CN110782171A - Method and device for determining demand side resource demand response benefit value and computing equipment - Google Patents
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Abstract
The invention discloses a method for determining a demand side resource demand response benefit value, which is executed in computing equipment, wherein a scheme set to be selected and an evaluation index set of demand response are stored in the computing equipment, and the method comprises the following steps: acquiring original data of the microgrid, and calculating result values of demand response under various indexes of various schemes; establishing a normalized matrix after dimensionless conversion is carried out on the result values, and calculating the central point value of each index in all schemes and the variation degree of each index; normalizing the variation degree to obtain a second weight of each index, and combining the first weight of each index to obtain a combined weight of each index; and calculating the index vector representation of each scheme and the index vector representations of the positive ideal scheme and the negative ideal scheme of the demand response, and calculating the comprehensive closeness of each scheme to the positive ideal scheme by combining the combined weight to obtain the comprehensive benefit value. The invention also discloses a device and a computing device for determining the demand side resource demand response benefit value correspondingly.
Description
Technical Field
The invention relates to the technical field of computers and internet, in particular to a method, a device and computing equipment for determining a demand side resource demand response benefit value.
Background
The demand response is different from a means of guaranteeing the balance of supply and demand of a system by relying on traditional physical equipment, on one hand, the development of the demand response must be supported by multiple parties including a power grid, users and the like, so that more beneficial agents are involved; on the other hand, because the load resources have different response characteristics and are influenced by the user's will, the effect of developing demand response under different conditions often has a great difference. Therefore, in practical engineering, how to effectively judge the expected benefit of the demand response becomes a problem that must be faced in the microgrid planning decision.
However, in the existing calculation method for the expected benefits of the microgrid, the selected index value is mostly based on a power grid system, the weight of the index value is mostly manually defined by expert scoring, and the actual benefit weight of each index is ignored. Therefore, a certain deviation exists in the final calculation of the benefit value, the optimal benefit value of each scheme cannot be represented, and the optimal scheme cannot be determined. Therefore, a more accurate and objective calculation method for the expected benefit of demand response is needed.
Disclosure of Invention
In view of the above, the present invention provides a method, apparatus and computing device for determining demand-side resource demand response benefit value in an effort to solve, or at least solve, the above-identified problems.
According to an aspect of the present invention, a method for determining a demand-side resource demand response benefit value is provided, which is suitable for being executed in a computing device, wherein a candidate scheme set P ═ { P } of demand responses in a microgrid is stored in the computing device
1,p
2,···,p
i,···,p
mAnd the set of evaluation indices I ═ I
1,i
2,···,i
j,···,i
nIn which s is
iAnd i
jRepresenting a scheme i and an index j respectively, and each index is marked with a first weight value, the method comprises the following steps: acquiring original data of the microgrid, and respectively calculating a result value of each index of the demand response under each scheme according to the original data; converting each calculated result value into a dimensionless index value after standardization processing, wherein the index value comprises an index upper limit value
And lower limit of index
And is
Establishing a normalized matrix
And calculating each index based on the matrixCenter point value of the solution, and degree of variation w of index value of each index to the center point value
jThe center point value includes an index upper limit center value
And lower center value of index
Degree of variation w for each index
jNormalization processing is carried out to obtain a second weight value of each index
And combining the first weight to obtain the combined weight of each index
And calculating index vector representation S of each scheme respectively
iAnd the index vector representation S of the positive ideal solution and the negative ideal solution of the demand response
+And S
-And combining the combined weight
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iTo obtain the comprehensive benefit value of each scheme.
Optionally, in the method according to the present invention, further comprising the step of: establishing an interval decision matrix based on the calculated result value of each index under each scheme
Wherein
And
respectively an upper limit value and a lower limit value of a result value of the index j of the scheme i, and if the result value is a unique value A, the upper limit value and the lower limit value are respectively the upper limit value and the lower limit value of the result value of the index j of the scheme i
Alternatively, in the method according to the invention, if the resulting value is a ratiometric value, the dimensionless transformation is performed according to the following standardized formula:
alternatively, in the method according to the invention, if the resulting value is a cost-type value, a dimensionless transformation is performed according to the following standardized formula:
optionally, in the method according to the invention, the index j is at the center point value of all schemes
The calculation formula of (2) is as follows:
first weight ω 'of index j'
jA pre-recorded subjective weighting value is given, and the combined weighting value of the index j
Wherein α is a proportional adjustment coefficient, 0 is equal to or more than α is equal to or less than 1, and the variation degree w from the index value of the index j to the center point value
jThe calculation formula of (2) is as follows:
optionally, in the method according to the invention, the indicator vector of the scheme i is represented as
The indicator vector of the positive ideal scheme is expressed as
The indicator vector for the negative ideal case is represented as
Wherein the content of the first and second substances,
is the mean value of the upper and lower index limits of the index j of the scheme i, i.e.
Is the positive ideal solution corresponding to the index j,
representing m schemes corresponding to index j
Maximum value of (d);
is a negative ideal solution corresponding to the index j,
representing m schemes corresponding to index j
Is measured.
Optionally, in the method according to the invention, combining weights is combined
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iComprises the following steps: respectively calculating positive and negative differentiation distances of evaluation indexes corresponding to each scheme
And
determining a resolution system corresponding to each index based on the positive and negative differentiation distancesNumber rho
jAnd calculating the positive and negative correlation coefficients corresponding to each scheme
And
respectively calculating the interval gray correlation degree of each scheme and the positive and negative ideal schemes
And
and the distance between each solution and the positive and negative ideal solutions
And
to pair
And
performing dimensionless transformation, and marking the obtained results as
And determining the comprehensive closeness U between the scheme i to be selected and the ideal solution according to the result of the dimensionless conversion
i。
Alternatively, in the method according to the invention,
wherein the content of the first and second substances,
and
representing all indexes of all solutions respectively
And
minimum value of (d);
and
representing all indexes of all solutions respectively
And
is measured.
Optionally, in the method according to the invention, U
i＝U
i +/(U
i ++U
i -)，
Wherein, U
i +And U
i -Respectively the deviation distance between the scheme i and the positive and negative ideal schemes; e.g. of the type
1And e
2Is a preference coefficient and satisfies e
1+e
2＝1；v
iRespectively represent
And
respectively represent
And
obtained after dimensionless conversion
D
+。
Optionally, in the method according to the present invention, a resolution coefficient ρ corresponding to each index is determined
jComprises the following steps: calculating the average dissimilarity distance of each index in each scheme
Calculating a boundary judgment factor psi for measuring the integral numerical difference of the normalized matrix R
j(ii) a And determining the numerical value of the resolution coefficient according to the numerical value ranges of the respective judgment factors.
Alternatively, in the method according to the invention,
when t is
jWhen equal to 0, ρ
jIn (0, 1)]Any value is taken in between; when 0 is present<Ψ
jWhen the concentration is less than or equal to 0.5, taking
When t is
j>At 0.5, ρ
jIn [0.8,1 ]]Any value of (1).
Optionally, in the method according to the present invention, the microgrid comprises a microgrid operator, and the evaluation index set comprises at least one of power quality benefit, reliability benefit and economic benefit of the microgrid operator; wherein the power quality benefits include a voltage yield improvement O
11And/or frequency yield improvement O
12(ii) a The reliability benefits include the system average outage time reduction rate O
21And system power failure frequency reduction rate O
22And rate of decrease in power supply shortage of system O
23At least one of; the economic benefits include at least one of a payable investment cost, a payable grid speed cost, a loss of electricity sales revenue, and a demand response incentive cost.
Alternatively, in the method according to the invention,
in the formula, T is a statistical period;
when the demand response is not implemented, counting the time of the voltage of the system monitoring point in a qualified range in a period;
counting the time of the voltage of the system monitoring point in a qualified range in a period when a demand response is implemented;
when the demand response is not implemented, counting the time of the system monitoring point frequency in a qualified range in a period;
counting the time of the system monitoring point frequency in a qualified range in a period when implementing a demand response;
when the demand response is not implemented, counting the total power failure times of the system in the period;
when the demand response is not implemented, counting the power failure time of the ith power failure of the system in the period;
counting the total power failure times of the system in a period when a demand response is implemented;
counting the power failure time of the ith power failure of the system in a period when a demand response is implemented;
when the demand response is not implemented, counting the total electric quantity purchased from the large power grid by the system in the period;
and counting the total electric quantity purchased from the large power grid by the system in the period in order to implement the demand response.
Optionally, in the method according to the present invention, the microgrid includes a distributed generator, and the evaluation index set includes economic benefits and/or environmental benefits of the distributed generator; wherein, the economic benefit comprises the cost of power generation and/or the loss of power selling income, and the environmental benefit comprises the utilization increase rate of renewable energy sources and/or the emission reduction rate of polluted gas.
Optionally, in the method according to the present invention, the microgrid includes power consumers, and the evaluation index set includes service goodness benefits and/or economic benefits of the power consumers; the service goodness benefits comprise at least one of a user power failure frequency reduction rate, a user average power failure time reduction rate and a user power quality satisfaction degree; the economic benefit includes at least one of an electricity charge saving rate, a compensation profitability, and an equipment investment cost.
Optionally, in the method according to the present invention, the demand-side resource comprises at least one of an interruptible load, a transferable load and a bi-directional flexible interactive load; the candidate scheme set comprises at least one of a demand response scheme of an interruptible load, a demand response scheme of a transferable load and a demand response scheme of a bidirectional flexible interactive load.
According to another aspect of the present invention, there is provided an apparatus for determining a demand-side resource demand response benefit value, adapted to reside in a computing device, where a candidate set P ═ { P } of demand responses in a microgrid is stored
1,p
2,···,p
i,···,p
mAnd the set of evaluation indices I ═ I
1,i
2,···,i
j,···,i
nIn which s is
iAnd i
jRepresent scheme i and index j respectively, and every index is marked with first weight, the apparatus includes: the first calculation module is suitable for acquiring original data of the microgrid and calculating a result value of each index of the demand response under each scheme according to the original data; a second calculation module, adapted to convert each of the calculated result values into a dimensionless index value after normalization, the index value including an index upper limit value
And lower limit of index
And is
A third calculation module adapted to establish a normalized matrix
And calculating the central point value of each index in all schemes and the variation degree w from the index value of each index to the central point value based on the matrix
jThe center point value includes an index upper limit center value
And lower center value of index
A fourth calculation module adapted to calculate the degree of variation w of each index
jNormalization processing is carried out to obtain a second weight value of each index
And combining the first weight to obtain the combined weight of each index
And a fifth calculation module adapted to calculate an index vector representation S for each solution separately
iAnd the index vector representation S of the positive ideal solution and the negative ideal solution of the demand response
+And S
-And combining the combined weight
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iTo obtain the comprehensive benefit value of each scheme.
According to another aspect of the present invention, there is provided a computing device comprising: one or more processors; a memory; and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs, when executed by the processors, implementing the steps of the method for determining a demand-side resource demand response benefit value as described above.
According to yet another aspect of the present invention, there is provided a readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, perform the steps of the method of determining a demand-side resource demand response benefit value as described above.
According to the technical scheme, the regional evaluation data index is introduced, and the influence of various uncertain factors on the demand response benefit value can be effectively calculated. In order to meet the calculation requirement of the comprehensive benefit value under the condition of uncertainty information, the interval central point distance method and the expert scoring method are combined, and the accurate weight of each index is obtained through the combined weighting of the interval central point distance method and the expert scoring method, so that the reasonable consideration of the subjective experience of a decision maker and the information value of each index is realized. On the basis, the comprehensive benefit value of the demand response is accurately obtained by solving the positive and negative ideal schemes in the demand response, and the precision of the calculation result is effectively improved. Furthermore, in order to avoid adverse effects on a calculation result caused by improper selection of a resolution coefficient in an analysis process, the invention further provides a dynamic adjustment strategy aiming at the resolution coefficient, and the robustness and the discrimination capability of the model in practical application are improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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 one embodiment of the invention;
fig. 2 is a schematic diagram illustrating an evaluation index system for demand response benefit values in a microgrid according to an embodiment of the present invention;
FIG. 3 illustrates a flow chart of a method 300 of determining a demand-side resource demand response benefit value in accordance with one embodiment of the present invention;
FIG. 4 is a block diagram of an apparatus 400 for determining a demand-side resource demand response benefit value according to one embodiment of the present invention; and
fig. 5-7 show graphs illustrating the results of the first, second, and third evaluation metrics in a microgrid, respectively, 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 a computing device 100 according to one embodiment of the invention. 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. Program data 124 includes instructions, and in computing device 100 according to the present invention, program data 124 includes instructions for performing method 300 of the present invention for determining a demand-side resource demand response benefit value.
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 the method 300 of determining a demand-side resource demand response benefit value of the present invention.
In addition, the computing device stores a candidate scheme set P ═ P of demand response in the microgrid
1,p
2,···,p
i,···,p
mAnd the set of evaluation indices I ═ I
1,i
2,···,i
j,···,i
nIn which s is
iAnd i
jRepresent scheme i and index j, respectively, and each index is marked with a first weight. Specifically, the demand response resources in the microgrid system can be divided into 3 types of interruptible loads, transferable loads and bidirectional flexible interactive loads, and each type of load has a corresponding demand response implementation scheme. Therefore, there are three alternatives in the alternative scheme set, including a demand response scheme of interruptible load, a demand response scheme of transferable load, and a demand response scheme of bidirectional flexible interactive load, i.e., m is 3, P is { P ═ P {
1,p
2,p
3}。
Specifically, the interruptible load refers to a load in which the amount of electricity used can be partially or entirely reduced as necessary, such as an air conditioner and a large laundry device. The transferable load refers to a load in which the total power consumption is unchanged in a fixed period (such as 24h), and the power consumption in each period can be flexibly adjusted, and mainly comprises ice cold storage, industrial load capable of automatically arranging a production plan and the like. The bidirectional interactive load refers to a load capable of performing bidirectional energy interaction with a power grid, such as a distributed energy storage, an electric vehicle with V2G, and the like. In order to make the calculation results of the benefit values have contrast, the system is assumed to respectively contain interruptible loads, transferable loads and bidirectional interactive loads, different demand response implementation schemes are respectively provided for different types of demand response resources, and the different demand response schemes can only guide the behaviors of the corresponding types of response resources.
In the power market environment, investment and operation of the microgrid are gradually changed from a traditional vertical integration mode to a multilateral co-participation mode. The repartition of market status makes the demand response bring different cost and benefit to each subject. In this case, the relevant participating entities of the microgrid include the microgrid operator, the distributed power generator and the power consumers. The micro-network operator is responsible for planning, operating and maintaining the whole system. The distributed power generator is mainly responsible for investment and operation of various commercial distributed power generation projects in a park, and economic benefits are obtained by selling electricity to a microgrid operator or a user. The users are the consumers of terminal electric energy and the providers of demand response resources in the microgrid. According to the method and the system, the demand response users in the microgrid are natural cluster type large users, and can participate in the electric power market activity without the help of a load integrator, so that the influence and the effect of the load integrator are not considered for the moment.
FIG. 2 shows a schematic diagram of a demand response indicator evaluation architecture according to one embodiment of the present invention. As shown in fig. 2, the primary indexes of the index evaluation system are mainly a microgrid operator O, a distributed power generator G, and a power consumer U. Each first-level index has a second-level index, such as the second-level index of the micro-network operator O including the power quality benefit O
1Reliability benefit O
2And economic benefits O
3. The secondary indexes under the distributed power generator G comprise economic benefits G
1And environmental benefit G
2. The secondary indexes under the power consumer U comprise service goodness benefits U
1And environmental benefits U
2. There will be multiple tertiary indexes under each secondary index. The multi-level indexes can form an index evaluation system stored in the computing equipment. If all the 19 three-level indexes in fig. 3 are selected, the evaluation index set I has 19 indexes in common, that is, n is 19.
Among them, for the micro-network operator, the power quality benefit O
1The quality of the electric energy is evaluated mainly from two aspects of voltage and frequency, and the quality is reflected by the improvement degree O of the voltage qualification rate
11And/or frequency yield improvement O
12. The power supply reliability refers to the continuous power supply capacity of a power supply system, and can be quantitatively represented by the average system power failure time, the average system power failure times and the system power shortage amount. Therefore, the reliability benefit O
2The average power-off time reduction rate O of the system can be selected
21And system power failure frequency reduction rate O
22And rate of decrease in power supply shortage of system O
23Is expressed by at least one of the above. Economic benefit O
3The improvement is mainly through increasing the income of the electricity selling income, and reducing the construction cost and the operation cost. The construction cost mainly refers to the investment cost, and the operation cost mainly comprises the network loss cost and the demand response incentive cost. Economic benefits of the operator of the microgrid O
3Mainly comprising an exemptable investment cost O
31Free from loss of network
32Loss of income from electricity sale
33And demand response incentive cost O
34At least one of (1).
For distributed power generator G, its economic benefit G
1Mainly in the cost of electricity generation and income from selling electricity, therefore G
1May include a cost of free generation G
11And/or loss of electricity sales revenue G
12And (4) indexes. Environmental benefit G
2Mainly due to the primary energy used by the device and the reduction of pollutant emission in the process of producing electric energy. Thus, G
2May include a renewable energy utilization growth rate G
21And/or pollution gas emission reduction rate G
22And (4) indexes.
For the electric power user U, the service goodness benefit U
1Mainly comprising the rate of frequency reduction of power failure of usersU
11Average user power failure time reduction rate U
12And user power quality satisfaction degree U
13At least one of (1). Economic benefit U is obtained by combining economic relations between power users and other main bodies in the microgrid
2May include an electricity charge saving rate U
21Compensation income U
22And equipment investment cost U
23At least one of (1).
1) The voltage qualification rate refers to the percentage of the total time sum of the voltage of the monitoring point in the qualified range and the total statistical time of voltage monitoring in the statistical time in the operation of the power grid. Voltage qualification rate improvement degree O
11Refers to the change in the ratio of the time that the system monitor point voltage is within a qualified range to the total statistical time after the demand response is implemented.
2) The frequency qualification rate refers to the percentage of the time of the grid frequency within the allowable deviation and the statistical time, and the frequency qualification rate improvement degree O
12Refers to the change in the ratio of the time that the system node frequency is within the qualified range to the total statistical time after the demand response is implemented.
Wherein T is a statistical period;
when the demand response is not implemented, counting the time of the voltage of the system monitoring point in a qualified range in a period;
and counting the time of the voltage of the monitoring point of the system in a qualified range in the period for implementing the demand response.
When the demand response is not implemented, counting the time of the system monitoring point frequency in a qualified range in a period;
in order to implement demand response, the frequency of system monitoring points in the statistical period is in the stateTime within a grid.
3) The average system power failure time refers to the average number of power failure hours of the system in a statistical period, and is the ratio of the total system power failure time to the total system power failure times in the statistical time. Average system outage time reduction rate O
21The time is the descending amplitude of the ratio of the total power failure time of the system to the power failure times after the demand response is implemented.
4) The system power failure frequency is one of important indexes representing the reliability of a power distribution system, and represents the total power failure times of the system in statistical time. Rate of decrease of system power failure frequency O
22The system power failure accident frequency reduction rate after the demand response is implemented.
5) The system power shortage is the power which is used for supplying less power to users during the power failure of the power supply system. Rate of decrease of system power shortage
23The method refers to the reduction rate of the electric quantity purchased from the large power grid by the system in statistical time after demand response is implemented.
In the formula (I), the compound is shown in the specification,
when the demand response is not implemented, counting the total power failure times of the system in the period;
when the demand response is not implemented, counting the power failure time of the ith power failure of the system in the period;
counting the total power failure times of the system in a period when a demand response is implemented;
and counting the power failure time of the ith power failure of the system in the period for implementing the demand response.
When the demand response is not implemented, counting the total electric quantity purchased from the large power grid by the system in the period;
and counting the total electric quantity purchased from the large power grid by the system in the period in order to implement the demand response.
6) The implementation of demand response can reduce the system utilization rate and realize peak clipping and valley filling, thereby reducing the system investment cost. So that the investment cost O can be avoided
31Refers to the grid investment costs that are avoided or delayed by implementing demand response.
7) The grid loss refers to the loss of electric energy generated in the transmission and distribution process, and is related to the square of transmission current, and the square of transmission quantity due to the basically stable voltage of the microgrid system. Cost O of network loss can be avoided
32Refers to the reduced cost of network loss due to implementation of demand response.
8) Demand response is generally divided into two modes based on electricity price and incentive, and electricity selling income loss O
33Which refers to a reduction in electricity sales revenue resulting from implementing demand response items based on electricity prices.
9) Demand response incentive cost O
34The incentive cost paid by the micro-network operator for encouraging the user to participate in incentive type demand response in statistical time is referred to, and specific data can be obtained by counting historical data.
In the formula (I), the compound is shown in the specification,
when the demand response is not implemented, the system planning model solves the obtained total construction cost;
in order to implement the demand response, the system planning model solves for the resulting total construction cost.
Is the average electricity price; mu is a network loss factor;
and
respectively representing the load peak value and the load valley value of the system when the demand response is not implemented;
and
respectively, the load peak and the load valley of the system when implementing the demand response. p is a radical of
0A uniform electricity price for not implementing a demand response;
when the demand response is implemented, counting the system electricity sales amount in the period; r
DRIn order to implement the demand response, the system actually collects the electricity selling income in the counting period.
10) Cost G of power generation can be avoided
11Mainly means the investment cost of power generation can be avoided. The demand response can call demand response resources to promote the balance of supply and demand of the system, thereby reducing the standby capacity of the system and reducing the investment cost of power generation. To this end, the cost of avoidable power generation G may be characterized by calculating the reduction in system peak due to implementing demand response
11。
11) On one hand, the implementation of demand response can coordinate demand response resources to adapt to the output of the distributed power supply and promote the output consumption of renewable energy sources to a certain extentThe electricity selling quantity of the renewable power supply is increased, and the electricity selling income is increased; on the other hand, the change of electricity price or the incentive measures can lead the user to reasonably and efficiently use electricity, and the electricity selling quantity can be reduced, so that the income loss G of electricity selling is caused
12。
In the formula, c
disThe investment cost of the average unit capacity of the distributed power supply is reduced.
When the demand response is not implemented, counting the average on-line electricity price in the period;
when the demand response is not implemented, the online electric quantity in the period is counted;
when the demand response is implemented, counting the average internet-surfing electricity price in a period;
and when the demand response is implemented, counting the internet surfing electric quantity in the period.
12) Renewable energy utilization G
21The method refers to the proportion of the renewable distributed power supply on-grid electricity quantity in the total electricity consumption of the system in the statistical time. Renewable energy utilization growth rate G
21Refers to the rate of change in system renewable energy utilization after the demand response is implemented.
13) Emission reduction rate of polluted gas G
22The proportion of the power generation pollution gas emission to the original emission amount is reduced due to the implementation of demand response in the statistical period. Renewable distributed power sources are available because the pollution gas generated by the distributed power sources, particularly renewable distributed power sources, is negligible compared with the traditional thermal power generating unitIncreasing of on-grid electricity quantity represents pollution gas emission reduction rate G
22。
In the formula, Q
0When the demand response is not implemented, counting the total power consumption of the system in the period; q
DRAnd counting the total power consumption of the system in the period when the demand response is implemented.
14) Rate of decrease of frequency of user power failure U
11The method refers to the descending range of the ratio of the power failure times of system users to the total number of the users in a statistical period.
15) Average user outage rate U
12The descending amplitude of the ratio of the total power failure time of system users to the total number of the users in a statistical period is referred to
16) Satisfaction degree U of user electric energy quality
13The evaluation method is characterized in that the satisfaction degree of a user on the electric energy quality is indicated, the user in the system subjectively evaluates five grades of excellence, good, common, slightly poor and poor according to the actual electricity utilization condition, the grades are respectively 1,0.8,0.6,0.4 and 0.2, and the evaluation is carried out according to the grades.
In the formula (I), the compound is shown in the specification,
when the demand response is not implemented, counting the total number of power failure households in the ith power failure of the system in the period;
counting the total number of power failure households in the ith power failure of the system in a period when a demand response is implemented; n is the total number of the system households. r is
iAnd scoring the satisfaction degree of the ith user on the electric energy quality.
17) Electric charge saving rate U
21Finger systemAnd in the counting period, the electricity charge saved by the user due to the implementation of demand response accounts for the proportion of the original electricity charge.
18) Compensating for rate of return U
22The proportion of the incentive income obtained by the user for participating in demand response to the expenditure of the original electric charge is indicated.
19) Equipment investment cost U
23The equipment investment cost paid by the user for participating in the demand response is calculated by averaging the actual cost of purchasing, installing and maintaining the equipment related to the demand response by each user in the system.
In the formula (I), the compound is shown in the specification,
when the demand response is not implemented, the user needs to pay the electricity fee on average;
when the demand response is implemented, the user pays the electricity fee on average.
To implement the demand response, the user averages the incentive revenue.
FIG. 3 illustrates a flow diagram of a method 300 for determining a demand-side resource demand response benefit value in accordance with one embodiment of the present invention. The method 300 is executed in a computing device, such as the computing device 100, to calculate a demand-response benefit value for a demand-side resource.
As shown in fig. 3, the method begins at step S310. In step S310, raw data of the microgrid is obtained, and a result value of each index of the demand response under each scheme is calculated according to the raw data.
According to one embodiment, the parameter values on the right side of the equal sign of each index under each scheme can be obtained, specifically, the parameter values on the right side of the equal sign of the 19 indexes, such as various voltage ranges, qualified time, power failure times, power failure duration, total electric quantity, various costs, power loss,The parameter values of electricity price, electricity selling income, total number of households, electricity charge and the like mainly comprise data of demand side resources. From these raw base data, the result value of each index for each scheme is calculated. The result value may be a bin number
A value or a single value, wherein
And
respectively an upper limit value and a lower limit value of a result value of the index j of the scheme i, and if the result value is a unique value A, the scheme i is considered to be a scheme I
Table 1 shows an example of the calculation results of each index under each scheme:
TABLE 1
Subsequently, in step S320, each of the calculated result values is converted into a dimensionless index value including an index upper limit value after being subjected to a normalization process
And lower limit of index
And is
The inherent differences of the evaluation indexes in the aspects of types and dimensions are mainly considered, and the calculation result has incommercity, so that the related data needs to be further normalized. In one implementation, if the result value is a ratio-type value, i.e., the index is a benefit-type index, such as a promotion, a decline, an increase, an emission reduction, a satisfaction, a saving, a yield, etc., dimensionless transformation is performed according to the following standardized formula:
in another implementation, if the resulting value is a costwise value, i.e., the index is a costwise index, such as various loss values, cost values, costs, etc., dimensionless conversions are performed according to the following standardized formulas:
subsequently, in step S330, a normalized matrix is established
And calculating the central point value of each index in all schemes and the variation degree w from the index value of each index to the central point value based on the matrix
jThe center point value includes an index upper limit center value
And lower center value of index
According to one embodiment, an interval decision matrix may be established based on the calculated result value of each index for each scheme in step S310
And performing dimensionless transformation on each result value in the decision matrix in step S320, thereby obtaining the normalized matrix in step S330
According to another embodiment, the index j is at the center point value of all schemes
The central value of the upper limit of the index
And lower center value of index
The calculation methods are respectively as follows:
the distance between the index value of the index j and the center point value, i.e., the degree of variation w
jThe calculation formula of (2) is as follows:
subsequently, in step S340, the coefficient of variation w for each index is calculated
jNormalization processing is carried out to obtain a second weight value of each index
And combining the first weight to obtain the combined weight of each index
Wherein the first weight ω 'of the index j'
jGenerally, experts can reasonably assign importance of each index to a pre-recorded subjective scoring weight to obtain the subjective scoring weight. The second weight ω
jFor the interval distance weight, based on an expert scoring-interval central point distance (ESICD) combined weighting method, the obtained weight can fully reflect the actual importance degree of the attribute, and can reasonably reflect the information value index of the related calculation dataAnd marking the combined weight value of j. In accordance with one embodiment of the present invention,
wherein α is a proportional adjustment coefficient, and 0 ≦ α ≦ 1, and it may be preferable to take α ≦ 0.5.
Subsequently, in step S350, an index vector representation S for each solution is calculated separately
iAnd the index vector representation S of the positive ideal solution and the negative ideal solution of the demand response
+And S
-And combining the combined weight
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iTo obtain the comprehensive benefit value of each scheme.
According to one embodiment, the indicator vector for scheme i is represented as
The indicator vector of the positive ideal scheme is expressed as
The indicator vector for the negative ideal case is represented as
Wherein the content of the first and second substances,
is the mean value of the upper and lower index limits of the index j of the scheme i, i.e.
Representing m solutions corresponding to the index j for the positive ideal solution corresponding to the index j
Is measured.
Is a negative ideal solution corresponding to the index j and represents m schemes corresponding to the index j
Is measured.
According to another embodiment, combining weights
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iMay be implemented by steps S351-S355:
in step S351, the positive and negative differentiation distances of the evaluation index corresponding to each case are calculated
And
and
the positive and negative differential distances of the index j corresponding to the scheme i are respectively shown, and the calculation formula is as follows:
in step S352, the resolution coefficient ρ corresponding to each index is determined based on the calculated positive and negative differentiation distances
jAnd calculating the positive and negative correlation coefficients corresponding to each scheme
And
here, the present invention employs an IGR-TOPSIS method (Technique for Order Preference by similarity to an Ideal Solution) to determine the benefit value of demand response. Quantifying the gap between the evaluation object and the ideal solution is an important step in IGR-TOPSIS. For any candidate scheme i, the difference degree between the evaluation index and the positive and negative ideal schemes can be provided with positive and negative correlation coefficients
And
and (3) quantification:
wherein the content of the first and second substances,
and
representing all indexes of all solutions respectively
And
i.e., the worst indicator representing the worst scenario;
and
representing all indexes of all solutions respectively
And
i.e. the best index representing the best solution.
It can be seen that the correlation coefficient is the resolved coefficient ρ
jThe size of the direct influence of (1) determines the correlation distribution interval corresponding to each solution and the sensitivity of the model to the difference in the evaluation target. If the value of rho is set manually, the result is influenced by the judgment of a decision maker, and the randomness of the value is strong, so that the persuasion of the final evaluation result is reduced; additionally a constant resolution factor may reduce the effectiveness of the correlation factor. Therefore, the invention provides a dynamic adjustment strategy aiming at the resolution coefficient, so that the GRA result is more objective. The basic principle for ρ adjustment is: 1) rho is dynamically valued according to the numerical distribution condition of specific evaluation data of a target object; 2) when a singular value appears in the index calculation result, rho should take a smaller value to overcome the dominance of the singular value on the comprehensive evaluation result; 3) when the calculated data is stable, rho should be a large value, so that the difference of the integral data information contained in the correlation degree is fully highlighted.
For this reason, the resolution coefficient ρ corresponding to each index can be determined by the following method
j: firstly, the average differentiation distance of each index in each scheme is calculated
Then, calculating a boundary judgment factor psi for measuring the integral numerical difference of the normalized matrix R
jAnd determining a resolution coefficient rho according to the numerical range of the respective judgment factors
jThe numerical value of (c). Wherein the content of the first and second substances,
when t is
jWhen equal to 0, ρ
jIn (0, 1)]Any value is taken in between; when 0 is present<Ψ
jWhen the concentration is less than or equal to 0.5, taking
When t is
j>At 0.5, ρ
jIn [0.8,1 ]]Any value of (1).
Therefore, different evaluation indexes correspond to different resolution coefficients, the resolution capability of the GRA to similar attributes of each scheme is effectively enhanced, the influence of subjective factors is avoided to the maximum extent, and the analysis result has better credibility.
Step S352 calculates positive and negative correlation coefficients, and then in step S353, the interval gray correlation degree between each scheme and the positive and negative ideal schemes can be calculated respectively
And
and the distance between each solution and the positive and negative ideal solutions
And
thereafter, in step S354, the following formula may be applied
And
performing dimensionless transformation, and marking the obtained results as
Wherein v is
iRespectively represent
And
are the results obtained from TOPSIS and GRA;
respectively represent
And
obtained after dimensionless conversion
Finally, in step S355, the comprehensive closeness U between the scheme i to be selected and the ideal solution is determined according to the result of the dimensionless transformation
i。
According to one embodiment, U
i＝U
i +/(U
i ++U
i -). Wherein, U
i +And U
i -Respectively, the deviation distance between the solution i and the positive and negative ideal solutions. The combination of TOPSIS and GRA is due to
And
are all the larger the better, and
and
the smaller the better, and thus
e
1And e
2Is a preference coefficient and satisfies e
1+e
21, preferably e can be taken
1＝e
2＝0.5。U
iThe smaller the solution, the farther the solution i is different from the positive ideal solution, namely the lower the comprehensive benefit; u shape
iThe larger the size, the closer the solution is to the ideal solution, i.e., the better the expected benefit of implementing the demand response. Here, U can be replaced
iAs an indication of the expected benefit of the demand response. According to U
iThe schemes are sorted, and the scheme corresponding to the maximum value is the best scheme determined by IGR-TOPSIS.
In conclusion, the invention provides a novel comprehensive evaluation index system and a calculation model capable of effectively being compatible with uncertainty aiming at the problem of cost-benefit measurement and calculation of demand response introduced in the current microgrid development. The index system fully considers the diversification of the demand response participating main bodies and the difference of benefit demands in the power market environment. By adopting a combined weighting method based on ESICD and a dynamic resolution coefficient adjustment strategy, the proposed interval gray correlation TOPSIS combined evaluation method can realize scientific evaluation of comprehensive benefits of demand response under the influence of uncertain factors on the premise of reasonably considering both the subjective experience of a decision maker and the information value of indexes. The method has the advantages of simple and convenient calculation, strong distinguishing capability and the like, and can effectively reflect weak links of relevant demand response strategies and the difference between the weak links and an ideal scheme.
FIG. 4 illustrates a block diagram of an apparatus 400 for determining a demand-side resource demand response benefit value, according to one embodiment of the invention, where the apparatus 400 may reside in a computing device, such as computing device 100. The computing device stores a candidate scheme set P ═ { P ] of demand response in the microgrid
1,p
2,···,p
i,···,p
mAnd the set of evaluation indices I ═ I
1,i
2,···,i
j,···,i
nIn which s is
iAnd i
jRepresent schemes i and i, respectivelyAn index j, and each index is marked with a first weight. As shown in fig. 4, the apparatus 400 includes: a first calculation module 410, a second calculation module 420, a third calculation module 430, a fourth calculation module 440, and a fifth calculation module 450.
The first calculating module 410 may obtain raw data of the microgrid, and calculate a result value of each index of the demand response under each scheme according to the raw data. The first calculation module 410 may perform processing corresponding to the processing described above in step S310, and the detailed description thereof will not be repeated.
The second calculation module 420 converts each of the calculated result values into a dimensionless index value after normalization, wherein the index value includes an index upper limit value
And lower limit of index
And is
The second calculation module 420 may perform processing corresponding to the processing described above in step S320, and a detailed description thereof will not be repeated.
The third computing module 430 builds a normalization matrix
And calculating the central point value of each index in all schemes and the variation degree w from the index value of each index to the central point value based on the matrix
jThe center point value includes an index upper limit center value
And lower center value of index
The third calculation module 430 may perform processing corresponding to the processing described above in step S330, and a detailed description thereof will not be repeated.
The fourth calculating module 440 calculates the variation degree w of each index
jNormalization processing is carried out to obtain a second weight value of each index
And combining the first weight of each index to obtain the combined weight of each index
The fourth calculation module 440 may perform processing corresponding to the processing described above in step S340, and detailed description thereof is omitted.
The fifth calculation module 450 calculates an index vector representation S of each solution respectively
iAnd the index vector representation S of the positive ideal solution and the negative ideal solution of the demand response
+And S
-And combining the combined weight
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iTo obtain the comprehensive benefit value of each scheme. The fifth calculation module 450 may perform processing corresponding to the processing described above in step S350, and a detailed description thereof will not be repeated.
In order to verify the rationality and scientificity of the comprehensive benefit of demand response in the provided microgrid, the method selects an actual microgrid in a certain area in North China as a research object, and evaluates and analyzes the expected comprehensive benefit of the introduced demand response. The voltage class of the system is 10 kilovolts, 3 110/10kV transformer substations are built, and 22MW distributed wind power is contained. The total number of regional users is 10000, the maximum electricity load of the initial detection year is 20MW, and the annual electricity consumption is 0.91 multiplied by 10
5MWh. The statistical period is 1 year, the statistical total duration is 20 years, and the annual growth rate of the load and the electric quantity is assumed to be 3.1% and 4.6%, respectively. The volume of the interruptible load, the transferable load and the bidirectional interactive load contained in the system is 4 MW. The evaluation index values corresponding to the demand response schemes are determined by the aid of simulation experiments and expert opinions through relevant basic data, and the obtained results are shown in table 2.
TABLE 2 result values of different indexes of different schemes
And calculating the weight of the demand response comprehensive benefit evaluation index in the microgrid by using an ESICD combined weighting method, wherein the obtained result is shown in Table 3.
TABLE 3 weight calculation of evaluation index
According to the calculation results of the index values and the weights, the comprehensive benefit of the demand response under different load response characteristics is evaluated by adopting the improved comprehensive evaluation method, the obtained comprehensive evaluation result is shown in table 4, and the evaluation results of indexes at all levels are shown in fig. 5 to 7.
Table 4 comprehensive evaluation results
As can be seen from Table 4, the demand response scenarios from good to bad are the demand response scenario for bidirectional loads, the demand response scenario for interruptible loads, and the demand response scenario for transferable loads, respectively. This shows that, under the condition of a certain total capacity, the realization of the demand response based on the bidirectional flexible load can bring greater comprehensive benefits to each participant in the microgrid.
As can be seen from the radar maps of the evaluation results of the indexes at different levels in fig. 5 to 7, the specific contributions of the demand response resources at different types to the system benefit improvement are different. The demand response scheme aiming at interruptible load has the strongest promotion effect on the aspects of improving the economic benefit and the service benefit of the user, and is excellent in the aspect of improving the reliability benefit of the microgrid operator. The transferable load has better performance in promoting the economic benefit of a microgrid operator and promoting the economic benefit and the environmental benefit of a distributed power generator. The comprehensive benefit of the bidirectional flexible load is optimal. The scheme has better performance in the aspects of economy and reliability, and can effectively improve the power supply quality of the system; in addition, the system can also bring greater contribution to the system in the aspects of renewable energy utilization and power generation pollution emission reduction.
According to the technical scheme of the invention, through deep analysis of the coupling relation between the uncertain factors and each input and output element and comprehensive consideration of the qualitative factors and the quantitative factors, a comprehensive evaluation index system for the demand response benefits in the microgrid covering all sections of electric energy production, transmission, consumption and the like is constructed from three angles of a microgrid operator, a distributed power generator and a power user, and the influence of various uncertain factors on the demand response benefits can be effectively calculated by introducing the interval type data indexes. And further, the application implementation benefits of different types of demand response resources in the microgrid are calculated through a corresponding comprehensive evaluation method, and the precision of the calculation result is improved.
On the basis, the invention provides an interval gray correlation ideal point analysis method which can adapt to decision under the uncertainty condition. The method adopts a mixed weighting strategy combining expert scoring and an interval central point distance method to realize reasonable consideration of the subjective intention of a decision maker and the information value of an evaluation index. In addition, in order to avoid adverse effects on an evaluation result caused by improper selection of the resolution coefficient in the analysis process, the invention also provides a dynamic adjustment strategy aiming at the resolution coefficient, and the robustness and the discrimination capability of the model in practical application are improved.
A8 the method of A7, wherein,
wherein the content of the first and second substances,
and
representing all indexes of all solutions respectively
And
minimum value of (d);
and
representing all indexes of all solutions respectively
And
is measured.
A9, the method as described in A7, wherein U
i＝U
i +/(U
i ++U
i -)，
Wherein the content of the first and second substances,
and
respectively the deviation distance between the scheme i and the positive and negative ideal schemes; e.g. of the type
1And e
2Is a preference coefficient and satisfies e
1+e
2＝1；v
iRespectively represent
And
respectively represent
And
obtained after dimensionless conversion
A10, the method as in A7, wherein the determining the resolution coefficient rho corresponding to each index
jComprises the following steps: calculating the average dissimilarity distance of each index in each scheme
Calculating a boundary judgment factor psi for measuring the integral numerical difference of the normalized matrix R
j(ii) a And determining the numerical value of the resolution coefficient according to the numerical value range of the respective judgment factor.
A11 the method of A10, wherein,
when t is
jWhen equal to 0, ρ
jIn (0, 1)]Any value is taken in between; when 0 is present<Ψ
jWhen the concentration is less than or equal to 0.5, taking
When t is
j>At 0.5, ρ
jIn [0.8,1 ]]Any value of (1). A12, the method as in any one of a1-a11, wherein the microgrid comprises a microgrid operator, the set of evaluation indicators comprising at least one of power quality benefits, reliability benefits, and economic benefits of the microgrid operator; wherein the power quality benefits include a voltage yield improvement O
11And/or frequency yieldDegree of lift O
12(ii) a The reliability benefits include a system average outage time reduction rate O
21And system power failure frequency reduction rate O
22And rate of decrease in power supply shortage of system O
23At least one of; the economic benefits include at least one of a payable investment cost, a payable grid speed cost, a loss of electricity sales revenue, and a demand response incentive cost.
A13 the method of A12, wherein,
in the formula, T is a statistical period;
when the demand response is not implemented, counting the time of the voltage of the system monitoring point in a qualified range in a period;
counting the time of the voltage of the system monitoring point in a qualified range in a period when a demand response is implemented;
when the demand response is not implemented, counting the time of the system monitoring point frequency in a qualified range in a period;
counting the time of the system monitoring point frequency in a qualified range in a period when implementing a demand response;
when the demand response is not implemented, counting the total power failure times of the system in the period;
when the demand response is not implemented, counting the power failure time of the ith power failure of the system in the period;
counting the total power failure times of the system in a period when a demand response is implemented;
counting the power failure time of the ith power failure of the system in a period when a demand response is implemented;
when the demand response is not implemented, counting the total electric quantity purchased from the large power grid by the system in the period;
and counting the total electric quantity purchased from the large power grid by the system in the period in order to implement the demand response.
A14, the method as claimed in any one of a1-a11, wherein the microgrid comprises a distributed generator, the set of evaluation metrics comprises economic and/or environmental benefits of the distributed generator; wherein the economic benefit comprises the cost of power generation and/or the loss of power selling income, and the environmental benefit comprises the renewable energy utilization increase rate and/or the pollution gas emission reduction rate. A15, the method as in any one of a1-a11, wherein the microgrid comprises power users, the set of evaluation metrics comprises goodness and/or economics of service for the power users; the service goodness benefits comprise at least one of a user power failure frequency reduction rate, a user average power failure time reduction rate and a user power quality satisfaction degree; the economic benefit includes at least one of an electricity charge saving rate, a compensation profitability, and an equipment investment cost. A16, the method as in any one of A1-A11, wherein the demand-side resources include at least one of interruptible loads, transferable loads and bi-directional flexible interactive loads; the candidate scheme set comprises at least one of a demand response scheme of an interruptible load, a demand response scheme of a transferable load and a demand response scheme of a bidirectional flexible interactive load.
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 removable hard drives, U.S. disks, floppy disks, CD-ROMs, 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 determining a demand-side resource demand response benefit value of the present invention according to instructions in the program code stored in the memory.
By way of example, and not limitation, readable media may comprise readable storage media and communication media. Readable 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 readable media. In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with examples of this invention. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention. 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 components of the 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. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. 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 skilled 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. 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 with respect to the scope of the invention, as defined in the appended claims.
Claims (10)
1. A method for determining demand-side resource demand response benefit values is suitable for being executed in computing equipment, and a candidate scheme set P ═ { P } of demand responses in a microgrid is stored in the computing equipment
1,p
2,…,p
i,…,p
mAnd the set of evaluation indices I ═ I
1,i
2,…,i
j,…,i
nIn which s is
iAnd i
jRepresenting a scheme i and an index j, respectively, and each index is marked with a first weight, the method comprising the steps of:
acquiring original data of the microgrid, and respectively calculating a result value of each index of the demand response under each scheme according to the original data;
converting each calculated result value into a dimensionless index value after standardization processing, wherein the index value comprises an index upper limit value
And lower limit of index
And is
Establishing a normalized matrix
And calculating the central point value of each index in all schemes and the variation degree w from the index value of each index to the central point value based on the matrix
jThe center point value includes an index upper limit center value
And lower center value of index
Degree of variation w for each index
jNormalization processing is carried out to obtain a second weight value of each index
And combining the first weight to obtain a combined weight of each index
And
separately calculating an index vector representation S for each solution
iAnd the index vector representation S of the positive ideal solution and the negative ideal solution of the demand response
+And S
-And combining the combined weight
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iTo obtain the comprehensive benefit value of each scheme.
2. The method of claim 1, further comprising the steps of:
establishing an interval decision matrix based on the calculated result value of each index under each scheme
Wherein
And
respectively, the results of the index j of the scheme iUpper and lower values of the value, if the resulting value is the unique value A
3. The method of claim 2, wherein if the result value is a ratiometric value, performing a dimensionless transformation according to the following normalized equation:
4. the method of claim 2, wherein if the result value is a cost-type value, performing a dimensionless transformation according to the following standardized formula:
5. the method of claim 1, wherein the index j is at the center point value of all schemes
The calculation formula of (2) is as follows:
the degree of variation w from the index value of the index j to the center point value
jThe calculation formula of (2) is as follows:
first weight ω 'of index j'
jA pre-recorded subjective weighting value is given, and the combined weighting value of the index j
α is a proportional adjustment coefficient, and 0 is more than or equal to α is more than or equal to 1.
6. The method of any one of claims 1-5,
the indicator vector of scheme i is represented as
The indicator vector of the positive ideal scheme is expressed as
The indicator vector of the negative ideal scheme is expressed as
Wherein the content of the first and second substances,
is the mean value of the upper and lower index limits of the index j of the scheme i, i.e.
7. The method of any of claims 1-6, wherein the combining weights are combined
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iComprises the following steps:
respectively calculating positive and negative differentiation distances of evaluation indexes corresponding to each scheme
And
determining based on the positive and negative differencing distancesResolution factor ρ corresponding to each index
jAnd calculating the positive and negative correlation coefficients corresponding to each scheme
And ξ
ij；
Respectively calculating the interval gray correlation degree of each scheme and the positive and negative ideal schemes
And
and the distance between each solution and the positive and negative ideal solutions
And
to pair
And
performing dimensionless transformation, and marking the obtained results as
And
determining the comprehensive closeness U between the scheme i to be selected and the ideal solution according to the result of the dimensionless conversion
i。
8. A device for determining demand side resource demand response benefit value is suitable for residing in computing equipment, and candidate schemes of demand response in a microgrid are stored in the computing equipmentSet P ═ P
1,p
2,…,p
i,…,p
mAnd the set of evaluation indices I ═ I
1,i
2,…,i
j,…,i
nIn which s is
iAnd i
jRepresenting a scheme i and an index j respectively, and each index is marked with a first weight value, the device comprises:
the first calculation module is suitable for acquiring original data of the microgrid and calculating a result value of each index of the demand response under each scheme according to the original data;
a second calculation module, adapted to convert each of the calculated result values into a dimensionless index value after normalization, wherein the index value includes an index upper limit value
And lower limit of index
And is
A third calculation module adapted to establish a normalized matrix
And calculating the central point value of each index in all schemes and the variation degree w from the index value of each index to the central point value based on the matrix
jThe center point value includes an index upper limit center value
And lower center value of index
A fourth calculation module adapted to calculate the degree of variation w of each index
jNormalization processing is carried out to obtain a second weight value of each index
And combining the first weight to obtain a combined weight of each index
And
a fifth calculation module adapted to calculate an index vector representation S for each solution separately
iAnd the index vector representation S of the positive ideal solution and the negative ideal solution of the demand response
+And S
-And combining the combined weight
Calculating the comprehensive closeness U from each scheme to the ideal scheme
iTo obtain the comprehensive benefit value of each scheme.
9. A computing device, comprising:
at least one processor; and
a memory storing program instructions, wherein the program instructions are configured to be adapted to be executed by the at least one processor, the program instructions comprising instructions for performing the method of any of claims 1-7.
10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method of any of claims 1-7.
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