CN111585615B - Direct current energy supply method - Google Patents

Direct current energy supply method Download PDF

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CN111585615B
CN111585615B CN202010304130.XA CN202010304130A CN111585615B CN 111585615 B CN111585615 B CN 111585615B CN 202010304130 A CN202010304130 A CN 202010304130A CN 111585615 B CN111585615 B CN 111585615B
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mec server
energy
mec
power
router
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CN111585615A (en
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李保罡
王梦媛
赵伟
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North China Electric Power University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines
    • H04B3/548—Systems for transmission via power distribution lines the power on the line being DC
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/10—Current supply arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B2203/00—Indexing scheme relating to line transmission systems
    • H04B2203/54—Aspects of powerline communications not already covered by H04B3/54 and its subgroups
    • H04B2203/5462—Systems for power line communications
    • H04B2203/547—Systems for power line communications via DC power distribution

Abstract

The embodiment of the invention discloses a direct-current energy supply method, and in order to promote the development of a ubiquitous power Internet of things, a renewable energy-oriented power pack scheduling system is used for supplying power to an MEC server, so that on one hand, green and environment-friendly effects can be realized, and on the other hand, quantitative, real-time and accurate energy scheduling can be realized in an emerging mode of power pack electric energy transmission.

Description

Direct current energy supply method
Technical Field
The invention relates to the field of power communication, in particular to a direct-current energy supply method.
Background
The Mobile Edge Computing (MEC) technology is one of the key technologies of 5G communication and ubiquitous power internet of things, and can greatly improve the experience rate of users. When the user request amount or the unloading amount is low, the MEC server powered by the power grid may cause electric energy waste; in addition, some areas with weak electric power bases are difficult to be directly connected with a power grid, and the MEC server is powered by a diesel generator, so that the energy efficiency is low, the operation cost is high, and the greenhouse gas emission is large.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the invention provides a direct-current energy supply method, and in order to promote the development of the ubiquitous power internet of things, the renewable energy-oriented power pack scheduling system is used for supplying power to the MEC server, so that on one hand, green and environment-friendly effects can be realized, and on the other hand, quantitative, real-time and accurate energy scheduling can be realized in an emerging mode of power pack transmission of electric energy.
The embodiment of the invention provides the following technical scheme:
a direct current energy supply method is applied to an MEC environment, a renewable energy source-oriented power pack scheduling system is used for supplying power to an MEC server, and the method comprises the following steps:
loss coefficient alpha based on power pack routerr,mAnd a forward energy cost urGenerating a preference list about the power packet router and the MEC server;
obtaining an MEC server-electric power packet router matching pair by utilizing a one-to-many matching game according to the preference list;
in each MEC server-electric power packet router matching pair, obtaining the optimal energy supplied to each MEC server by adopting an improved second-generation non-dominated sorting genetic algorithm;
wherein the power packet router-based loss coefficient αr,mAnd a forward energy cost urGenerating a packet router for powerAnd the preference list of the MEC server specifically comprises:
loss factor α for a packet router for an MEC serverr,mThe smaller the more energy is received. Thus, for MEC server m, a preference relationship may be established through the set of power packet routers R
Figure BDA0002455110850000021
Figure BDA0002455110850000022
The above relationship indicates that MEC server m prefers i among the power packet routers i and j.
For the electric power pack router, the larger the loss coefficient of the corresponding MEC server is, the more energy is supplied by renewable energy sources, and the higher the cost of the obtained forwarding energy is. Thus, for the power packet router r, the preference relationship established by the MEC server set M
Figure BDA0002455110850000023
Figure BDA0002455110850000024
The above relationship indicates that the power packet router r prefers a over the MEC servers a and b.
Wherein, according to the preference list, obtaining an MEC server-electric power packet router matching pair by utilizing a one-to-many matching game specifically comprises the following steps:
each of the power packet routers may transmit power for a plurality of MEC servers, and the number of MEC servers is limited by the maximum transmission capacity of the power packet router, while each MEC server may only receive power transmitted by one power packet router. Thus, a one-to-many matching model can be employed
Figure BDA0002455110850000025
Is shown in which
Figure BDA0002455110850000026
And
Figure BDA0002455110850000027
respectively representing the preference relationship of the MEC server m and the power packet router r.
The specific matching process is as follows:
defining Q as a connection matrix, wherein the elements Q in the matrixm,rWhether each power packet router and each MEC server are connected or not is indicated, and if the connection is '1', the connection is indicated; if the value is "0", it indicates that no connection is established, and the specific process is as follows:
1) when there is an unmatched MEC server, an MEC server is optionally selected, and the following operations are performed.
2) MEC server request match: the selected MEC server m sends a request to the most preferred power packet router in the list according to the acceptable power packet router preference list which does not reject the MEC server m, wherein the request comprises the energy information required by the MEC server m.
The power packet router responds with: the power packet router r calculates the remaining transmissible capacity thereof
Figure BDA0002455110850000031
Minimum supply energy required by MEC server m if requested
Figure BDA0002455110850000032
Satisfies the conditions
Figure BDA0002455110850000033
Comparing the MEC server with the last MEC server currently accepted by the power packet router r, selecting an MEC server with higher order in the preference list, and adding the rejected MEC server to the unmatched MEC server set; minimum supply energy required by MEC server m if requested
Figure BDA0002455110850000034
Satisfies the conditions
Figure BDA0002455110850000035
The power packet router r directly accepts the request of the MEC server.
As described above
Figure BDA0002455110850000036
Represents the minimum energy requirement, α, of the mth MEC serverr,mRepresenting the loss factor of the power packet router,
Figure BDA0002455110850000037
represents the maximum transmittable capacity of the power packet router r, and g represents the number of iterations.
3) Stop until the set of unmatched MEC servers is empty, otherwise return to 1).
4) And after matching is finished, returning to the connection matrix.
Wherein, in each MEC server-electrical packet router matching pair, an improved second-generation non-dominated sorting genetic algorithm is adopted to obtain the optimal energy supplied to each MEC server, and the method specifically comprises the following steps:
in each MEC server-power packet router matching pair, the traditional multi-objective optimization method is easy to fall into a local optimal solution, and the evolutionary algorithm has global search capability. Therefore, an improved second-generation non-dominated sorting genetic algorithm is adopted, which is to add an optimal front-end individual coefficient on the basis of the second-generation non-dominated sorting genetic algorithm, calculate the number of the individuals allowed to be reserved by the front end, further find an optimal solution set enabling each objective function value to be as large as possible, and obtain the optimal supply energy V supplied to each MEC serverr,m；
The improved second generation non-dominated sorting genetic algorithm is as follows:
based on the process, the integer variable q of 0-1 can be solvedm,rThe optimization problem K1 can be converted into:
Figure BDA0002455110850000041
Figure BDA0002455110850000042
Figure BDA0002455110850000043
wherein the above formula is to maximize the satisfaction of each MEC server matched with the r-th power packet router for the received energy,
Figure BDA0002455110850000044
representing the maximum energy demand of the mth MEC server.
The problem K2 is a multi-objective programming problem, the traditional multi-objective optimization method is easy to fall into a local optimal solution, and the genetic algorithm has global search capability, so that the evolutionary algorithm can be adopted to solve the problem. The invention utilizes an improved second-generation non-dominated sorting genetic algorithm to solve the problem, namely, an optimal front-end individual coefficient is added on the basis of the second-generation non-dominated sorting genetic algorithm, the allowable reserved individual number of the front end is calculated, an optimal solution set enabling each objective function value to be as large as possible is further found, and the optimal supply energy V supplied to each MEC server is obtainedr,m。
Because the supply energy of the invention is transmitted in the form of electric power packets, and the number of time slots occupied by one electric power packet must be an integer, the V obtained by the algorithm is solvedr,mAccording to Vr,m＝Pmhnm＝dmnmTo obtain nmAfter that, it is necessary to round off to get the whole to n'm. Thereby obtaining the optimal energy V 'actually supplied for each MEC server'r,m＝dmn'm。
Compared with the prior art, the technical scheme has the following advantages:
the embodiment of the invention provides a direct-current energy supply method, and in order to promote the development of the ubiquitous power Internet of things, a renewable energy-oriented power pack scheduling system is used for supplying power to an MEC server, so that on one hand, green and environment-friendly effects can be realized, and on the other hand, quantitative, real-time and accurate energy scheduling can be realized in an emerging mode of power pack electric energy transmission.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic diagram of a dc power supply method according to an embodiment of the present invention.
Detailed Description
As described in the background section, some areas with weak power bases are difficult to directly connect with a power grid, and an MEC server serving as a key technology of a 5G communication and ubiquitous power internet of things is powered by a diesel generator, so that the energy efficiency is low, the operation cost is high, and the greenhouse gas emission is large.
Aiming at the energy consumption of a plurality of MEC servers, in order to promote the development of the ubiquitous power internet of things, the invention provides a direct-current energy supply method based on power packets, wherein the direct-current energy supply method mainly relates to a plurality of power packet routers and a plurality of MEC servers, the method utilizes the power packets generated facing renewable energy sources to be forwarded to the corresponding MEC servers through the power packet routers, due to the fact that the passing power packet routers are different, a plurality of paths are formed for reaching a target MEC server, different paths have different losses, and the losses are increased along with the increase of the distance between the renewable energy sources and the MEC servers. In order to maximize the satisfaction of each MEC server with the received energy, it is necessary to match the MEC server with the optimal path of the power packet router and allocate the optimal energy to each MEC server. On one hand, the method utilizes the electric energy generated by facing renewable energy sources to supply power for the MEC server, so that green and environment-friendly effects can be realized, and on the other hand, quantitative, real-time and accurate energy scheduling can be realized in a new mode of transmitting electric energy by using an electric power pack.
Firstly according to the loss coefficient alpha of the power pack routerr,mAnd a forward energy cost urGenerating a preference list about the power packet router and the MEC server; then obtaining an MEC server-electric power packet router matching pair by utilizing a one-to-many matching game according to the preference list; and finally, in each MEC server-power packet router matching pair, obtaining the optimal energy supplied to each MEC server by adopting an improved NSGA-II algorithm. The above process realizes the MEC server-electric power packet router matching pair and realizes the optimal energy distribution in the matching pair. However, since the service requested by the user is different, the energy consumption range of each MEC server can be predicted only in advance, and the specific energy consumption of each MEC server in the one-to-many matching game is not clear. Therefore, we consider the lower limit of the energy consumption range of each MEC server as a reference in the matching process to meet the minimum requirement of each MEC server. In addition, since the number of matching packets is directly defined in the matching process, which is measured by the duration length, and the power packet router may not be fully utilized, we indirectly limit the number of matched MEC servers through the maximum transmission capacity of the power packet router.
Further, although the current research mostly focuses on a single power packet router, due to the limited capacity of the single power packet router, some transmission tasks may wait, resulting in the reduction of transmission efficiency. Therefore, the present invention can alleviate this phenomenon by using multiple power packet routers.
Referring to fig. 1, an embodiment of the present invention provides a direct current energy supply method, which is applied in an MEC environment, and supplies power to an MEC server by using a power pack scheduling system facing renewable energy, where the method includes:
step 101: loss coefficient alpha based on power pack routerr,mAnd a forward energy cost urA preference list is generated for the power packet router and the MEC server.
Loss factor α for a packet router for an MEC serverr,mThe smaller the more energy is received. Thus, for MEC server m, it canEstablishing preference relationships through a power pack router set R
Figure BDA0002455110850000061
Figure BDA0002455110850000062
The above relationship indicates that MEC server m prefers i among the power packet routers i and j.
For the electric power pack router, the larger the loss coefficient of the corresponding MEC server is, the more energy is supplied by renewable energy sources, and the higher the cost of the obtained forwarding energy is. Thus, for the power packet router r, the preference relationship established by the MEC server set M
Figure BDA0002455110850000063
Figure BDA0002455110850000071
The above relationship indicates that the power packet router r prefers a over the MEC servers a and b.
Preference-based MEC server and power pack router
Figure BDA0002455110850000072
And
Figure BDA0002455110850000073
and respectively sorting to obtain a preference list, wherein the higher the sorting is, the higher the satisfaction degree of the preference list is.
Step 102: and obtaining the MEC server-power packet router matching pair by utilizing a one-to-many matching game according to the preference list.
Each of the power packet routers may transmit power for a plurality of MEC servers, and the number of MEC servers is limited by the maximum transmission capacity of the power packet router, while each MEC server may only receive power transmitted by one power packet router. Therefore, the temperature of the molten metal is controlled,one-to-many matching models can be employed
Figure BDA0002455110850000074
Is shown in which
Figure BDA0002455110850000075
And
Figure BDA0002455110850000076
respectively representing the preference relationship of the MEC server m and the power packet router r.
The specific matching process is as follows:
defining Q as a connection matrix, wherein the elements Q in the matrixm,rWhether each power packet router and each MEC server are connected or not is indicated, and if the connection is '1', the connection is indicated; if "0", it means that no connection is established. The specific process is as follows:
1) when there is an unmatched MEC server, an MEC server is optionally selected, and the following operations are performed.
2) MEC server request match: the selected MEC server m sends a request to the most preferred power packet router in the list according to the acceptable power packet router preference list which does not reject the MEC server m, wherein the request comprises the energy information required by the MEC server m.
The power packet router responds with: the power packet router r calculates the remaining transmissible capacity thereof
Figure BDA0002455110850000077
Minimum supply energy required by MEC server m if requested
Figure BDA0002455110850000078
Satisfies the conditions
Figure BDA0002455110850000079
Comparing the MEC server with the last MEC server currently accepted by the power packet router r, selecting an MEC server with higher order in the preference list, and adding the rejected MEC server to the unmatched MEC server set; MEC clothes if requestedMinimum supply energy required by server m
Figure BDA0002455110850000081
Satisfies the conditions
Figure BDA0002455110850000082
The power packet router r directly accepts the request of the MEC server.
As described above
Figure BDA0002455110850000083
Represents the minimum energy requirement, α, of the mth MEC serverr,mRepresenting the loss factor of the power packet router,
Figure BDA0002455110850000084
represents the maximum transmittable capacity of the power packet router r, and g represents the number of iterations.
3) Stop until the set of unmatched MEC servers is empty, otherwise return to 1).
4) And after matching is finished, returning to the connection matrix.
Step 103: in each MEC server-power packet router matching pair, a modified second-generation Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the optimal energy supplied to each MEC server.
In each MEC server-power packet router matching pair, the traditional multi-objective optimization method is easy to fall into a local optimal solution, and the evolutionary algorithm has global search capability. Therefore, an improved NSGA-II algorithm is adopted, wherein the improved NSGA-II algorithm is to add the optimal front-end individual coefficient on the basis of the NSGA-II algorithm, calculate the number of the individuals allowed to be reserved by the front end, further find the optimal solution set enabling each objective function value to be as large as possible, and obtain the optimal supply energy V supplied to each MEC serverr,m。
The modified NSGA-II algorithm is as follows:
based on the process, the integer variable q of 0-1 can be solvedm,rThe optimization problem K1 can be converted into:
Figure BDA0002455110850000085
Figure BDA0002455110850000086
Figure BDA0002455110850000087
wherein the above formula is to maximize the satisfaction of each MEC server matched with the r-th power packet router for the received energy,
Figure BDA0002455110850000091
representing the maximum energy demand of the mth server.
The problem K2 is a multi-objective programming problem, the traditional multi-objective optimization method is easy to fall into a local optimal solution, and the genetic algorithm has global search capability, so that the evolutionary algorithm can be adopted to solve the problem. The invention is solved by using an improved NSGA-II algorithm, namely, an optimal front-end individual coefficient is added on the basis of NSGA-II, the allowable reserved individual number of the front end is calculated, an optimal solution set enabling each objective function value to be as large as possible is further found, and the optimal supply energy V supplied to each MEC server is obtainedr,m。
Step 104: because the supply energy of the invention is transmitted in the form of electric power packets, and the number of time slots occupied by one electric power packet must be an integer, the V obtained by the algorithm is solvedr,mAccording to Vr,m＝Pmhnm＝dmnmTo obtain nmAfter that, it is necessary to round off to get the whole to n'm. Thereby obtaining the optimal energy V 'actually supplied for each MEC server'r,m＝dmn'm。
P is abovemRepresents the power of the renewable energy source transmission power packet to the MEC server m, h represents the time length of each time slot,nmand the number of time slots occupied by the power packet transmitted to the MEC server m is shown.
As can be seen from the above, the present invention provides a method for matching an MEC server and an electrical packet router with each other and supplying optimal energy to the MEC server under the limitation of maximum transmittable capacity of the electrical packet router with respect to energy consumption of a plurality of MEC servers.
In summary, aiming at the timeliness problem that a plurality of MEC servers receive energy, the invention realizes the MEC server-power packet router matching pair by utilizing a one-to-many matching game, each power packet router is responsible for a plurality of MEC servers, and is similar to clustering all the MEC servers, and a plurality of power packet routers work simultaneously to reduce the waiting time of the MEC servers, so that the MEC servers can receive the energy in time.
In the invention, one electric power packet router can be connected with a plurality of MEC servers, one MEC server is connected with one electric power packet router, and from the perspective of the electric power packet router, one electric power packet router only needs to serve a part of MEC servers, thereby solving the congestion phenomenon existing in the peak period of the electric power packet router, reducing the pressure of the electric power packet router, and from the perspective of the MEC servers, each MEC server can receive the optimal energy in time through the matched electric power packet router, and realizing the satisfaction maximization of the MEC servers. From the perspective of the overall system, the connection can improve the efficiency of the transmission of electrical energy.
The invention improves the NSGA-II algorithm, namely, an optimal front-end individual coefficient is added, the allowable reserved individual number of the front end is calculated, the optimal supply energy supplied to each MEC server is obtained, and the global search capability is improved.
The invention considers that the maximum transmissible capacity of the power packet router in the system is limited, and when matching is carried out, the invention takes the minimum required energy of the MEC server as one of matching parameters so as to ensure that the MEC server can normally work and avoid interruption.
In order to maximize the satisfaction degree of each MEC server, compared with the traditional multi-objective planning method, the method can better meet the requirements of each MEC server by using an evolutionary algorithm.
The invention utilizes the power package scheduling system facing renewable energy sources to supply power for the MEC server, and can supply the electric energy to the MEC server in a timing, quantitative and accurate manner based on the unique properties of the electric power package including information and the electric energy.
In the prior art, the utility model has the advantages that the utility model provides a theoretical frame and a concrete realization method are provided to the energy consumption problem of MEC server under the condition that the power grid or diesel generator is utilized to supply power to the MEC server, which not only causes the waste of electric energy, but also is not beneficial to environmental protection.
The direct-current energy supply method based on the power pack in the MEC environment not only effectively reduces greenhouse gas emission and power supply cost, realizes green and environment-friendly performance, but also can improve the operation efficiency of the whole system.
A specific embodiment is provided below:
the energy consumption of the MEC server includes static energy consumption and dynamic energy consumption, the static energy consumption is generated as long as the MEC server runs, and the dynamic energy consumption refers to the calculation energy consumption generated by the MEC server processing the service request of the user. Suppose each MEC server caches w services daily required by the user, and the MEC server processes the user's request with the maximum CPU speed. The total energy consumption of the mth server at that time can be described as:
Figure BDA0002455110850000111
wherein:
Figure BDA0002455110850000112
which represents the static energy consumption,
Figure BDA0002455110850000113
the dynamic energy consumption is represented by the dynamic energy consumption,
Figure BDA0002455110850000114
representing the total number of CPU cycles required by the MEC server to process the traffic. Beta is amMeans m MEC server to maximumHigh speed fm(number of CPU cycles per second) energy consumption per cycle, beta, in processing trafficm＝kmfm，kmIs related to the hardware structure of the MEC server m. EtawThe number of CPU cycles required to process the w-th traffic.
Figure BDA0002455110850000115
Represents the number of the users' demands for the service w at the t moment, and the obeying rate is
Figure BDA0002455110850000116
In the process of Poisson, wherein
Figure BDA0002455110850000117
Uniform distribution is obeyed. According to the historical data of the user request, the energy consumption change rule of each MEC server can be predicted.
The MEC server set is M ═ {1,2, …, M }, and the power packet router set is R ═ {1,2, …, R }. Since renewable energy sources output energy intermittently, the invention mainly analyzes energy supply and demand balance in a specific time. The time period is denoted by T, and for the convenience of analysis, the time period is divided into a plurality of equal time slots, the time length of each time slot is h, T is nh, and n is the number of time slots. And the energy requirement provided in the period is used as the maximum transmissible capacity of the power packet router r
Figure BDA0002455110850000118
Where R ∈ R. The loss coefficient of the power pack router is alphar,mThe cost per unit energy transferred is urWhere R ∈ R. Based on the energy consumption change rule of each MEC server, the energy supplied by different renewable energy sources to the corresponding MEC server is Dm，Dm＝Vr,m(1-αr,m)，Vr,mRepresenting the energy that renewable energy supplies to the mth MEC server via the power packet router r. Vr,m＝UmImnmh，UmRepresenting the renewable energy supply voltage, ImRepresenting the transmission current, nmPresentation renderingAnd the number of time slots occupied by the power packet input to the MEC server m. The power of the renewable energy source transmission power pack to the MEC server m is Pm，Pm＝UmIm，Pm≤Pr max，Pr maxThe maximum power that the power packet router r can accept. The energy that can be transmitted in a single time slot is therefore denoted dm＝Pmh. The supplied energy is transmitted in the form of power packets, one MEC server for each power packet, and a power packet may contain a plurality of time slots.
To maximize each MEC server's satisfaction with the received energy, this optimization model is built as:
Figure BDA0002455110850000121
Figure BDA0002455110850000122
Figure BDA0002455110850000123
Figure BDA0002455110850000124
Figure BDA0002455110850000125
wherein, the formula (2a) represents that the amount of power after the transmission loss must meet the minimum demand of the user; equation (2b) ensures that the energy of the matched pair transmitted in a single power packet router does not exceed the maximum transmittable capacity of the power packet router; equation (2c) ensures that each power packet can only be transmitted in one power packet router at most at a time; formula (2d) represents qm,rIs an integer variable of 0-1, i.e. represents the r-th electric packet router and the m-th MEC serviceWhether the device establishes a connection. The optimization model is established to contain a real variable Vr,mAnd the variable q is an integer from 0 to 1m,rThis problem is therefore a non-linear mixed integer programming problem. The invention provides an effective step solving scheme, which comprises the following specific steps:
1. one-to-many matching game
Each of the power packet routers may transmit power for a plurality of MEC servers, and the number of MEC servers is limited by the maximum transmission capacity of the power packet router, while each MEC server may only receive power transmitted by one power packet router. Thus, a one-to-many matching model can be employed
Figure BDA0002455110850000126
Is shown in which
Figure BDA0002455110850000127
And
Figure BDA0002455110850000128
respectively representing the preference relationship of the MEC server m and the power packet router r.
Generation of preference list:
loss factor α for a packet router for an MEC serverr,mThe smaller the more energy is received. Thus, for MEC server m, a preference relationship may be established through the set of power packet routers R
Figure BDA0002455110850000129
Figure BDA0002455110850000131
Equation (3) indicates that MEC server m prefers i among the power packet routers i and j.
For the electric power pack router, the larger the loss coefficient of the corresponding MEC server is, the more energy is supplied by renewable energy sources, and the higher the cost of the obtained forwarding energy is. Thus, for the packet router r, the server is passed through the MECPreference relationship established by set M
Figure BDA0002455110850000132
Comprises the following steps:
Figure BDA0002455110850000133
equation (4) indicates that the power packet router r prefers a among the MEC servers a and b.
Preference-based MEC server and power pack router
Figure BDA0002455110850000134
And
Figure BDA0002455110850000135
and respectively sorting to obtain a preference list, wherein the higher the sorting is, the higher the satisfaction degree of the preference list is.
Matching:
defining Q as a connection matrix, wherein the elements Q in the matrixm,rWhether each power packet router and each MEC server are connected or not is indicated, and if the connection is '1', the connection is indicated; if "0", it means that no connection is established. The specific process is as follows:
1) when there is an unmatched MEC server, an MEC server is optionally selected, and the following operations are performed.
2) MEC server request match: the selected MEC server m sends a request to the most preferred power packet router in the list according to the acceptable power packet router preference list which does not reject the MEC server m, wherein the request comprises the energy information required by the MEC server m.
The power packet router responds with: the power packet router r calculates its capacity
Figure BDA0002455110850000136
Minimum supply energy required by MEC server m if requested
Figure BDA0002455110850000137
Satisfies the conditions
Figure BDA0002455110850000138
Comparing the MEC server with the last MEC server currently accepted by the power packet router r, selecting an MEC server with higher order in the preference list, and adding the rejected MEC server to the unmatched MEC server set; minimum supply energy required by MEC server m if requested
Figure BDA0002455110850000141
Satisfies the conditions
Figure BDA0002455110850000142
The power packet router r directly accepts the request of the MEC server.
3) Stop until the set of unmatched MEC servers is empty, otherwise return to 1).
4) And after matching is finished, returning to the connection matrix.
2. Improved NSGA-II algorithm
Based on the process, the integer variable q of 0-1 can be solvedm,rThe optimization problem K1 can be converted into:
Figure BDA0002455110850000143
Figure BDA0002455110850000144
Figure BDA0002455110850000145
wherein equation (5) is to maximize the satisfaction of each MEC server matched with the r-th power packet router with respect to the received energy,
Figure BDA0002455110850000146
representing the maximum energy demand of the mth MEC server.
The problem K2 is a multi-objective programming problem, the traditional multi-objective optimization method is easy to fall into a local optimal solution, and the genetic algorithm has global search capability, so that the evolutionary algorithm can be adopted to solve the problem. The invention is solved by using an improved NSGA-II algorithm, namely, an optimal front-end individual coefficient is added on the basis of NSGA-II, the allowable reserved individual number of the front end is calculated, an optimal solution set enabling each objective function value to be as large as possible is further found, and the optimal supply energy V supplied to each MEC server is obtainedr,m。
Because the supply energy of the invention is transmitted in the form of electric power packets, and the number of time slots occupied by one electric power packet must be an integer, the V obtained by the algorithm is solvedr,mAccording to Vr,m＝Pmhnm＝dmnmTo obtain nmAfter that, it is necessary to round off to get the whole to n'm. Thereby obtaining the optimal energy V 'actually supplied for each MEC server'r,m＝dmn'm。
Therefore, in the MEC server set and the electric power packet router set, the required energy of the MEC server, the loss of the electric power packet router and the cost information of the energy of a forwarding unit are obtained, the lowest required energy of the MEC server is taken as a standard, a matching stage is started, and the electric power packet router determines whether to accept the request of the MEC server or not according to the residual transmissible capacity of the electric power packet router based on the related request content sent by the MEC server. And then, continuously executing a matching process until all MEC servers in the MEC server set are successfully matched, and obtaining an MEC server-electric power packet router matching pair.
From the above, the direct current energy supply method provided by the invention can be applied to the MEC environment, and the one-to-many matching game is firstly utilized to realize the mutual matching between the MEC server and the power packet router. The optimal path of the renewable energy for transmitting the electric energy to the MEC server is determined by utilizing a one-to-many matching game, and then the optimal energy supplied to each power packet router is obtained by adopting an improved NSGA-II algorithm.
Moreover, the direct-current energy supply method provided by the embodiment of the invention utilizes the renewable energy-oriented power pack scheduling system to supply energy for the energy consumption of a plurality of MEC servers, thereby not only effectively reducing greenhouse gas emission and power supply cost, but also realizing quantitative, real-time and accurate energy scheduling. Firstly, a one-to-many matching game is utilized to realize mutual matching between the power packet router and the MEC server. In the one-to-many matching game, the minimum required energy of the MEC server is taken as a standard, the number of matched MEC servers is indirectly limited through the maximum transmission capacity of the power packet router, so that the condition of directly limiting the number of matched MEC servers in the traditional method is avoided, and the system transmission efficiency is improved on the premise of ensuring the optimum. In the energy distribution stage, for each MEC server-electric power packet router matching pair, compared with the traditional multi-objective optimization algorithm, the improved NSGA-II algorithm has stronger global search capability, avoids falling into a local optimal solution, and realizes the satisfaction maximization of each MEC server.
In conclusion, the invention obtains the following technical effects:
(1) in consideration of green energy conservation and accurate scheduling, the invention provides a direct-current energy supply method, which is characterized in that an electric power packet generated by renewable energy is used for supplying power to an MEC server, the MEC server is matched with an electric power packet router with an optimal path, and optimal energy is distributed and transmitted to each MEC server in the form of an electric power packet.
(2) Considering that the maximum transmissible capacity of the power packet router in the system is limited, when matching is carried out, the minimum required energy of the MEC server is taken as one of matching parameters, so that the MEC server can work normally, and interruption is avoided.
(3) Since the power packet is measured by the duration length, and the direct definition of the matching number in the matching process may not fully utilize the power packet router, the present invention proposes to indirectly limit the number of matched MEC servers through the maximum transmittable capacity of the power packet router.
(4) According to the invention, the MEC server is powered by the renewable energy-oriented power pack scheduling system, so that the greenhouse gas emission and the power supply cost are effectively reduced, and the electric energy can be scheduled regularly, quantitatively and accurately. Therefore, the direct-current energy supply method based on the power pack is more suitable for intelligent management of future electric energy.
(5) The invention realizes the optimal matching between the power pack router and the MEC server by utilizing a one-to-many matching game, and improves the operation efficiency of the whole system.
(6) The invention obtains the optimal supply energy supplied to each MEC server by using the evolutionary algorithm, and improves the global search capability compared with the traditional multi-objective optimization method.
In the description, each part is described in a progressive manner, each part is emphasized to be different from other parts, and the same and similar parts among the parts are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (1)

1. A direct current energy supply method is applied to an MEC environment, and is characterized in that a renewable energy source-oriented power pack scheduling system is used for supplying power to an MEC server, and the method comprises the following steps:
loss coefficient alpha based on power pack routerr,mAnd a forward energy cost urGenerating a preference list about the power packet router and the MEC server;
obtaining an MEC server-electric power packet router matching pair by utilizing a one-to-many matching game according to the preference list;
in each MEC server-electric power packet router matching pair, obtaining the optimal energy supplied to each MEC server by adopting an improved second-generation non-dominated sorting genetic algorithm;
wherein the power packet router-based loss coefficient αr,mAnd a forward energy cost urGenerating a preference list related to the power pack router and the MEC server, specifically comprising:
loss factor α for a packet router for an MEC serverr,mThe smaller the received energy is, the more energy is received, and thus, for the MEC server m, a preference relationship may be established through the set of power packet routers R
Figure FDA0003027129850000013
Figure FDA0003027129850000011
The above relation indicates that the MEC server m prefers i among the power packet routers i and j;
for the electric power pack router, the larger the loss coefficient of the corresponding MEC server is, the more the energy supplied by the renewable energy source is, and the higher the cost of the obtained forwarding energy is, therefore, for the electric power pack router r, the preference relationship established by the MEC server set M
Figure FDA0003027129850000014
Figure FDA0003027129850000012
The above relation indicates that the power packet router r prefers a among the MEC servers a and b;
wherein, according to the preference list, obtaining an MEC server-electric power packet router matching pair by utilizing a one-to-many matching game specifically comprises the following steps:
each MEC server may transmit power for multiple MEC servers, and the number of MEC servers is limited by the maximum transmission capacity of the MEC server, while each MEC server can only receive one MEC routerTransmitted energy, and therefore, a one-to-many matching model can be employed
Figure FDA0003027129850000028
Is shown in which
Figure FDA0003027129850000029
And
Figure FDA00030271298500000210
respectively representing preference relations of the MEC server m and the power pack router r;
the specific matching process is as follows:
1) when an unmatched MEC server exists, selecting an MEC server optionally, and executing the following operations;
2) MEC server request match: the selected MEC server m sends a request to the most preferred power pack router in the list according to the acceptable power pack router preference list which does not reject the MEC server m, wherein the request comprises energy information required by the MEC server m;
the power packet router responds with: the power packet router r calculates the remaining transmissible capacity thereof
Figure FDA0003027129850000021
Minimum supply energy required by MEC server m if requested
Figure FDA0003027129850000022
Satisfies the conditions
Figure FDA0003027129850000023
Comparing the MEC server with the last MEC server currently accepted by the power packet router r, selecting an MEC server with higher order in the preference list, and adding the rejected MEC server to the unmatched MEC server set; minimum supply energy required by MEC server m if requested
Figure FDA0003027129850000024
Satisfies the conditions
Figure FDA0003027129850000025
The power packet router r directly accepts the request of the MEC server;
as described above
Figure FDA0003027129850000026
Represents the minimum energy requirement, α, of the mth MEC serverr,mRepresenting the loss factor of the power packet router,
Figure FDA0003027129850000027
the maximum transmissible capacity of the power packet router r is represented, and g represents the iteration number;
3) stopping until the unmatched MEC server set is empty, and otherwise, returning to 1);
wherein, in each MEC server-electrical packet router matching pair, an improved second-generation non-dominated sorting genetic algorithm is adopted to obtain the optimal energy supplied to each MEC server, and the method specifically comprises the following steps:
in each MEC server-power pack router matching pair, because the traditional multi-objective optimization method is easy to fall into a local optimal solution and the evolutionary algorithm has global search capability, the improved evolutionary algorithm of the second generation non-dominated sorting genetic algorithm is adopted, the algorithm adds an optimal front end individual coefficient on the basis of the second generation non-dominated sorting genetic algorithm, calculates the number of the individuals which can be allowed to be reserved by the front end, further finds an optimal solution set which enables each objective function value to be as large as possible, and obtains the optimal supply energy V supplied to each MEC serverr,m；
The improved second generation non-dominated sorting genetic algorithm is as follows:
based on the matching process, the integer variable q from 0 to 1 can be solvedm,rThe optimization problem K1 can be converted into:
Figure FDA0003027129850000031
Figure FDA0003027129850000032
Figure FDA0003027129850000033
wherein the above formula is to maximize the satisfaction of each MEC server matched with the r-th power packet router for the received energy,
Figure FDA0003027129850000034
representing the maximum required energy of the mth MEC server;
the problem K2 is a multi-objective planning problem, a traditional multi-objective optimization method is easy to fall into a local optimal solution, a genetic algorithm has global searching capability, and therefore, the direct current energy supply method is solved by utilizing an improved second-generation non-dominated sorting genetic algorithm, namely, an optimal front-end individual coefficient is added on the basis of the second-generation non-dominated sorting genetic algorithm, the allowable reserved individual number of the front end is calculated, an optimal solution set enabling each objective function value to be as large as possible is further found, and the optimal supply energy V supplied to each MEC server is obtainedr,m；
Because the supply energy of the direct current energy supply method is transmitted in the form of power packets, and the number of time slots occupied by one power packet needs to be an integer, the V obtained by solving based on the algorithm is obtainedr,mAccording to Vr,m＝Pmhnm＝dmnmTo obtain nmAfter that, it is necessary to round off to get the whole to n'mFurther, obtaining the optimal energy V 'actually supplied for each MEC server'r,m＝dmn'mWhere h is the time length of each slot, PmTransmitting power, n, of power packets to MEC server m for renewable energy sourcesmNumber of time slots occupied by power packets for transmission to MEC server m, dm＝Pmh is the energy that a single slot can transmit.
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