CN111641974B - Method and storage device based on 5G small-sized cellular hybrid renewable energy network - Google Patents

Method and storage device based on 5G small-sized cellular hybrid renewable energy network Download PDF

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CN111641974B
CN111641974B CN202010277257.7A CN202010277257A CN111641974B CN 111641974 B CN111641974 B CN 111641974B CN 202010277257 A CN202010277257 A CN 202010277257A CN 111641974 B CN111641974 B CN 111641974B
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CN111641974A (en
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余志民
默汉默德·泰森
林剑萍
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Yango University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G06Q50/40—
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/24—Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention relates to the field of renewable energy networks, in particular to a method and storage equipment based on a 5G small-sized cellular hybrid renewable energy network. The method based on the 5G small-sized cellular mixed renewable energy network comprises the following steps of: updating the value of each target initial stage based on the 3D-MDP strategy, and determining a feasible space solution; obtaining a first best solution from the feasible spatial solution; checking whether the first best solution is viable one by one; selecting a second best solution among all possible first best solutions; and checking the second optimal solution through preset parameters to obtain a final feasible solution. In this way an optimal transmission strategy is obtained which simultaneously minimizes the power consumption of the grid and the waste of green harvested energy.

Description

Method and storage device based on 5G small-sized cellular hybrid renewable energy network
Technical Field
The invention relates to the field of renewable energy networks, in particular to a method and storage equipment based on a 5G small-sized cellular hybrid renewable energy network.
Background
In fifth generation (5G) small cellular hybrid renewable energy networks, maximum energy utilization is one of the major challenges, especially in situations where green energy is limited. How to determine the optimal transmission strategy for a hybrid model with grid and green energy in a small cellular network is an urgent issue to be addressed. By optimal transmission strategy is meant: the power consumption of the power grid and the waste of green harvesting energy sources are simultaneously reduced to the minimum; while meeting quality of service (QoS) requirements, such as minimum packet loss probability, etc.
Disclosure of Invention
Therefore, a method based on a 5G small-sized cellular hybrid renewable energy network is needed to solve the problem of low energy utilization rate of the fifth generation (5G) small-sized cellular hybrid renewable energy network. The specific technical scheme is as follows:
a method based on a 5G small cellular hybrid renewable energy network, comprising the steps of:
updating the value of each target initial stage based on the 3D-MDP strategy, and determining a feasible space solution;
obtaining a first best solution from the feasible spatial solution;
checking whether the first best solution is viable one by one;
selecting a second best solution among all possible first best solutions;
checking the second optimal solution through preset parameters to obtain a final feasible solution;
the preset parameters include: queuing packet status information, green energy status information, and channel status information.
Further, the "obtaining the first best solution from the feasible space solution" further includes the steps of:
determining a feasible path;
the first best solution is calculated by weighting the feasible paths of each target.
Further, the "select the second best solution from all possible first best solutions" further includes the steps of:
a weight value for each first best solution path is calculated,
a second best solution is calculated based on the intersection minimum weight value.
Further, the "check whether the first best solution is feasible one by one" further includes the steps of:
a solution is a viable solution when it indicates that the state and the path of the markov chain for each object are the same.
Further, before the step of updating the value of each target initial stage based on the 3D-MDP strategy and determining the feasible space solution, the method further comprises the steps of:
the packet buffer and the battery buffer are set to zero.
In order to solve the technical problems, the invention also provides a storage device, which comprises the following specific technical scheme:
a storage device having stored therein a set of instructions for performing:
updating the value of each target initial stage based on the 3D-MDP strategy, and determining a feasible space solution;
obtaining a first best solution from the feasible spatial solution;
checking whether the first best solution is viable one by one;
selecting a second best solution among all possible first best solutions;
checking the second optimal solution through preset parameters to obtain a final feasible solution;
the preset parameters include: queuing packet status information, green energy status information, and channel status information.
Further, the set of instructions is further configured to perform:
the "obtain the first best solution from the feasible spatial solution" further includes the steps of:
determining a feasible path;
the first best solution is calculated by weighting the feasible paths of each target.
Further, the set of instructions is further configured to perform:
the "select the second best solution among all possible first best solutions" further comprises the steps of:
a weight value for each first best solution path is calculated,
a second best solution is calculated based on the intersection minimum weight value.
Further, the "check whether the first best solution is feasible one by one" further includes the steps of:
a solution is a viable solution when it indicates that the state and the path of the markov chain for each object are the same.
Further, the set of instructions is further configured to perform:
before the step of updating the value of each target initial stage based on the 3D-MDP strategy and determining the feasible space solution, the method further comprises the steps of:
the packet buffer and the battery buffer are set to zero.
The beneficial effects of the invention are as follows: updating the value of each target initial stage through a 3D-MDP strategy, determining a feasible space solution, and then acquiring a first optimal solution from the feasible space solution; checking whether the first best solution is viable one by one; selecting a second best solution among all possible first best solutions; and checking the second optimal solution through preset parameters to obtain a final feasible solution. The preset parameters comprise: queuing packet status information, green energy status information, and channel status information. Since the queuing data packet status information is related to packet loss probability (PDP), the green energy status information is related to Green Energy Waste (GEW), the channel status information is related to minimized Grid Power (GP), and when the inspection result meets the preset condition, a final viable solution is obtained, by which an optimal transmission strategy can be obtained that minimizes both grid power consumption and green harvesting energy waste.
Drawings
FIG. 1 is a flow chart of a method for 5G small cell hybrid renewable energy network based in accordance with an embodiment;
FIG. 2 is a Markov chain (N) of a Markov decision process of an embodiment Q ＝N,N B =n, and N C =2) schematic;
FIG. 3 is a schematic illustration of the embodiment of achieving the last viable solution;
FIG. 4 shows a target M according to an embodiment 1 ，M 2 And M 3 Is a potential model checking path schematic diagram;
FIG. 5 is a schematic diagram of a multi-objective model inspection for hybrid energy utilization according to an embodiment;
fig. 6 is a schematic block diagram of a storage device according to an embodiment.
Reference numerals illustrate:
600. a storage device.
Detailed Description
In order to describe the technical content, constructional features, achieved objects and effects of the technical solution in detail, the following description is made in connection with the specific embodiments in conjunction with the accompanying drawings.
Referring to fig. 1 to 5, in the present embodiment, a method based on a 5G small cellular hybrid renewable energy network may be applied to a storage device, including but not limited to: personal computers, servers, general purpose computers, special purpose computers, network devices, embedded devices, programmable devices, intelligent mobile terminals, and the like. The following description will be given specifically:
step S101: the feasible spatial solution is determined based on the 3D-MDP strategy updating the value of each target initial stage.
Step S102: a first best solution is obtained from the feasible spatial solution.
Step S103: it is checked one by one whether the first best solution is feasible.
Step S104: the second best solution is selected among all possible first best solutions.
Step S105: checking the second optimal solution through preset parameters to obtain a final feasible solution; the preset parameters include: queuing packet status information, green energy status information, and channel status information.
Updating the value of each target initial stage through a 3D-MDP strategy, determining a feasible space solution, and then acquiring a first optimal solution from the feasible space solution; checking whether the first best solution is viable one by one; selecting a second best solution among all possible first best solutions; and checking the second optimal solution through preset parameters to obtain a final feasible solution. The preset parameters comprise: queuing packet status information, green energy status information, and channel status information. Since the queuing data packet status information is related to packet loss probability (PDP), the green energy status information is related to Green Energy Waste (GEW), the channel status information is related to minimized Grid Power (GP), and when the inspection result meets the preset condition, a final viable solution is obtained, by which an optimal transmission strategy can be obtained that minimizes both grid power consumption and green harvesting energy waste.
The following description will be given specifically:
the 3D-MDP is designed in two steps simultaneously in order to obtain the best possible target solutions (M1, M2 and M3). The first step yields candidate solutions for the best domain for M1, M2 and M3, respectively. The goal is to reduce GP consumption (M1), PDP (M2) and GEW (M3) together. These alternative solutions rely on the energy costs of all operations. The second step produces the best possible solution involving LTL (linear time logic) with respect to Control Propositions (CPs). In MOMC-MDP, it is checked whether the CP can meet all the decision targets.
In each slot, the 3D-MDP will select one of the transition probabilities. As shown in fig. 2, the selected transition probabilities contain the best actions with the smallest energy costs. In addition, S Q (packet buffer state)，S B (Battery state) and S C The transition probability of a (channel state) markov chain depends on the actions and conditions of the different states. Grouping bufferPunch N Q Battery N B And channel state N C The state numbers of (2) are N, N and 2, respectively. In fig. 3, each case is represented by a unique color. S is S Q And S is B The synthetic transition probabilities are mapped to case 1, case 2, case 3, and case 4, respectively, as shown. In case 1, it is indicated that the datagram buffer is not empty and the hybrid power supply is used for datagram transmission. In case 2, neither the battery nor the datagram buffer is empty, only GHE (Green Harvesting Energy) is used for datagram transmission. In case 3, it is indicated that the datagram buffer is not empty and only GP is used for datagram transmission. In case 4, the representation battery and datagram buffer are both empty and no datagram is transmitted.
In the proposed model, MC technology is used for 3D-MDP to control the requirements of the system architecture, e.g. the rule properties (ω), which define the spatial state of each objective at different stages. In addition, the framework criteria are defined by control assertions (CPs) based on LTL rules. In addition, it redefines the 3D-MDP for the size of the 3D-MDP by adding a CP usage time polynomial. In practice, CP is a limited set of control decisions that can find the best path to approach the demand. In particular, the CP defines the action path of the sequence true/false, i.e. whether or not there is an expected action. It then decides whether the path exists using the LTL rule that the trace is satisfied.
In this embodiment, before the step of updating the value of each target initial stage based on the 3D-MDP policy and determining the feasible spatial solution, the method further includes the steps of: the packet buffer and the battery buffer are set to zero.
The values of the initial phase will then be updated for each target according to the 3D-MDP strategy of (M1, M2, M3) feasible spatial solutions.
Further, the "obtaining the first best solution from the feasible space solution" further includes the steps of: determining a feasible path; the first best solution is calculated by weighting the feasible paths of each target.
Further, the "select the second best solution from all possible first best solutions" further includes the steps of: a weight value for each first best solution path is calculated, and a second best solution is calculated based on the intersection minimum weight value.
Further, the "check whether the first best solution is feasible one by one" further includes the steps of: a solution is a viable solution when it indicates that the state and the path of the markov chain for each object are the same.
Please refer to fig. 3 and fig. 4 for a specific explanation:
as shown in fig. 3. The figure shows four phases of determination. The initial phase of the first phase is set to zero for the packet buffer and the battery buffer. For each target, the values of the initial phase will be updated according to the 3D-MDP strategy of (M1, M2, M3) feasible spatial solutions.
The second stage will select three individual best solutions from the spatial solution of the first stage.
And in the third stage, if the solutions are feasible solutions, checking the feasible solution sets one by one. The feasible solution shows that the states and the path of the markov chain for each object are the same.
The fourth stage selects the best solution F1, F2, F3 among all possible solutions set of the third stage.
These four phases find viable F1, F2, and F3 from M1 (minimizing GP), M2 (minimizing PDP), and M3 (minimizing GEW).
Further, in the left box of fig. 3, the most recently updated information, including queuing data packet status information (QSI), which is associated with PDP, green energy status information (GSI), which is associated with GEW, channel Status Information (CSI), which is associated with GP, will be used in the fourth stage. AoI information is updated in real time in combination with QSI, GSI and CSI, so that PDP, GP and GEW can be reduced to the greatest extent.
Fig. 4 gives an example of three-phase states and paths including an initial stage for each object. In this example, three targets (M1, M2, M3) are considered. The path of each target will be determined by the selected state in the above stage. Each path starts from an initial stage until the availability status (S1, S2, S3, etc.) and the 3D-MDP reaches a final stageAnd (5) acting. In the first and second phases, each target has a different path, depending on the different possible states and actions. In the initial stage, the starting points of all targets are S 0 . The feasible paths are then determined from the feasible solution set. Finally, the best solution is calculated by the weight value of the feasible path for each target. For example, the best solution for M1 is F1. The shortest path for the other targets is achieved by calculating the weight value of each path separately for each target by taking into account the following factors. Also, based on the intersection minimum weight value, the MOMC-MDP selects the best solution, and tradeoffs between all targets if and only if the path is applicable to all targets simultaneously.
In automation control, the MC-MDP method includes that the LTL has implemented to optimize the transmission policy of the hybrid system of the SC. Thus, the MOMC-MDP model aims to determine the best transmission strategy that will trade off between all objectives. Obviously, the transmission strategy finds the best decision for each slot. As shown in fig. 5, few decisions can determine the best possible measure. In practice, the common feasible region (Fm) between the red, green and blue circles represents the solution for all targets at the same time. The goal is to reduce GP consumption (M1), PDP (M2) and GEW (M3) together. Moreover, it indicates dependency decisions between all targets. Obviously, a 3D-MDP model is used to obtain the interaction domain for each circle. Next, three MC algorithms are described for each target based on the feasible solution set of LTL rules. To verify the correct path of the operation and solution, grammar and semantic rules are defined for each objective. Furthermore, these rules are implemented by a limited set of operations based on the transmission policy of the 3D-MDP. The strategy determines the magnitude of the MC polynomial time according to the stop iteration value in the 3D-MDP iteration algorithm.
At the same time AoI is defined from other best models to update the selection of the correct viable measures and decisions. Furthermore, three types of AoI are believed to be able to obtain the best decision at the appropriate time. These types are QSI, GSI and CSI. To maximize green energy utilization while providing QoS requirements, both QSI, GSI and CSI in each slot affect the target. On the other hand, the model examines the sequence of verification operations by the LTL to take into account the CP and the feasible state (as the relative time of each operation in the past, present or future). However, the model check can verify the order of each action in the actual timeline using AoI. Both GSI and CSI types will periodically update AoI information of the proposed model, which are regarded as additional cost values for GSI and CSI, respectively. Furthermore, GSI and CSI will use internal CP rules to verify decisions for viable solutions (including status and order of operations). On the other hand, QSI type would be directly used AoI in C2 to avoid.
Referring to fig. 6, in this embodiment, a specific embodiment of a storage device 600 is as follows: the storage device 600 includes, but is not limited to: personal computers, servers, general purpose computers, special purpose computers, network devices, embedded devices, programmable devices, intelligent mobile terminals, and the like. The following description will be given specifically:
a storage device 600 having stored therein a set of instructions for performing:
updating the value of each target initial stage based on the 3D-MDP strategy, and determining a feasible space solution;
obtaining a first best solution from the feasible spatial solution;
checking whether the first best solution is viable one by one;
selecting a second best solution among all possible first best solutions;
checking the second optimal solution through preset parameters to obtain a final feasible solution;
the preset parameters include: queuing packet status information, green energy status information, and channel status information.
Further, the set of instructions is further configured to perform:
the "obtain the first best solution from the feasible spatial solution" further includes the steps of:
determining a feasible path;
the first best solution is calculated by weighting the feasible paths of each target.
Further, the set of instructions is further configured to perform:
the "select the second best solution among all possible first best solutions" further comprises the steps of:
a weight value for each first best solution path is calculated,
a second best solution is calculated based on the intersection minimum weight value.
Further, the set of instructions is further configured to perform:
the "check whether the first best solution is feasible one by one" further comprises the steps of:
a solution is a viable solution when it indicates that the state and the path of the markov chain for each object are the same.
Further, the set of instructions is further configured to perform:
before the step of updating the value of each target initial stage based on the 3D-MDP strategy and determining the feasible space solution, the method further comprises the steps of:
the packet buffer and the battery buffer are set to zero.
Executing the command through the storage device 600 instruction set: updating the value of each target initial stage through a 3D-MDP strategy, determining a feasible space solution, and then acquiring a first optimal solution from the feasible space solution; checking whether the first best solution is viable one by one; selecting a second best solution among all possible first best solutions; and checking the second optimal solution through preset parameters to obtain a final feasible solution. The preset parameters comprise: queuing packet status information, green energy status information, and channel status information. Since the queuing data packet status information is related to packet loss probability (PDP), the green energy status information is related to Green Energy Waste (GEW), the channel status information is related to minimized Grid Power (GP), and when the inspection result meets the preset condition, a final viable solution is obtained, by which an optimal transmission strategy can be obtained that minimizes both grid power consumption and green harvesting energy waste.
It should be noted that, although the foregoing embodiments have been described herein, the scope of the present invention is not limited thereby. Therefore, based on the innovative concepts of the present invention, alterations and modifications to the embodiments described herein, or equivalent structures or equivalent flow transformations made by the present description and drawings, apply the above technical solution, directly or indirectly, to other relevant technical fields, all of which are included in the scope of the invention.

Claims (2)

1. A method based on a 5G small cellular hybrid renewable energy network, comprising the steps of:
step S101: updating the value of each target initial stage based on the 3D-MDP strategy, and determining a feasible space solution;
step S102: acquiring first optimal solutions M1, M2 and M3 from the feasible space solutions, wherein M1 is a minimized power grid power GP, M2 is a minimized packet loss probability PDP, M3 is a minimized green energy waste GEW, GP is related to channel state information CSI, PDP is related to queuing data packet state information QSI, GEW is related to green energy state information GSI;
step S103: checking whether the first best solution is viable one by one;
step S104: selecting a second best solution F1, F2 and F3 among all possible first best solutions, wherein F1 is the best solution for M1, F2 is the best solution for M2 and F3 is the best solution for M3;
step S105: checking the second optimal solution through preset parameters to obtain a final feasible solution Fm;
wherein, the preset parameters include: queuing data packet state information QSI, green energy state information GSI and channel state information CSI;
selecting the best solution by MOMC-MDP, making a trade-off between all targets if and only if the path is applicable to all targets at the same time, the MOMC-MDP model being intended to determine the best transmission strategy that will make a trade-off between all targets;
the four phases find out feasible F1, F2 and F3 from M1, M2 and M3, and the AoI information is updated in real time by combining QSI, GSI and CSI;
wherein, the step S101 updates the value of each target initial stage based on the 3D-MDP policy, and before determining the feasible spatial solution, the method further comprises the steps of:
setting the data packet buffer area and the battery buffer area to be zero;
wherein, the step S102 obtains the first best solutions M1, M2 and M3 from the feasible spatial solutions, and further includes the steps of:
determining a feasible path;
calculating a first optimal solution by means of weighting values of feasible paths of each target;
wherein the step S103 checks whether the first best solution is feasible one by one, further comprising the steps of:
when the solution indicates that the state and the path of the Markov chain for each object are the same, the solution is a viable solution;
wherein said step S104 selects a second best solution among all possible first best solutions, further comprising the steps of:
a weight value for each first best solution path is calculated, and a second best solution is calculated based on the intersection minimum weight value.
2. A storage device having stored therein a set of instructions for performing:
step S101: updating the value of each target initial stage based on the 3D-MDP strategy, and determining a feasible space solution;
step S102: acquiring first optimal solutions M1, M2 and M3 from the feasible space solutions, wherein M1 is a minimized power grid power GP, M2 is a minimized packet loss probability PDP, M3 is a minimized green energy waste GEW, GP is related to channel state information CSI, PDP is related to queuing data packet state information QSI, GEW is related to green energy state information GSI;
step S103: checking whether the first best solution is viable one by one;
step S104: selecting a second best solution F1, F2 and F3 among all possible first best solutions, wherein F1 is the best solution for M1, F2 is the best solution for M2 and F3 is the best solution for M3;
step S105: checking the second optimal solution through preset parameters to obtain a final feasible solution Fm;
wherein, the preset parameters include: queuing data packet state information QSI, green energy state information GSI and channel state information CSI;
selecting the best solution by MOMC-MDP, making a trade-off between all targets if and only if the path is applicable to all targets at the same time, the MOMC-MDP model being intended to determine the best transmission strategy that will make a trade-off between all targets;
the four phases find out feasible F1, F2 and F3 from M1, M2 and M3, and the AoI information is updated in real time by combining QSI, GSI and CSI;
wherein, the step S101 updates the value of each target initial stage based on the 3D-MDP policy, and before determining the feasible spatial solution, the method further comprises the steps of:
setting the data packet buffer area and the battery buffer area to be zero;
wherein, the step S102 obtains the first best solutions M1, M2 and M3 from the feasible spatial solutions, and further includes the steps of:
determining a feasible path;
calculating a first optimal solution by means of weighting values of feasible paths of each target;
wherein the step S103 checks whether the first best solution is feasible one by one, further comprising the steps of:
when the solution indicates that the state and the path of the Markov chain for each object are the same, the solution is a viable solution;
wherein said step S104 selects a second best solution among all possible first best solutions, further comprising the steps of:
a weight value for each first best solution path is calculated, and a second best solution is calculated based on the intersection minimum weight value.
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