CN114357743B - Edge cloud collaborative optimization method and device for regional energy Internet - Google Patents
Edge cloud collaborative optimization method and device for regional energy Internet Download PDFInfo
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Abstract
The invention discloses a method and a device for edge cloud collaborative optimization of a regional energy Internet, wherein the method comprises the following steps: constructing a regional energy Internet model consisting of an energy supply party, an energy using party, a supplier and equipment; establishing a side cloud cooperative framework with a regional energy Internet as a distributed control unit; the architecture consists of a cloud service layer, an edge service layer and an equipment layer; the method for constructing the optimization strategy based on the edge cloud collaborative architecture comprises the following steps: a cloud service layer optimization strategy and an edge service layer optimization strategy; and carrying out emergency treatment on the energy Internet based on the edge cloud cooperative architecture. The device comprises: the system comprises a model building module, an architecture design module, a task allocation module, a game computing module, an optimization strategy module and an emergency processing module. The method overcomes the defects in the prior art, improves the operation optimization rate of the energy Internet from the aspect of improving the operation model, can reduce the operation delay of the energy Internet and improve the income of the energy Internet.
Description
Technical Field
The invention relates to the field of operation optimization of energy Internet, in particular to a method and a device for edge cloud collaborative optimization of regional energy Internet.
Background
The energy internet is a novel energy system which is based on electric power as a center, realizes cross-time scale coupling, multi-energy complementation and multi-party coordination through a large number of novel intelligent technologies, information technologies and control technologies, and has the advantages of high energy efficiency, high reliability, high flexibility and the like. However, as the scale of the energy internet is rapidly enlarged, the permeability of renewable energy is remarkably increased under the "dual-carbon" target, and the energy informatization level is improved, the traditional centralized cloud service architecture is difficult to meet the computing requirement of the existing energy internet, and a research on the advanced computing technology of the energy internet needs to be deeply carried out. Currently, research on energy internet computing mainly focuses on two aspects: the method comprises the steps that firstly, the trend of multi-subject and multi-energy coupling of an energy market is fully considered, a computing model of an energy internet is researched in principle, and factors such as multi-subject game, demand response and the like are considered, so that the computing model is closer to actual engineering; and secondly, based on the current situation of the rise of the data dimension of the energy Internet, a more efficient operation framework is established by utilizing a novel information processing technology, and the calculation speed is improved.
In the aspect of an energy Internet computing model, the continuous deepening of the system complexity is considered, and in order to enable the system to be closer to actual engineering, more factors such as multi-subject games, demand response and the like need to be considered in the computing model. At present, an extremely accurate energy internet mathematical model is established in the prior art, but a huge amount of computing tasks are placed in a cloud server, so that the operation optimization efficiency of the energy internet is reduced.
In the aspect of an energy internet operation architecture, along with the increase of data dimensions in a system and the exponential increase of data total amount, novel information processing technologies such as artificial intelligence and edge cloud cooperation are widely applied in the field, however, the research of the technologies is still less at present, and the improvement on the optimization efficiency of the energy internet is insufficient.
In the process of implementing the invention, the inventor finds that the prior art has at least the following disadvantages and shortcomings:
1. in the prior art, a huge amount of computing tasks are placed on a cloud server, so that the operation optimization efficiency of the energy Internet is reduced;
2. in the prior art, task allocation of a cloud server and a border server cannot be determined, and a game relation among different subjects is not considered in a border side optimization strategy;
3. the prior art is lack of emergency treatment schemes for formulating energy Internet edge cloud coordination, and is difficult to deal with emergencies such as large-amplitude deviation of renewable energy source predicted power, intelligent agent calculation error, regional energy Internet equipment outage and the like.
Disclosure of Invention
The invention provides a method and a device for edge cloud collaborative optimization of regional energy Internet, which overcome the defects in the prior art, improve the operation optimization rate of the energy Internet from the aspect of improving an operation model, reduce the operation delay of the energy Internet and improve the income of the energy Internet, and are described in detail in the following:
in a first aspect, a method for edge cloud collaborative optimization of regional energy internet includes:
constructing a regional energy Internet model consisting of an energy supply party, an energy utilization party, a supplier and equipment;
establishing a side cloud cooperative architecture taking a regional energy Internet as a distributed control unit; the architecture consists of a cloud service layer, an edge service layer and an equipment layer;
the method for constructing the optimization strategy based on the edge cloud collaborative architecture comprises the following steps: a cloud service layer optimization strategy and an edge service layer optimization strategy;
and performing emergency treatment on the energy Internet based on the edge cloud cooperative architecture.
The cloud service layer optimization strategy specifically comprises the following steps: an objective function:
in the formula, n is the number of the regions; g i Total earnings for region i; e i 、H i 、S i Respectively representing the energy quantities of a power grid, a heat supply network and an air network distributed to the area i, and calculating the numerical values by the cloud server;
the inequality constrains:
in the formula (I), the compound is shown in the specification,
initial values of electricity, heat and gas consumption, e, respectively, for users in zone i i 、h i 、s i Representing the actual electricity, heat and air consumption of the user after the area i is responded by the demand; p i,j Indicating work of j-th equipment in zone iThe ratio of the total weight of the particles,
and i,j Prespectively representing the upper and lower power limits of the jth device in zone i.
And (3) constraint of an equation:
in the formula, M e 、M h 、M s Respectively representing the maximum power which can be provided by the current power grid, the heat supply network and the air network.
The emergency treatment of the energy internet based on the edge cloud collaborative architecture specifically comprises the following steps:
the REI equipment layer detects an emergency and sends out an alarm to an edge server of the area where the REI equipment layer is located;
the edge server judges the type of the emergency, accurately measures the power shortage and sends all fault information to the cloud server;
the edge server schedules network resources to be preferentially distributed to the area until the power shortage of the node is complemented;
the cloud server carries out optimization calculation again according to the current allocable network resource information and sends the network resource scheduling information to each REI;
each edge server carries out game bidding and equipment power optimization according to the new scheduling information of the cloud server, and the system enters the optimal running state again;
and resetting a warning signal at the equipment layer, and recovering the optimal operation of the whole system.
In a second aspect, an apparatus for edge cloud collaborative optimization of regional energy internet, the apparatus includes:
a model construction module: establishing a regional energy Internet mathematical model comprising an energy supply party, an energy using party, a supplier and equipment;
an architecture design module: designing a side cloud cooperative architecture taking a regional energy Internet as a distributed control unit;
a task allocation module: distributing the inter-area and intra-area optimized computing tasks to the cloud server and the edge server;
the game computing module: considering the game relation of different main bodies in the area, and calculating an equilibrium solution;
an optimization strategy module: an optimization strategy for pursuing the maximum profit based on the cooperation form is formulated for the cloud service layer, and an optimization strategy for pursuing the maximum profit based on the non-cooperation form is formulated for the edge service layer;
an emergency processing module: when the edge layer detects a fault, firstly, an edge server processing scheme is formulated, and then a cloud server stable operation optimization scheme is formulated.
In a third aspect, an apparatus for edge cloud collaborative optimization of regional energy internet, the apparatus includes: a processor and a memory, the memory having stored therein program instructions, the processor calling the program instructions stored in the memory to cause the apparatus to perform the method steps of the first aspect.
The technical scheme provided by the invention has the beneficial effects that:
1) compared with a traditional cloud service model, the regional energy Internet edge cloud coordination architecture and the optimization strategy thereof utilize the coordination of two types of servers, so that the optimization calculation rate of the energy Internet can be improved;
2) compared with the traditional optimization strategy, the cloud collaborative framework facing the regional energy Internet and the optimization strategy thereof consider the cooperation among the regions and the game relationship in the regions, and can improve the overall benefit of the energy Internet;
3) compared with the traditional emergency treatment method, the regional energy Internet edge cloud coordination framework and the optimization strategy thereof provided by the invention utilize the edge cloud coordination form to carry out emergency treatment, thereby reducing the time for recovering stable operation and optimal operation of the energy Internet.
Drawings
FIG. 1 is a flow chart of a regional energy Internet oriented edge cloud collaborative architecture and an optimization method thereof;
FIG. 2 is a schematic diagram of a regional energy Internet model;
FIG. 3 is a schematic diagram of an energy Internet edge cloud coordination architecture;
FIG. 4 is a schematic diagram of a computing task allocation scheme;
FIG. 5 is a flow chart of energy Internet emergency processing;
FIG. 6 is a diagram illustrating the result of power supply;
FIG. 7 is a schematic diagram of the result of the heat energy supply;
FIG. 8 is a schematic diagram of the results of a natural gas supply;
FIG. 9 is a schematic diagram of energy Internet optimization results;
FIG. 10 is a diagram of real-time multi-player game comparison results;
fig. 11 is a schematic structural diagram of a regional energy internet edge cloud collaborative optimization device.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
Example 1
A regional energy Internet-oriented edge cloud cooperative architecture and an optimization method thereof are disclosed, referring to FIG. 1, the method comprises the following steps:
step 101: constructing a Regional Energy Internet (REI) model;
an REI system according to an embodiment of the present invention is shown in fig. 2. Wherein, the energy supply side includes: grid companies, suppliers, and heat grid companies; the REI has a Photovoltaic Generator set (PV), a Wind Turbine Generator (WTG), an electric-to-Gas plant (P2G), a Gas-fired cogeneration set (CHP) and a Gas Boiler (Gas Boiler, GB); the energy utilization method comprises the following steps: three loads of electricity, heat and gas.
In REI, both power supply of a power grid and power supply of a heat supply network belong to regional external power supply, and the prices of the two types of energy are directly set by a superior energy company; the supplier comprises all energy production parties built in the REI, and related energy prices are independently set in the energy supply process; the service provider manages REI internal energy conversion and renewable energy source production equipment, deals with an energy supply party in the energy supply process, sells energy sources for users, and the service provider autonomously determines the power of the REI equipment and the energy selling price facing the users; the user can determine the demand response quantity according to the self energy demand and the energy price, and the demand response form in the embodiment of the invention is interruptible load.
The embodiment of the invention takes the photovoltaic power and the fan predicted power as the maximum output power of the photovoltaic power and the fan predicted power, and establishes the following mathematical models of various REI devices.
(1) Photovoltaic generator set
The PV operation constraints are as follows:
in the formula, P t PV Represents the PV output power at the time t,
representing the PV predicted power at time t.
(2) Wind generating set
The WTG operating constraints are as follows:
in the formula, P t WTG Representing the WTG output power at time t,
indicating the WTG predicted power at time t.
(3) Electric gas conversion equipment
P2G converts electrical energy into natural gas with the operating constraints as follows:
in the formula (I), the compound is shown in the specification,
representing plant natural gas output power at time P2G,
representing the device power input power at time P2G,
indicating the conversion efficiency of the P2G device,
and
respectively representing the upper limit and the lower limit of the natural gas output power of the P2G equipment.
(4) Gas type combined heat and power generation unit
The CHP units consume natural gas, produce electricity and heat, and are subject to the following operational constraints:
in the formula (I), the compound is shown in the specification,
representing the CHP unit electric energy output power at the time t,
representing the heat energy output power of the CHP unit at the time t,
the natural gas input power of the CHP unit is shown at the time t,
and
respectively represents the conversion efficiency of the electrical energy and the heat energy of the CHP unit,
and
respectively representing the upper limit and the lower limit of the electrical energy output power of the CHP unit,
and
respectively representing the upper limit and the lower limit of the heat energy output power of the CHP unit.
(5) Gas boiler
GB consumes natural gas to generate heat energy, the operating constraints of which are as follows:
in the formula (I), the compound is shown in the specification,
representing the thermal energy output power at time GB,
representing the GB natural gas input power at time t,
it is shown that the efficiency of the GB conversion,
and
respectively representing the upper limit and the lower limit of GB heat energy output power.
Step 102: establishing a side cloud cooperative architecture taking a regional energy Internet as a distributed control unit;
in order to ensure the operating efficiency of the energy Internet, the embodiment of the invention allocates the computing tasks according to the actual workload, respectively arranges the computing tasks optimized between the regions and in the regions in the cloud server and the edge server, and provides the energy Internet edge cloud cooperative architecture by considering the game relationship in the regions. The architecture is shown in fig. 3, the task allocation scheme in the architecture is shown in fig. 4, and the architecture can be described as follows:
(1) a cloud service layer: the energy internet inter-regional optimization system is composed of a cloud server and a general dispatching center, and is responsible for calculating an optimal distribution scheme of power grid resources, heat supply network resources and air network resources.
(2) An edge service layer: the method is composed of an edge server and a regional dispatching center, internal optimization of the controlled REI region is carried out, the method is responsible for calculating the optimal energy price, the energy consumption and the optimal equipment output in the REI, and an intelligent agent is trained to realize the rapid optimization of the REI.
(3) Equipment layer: the equipment layer comprises various types of REI energy conversion, measurement, transmission and other equipment, and the main task of the equipment layer is to assist the edge server in perceiving and managing the REI.
Step 103: formulating an optimization strategy based on the edge cloud cooperative architecture;
(1) cloud service layer optimization strategy
The cloud server is responsible for performing optimized calculation among REIs, and calculating the optimal allocation scheme of the power grid and heat supply network resources in each REI, and the aim of the cloud server is to improve the total income of the energy Internet.
An objective function:
in the formula, n is the number of the regions; g i Total earnings for region i; e i 、H i 、S i And respectively representing the energy quantities of the power grid, the heat supply network and the air network distributed to the area i, and the numerical values are calculated by the cloud server.
The inequality constrains:
in the formula (I), the compound is shown in the specification,
initial values of electricity, heat and gas consumption, e, respectively, for users in zone i i 、h i 、s i Representing the actual electricity, heat and gas consumption of the user after the area i is subjected to demand response (the demand response form in the text is interruptible load); p i,j Indicating the power of the jth device in zone i,
and i,j Prespectively representing the upper and lower power limits of the jth device in zone i.
And (3) constraint of an equation:
in the formula, M e 、M h 、M s The maximum powers which can be provided by the current power grid, the heat supply network and the air network are respectively represented, and are all normal numbers.
(2) Edge service layer optimization strategy
The embodiment of the invention uniformly divides all benefit subjects participating in the energy game link in the REI into three types of suppliers, service providers and users.
1) The supplier: including all benefit agents who have energy on sale as the primary means of profit, such agents typically own energy production facilities whose decision variable is the price of the energy on sale. Its objective function I E The following were used:
wherein T represents the total duration;
the selling price of the supplier at the time t is shown; p is t E Representing the energy selling power of the supplier at the time t; Δ t represents a time interval; k is a radical of formula 1 Represents the cost of the supplier's unit power service, usually including pollutionWater treatment costs, environmental remediation costs, and the like;
represents the total cost of energy production, including equipment maintenance cost, coal burning cost and the like; z t And expressing the satisfaction cost at the time t and representing the satisfaction degree of the energy purchasers on the energy price, wherein the expression is as follows:
in the formula, a is a multiplication coefficient, b is an exponential coefficient, and both are normal numbers; ρ is the historical energy average of the energy market.
2) The service provider: including all benefit agents that benefit by storing, converting energy, or selling renewable energy, such agents typically own associated energy devices with decision variables of energy selling price and area device power. Its objective function I S The following were used:
in the formula (I), the compound is shown in the specification,
the selling price of the service provider at the time t is shown; p t S Representing the energy selling power of the service provider at the time t; m { PV, WTG, CHP, P2G, GB }; k is a radical of m The unit power service cost of the equipment m is expressed, and generally, the unit power service cost includes the maintenance cost of the equipment and the like; lambda [ alpha ] t S Indicating the purchase energy unit price of the service; p is m,t Representing the running power of the equipment m at the time t;
indicating that the service purchased the energy power.
3) The user: including all benefit agents that consume energy and benefit from the rest of the process, such agents are usually only users of energy, and the decision variable is the actual energy usage. Its objective function I U The following were used:
in the formula, λ t U The unit price of the energy purchase of the user at the time t is shown; p t U Representing the actual energy purchasing power of the user after the demand response at the time t, wherein the demand response is in the form of interruptible load; d t Represents the user comfort coefficient, whose expression is as follows:
in the formula, y k For comfort factor, L t The initial energy demand is made for the user.
Step 104: an energy internet emergency processing method based on a side cloud collaborative architecture.
Under the emergency conditions of large-amplitude deviation of the renewable energy source predicted power, shutdown of REI equipment and the like, the energy internet needs to have corresponding emergency processing capacity so as to ensure stable operation of the energy internet. Therefore, on the basis of the edge cloud cooperative architecture provided in the embodiment of the present invention, a method for edge cloud cooperative emergency processing is provided, a flow of which is shown in fig. 5, and the specific steps are as follows:
step 1: the REI equipment layer detects an emergency and sends out a warning to an edge server of the area where the emergency is located;
step 2: the edge server judges the type of the emergency, accurately measures the power shortage and sends all fault information to the cloud server;
and 3, step 3: the edge server schedules network resources to be preferentially distributed to the area until the power shortage of the node is complemented;
therefore, the participation of the cloud server is not needed in the process of recovering the stable operation. In consideration of the problem of multi-time scale energy transmission time, in the process, the shortage of electric energy is directly complemented by a power grid; the shortage of heat energy is complemented by the power grid through the energy conversion equipment, and is complemented by the heat supply network after the heat transmission is finished; the natural gas shortage is replenished by the power grid through energy conversion equipment.
And 4, step 4: the cloud server performs optimization calculation again according to the current allocable network resource information, and sends network resource scheduling information to each REI;
and 5: each edge server carries out game bidding and equipment power optimization according to the new scheduling information of the cloud server, and the system enters the optimal running state again;
and 6: and resetting the warning signal by the equipment layer, and recovering the optimal operation of the whole system.
In summary, the embodiment of the present invention overcomes the defects in the prior art through the steps 101-104, and improves the operation optimization rate of the energy internet in terms of improving the operation model.
Example 2
Specific examples are given below, in order to verify the feasibility of the above method, as described in detail below:
in order to verify the validity of the proposed scheme, the embodiment of the present invention sets up 5-region and 11-region systems respectively for simulation: setting the P2G equipment efficiency to be 0.70, the gas boiler efficiency to be 0.90, the CHP heat energy production efficiency to be 0.65 and the CHP electric energy production efficiency to be 0.25; setting P2G maintenance cost to be 4USD/MW, CHP maintenance cost to be 3USD/MW, GB maintenance cost to be 7USD/MW, user demand response compensation cost to be 5USD/MW, and environment treatment cost to be 0.3 USD/MW; setting network resources including power grid electric energy and heat supply network heat energy, wherein the power grid price is 110USD/MW, and the heat supply network heat price is 100 USD/MW; establishing that the learning rate of each edge intelligent agent is 0.3 and the satisfaction index coefficient is 4; the initial load value of each edge node, the product coefficient of the satisfaction degree of the supplier, the product coefficient of the satisfaction degree of the service provider and the response coefficient of the user demand of the area system 11 are shown in table 1, and the first 5REI data are taken as the parameters of the area system 5. In this example, the game solution of the fringe service layer includes 9 components, i.e., the provider electricity price, the provider heat price, the provider gas price, the user actual electricity consumption, the user actual heat consumption, and the user actual gas consumption, the initial load of each area is shown in table 2, and the 24-hour detailed operation data in the area 6 is shown in table 3.
TABLE 1 regional energy Internet parameters
TABLE 2 initial load
TABLE 324 hrs zone 6 run data
(1) Edge service layer optimization effect analysis
The edge service layer optimization strategy considers the game relation among different interest subjects, and in order to verify the superiority of the optimization strategy, a region 6 is taken as a research object, and four game scenes are shown in table 4:
TABLE 4 zone 6 Game scenarios
The simulation time is 24 hours, the initial value of the electrical load, the initial value of the thermal load, the initial value of the air load, the predicted value of the fan and the predicted value of the photovoltaic within one day of the area 6 are respectively shown in table 2, and the equipment contained in the area 6 is shown in fig. 2.
The server and provider earnings in the gaming scenes of the optimized section 6 are shown in table 5.
Table 5 area 6 game benefits
As can be seen from table 5, when the energy game is considered, the energy selling main body fully considers the market behavior of the energy purchasing main body and selects a reasonable energy price, so that the energy purchasing main body can purchase more energy voluntarily, and the income of the energy selling main body is further improved; it is also advantageous for the energy purchasing entity to consider that the energy game causes the energy price to be reduced. Obviously, compared with the traditional optimization strategy, the energy game greatly improves the regional income.
In response to the dual carbon goal, embodiments of the present invention prefer to invest in renewable energy, so that renewable energy is almost completely invested in the optimization process of region 6; the CHP unit can simultaneously supply electricity and heat load, so that the CHP unit is called only when the electricity and the heat energy have high requirements; the user of the node can determine the demand response quantity at the moment according to the energy price, the self demand response coefficient and the REI energy supply condition given by the upper-layer supplier; P2G devices are not typically invoked because P2G devices are less efficient.
(2) Cloud service layer optimization effect and edge cloud collaborative optimization speed analysis
Taking a 5-region system and a 11-region system as examples at a certain hour, verifying the effectiveness of the cloud service layer optimization strategy. The initial load data for each region of the hour is shown in appendix A, Table A4, and the calculated total gains for both systems are shown in FIG. 9.
As shown in fig. 9, the cloud service layer optimization strategy provided in the embodiment of the present invention can achieve optimal allocation of network resources, so that the gains of the 5-region system and the 11-region system are respectively increased by about 31% and about 17%.
In order to examine the information processing capability of the edge cloud coordination framework, the embodiment of the invention compares the real-time optimized calculation time of the edge cloud coordination framework and the real-time optimized calculation time of the cloud service rack, and the calculation result is shown in fig. 10. All calculation methods of the two architectures are the same, so that the optimization effect is the same. Considering the performance difference of the edge server and the cloud side server, setting the computing efficiency of the cloud server to be 2 times that of the edge server: both servers adopt computers with processors of intel i7-9750H and internal memories of 16GB, and the cloud computing time is 50%.
From FIG. 10: compared with a traditional cloud service architecture, the edge cloud cooperation architecture can accelerate energy internet real-time optimization calculation, wherein the real-time optimization calculation time of the 5-region system and the real-time optimization calculation time of the 11-region system are respectively reduced by about 57% and 80%.
(3) Analysis of edge cloud collaborative emergency treatment effect
In order to check the emergency processing capability of the proposed method, the embodiment of the present invention establishes three emergency scenarios as shown in table 6 for simulation verification.
The transmission time of each edge server during the simulation is shown in table 1. In table 1, the device layer transmission time represents a time when the fault information is transmitted from the device layer to the edge server, and the edge service layer transmission time represents a time when the fault information is transmitted from the edge server to the cloud server.
In order to accurately compare the information processing capacity of the edge cloud coordination architecture and the cloud service architecture, the fault information length and the electric energy transmission time are not counted in the embodiment of the invention.
TABLE 6 regional energy Internet Emergency scenarios
The edge cloud coordination architecture and the cloud service architecture are respectively adopted, and the calculated emergency processing time of each scene is shown in table 7.
Under the edge cloud cooperative operation architecture, a fault node does not need to wait for the response of a cloud server, and can recover stable operation only after information is transmitted to an edge server; under the traditional cloud service architecture, emergency treatment needs to be carried out on a cloud server, and stable operation can be restored after all information is transmitted to the cloud server. Both architectures can enter the optimal operation state only after the fault information is transmitted to the cloud server and the optimization calculation is completed, so that the promotion range for recovering the optimal operation state is small.
TABLE 7 Emergency treatment time
From table 7, the speed of the edge cloud cooperative architecture for recovering stable operation under different emergency scenes is improved by about 70%, and the speed for recovering optimal operation is improved by a small margin; because the cloud service architecture and the edge cloud cooperative architecture adopt the same algorithm, the emergency treatment effect is the same except that the emergency treatment time is different.
In summary, the edge cloud collaborative optimization method of the regional energy internet has the following advantages:
1) compared with a traditional cloud service model, the regional energy Internet-oriented cloud collaborative framework and the optimization strategy thereof provided by the invention utilize two types of servers to collaboratively cooperate, so that the energy Internet optimization calculation rate can be improved;
2) compared with the traditional optimization strategy, the cloud collaborative framework facing the regional energy Internet and the optimization strategy thereof consider the cooperation among the regions and the game relationship in the regions, and can improve the overall benefit of the energy Internet;
3) compared with the traditional emergency treatment method, the regional energy Internet edge cloud coordination framework and the optimization strategy thereof provided by the invention utilize the edge cloud coordination form to carry out emergency treatment, thereby reducing the time for recovering stable operation and optimal operation of the energy Internet.
Example 3
An apparatus for edge cloud collaborative optimization of regional energy internet, referring to fig. 11, the apparatus comprises:
a model construction module: establishing a regional energy Internet mathematical model comprising an energy supply party, an energy using party, a supplier and equipment;
an architecture design module: designing a side cloud cooperative framework taking a regional energy Internet as a distributed control unit;
a task allocation module: distributing the inter-region and intra-region optimized computing tasks to the cloud server and the edge server;
the game computing module: considering the game relation of different main bodies in the area, and calculating an equilibrium solution;
an optimization strategy module: an optimization strategy for pursuing the maximum profit based on the cooperation form is formulated for the cloud service layer, and an optimization strategy for pursuing the maximum profit based on the non-cooperation form is formulated for the edge service layer;
an emergency processing module: when the edge layer detects a fault, firstly, an edge server processing scheme is formulated, and then a cloud server stable operation optimization scheme is formulated.
It should be noted that the device description in the above embodiments corresponds to the method description in the embodiments, and the details of the embodiments of the present invention are not repeated herein.
Example 4
A regional energy Internet's limit cloud collaborative optimization device, the device includes: a processor and a memory, the memory having stored therein program instructions, the processor calling the program instructions stored in the memory to cause the apparatus to perform the method steps of embodiment 1:
constructing a regional energy Internet model consisting of an energy supply party, an energy utilization party, a supplier and equipment;
establishing a side cloud cooperative framework with a regional energy Internet as a distributed control unit; the architecture consists of a cloud service layer, an edge service layer and an equipment layer;
the method for constructing the optimization strategy based on the edge cloud collaborative architecture comprises the following steps: a cloud service layer optimization strategy and an edge service layer optimization strategy;
and performing emergency treatment on the energy Internet based on the edge cloud cooperative architecture.
The cloud service layer optimization strategy specifically comprises the following steps: an objective function:
in the formula, n is the number of the regions; g is a radical of formula i Total revenue for zone i; e i 、H i 、S i Respectively representing the energy quantities of a power grid, a heat supply network and an air network distributed to the area i, and calculating the numerical values by the cloud server;
the inequality constrains:
in the formula (I), the compound is shown in the specification,
initial values of electricity, heat and gas consumption, e, respectively, for users in zone i i 、h i 、s i Representing the actual electricity, heat and air consumption of the user after the area i is responded by the demand; p i,j Indicating the power of the jth device in zone i,
and i,j Prespectively representing the upper and lower power limits of the jth device in zone i.
The equation constrains:
in the formula, M e 、M h 、M s Respectively represents the maximum power which can be provided by the current power grid, the heat supply network and the air network.
Wherein, carry out emergency treatment specifically to energy internet based on limit cloud cooperative architecture does:
the REI equipment layer detects an emergency and sends out a warning to an edge server of the area where the emergency is located;
the edge server judges the type of the emergency, accurately measures the power shortage and sends all fault information to the cloud server;
the edge server schedules network resources to be preferentially distributed to the area until the power shortage of the node is complemented;
the cloud server performs optimization calculation again according to the current allocable network resource information, and sends network resource scheduling information to each REI;
each edge server carries out game bidding and equipment power optimization according to the new scheduling information of the cloud server, and the system enters the optimal running state again;
and resetting the warning signal by the equipment layer, and recovering the optimal operation of the whole system.
It should be noted that the device description in the above embodiments corresponds to the method description in the embodiments, and the embodiments of the present invention are not described herein again.
The execution main bodies of the processor and the memory can be devices with calculation functions such as a computer, a single chip microcomputer and a microcontroller, and in the specific implementation, the execution main bodies are not limited in the embodiment of the invention and are selected according to the requirements in practical application.
The data signals are transmitted between the memory and the processor through the bus, which is not described in detail in the embodiments of the present invention.
In the embodiment of the present invention, except for the specific description of the model of each device, the model of other devices is not limited, as long as the device can perform the above functions.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (4)
1. A regional energy Internet edge cloud collaborative optimization method is characterized by comprising the following steps:
constructing a regional energy Internet model consisting of an energy supply party, an energy utilization party, a supplier and equipment;
establishing a side cloud cooperative architecture taking a regional energy Internet as a distributed control unit; the architecture consists of a cloud service layer, an edge service layer and an equipment layer;
the method for constructing the optimization strategy based on the edge cloud collaborative architecture comprises the following steps: a cloud service layer optimization strategy and an edge service layer optimization strategy;
performing emergency treatment on the energy Internet based on the edge cloud cooperative architecture;
the emergency treatment of the energy internet based on the edge cloud collaborative architecture specifically comprises the following steps:
the REI equipment layer detects an emergency and sends out a warning to an edge server of the area where the emergency is located;
the edge server judges the type of the emergency, accurately measures the power shortage and sends all fault information to the cloud server;
the edge server schedules network resources to be preferentially allocated to the area until the power shortage of the nodes is complemented;
the cloud server performs optimization calculation again according to the current allocable network resource information, and sends network resource scheduling information to each REI;
each edge server carries out game bidding and equipment power optimization according to the new scheduling information of the cloud server, and the system enters the optimal running state again;
and resetting the warning signal by the equipment layer, and recovering the optimal operation of the whole system.
2. The edge cloud collaborative optimization method for the regional energy Internet according to claim 1, wherein the cloud service layer optimization strategy is specifically as follows: an objective function:
in the formula, n is the number of the regions; g i Total earnings for region i; e i 、H i 、S i Respectively representing the energy quantities of the power grid, the heat supply network and the air network distributed to the area i, and the numerical values of the energy quantities are represented by cloud clothesServer computing;
the inequality constrains:
in the formula (I), the compound is shown in the specification,
initial values of electricity, heat and gas consumption, e, respectively, for users in zone i i 、h i 、s i Representing the actual electricity, heat and air consumption of the user after the area i is responded by the demand; p i,j Indicating the power of the jth device within zone i,
and P i , j Respectively representing the upper limit and the lower limit of the power of jth equipment in the area ith;
and (3) constraint of an equation:
in the formula, M e 、M h 、M s Respectively representing the maximum power which can be provided by the current power grid, the heat supply network and the gas network;
the edge service layer utilizes an advanced multi-body optimization technology to enable an optimization result to be closer to an energy internet operation truth value; meanwhile, the edge service layer helps the cloud service layer to share the calculation load, and the energy internet calculation efficiency is improved.
3. An edge cloud collaborative optimization device of regional energy Internet, which is characterized by comprising:
a model construction module: constructing a regional energy Internet model consisting of an energy supply party, an energy utilization party, a supplier and equipment;
an architecture design module: establishing a side cloud cooperative framework with a regional energy Internet as a distributed control unit; the architecture consists of a cloud service layer, an edge service layer and an equipment layer;
a task allocation module: distributing the inter-area and intra-area optimized computing tasks to the cloud server and the edge server;
the game computing module: considering the game relation of different main bodies in the area, and calculating an equilibrium solution;
an optimization strategy module: an optimization strategy for pursuing the maximum profit based on the cooperation form is formulated for the cloud service layer, and an optimization strategy for pursuing the maximum profit based on the non-cooperation form is formulated for the edge service layer;
an emergency processing module: performing emergency treatment on the energy Internet based on the edge cloud cooperative architecture;
the emergency treatment of the energy internet based on the edge cloud collaborative architecture specifically comprises the following steps:
the REI equipment layer detects an emergency and sends out a warning to an edge server of the area where the emergency is located;
the edge server judges the type of the emergency, accurately measures the power shortage and sends all fault information to the cloud server;
the edge server schedules network resources to be preferentially distributed to the area until the power shortage of the node is complemented;
the cloud server performs optimization calculation again according to the current allocable network resource information, and sends network resource scheduling information to each REI;
each edge server carries out game bidding and equipment power optimization according to the new scheduling information of the cloud server, and the system is enabled to enter the optimal running state again;
and resetting the warning signal by the equipment layer, and recovering the optimal operation of the whole system.
4. An edge cloud collaborative optimization device of regional energy Internet, which is characterized by comprising: a processor and a memory, the memory having stored therein program instructions, the processor calling upon the program instructions stored in the memory to cause the apparatus to perform the method steps of any of claims 1-2.
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