102, computing device 100 typically includes system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between the processor 104 and the system memory 106.

Depending on the desired configuration, the processor 104 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as a level one cache 110 and a level two cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations the memory controller 118 may be an internal part of the processor 104.

Depending on the desired configuration, system memory 106 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some embodiments, application 122 may be arranged to operate with program data 124 on an operating system. The program data 124 includes instructions, and in the computing device 100 according to the present invention, the program data 124 includes instructions for performing the demand response capability assessment method 200 of the smart grid.

Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communications with one or more other computing devices 162 over a network communication link via one or more communication ports 164.

A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.

Computing device 100 may be implemented as a server, such as a file server, a database server, an application server, a WEB server, etc., or as part of a small-form factor portable (or mobile) electronic device, such as a cellular telephone, a Personal Digital Assistant (PDA), a personal media player device, a wireless WEB-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device 100 may also be implemented as a personal computer including both desktop and notebook computer configurations. In some embodiments, the computing device 100 is configured to perform a demand response capability assessment method 200 of a smart grid in accordance with the present invention.

As previously described, the demand response capability of the user may be highly uncertain and may deviate significantly from the theoretical prediction, thus resulting in a false assessment of the demand response capacity. There are a few studies in the prior art that take this uncertainty into account, but the uncertainty associated with demand response in the prior art is typically described by a probability distribution that is predetermined and determined prior to evaluation. In other words, existing demand response models are designed primarily to handle extrinsic uncertainties, and are based on the assumption that the stochastic responsiveness of future users is independent of the control strategy employed by the system operator. However, in practical situations, the participation of demand responses may bring users economic returns, but may also reduce their happiness (comfort). Thus, in actual implementation, improper scheduling policy may cause response fatigue and reduce the involvement of the demand party (i.e., not subject to demand response invocation). The actual response of the user responsiveness does not depend only on its future outcome itself, but is also influenced by grid operation decisions. Therefore, if this inherent uncertainty in the demand response and its relationship to system decisions are not considered, it is likely that a false estimate of the reliability value of the demand response will be made. However, to date, little research has been done to discover and address this problem.

Therefore, the invention provides a demand response capability evaluation method of a smart grid, which is mainly based on a new demand response confidence capacity model, namely a demand response modeling-probability-fuzzy framework, and is used for evaluating the confidence capacity of demand response. The concept of confidence capacity was originally designed and applied to quantify the ability of power generation resources to provide power to a power system. However, in the context of smart grids, this definition is extended to demand response scenarios, since flexible loads act as virtual power generation units, estimating how flexibility on the demand side affects the supply reliability of the system under different conditions. Generally, the confidence capacity indicators for demand response typically include payload capacity (ELCC), Equivalent Fixed Capacity (EFC), equivalent traditional capacity (ECC), and equivalent alternative generation capacity (EGCS), each applicable to a different indicator. Considering that the present invention mainly focuses on the potential of demand response to provide operational reserve, two metrics, namely EFC and EGCS, are selected, and of course, the other two metrics may be calculated.

According to one embodiment of the invention, the confidence capacity model of demand response takes the expected value of power shortage, EENS, as the system reliability index, and the EENS is calculated differently in the two measurement methods. In the EFC method, the confidence capacity of the demand response is defined as the additional genset capacity that needs to be installed when no response participation is required to achieve the same system reliability as when there is a demand response. In order to calculate the EFC value of the demand response, the EFC value of the demand response needs to be respectively containedAnd the system containing the demand response carries out reliability evaluation to obtain a system reliability index containing the demand response

Comprises the following steps:

wherein D represents the time series of the system load demands, C^gIs the total power generation in the system, C^rlIs the capacity of the demand response resource, and R is also an index for measuring the reliability of the system.

Then, when there is only the generator set (with a reference capacity of C)^bm) Reliability index of system in presence of but no demand response

Comprises the following steps:

it should be noted that in the EFC method, the reference unit is considered to be completely reliable, which means that it has zero forced outage rate. Reference unit C^bmUntil the EENS of the system reaches the same level of reliability as the demand response, i.e., the capacity of

The amount of power generation C required at this time^bmThe value considered as EFC, i.e. the confidence capacity of the demand response.

Unlike the aforementioned EFC, the EGCS quantifies the confidence capacity of the demand response by replacing gensets in the system. Thus, the confidence capacity of demand response under an EGCS is defined as the capacity of a conventional genset that can be replaced while maintaining the same reliable supply level. In practice, to obtain an EGCS, it is necessary to first examine the system reliability index that does not contain a demand response

The formula is as follows:

then, considering the influence of demand response, reducing the capacity of the generator set according to the descending order of the operation cost, and calculating to obtain a system reliability index containing the demand response

The formula is as follows:

wherein, C^agIndicating alternative power generation. C^agWill be continually adjusted until the system

Achieving the condition of not requiring response

The EGCS value of the demand response at this time may be determined as C^agIs the final result.

FIG. 2 illustrates a flow diagram of a demand response capability assessment method 200 of a smart grid, suitable for execution in a computing device (e.g., computing device 100 shown in FIG. 1), according to one embodiment of the invention.

As shown in fig. 2, the method begins at step S210. In step S210, a reliability model of the genset is established, the uncertainty model including an output power function of the genset.

According to one embodiment, the generator set comprises a conventional generator set, the reliability model of which consists essentially of two parts: fuel supply and machine availability, where the machine part is typically described using a two-state markov model representing the normal and fault states of the unit. In terms of fuel supply, due to most conventional generator setsDepending on the traditional energy source (such as gas or diesel), its primary energy source is generally considered 100% reliable and therefore without uncertainty. Based on the model, the available output power P of the conventional generator set is determined for a certain time t_t ^cgThe function of (d) is:

wherein, C^cgIndicating the rated capacity of a conventional generator set, β_t ^cgIs a variable 0-1, representing the mechanical state of the conventional generator set at time slot t, wherein β is used when the device is operating normally_t ^cgIs 1, otherwise is 0. in the present invention, β_t ^cgIs a variable determined using random sampling based on long-term reliability data.

According to another embodiment, the generator set comprises a renewable energy generator set, for the sake of simplicity, the invention being exemplified by a wind generator set. The output power of the wind generating set is mainly determined by the wind speed on site. In practice, the stochastic variation of the spatial correlation over time and the wind speed variation over time, which is usually represented by an autoregressive moving average time series model, is the wind speed v over a time period t_tCan be expressed as:

wherein the content of the first and second substances,

and σ_tMean and standard deviation of wind speed, y_tIs the time series value of the epoch t. Available output power P of renewable energy generator set (such as wind generator set) along with wind state_t ^rgpCan be deduced according to the operating characteristics:

wherein, C^rgRepresenting the rated capacity of the renewable energy generator set; v. of_ci、v_ratAnd v_coRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan. Final output power P of renewable energy power generator set_t ^rgPower output and machine availability derived from operating characteristics

The integration of (a) results in:

wherein the content of the first and second substances,

similar to β_t ^cgIs a variable 0-1 representing the mechanical state of the renewable energy generator set at time slot t, wherein β is used when the device is operating normally_t ^cgIs 1, otherwise is 0.

Subsequently, in step S220, a responsive capacity model, a user engagement model and a load bounce model of the demand response are respectively established, wherein the three models respectively comprise a response demand function, an engagement function and a load bounce electric quantity function of the responsive user.

Modeling the load-side response is a prerequisite for efficient estimation of the confidence capacity of the demand response. In practical applications, the available capacity of demand response is associated with physical and human factors. From a physical perspective, demand response capacity is actually largely dependent on the power usage pattern and load characteristics of the demand side electrical load. Depending on the flexibility of operation, the consumer's electrical devices can be generally classified into two types, responsive loads and nonresponsive loads. The former refers to situations where the use of the device can be interrupted or postponed without sacrificing user welfare, while the use of the latter has no flexibility at any time. In fact, since users may have different load configurations and power usage habits, demand response capacities and characteristics for different users may vary greatly and vary greatly over time. Therefore, the inclusion of uncertainty in the modeling relating to the user's demand is essential to effectively estimate the confidence capacity of the demand response.

On the other hand, the degree of engagement of the demand party is another factor that affects the available capacity of the demand response. As described above, in the open power market, the user can freely decide whether to participate in the demand response item in the interests of the user. Furthermore, for non-direct controlled loads, since the demand response is obtained on a "voluntary" basis, the user may take different actions, not necessarily always responding to the demand in the demand response project. Both of these can introduce additional uncertainty in the amount of available capacity for demand response. Although the uncertainty of demand response has been involved in prior studies, the randomness of demand-side responses in these works is mostly represented by fixed probabilistic models, where the characteristics of the behavior of the user over time are largely ignored. However, in the long-term reliability analysis, since the time span is long, the user may dynamically adjust the demand response policy according to the external condition to exert its own advantage to the maximum extent.

Practice proves that the degree of participation of the resident users in the demand response project is mainly influenced by several factors such as incentive policy, inconvenient cost, education degree and the like; and in actual implementation, the speculative nature of the user will result in a random variation in the available capacity of the demand response of the system. Furthermore, while the user's power reduction may have some negative impact on it, if appropriate scheduling strategies are employed, sustainable demand response may be achieved. The above research results show that the operation strategy of the demand response has an important role in determining the potential capacity of the demand response for the user. However, current single probabilistic models are not sufficient to contain this progressiveness and decision dependency of demand response capacity. Furthermore, in real life, since it is often difficult for a utility company to obtain complete data about an individual user, there is also a need for more advanced tools to simulate the randomness of demand responses due to lack of information.

In addition to the above, load recovery or load bounce (LR) behavior associated with a demand response item may be another factor affecting demand response characteristics. As an additional result of the demand response, the presence of load bounce may significantly alter the demand of the system, thereby contributing significantly to the reliability of the demand response. In practice, the load bounce pattern should be highly uncertain for each demand response event, since the user decides the load bounce process at his or her discretion. It is therefore necessary to take such variations into account in the confidence capacity assessment of the demand response.

In view of the above, the present invention proposes a novel hybrid probabilistic-fuzzy modeling framework that integrates the extrinsic and intrinsic uncertainties present in the confidence capacity analysis of demand responses. As shown in FIG. 3, the framework provided considers the available capacity of demand response as a composite result of three aspects of user load characteristics, engagement and load bounce effects, thereby establishing a responsive capacity model, a user engagement model and a load bounce model. In particular, recognizing that sufficient demand-side data is available in the smart grid, the customer's load demand is considered a random variable, which is represented by a time model. On this basis, the human aspect of the effect was further analyzed. Due to the incompleteness of personal information, a fuzzy set theory is introduced, and a fuzzy model based on decision dependence is established to describe the random change of the user to the demand response participation. The output of the above model will be defined as the capacity of available demand responses in the system over a single time period of the day. By integrating these results with the load bounce effect, a final load demand model can be obtained, thereby finally deriving a load curve of the corrected demand response. These data, as the final result of the proposed model framework, will be used for reliability-based confidence capacity evaluation procedures.

According to one embodiment of the present invention, the responsive demand function for a responsive user is:

wherein the content of the first and second substances,

is the estimated response of user k in time period t,

is the total capacity of the response demand of user k,

maximum responsive capacity factor, I, of one year, one month, one day and one hour, respectively_k,tIs white noise used to express the random dynamics of the load during operation. In general, the parameters in equation (9) can be determined by conducting a load survey: for various users (e.g., commercial, industrial, residential, etc.), a historical record of the hourly consumption and load profiles of the primary appliances is collected. In a smart grid, a utility company can obtain sufficient data about each individual's needs as end users are outfitted with smart meters. The corresponding load curves obtained will be further evaluated and filtered using statistical techniques. This allows the average load level suitable for the demand response to be estimated hourly over the year and the parameters of the model to be derived ultimately by analysis of these demand response curves.

According to another embodiment of the invention, in the electricity market, the willingness of a customer to participate in a demand response program depends primarily on a balance between the desired revenue and the corresponding discomfort level that needs to be maintained due to load shedding/shifting. However, in practice, since the incentive mechanism for demand response is typically predetermined by the utility company, the level of participation in the demand response program is driven primarily by the customer's discomfort factors. While the discomfort cost of demand response participants depends mainly on their response frequency and response process metrics, the present invention is expressed in terms of Response Frequency (RF) and response strength (RI), and the calculation formula is as follows:

wherein, RF_tAnd RI_tRespectively the response frequency and the response strength of the responsive user during the time period t,

is the load reduction over time period τ; e.g. of the type_τIs two state variables, and takes 1 when the demand response event occurs in the time period tau and takes 0 when the demand response event does not occur; r is_τIs the duration. It can be seen that RF and RI quantify the frequency and average response level of the customer, respectively, into the period τ historical demand response event. Clearly, the smaller these two values, the less the demand response has an impact on user comfort, and thus an individual may be more willing to follow the demand response schedule at a later time. Because the correlation between the demand response and the operation strategy thereof is strong, the invention assumes that the positive correlation exists between the demand participation and the response frequency/response intensity index for the inherent characteristics. Thus, the responsive user engagement (PL) function for period t is:

wherein PL_k,tIs the engagement of user k during time period t; RF (radio frequency)_k,tAnd RI_k,tRespectively responding to the response frequency and the response strength of the user k in the time period t; k' is all system users Ω_DAny of the users in (1); RF (radio frequency)_k',τAnd RI_k',τRespectively, the response frequency and the response intensity of the system user k' in the time period tau;

is a weight correlation coefficient used in the demand response program to quantify the response frequency and the response strength. When the user's profit is greatly affected by the response frequency,

will correspond to a larger value; otherwise, it should be applied smaller

Equation (12) defines the user's engagement by synthesizing the response frequency and response strength results based on the classical weighted sum method. The values of response frequency and response strength are based on their presence at all system users (k' ∈ Ω)_D) And the corresponding maximum record at time (τ ∈ 1: t-1) to ensure index consistency. According to equation (12), the user engagement function is a decreasing function with respect to the response frequency and the response intensity index. Thus, unlike traditional demand response models, where the engagement of the user is considered constant, the proposed engagement value

Will vary with the scheduling policy of the utility company so that the formula is essentially a time-varying model. The variables in equation (12) can be divided into two categories, i.e., observable quantities (consisting essentially of RF), depending on the nature of the associated uncertainty_k,tAnd RI_k,t) And unobservable quantity (weighting factor ω)_kAnd ξ_k). Observables are variables that are not known in advance but can be derived appropriately using statistical methods when there is sufficient data, and such variables can be described by probabilistic models when evaluating confidence capacities and will be determined from stochastic simulations under the SMCS framework. The information measured by unobservable variables is often very limited for various reasons, so that it is impossible to directly obtain the statistical characteristics of the data.

To this end, the present invention models uncertainty data using fuzzy variables, i.e., by representing the fuzzy variables using probability distributions (PD, or membership functions). Under fuzzy framework, for each variable

Its membership function mu_A(x) Will be provided withDescribing the degree to which the element x in the domain of discourse U belongs to the fuzzy set A, the greater the degree of membership, the more X belongs to A. In practical applications, different types of membership functions may be used to represent the uncertainty of the unobservable variables, depending on the availability of the corresponding knowledge. In particular, the invention describes random variables by using a trapezoidal membership function

And

FIG. 4 shows a schematic representation of the membership function, wherein

Wherein the content of the first and second substances,

and

is expressed by

A specific value of the distribution interval of (a). At this time, the responsive user engagement (PL) function may be further expressed as:

wherein the content of the first and second substances,

and

is used for the description

And

an estimate for user k.

According to yet another embodiment of the invention, for any load reduction that occurs during the time period t, its aggregate charge bounce may be considered as additional loads for other time periods that are superimposed at a decreasing rate to the time period after the time period t. At this point, the bounce charge amount function may be:

wherein the content of the first and second substances,

representing the amount of power applied to the bounce at time t',

is the load reduction over time period t;

is the duration of the load rebound process,

and

representing slope and power compensation rate, respectively.

As previously mentioned, in real demand response projects, since users may recover their curtailed power in different ways, the parameters in equation (14) are for the system operator

A non-observable variable. Therefore, the invention adopts a processing mode similar to the user participation degree and adopts a membership function based on a trapezoid to simulate the random change of the load rebound mode of the user.

Subsequently, in step S230, a load demand model of the demand response is built according to the responsive capacity model, the user engagement model and the load bounce model of the demand response, the load demand model including a load demand function of the responsive users. Namely, the load demand model finally obtained by integrating the three models in fig. 4.

The output of the three models is integrated, and the user can normally use the three models

And in emergency situations

The final load demand function can be expressed as follows:

wherein the content of the first and second substances,

and

the final load requirements of user k under normal conditions and emergency conditions, respectively;

is the amount of load reduction required by the system over time period t;

is the unresponsive load amount of the user over time period t. From the above function, when the system is in a normal state, the total power consumption of the user is the sum of the elastic load and the inelastic load requirement in the period. However, in an emergency situation, the customer demand is the original electrical energy demand of the customer

Minus the amount of load clipped in that period

Plus the amount of recoil of the load

Here, the actual load reduction of a customer per demand response event is determined by the customer's engagement and the required demand response capacity

The product of (a) and (b) is calculated. Since the user engagement reflects the individual's willingness to participate in the operation of the demand response item, the greater the user engagement, the greater the degree of response to the demand response signal.

In addition, equations (15) and (16) include both random and fuzzy variables, while taking into account extrinsic and intrinsic uncertainties in demand response. Therefore, the load demand model of the present invention belongs to a dynamic probabilistic-fuzzy model. Due to the introduction of the fuzzy theory, the invention can effectively solve the ambiguity of the contingency and the cognition related to the demand response, thereby leading the method to be more practical in practical application. The prior art solutions, however, require a strong dependence on historical data and therefore cannot be used to deal with situations where there is a lack of data for uncertain variables and uncertainty statistics that are not inherently repeatable, such as the case of demand response. In addition, the model takes into account the influence of extrinsic and intrinsic uncertainties on demand response potential, which enables the demand response model of the present invention to be a pioneering work for long-term evolution and dependency of customer involvement. Moreover, the present invention combines the effects of demand response items with physical aspects and human aspects, so that it can provide a more accurate and comprehensive representation of demand response in real situations.

It should be noted that the model is mainly established based on the following assumptions: 1) current confidence capacity research is primarily from a utility company perspective, and therefore is primarily focused on demand characteristics at the node level. To reduce complexity, the present invention assumes that users on the same node have similar load conditions and power consumption habits and can therefore be equated to one user whose load characteristics are an aggregation of individual needs. In practice, the model may be used to represent a large user or a set of responding users coordinated by the regional load aggregator. 2) Demand response in the present invention refers to non-load-directed, incident-driven demand response items, rather than price-based programs, such as real-time electricity prices. Also, to avoid undue complexity, it is assumed that all customers receive a flat rate tariff. 3) The load composition of the customer is fixed and does not evolve over time so that the confidence capacity of the demand response can be analyzed on a static baseline without regard to variability in the user's load characteristics. 4) All loads operate at a constant power factor.

Subsequently, in step S240, a confidence capacity model of the demand response is established based on the reliability model of the genset and the load demand model of the demand response, the confidence capacity model including the confidence capacity of the demand response. Specifically, a generic demand response confidence capacity model may be expressed as

Where the vectors x and

respectively representing probability variables and fuzzy variables in the researched smart grid system. In particular, the method of manufacturing a semiconductor device,

details on these variables are summarized below in table 1.

TABLE 1 uncertainty in the Smart grid System in question

According to one embodiment of the invention, the two uncertainties must be unified in the same evaluation boxUnder-shelf, i.e. unifying to the same variable, thus the variable in the model

Finally, the fuzzy variable is converted into a probability quantity, and the converted probability quantity can be directly applied to an evaluation algorithm of the confidence capacity. Normalization methods can be used in general, and fuzzy variables in particular

Having a function of degree of membership

At this time

The equivalent probability density function is:

subsequently, in step S250, historical data of the smart grid is obtained, and the confidence capacity model of the demand-side response is solved according to the historical data, so as to obtain the confidence capacity of the demand-side response of the smart grid.

According to one embodiment of the invention, solving the confidence capacity model of the demand-side response from the historical data comprises: generating time state sequences of a conventional generator set and a renewable energy generator set according to the input historical data, and generating output power curves of the two generator sets according to the time state sequences; calculating the reliability index EENS of the system with and without demand-side response^drAnd EENS^baseWherein EENS^baseRepresenting the reliability level of the system in the base case, the EENS demand response is used to quantify the increase in system reliability due to demand-side response participation; and index EENS^baseAnd index EENS^drComparing, and performing equivalent fixed capacity or alternative generating capacity in the system by using iterative algorithm according to the value difference of the twoAnd adjusting, and updating the two index values according to the adjustment result until the adjustment is stopped when the two updated index values meet a preset relation, wherein the equivalent fixed capacity EFC or the alternative generating capacity EGCS is the confidence capacity CC responded by the demand side. Wherein the predetermined relationship may be | EENS^dr-EENS^base|/EENS^baseζ where ζ is the threshold value, for example, may be 2%, although not limited thereto. In addition, the historical data may be a plurality of items of data such as system network information, forced outage rates of components, RES models, and classified load data.

As previously mentioned, the confidence capacity assessment of demand response is actually based on the system reliability in assessing and comparing whether there is a demand response, so the computational solution process described above can be used with the sequential monte carlo simulation SMCS method and the optimal power flow OPF method. The SMCS method obtains a reliability index by simulating the random behavior of the system in time sequence, and thus can effectively capture time information and is very suitable for time-related analysis. On the other hand, since the SMCS belongs to a time-series approach, some information indicators, especially frequency or time based information indicators, can only be quantized by the SMCS.

FIG. 5 shows the general steps for estimating confidence capacity of a demand response based on SMCS, according to one embodiment of the invention. Specifically, the algorithm operates the SMCS first to obtain a system reliability index value EENS when no demand response participates^baseThe index is the reliability level of the system in the basic case. Subsequently, the demand response is included and the SMCS calculation is performed again to quantify the improvement of the system reliability caused by the participation of the demand response, which will result in a new system reliability index value EENS^dr. Once the above steps are completed, the obtained system reliability index value results may be compared to derive a reliability index when there is no response required (fig. 6 and 7 show the calculation methods of the two indexes, respectively). Based on their numerical differences, the reference capacity (for EFC calculations)/replacement capacity (for EGCS calculations) in the system will be adjusted by using an iterative algorithm. For the EGCS calculation, assume that the genset is at its operating cost (push down)Ordered) and after each adjustment, the SMCS will check whether the new system has reached the preset accuracy. Satisfying EENS when there are two reliability index values of no demand response^dr-EENS^base|/EENS^baseζ ≦ ζ (e.g., 2%), the entire search will be stopped, at which time the installed capacity EFC/alternative power generation EGCS is considered the confidence capacity for demand response. It should be noted that in this calculation process, the present invention focuses primarily on the operational characteristics of the demand response, i.e., only on the uncertainty associated with power generation, and does not consider other factors that may affect the confidence capacity, such as transmission line failures, communication infrastructure safety and information delays, etc.

Clearly, how to determine the reliability of the system in the presence of a demand response is a critical issue in the evaluation of confidence capacity. In the demand response item, as a starting point, a load flow calculation is first repeated at each time t to check the operation state of the system. In practice, power flow analysis is used in this evaluation process, since a power outage may be caused by insufficient power generation or unsatisfied constraints (e.g., overload or undervoltage) in the network. If no accident is found, this means that no load loss is currently occurring, the energy deficit (ENS) of the system should be set to zero, otherwise it means that the system is involved in an emergency situation. When this occurs, some remedial action will first be taken as the primary corrective action. In smart grids, the most common remedies include capacitor switching, transformer tap switching, and network reconfiguration, among others. The optimal scheduling of these remediation resources is typically determined according to a pre-specified policy or through the use of optimization techniques. Flow analysis is performed after each adjustment until the system violation is resolved or all remedial actions are implemented. The demand response is the last resort to be initiated only if the above-mentioned remedial measures prove insufficient. In practice, in order to fully exploit the capacity of demand response, grid operators typically develop scheduling strategies based on OPF analysis. Therefore, there is a need to analyze the scheduling policy of demand responses to determine the optimal demand response scheduling policy and the amount of load shedding with respect to each customer. At this time, can be achieved byDemand response capacity level to be required in time t

Multiplied by the user engagement PL_k,tThe actual load reduction amount on the demand side is estimated. Based on this, the energy deficit value ENS of the system can be expressed as:

wherein the content of the first and second substances,

representing the load shedding amount of user k at time t. After each demand response event, the load bounce effect is incorporated using equation (14), with the load bounce behavior of the user being based on the parameters in the membership function

The derived response frequency and response intensity index can be updated according to the corresponding formula. As simulation time progresses, the EENS value of the simulation system can be finally obtained by integrating the recorded results of ENS in all simulation time.

The present invention also takes into account the impact of scheduling policies on scheduling efficiency (prior art techniques typically determine optimal scheduling of demand responses from the utility or customer perspective only), according to one embodiment. Specifically, the method may include the steps of: establishing a scheduling strategy model of demand response, wherein the scheduling strategy model comprises a reliability-driven scheduling strategy model (RD model for short) or a coordinated management scheduling strategy model (CM model for short), and the scheduling strategy model comprises an objective function and a constraint condition; and solving the scheduling strategy model according to the constraint conditions to determine the optimal scheduling plan of the demand side response. Wherein, in the RD scheduling policy, it is assumed that the utility deployment demand response is only used to maximize the reliability performance of the system (the objective function of the RD model also represents the inherent incentive of the utility to improve its service reliability); in the CM scheduling strategy, the system reliability and customer satisfaction in demand response operations are combined, i.e., the total downtime of the system is minimized, while the minimum inconvenience is caused to the demand response customers. Based on this, the objective functions of the RD model and the CM model are respectively:

represents the average cost of power interruption during t ($/kWh); l is_k(.) is a utility function that quantifies the user's need to reduce during t

Expected discomfort costs. It is clear that,

the larger the value, the more discomfort the scheduling of demand responses will cause to the user, and therefore a greater "penalty" is imposed on its fitness value. To calculate equation (20), the grid operator must know the specific form of Lk (-) for each user, but in practice this private information is not always accessible. To solve this problem, the present invention learns the behavior pattern of the user from the user's history data. Here, approximation based on guessed variables is considered

Such as:

i.e. using the value of the demand response required in the corresponding period t

A related linear function to estimate the discomfort level of the demand response user. Further, it depends on the interference factor coefficient κ_k,aMaking a determination wherein_k,aFor indicating the sensitivity of user comfort to electricity usage. Assume that the user's presentation is continuous in time and independent for each demand response event; the interference factor coefficient value can be estimated by having an empirically formed linear distribution hysteresis model:

wherein

Indicating the extent to which the customer did not comply with the demand response schedule in the previous demand response event,

λ is the average demand response cost of the society; furthermore, Bk (.) is a normalization operator for normalizing all its components x, relative to the corresponding average of all system users except k. Therefore, the temperature of the molten metal is controlled,

where K represents Ω in the system_DThe number of elements in (c). According to the equation (22), the estimation of the interference factor coefficient depends on the estimation value of the previous period and the historical demand response track of the user, and the larger unconformity demand response quantity

Will leadResulting in a larger interference factor coefficient and thus a higher discomfort level estimate for the user. In the real world, the interference factor coefficient is not a constant, but may vary during system operation, due to the different lifestyles and needs of individuals at different times of the day. In fact, due to

And

can be derived from historical data, so they are extrinsic to the model. On this basis, the objective function of the CM model can be further expressed as:

according to one embodiment of the present invention, the constraints of the RD model and the CM model are:

wherein the content of the first and second substances,

and

active power and reactive power generated by the traditional generating set of the ith node are respectively generated;

and

the active power and the reactive power are respectively emitted by the renewable energy generator set of the ith node;

line loss active and reactive of the line ij respectively;

and

the active and the reactive of the load rebound of the ith node are respectively; p_ij,tAnd Q_ij,tActive and reactive respectively flowing through the line ij; g_ijAnd B_ijRespectively, the conductance and susceptance of line ij; v_i,tAnd V_j,tThe voltage amplitudes of the ith and the j nodes are respectively; delta_i,tAnd delta_j,tThe voltage phase angles of the ith and j nodes are respectively; s_ij,maxIs the apparent power of line ij.

Constraint (24) and(25) scheduling level limits representing the responsable demand response and the nonresponsable demand response, respectively, for each customer in the system. The conventional constraints of the active/reactive power balance and the system power flow are considered in the constraints (26) to (29). In addition, in order to guarantee the service quality, (30) and (31) enforce the limitation of feeder voltage deviation and current capacity. Finally, constraints (32) specify that the power factor of the customer remain unchanged during operation. The RD/CM model described above will determine the optimal demand response plan (for flexible loads) and the shear load (for inflexible loads) of the system customer, i.e., the

And

load curves modified by demand response participation can be derived based on the results, and the new demand data will be used in a reliability-based algorithm for confidence capacity estimation.

The influence of the scheduling strategy on the confidence capacity of the demand response is calculated through example analysis, and the test is mainly carried out based on an IEEE-RTS system. The system is provided with 3405 megawatts of conventional power generation and 2850 megawatts of peak load. Further, assume that five 50 megawatt wind farms are added to the power generation assembly as renewable energy generator sets in the system. Each wind farm consists of twenty-five 2MW wind power units with the same parameters, the mean time to failure and the mean time to repair of the units being 760 hours and 40 hours, respectively. In addition, chronological load data for the system is also determined, including hourly load capacity and subscriber type assignments on each bus are identified. To fit the proposed demand response model, each fuzzy variable and the corresponding established membership function are shown in table 2.

TABLE 2 membership function of each parameter

Load Flexibility (FL) is defined as the userResponsive capacity of

Occupying the total electric energy consumption of the system in the time period t

Assuming that the customer's FL increases from 0 to 50% in steps of 5%, the Confidence Capacities (CC) of the responding users in different FLs, as represented by EFC and EGCS, may be as shown in table 3 and fig. 8.

TABLE 3 confidence capacities of demand response in EFC and EGCS under different FLs

It can be seen that under the same conditions, the confidence capacity of the demand response monotonically increases as the load flexibility increases; but the results tend to saturate when the load flexibility in the system reaches a certain value, i.e. the confidence capacity of the demand response is closely related to the user load flexibility. In other words, the more responsibilities available, the demand response may make greater improvements to the sufficiency of the supply. This is because a larger FL means more load regulation capability on the demand side; thereby reducing the risk of system starvation. However, as system reliability increases, marginal benefits decrease as demand response does not constantly promote adequate supply (due to physical limitations of the load).

TABLE 4 confidence capacity values for demand response under RD and CM scheduling policies

Table 4 shows confidence capacity values for demand responses under RD and CM scheduling strategies, as shown in table 4, the confidence capacity derived in CM mode is always higher than for RD case, and the gap between the two results increases with increasing load flexibility. This indicates that in a demand response project in which a user voluntarily participates, the confidence capacity of the demand response is closely related to the operation strategy adopted by the system operator. Assuming that the load flexibility of the system is 20% and 50%, respectively, the cumulative load reduction of two selected users and the user engagement in case of RD and CM can also be counted. The result shows that the load reduction of the user is inversely related to the participation degree; under the condition of RD, the demand response may bring certain discomfort to the user, and the average distribution of the demand response is influenced; while CM schemes can effectively alleviate this problem, demand response distribution is more uniform after considering the perceived human impact. Additionally, when the proportion of the responsibilities is low, the operating strategy does not present a significant problem for the system due to the limited potential for demand response. However, as the proportion increases, the user's behavior will have a greater impact on the revenue of the demand response, in which case the effect of the operating strategy may be manifested.

According to the technical scheme, the invention provides a new confidence capacity evaluation model for estimating the confidence capacity of demand response in a future intelligent power grid system. The main innovation of this model is the ability to introduce extrinsic and intrinsic uncertainties in demand response from the demanding parties, caused by physical and human factors. In order to represent the stochastic dynamics of user response levels, a mixed probabilistic fuzzy model is proposed, in which two satisfaction indicators (i.e., RF and RI) are defined and used to quantify the dependency of demand response availability on its operating strategy. And (3) integrating different types of related uncertainties into the same frame by adopting a probability fuzzy transformation technology, and evaluating the confidence capacity by adopting a reliability algorithm combining an SMCS (simple sampling circuit) method and an OPF (optimal particle filter) method. Simulation results also show that the system's richness can be greatly improved and considerable capacity support can be provided to the system if the appropriate demand response items are used. Unlike conventional generator sets, the confidence capacity of demand response is not completely dependent on the physical characteristics of the final electrical load, but is also affected by other issues such as customer consumption patterns and demand response operating strategies employed by the grid. Generally, greater load flexibility or higher correlation between customer response rate and system load curve will result in greater confidence capacity. Therefore, in practice, effective confidence capacity estimation of demand response is essential to take into account the impact of customer discomfort in demand response planning.

A9, the method as in A8, wherein the confidence capacity model of demand response

Where Y is the model output value, vector X and

a10, the method as in A9, wherein the fuzzy variable

Having a function of degree of membership

At this time

The equivalent probability density function is:

a11, the method as in any one of A1-A10, wherein the confidence capacity model of demand response has an undersea expected value, EENS, as a system reliability indicator.

A12, the method as in A11, wherein the confidence capacity of the demand response is expressed as EFC, and the system reliability index including the demand response is:

the system reliability index that does not contain a demand response is:

A13, the method as in a12, wherein the confidence capacity of the demand response is expressed in equivalent alternative power generation capacity EGCS, and the system reliability index without demand response is:

the system reliability indexes including the demand response are:

wherein, C^agIndicating alternative power generation.

A14, the method of any one of a1-a13, wherein solving the confidence capacity model of demand response from the historical data comprises: generating time state sequences of a conventional generator set and a renewable energy generator set according to the input historical data, and generating output power curves of the two generator sets according to the time state sequences; calculating the reliability index EENS of the system with and without the requirement response respectively^drAnd EENS^baseWherein EENS^baseRepresenting the reliability level of the system in the basic case, EENS^drFor quantifying the increase in system reliability due to demand response participation; index EENS^baseAnd index EENS^drComparing, adjusting the equivalent fixed capacity or the alternative generating capacity in the system by using an iterative algorithm according to the value difference of the two, updating the two index values according to the adjustment result until the two updated index values meet the preset relationship, and stopping the adjustment, wherein the equivalent fixed capacity or the alternative generating capacity is the equivalent fixed capacity or the alternative generating capacity at the momentConfidence capacity of demand response.

A15, the method as in A14, wherein the predetermined relationship is | EENS^dr-EENS^base|/EENS^baseζ, where ζ is the threshold.

A16, the method as recited in a14, wherein the process of solving the confidence capacity model of demand response based on the historical data uses a sequential monte carlo simulation method and an optimal power flow method.

A17, the method according to any one of A1-A16, further comprising the steps of: establishing a scheduling strategy model of demand response, wherein the scheduling strategy model comprises a reliability driving scheduling strategy model or a coordination management scheduling strategy model, and the scheduling strategy model comprises an objective function and a constraint condition; and solving the scheduling strategy model according to the constraint conditions to determine the optimal scheduling plan of the demand response.

A18, the method as in a17, wherein the objective functions of the reliability driven scheduling policy model and the coordinated management scheduling policy model are respectively:

represents the average cost of power interruption during t; kappa_k,aIs an interference factor coefficient used to represent the sensitivity of user comfort to power usage.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention. Those skilled in the art will appreciate that the modules or units or groups of devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. Modules or units or groups in embodiments may be combined into one module or unit or group and may furthermore be divided into sub-modules or sub-units or sub-groups. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Furthermore, those of skill in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination. Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.

The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention. In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the method of the invention according to instructions in said program code stored in the memory.

By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer-readable media includes both computer storage media and communication media. Computer storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media. As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.

Claims

1. A method of determining a confidence capacity of a smart grid demand response, adapted to be executed in a computing device, the method comprising:

establishing a reliability model of a generator set, the reliability model comprising an output power function of the generator set, the generator set comprising a conventional generator set, an available output power P of the conventional generator set_t ^cgThe function of (d) is:

wherein, C^cgIndicating the rated capacity of a conventional generator set, β_t ^cgIs a variable 0-1, representing the mechanical state of the conventional generator set at time slot t, wherein β is used when the device is operating normally_t ^cg1, otherwise 0;

respectively establishing a response capacity model, a user engagement model and a load rebound model of demand response, wherein the response capacity model, the user engagement model and the load rebound model respectively comprise a response demand function, an engagement function and a load rebound electric quantity function of a response user, and the load rebound electric quantity function is as follows:

wherein the content of the first and second substances,

representing the amount of power applied to the bounce at time t',

is the load reduction over time period t; TR (transmitter-receiver)_kIs the duration of the load rebound process,

and

respectively representing the slope and the electric energy compensation rate;

establishing a load demand model of demand response according to the responsive capacity model, the user participation model and the load rebound model, wherein the load demand model comprises a load demand function of a responsive user;

establishing a confidence capacity model of demand response according to the reliability model of the generator set and the load demand model of the demand response, wherein the confidence capacity model comprises the confidence capacity of the demand response; and

obtaining historical data of the intelligent power grid, wherein the historical data comprises classified load data, and solving a confidence capacity model of the demand response according to the historical data by adopting a sequential Monte Carlo simulation method and an optimal power flow method in a combined mode to obtain the confidence capacity of the demand response of the intelligent power grid; wherein the response requirement function of the responsive users is:

wherein the content of the first and second substances,

is the estimated response of user k in time period t,

is the total capacity of the response demand of user k,

maximum responsive capacity factor, I, of one year, one month, one day and one hour, respectively_k,tIs white noise for expressing the random dynamic characteristics of the load in the running process;

the engagement function is:

is a coefficient representing the degree of sensitivity of user k to the inconvenience caused by the demand response program; omega_kIs a weight correlation coefficient used for quantifying the response frequency and the response intensity in the demand response program;

the responsive user load demand function is:

wherein the content of the first and second substances,

and

representing the unresponsive load amount of the user during the time period t;

a confidence capacity model of the demand response

Where Y is the model output value, vector X and

wherein v is_tIs the wind speed for time period t, β_t ^rgIs a variable from 0 to 1 and represents the mechanical state of the renewable energy generator set at time slot t.

2. The method of claim 1, wherein the generator set comprises a renewable energy generator set having a final output power P_t ^rgThe function of (d) is:

wherein β is used when the equipment is working normally_t ^rgIs 1, otherwise it is0；P_t ^rgpRepresenting the available output power of the renewable energy generator set; c^rgRepresenting the rated capacity of the renewable energy generator set; v. of_ci、v_ratAnd v_coRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed of the fan;

3. The method of claim 1, wherein the confidence capacity model of demand response has an undersea expected value, EENS, as a system reliability indicator.

4. The method as claimed in claim 3, wherein the confidence capacity of the demand response is expressed in terms of equivalent fixed capacity, EFC, at which time,

the system reliability indexes including the demand response are:

the system reliability index that does not contain a demand response is:

5. The method as claimed in claim 4, wherein the confidence capacity of the demand response is expressed in terms of equivalent alternative generation capacity, EGCS, at which time,

the system reliability index that does not contain a demand response is:

the system reliability indexes including the demand response are:

wherein, C^agIndicating alternative power generation.

6. The method of claim 1, wherein solving the confidence capacity model of demand response from the historical data comprises:

generating time state sequences of a conventional generator set and a renewable energy generator set according to the input historical data, and generating output power curves of the two generator sets according to the time state sequences;

calculating the reliability index EENS of the system with and without the requirement response respectively^drAnd EENS^baseWherein EENS^baseRepresenting the reliability level of the system in the basic case, EENS^drFor quantifying the increase in system reliability due to demand response participation; and

index EENS^baseAnd index EENS^drAnd comparing, adjusting the equivalent fixed capacity or the replaceable generating capacity in the system by using an iterative algorithm according to the numerical difference of the two, updating the two index values according to the adjustment result until the two updated index values meet the preset relationship, and stopping adjustment, wherein the equivalent fixed capacity or the replaceable generating capacity is the confidence capacity of the demand response.

7. The method of claim 6, wherein the predetermined relationship is | EENS^dr-EENS^base|/EENS^baseζ, where ζ is the threshold.

8. The method according to any one of claims 1-7, further comprising the step of:

establishing a scheduling strategy model of demand response, wherein the scheduling strategy model comprises a reliability driving scheduling strategy model or a coordination management scheduling strategy model, and the scheduling strategy model comprises an objective function and a constraint condition; and

and solving the scheduling strategy model according to the constraint conditions to determine the optimal scheduling plan of the demand response.

9. The method of claim 8, wherein the objective functions of the reliability-driven scheduling policy model and the coordinated management scheduling policy model are respectively:

10. A computing device, comprising:

one or more processors;

a memory; and

one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-9.

11. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-9.