CN113065707B - Energy scheduling method and device - Google Patents

Energy scheduling method and device Download PDF

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Publication number
CN113065707B
CN113065707B CN202110379263.8A CN202110379263A CN113065707B CN 113065707 B CN113065707 B CN 113065707B CN 202110379263 A CN202110379263 A CN 202110379263A CN 113065707 B CN113065707 B CN 113065707B
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rural
energy system
iteration
power corresponding
comprehensive energy
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CN113065707A (en
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常云�
冯侃
孙沛
张新
边辉
陈丽娜
马凡琳
丁奎平
米睿煊
刘生红
史浩鹏
杨轲
任蒙蒙
范迪龙
何欣
姜金朋
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STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
Pingliang Power Supply Co Of State Grid Gansu Electric Power Co
Inner Mongolia University of Science and Technology
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STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
Pingliang Power Supply Co Of State Grid Gansu Electric Power Co
Inner Mongolia University of Science and Technology
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    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/042—Backward inferencing
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • 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

Abstract

The invention provides an energy scheduling method and device, wherein the method comprises the following steps: receiving fluctuation power corresponding to each rural comprehensive energy system in the last iteration; acquiring interaction power corresponding to each rural integrated energy system of the iteration based on the fluctuation power corresponding to each rural integrated energy system of the previous iteration and the first optimization model; if the interactive power corresponding to each rural comprehensive energy system in the iteration is judged to be converged and the accumulated iteration times are not smaller than the preset maximum iteration times, the interactive power corresponding to each rural comprehensive energy system in the iteration is taken as the target interactive power corresponding to each rural comprehensive energy system, and the rural comprehensive energy systems are scheduled based on the target interactive power corresponding to each rural comprehensive energy system. The energy scheduling method and the energy scheduling device provided by the invention can enable rural comprehensive energy configuration to be more reasonable, reduce resource waste, improve the running stability of a rural power distribution network and improve the consumption of clean energy.

Description

Energy scheduling method and device
Technical Field
The invention relates to the technical field of electric power, in particular to an energy scheduling method and device.
Background
The rural distribution network mainly comprises long lines and single-radiation overhead lines, the grid structure is relatively weak, and the power supply reliability and the quality of electric energy at the tail end of the lines are low. The rural comprehensive energy system (Rural Integrated Energy System, RIES) is a system for realizing the dynamic coordination and complementary advantages of the regional energy by coupling electric power, natural gas, marsh gas, clean energy, a cold and hot system and the like. With the development of technology level and socioeconomic, more rural comprehensive energy systems can be accessed into a rural power distribution network. More rural comprehensive energy systems are connected into a rural power distribution network, so that more flexible resources and large-scale distributed power sources can be connected into the tail end of the rural power distribution network, and the electric energy quality of the rural power network can be further deteriorated.
In the prior art, by scheduling the power distribution network, the micro-grid and the load, the better interaction power among the power distribution network, the micro-grid and the load can be determined, so that the energy utilization rate and the economic benefit are improved. However, based on the characteristics of relatively weak grid structure, low power supply reliability and low quality of electric energy at the tail end of a line of the rural power distribution network, energy diversity in a rural comprehensive energy system and the like, the scheduling of the power distribution network, the micro-grid and the load is difficult to reasonably configure the rural comprehensive energy.
Disclosure of Invention
The invention provides an energy scheduling method and device, which are used for solving the defect that rural comprehensive energy is difficult to reasonably configure in the prior art and realizing more reasonable configuration of rural comprehensive energy.
The invention provides an energy scheduling method, which is characterized by comprising the following steps:
receiving fluctuation power corresponding to each rural comprehensive energy system in the last iteration;
acquiring interaction power corresponding to each rural integrated energy system in the iteration based on the fluctuation power corresponding to each rural integrated energy system in the previous iteration and a first optimization model;
if the interactive power corresponding to each rural comprehensive energy system in the iteration is judged to be converged and the accumulated iteration number is not less than the preset maximum iteration number, the interactive power corresponding to each rural comprehensive energy system in the iteration is taken as the target interactive power corresponding to each rural comprehensive energy system;
the target interaction power corresponding to each rural integrated energy system is respectively sent to a first scheduling end corresponding to each rural integrated energy system, so that each first scheduling end schedules each rural integrated energy system based on the target interaction power corresponding to each rural integrated energy system;
The optimization targets of the first optimization model comprise the minimization of the running cost of the rural power distribution network and the maximization of the voltage safety margin of the rural power distribution network; the fluctuation power corresponding to each rural comprehensive energy system in the last iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in each generation in the last iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing the punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system.
The method for scheduling energy provided by the invention is characterized in that if the interactive power corresponding to each rural integrated energy system in the iteration is converged and the accumulated iteration number is not less than the preset maximum iteration number, the method further comprises the following steps before taking the interactive power corresponding to each rural integrated energy system in the iteration as the target interactive power corresponding to each rural integrated energy system:
if it is judged that the interactive power corresponding to each rural integrated energy system in the iteration is converged but the accumulated iteration times are smaller than the preset maximum iteration times, updating the interactive power corresponding to each rural integrated energy system in the iteration based on the interactive power corresponding to each rural integrated energy system in the iteration, the fluctuation power corresponding to each rural integrated energy system in the previous iteration and the first optimization model, and performing the next iteration based on the updated interactive power corresponding to each rural integrated energy system in the iteration.
The method for scheduling energy provided by the invention is characterized in that if the interactive power corresponding to each rural integrated energy system in the iteration is converged and the accumulated iteration number is not less than the preset maximum iteration number, the method further comprises the following steps before taking the interactive power corresponding to each rural integrated energy system in the iteration as the target interactive power corresponding to each rural integrated energy system:
and if the interactive power corresponding to each rural comprehensive energy system in the iteration is judged to be not converged, performing the next iteration.
The method for energy scheduling according to the present invention is characterized in that the updating of the interactive power corresponding to each rural integrated energy system of the present iteration based on the interactive power corresponding to each rural integrated energy system of the present iteration, the fluctuating power corresponding to each rural integrated energy system of the last iteration, and the first optimization model specifically comprises:
updating the value of each particle;
after the interactive power corresponding to each rural integrated energy system of the iteration and the fluctuation power of each rural integrated energy system corresponding to the iteration are input into the first optimization model, calculating the updated matching value corresponding to each particle;
Taking the value of the updated particle with the highest corresponding matching value as the interaction power corresponding to each rural comprehensive energy system of the updated iteration;
wherein the particles are randomly generated based on a multi-target particle swarm algorithm.
The energy scheduling method provided by the invention is characterized in that the interactive power corresponding to each rural integrated energy system of the iteration is obtained based on the fluctuation power corresponding to each rural integrated energy system of the previous iteration and a first optimization model, and specifically comprises the following steps:
after the fluctuation power corresponding to each rural comprehensive energy system in the last iteration is input into the first optimization model, calculating a matching value corresponding to each particle;
taking the value of the particle with the highest corresponding matching value as the interaction power corresponding to each rural comprehensive energy system of the iteration;
wherein the particles are randomly generated based on a multi-target particle swarm algorithm.
The invention provides an energy scheduling method, which is characterized by comprising the following steps:
the method comprises the steps that fluctuation power corresponding to the last iteration is sent to a second scheduling end corresponding to a rural power distribution network, so that the second scheduling end obtains interaction power corresponding to the current iteration based on the fluctuation power corresponding to the last iteration and a first optimization model, and if the interaction power corresponding to the current iteration is judged to be converged and the accumulated iteration number is not smaller than the preset maximum iteration number, the interaction power corresponding to the current iteration is used as target interaction power, and the target interaction power is sent;
After receiving the target interaction power, determining an energy scheduling scheme according to the target interaction power, and scheduling a rural comprehensive energy system according to the energy scheduling scheme;
the fluctuation power corresponding to the previous iteration is obtained based on the interaction power corresponding to the previous iteration and each second optimization model; the optimization targets of the second optimization model comprise the minimum punishment cost of the rural comprehensive energy system; the punishment cost is determined according to the theoretical power and the actual power of the clean energy in the rural comprehensive energy system; the optimization objective of the first optimization model includes minimizing the running cost of the rural power distribution network and maximizing the voltage safety margin of the rural power distribution network.
The invention also provides an energy scheduling device, which is characterized by comprising:
the first communication module is used for receiving the fluctuation power corresponding to each rural comprehensive energy system in the last iteration;
the iteration module is used for acquiring the interaction power corresponding to each rural integrated energy system in the iteration based on the fluctuation power corresponding to each rural integrated energy system in the previous iteration and the first optimization model;
The judging module is used for judging that the interactive power corresponding to each rural comprehensive energy system in the iteration is converged and the accumulated iteration number is not less than the preset maximum iteration number, and taking the interactive power corresponding to each rural comprehensive energy system in the iteration as the target interactive power corresponding to each rural comprehensive energy system;
the first scheduling module is used for respectively sending the target interaction power corresponding to each rural integrated energy system to a first scheduling end corresponding to each rural integrated energy system, so that each first scheduling end schedules each rural integrated energy system based on the target interaction power corresponding to each rural integrated energy system;
the optimization targets of the first optimization model comprise the minimization of the running cost of the rural power distribution network and the maximization of the voltage safety margin of the rural power distribution network; the fluctuation power corresponding to each rural comprehensive energy system in the last iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in each generation in the last iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing the punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system.
The invention also provides an energy scheduling device, which is characterized by comprising:
the second communication module is used for sending fluctuation power corresponding to the last iteration to a second scheduling end corresponding to the rural power distribution network, so that the second scheduling end obtains interaction power corresponding to the current iteration based on the fluctuation power corresponding to the last iteration and a first optimization model, and if the interaction power corresponding to the current iteration is judged to be converged and the accumulated iteration number is not less than the preset maximum iteration number, the interaction power corresponding to the current iteration is used as target interaction power, and the target interaction power is sent;
the second scheduling module is used for determining an energy scheduling scheme according to the target interaction power after receiving the target interaction power, and scheduling a rural comprehensive energy system according to the energy scheduling scheme;
the fluctuation power corresponding to the previous iteration is obtained based on the interaction power corresponding to the previous iteration and a second optimization model; the optimization targets of the second optimization model comprise the minimum punishment cost of the rural comprehensive energy system; the punishment cost is determined according to the theoretical power and the actual power of the clean energy in the rural comprehensive energy system; the optimization objective of the first optimization model includes minimizing the running cost of the rural power distribution network and maximizing the voltage safety margin of the rural power distribution network.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the energy scheduling method as described in any one of the above when executing the program.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the energy scheduling method as described in any of the above.
According to the energy scheduling method and device, based on the first optimization model, the second optimization model and dynamic games between the rural power distribution network and the rural comprehensive energy systems, the interactive power corresponding to the rural comprehensive energy systems is used as a coupling variable, after multiple iterations are carried out, an optimal scheduling scheme is obtained, and the rural power distribution network and the rural comprehensive energy systems are optimally scheduled according to the optimal scheduling scheme, so that the rural comprehensive energy configuration is more reasonable, the waste of resources can be reduced in the operation process of the rural power distribution network and the rural comprehensive energy systems, the load of the rural power distribution network can be reduced, the operation stability of the rural power distribution network can be improved, and the consumption of clean energy can be improved.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an energy scheduling method according to the present invention;
FIG. 2 is a second flow chart of the energy scheduling method according to the present invention;
FIG. 3 is a schematic diagram of an energy scheduling device according to the present invention;
FIG. 4 is a second schematic diagram of an energy scheduling apparatus according to the present invention;
fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Fig. 1 is a schematic flow chart of an energy scheduling method provided by the invention. The energy scheduling method of the present invention is described below with reference to fig. 1. As shown in fig. 1, the method includes: step 101, receiving fluctuation power corresponding to each rural comprehensive energy system in the last iteration.
The fluctuation power corresponding to each rural comprehensive energy system in the previous iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in the previous iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system; the optimization objectives of the first optimization model include minimizing the running cost of the rural power distribution network and maximizing the voltage safety margin of the rural power distribution network.
It should be noted that, the execution main body of the embodiment of the present invention is a second scheduling end corresponding to the rural power distribution network.
A rural power distribution network may include a plurality of electrical load nodes. Any rural comprehensive energy system can be connected into the rural power distribution network through an electric load node. The rural power distribution network can simultaneously supply power for one or more rural comprehensive energy systems.
The rural comprehensive energy system comprises an energy source side and a demand side. The energy side can comprise a power supply, a methane tank or a natural gas and other energy ends, and the energy side can also comprise a Photovoltaic (PV), wind power (Wind Turbine Generator, WTG) and other clean energy ends.
The demand side may include electrical, thermal, gas, and the like. The electrical load may include an agricultural utility load or a lighting load, etc., and may also include a removable distributed energy storage device, such as: agricultural electric forklifts (Rural Electric Vehicle, REV), and the like. The distributed energy storage equipment can flexibly regulate and control the charge and discharge power, so that peak clipping, valley flattening and load time shifting can be realized. The electrical load may also include a flexible electrical load. The flexible electrical load can adjust power over a range so that it can respond to the power regulation requirements.
Each device on the energy side and each load on the demand side in the rural integrated energy system can form an electric-gas-heat mutual coupling and complementary closed loop in the rural integrated energy system.
The rural comprehensive energy system can also comprise equipment capable of realizing energy conversion, such as a cogeneration subsystem (combined heat and power, CHP), a biogas boiler, a gas boiler, self-heating, an electric boiler, a heat pump and the like.
In particular, the energy conversion of the gas-to-electricity can be realized through the cogeneration subsystem. The energy conversion of gas-heat conversion can be realized through a cogeneration subsystem, a methane boiler, a gas boiler, self-heating equipment and the like. The energy conversion of electric heat conversion can be realized through equipment such as an electric boiler, a heat pump and the like.
In the embodiment of the invention, the rural power distribution network can be used as an upper layer leader, each rural comprehensive energy system accessed to the rural power distribution network is used as a lower layer follower, based on the optimization target of the rural power distribution network and the optimization target of each rural comprehensive energy system, master-slave dynamic game is carried out between the rural power distribution network and each rural comprehensive energy system, and an optimization scheduling scheme capable of simultaneously minimizing the running cost of the rural power distribution network, maximizing the voltage safety margin of the rural power distribution network, minimizing the comprehensive cost of each rural comprehensive energy system and maximizing the user satisfaction of each rural comprehensive energy system is obtained, so that the rural power distribution network and each rural comprehensive energy system can be optimally scheduled according to the optimization scheduling scheme.
Based on the optimization target of the rural power distribution network and the optimization target of each rural comprehensive energy system, the process of performing master-slave dynamic game between the rural power distribution network and each rural comprehensive energy system can comprise multiple iterations.
Specifically, before the first iteration is performed, the original interaction power corresponding to each rural comprehensive energy system can be obtained based on the optimization target of the rural power distribution network.
The original interaction power corresponding to each rural comprehensive energy system can be randomly determined in the target range.
It should be noted that the target range may be determined according to factors such as the safe operation condition and the cost requirement of the rural power distribution network. The specific value of the target range is not specifically limited in the embodiment of the present invention.
The active power of each electric load node of the rural power distribution network and the fluctuation power of each rural comprehensive energy system can be input into a first optimization model, and the first optimization model is solved to obtain the original interaction power corresponding to each rural comprehensive energy system.
The active power of each electric load node connected with the rural power distribution network by each rural comprehensive energy system can be randomly determined based on the active power of other electric load nodes of the rural power distribution network.
The fluctuation power of the rural comprehensive energy system refers to power fluctuation generated by intermittent power fluctuation equipment. Intermittent power fluctuation devices may refer to photovoltaic clean energy ends, wind clean energy ends, distributed energy storage devices, or the like. Although each rural integrated energy system can locally consume a part of electric energy generated by the intermittent power fluctuation equipment, most of electric energy generated by the intermittent power fluctuation equipment needs to be sold through a rural power distribution network. When the electric energy passes through a rural power distribution network, fluctuation power is generated. When the original interaction power to be output to each rural comprehensive energy system of the rural power distribution network is obtained based on the first optimization model, the fluctuation power of each rural comprehensive energy system input into the first optimization model is 0.
After the original interaction power of each rural integrated energy system is obtained, the original interaction power can be sent to a first scheduling end corresponding to each rural integrated energy system, so that each first scheduling end can respectively input the original interaction power into a second optimization model corresponding to each rural integrated energy system, and the original fluctuation power corresponding to each rural integrated energy system is obtained by solving each second optimization model.
It should be noted that, the optimization objective of the first optimization model is the same as that of the rural power distribution network, and may include minimizing the running cost of the rural power distribution network and maximizing the voltage safety margin of the rural power distribution network. The optimization objective of the second optimization model corresponding to any rural integrated energy system is the same as the optimization objective of the rural integrated energy system, and may include minimizing the integrated cost of the rural integrated energy system and maximizing the user satisfaction of the rural integrated energy system.
The integrated cost of any rural integrated energy system includes the punishment cost of the rural integrated energy system. The punishment cost of the rural comprehensive energy system can be determined according to the theoretical power and the actual power of the clean energy in the rural comprehensive energy system. The theoretical power consumption of the clean energy in the rural comprehensive energy system can be determined according to the theoretical maximum power generation of each clean energy end in the rural comprehensive energy system; the actual power consumed by the clean energy in the rural integrated energy system can be determined according to the actual power consumed by the rural integrated energy system in the actual power generation amount of each clean energy in the rural integrated energy system. The higher the punishment cost of the rural integrated energy system is, the lower the utilization rate of clean energy in the rural integrated energy system is.
It should be noted that, the original interaction power corresponding to each rural integrated energy system and the original fluctuation power corresponding to each rural integrated energy system may be used as initial values of iteration.
The method can solve the first optimization model and the second optimization model based on optimization methods such as a multi-target particle swarm algorithm and the like to obtain the original interaction power and the original wave band power corresponding to each rural comprehensive energy system.
After each first scheduling end obtains the original fluctuation power corresponding to each rural comprehensive energy system, a first iteration can be started.
After the first iteration starts, each first scheduling end can send the original fluctuation power corresponding to each rural comprehensive energy system to the second scheduling end.
After the second scheduling end receives the original fluctuation power corresponding to each rural integrated energy system, the original fluctuation power corresponding to each rural integrated energy system can be input into a first optimization model, the first optimization model is solved, the interaction power corresponding to each rural integrated energy system in the iteration is obtained, and the interaction power corresponding to each rural integrated energy system in the iteration is sent to the first scheduling end.
After the first scheduling end receives the interactive power corresponding to each rural integrated energy system of the iteration, the interactive power corresponding to each rural integrated energy system of the iteration is input into each second optimization model, each second optimization model is solved, the fluctuation power corresponding to each rural integrated energy system of the iteration is obtained, and the first iteration is completed.
After the first iteration is completed, each first scheduling end can send the fluctuation power corresponding to each rural integrated energy system in the previous iteration to the second scheduling end when each iteration starts.
The second scheduling end can receive the fluctuation power corresponding to each rural comprehensive energy system in the last iteration sent by each first scheduling end.
Step 102, obtaining the interaction power corresponding to each rural integrated energy system in the iteration based on the fluctuation power corresponding to each rural integrated energy system in the previous iteration and the first optimization model.
Specifically, when each iteration is performed, after the second scheduling end receives the fluctuation power corresponding to each rural integrated energy system in the previous iteration, the fluctuation power corresponding to each rural integrated energy system in the previous iteration can be input into the first optimization model. And obtaining the interactive power corresponding to each rural comprehensive energy system of the iteration by solving the first optimization model.
It should be noted that, when each iteration is performed, the first optimization model may be solved based on an optimization method such as a multi-objective particle swarm algorithm, so as to obtain the interaction power corresponding to each rural integrated energy system in this iteration.
And step 103, if the interactive power corresponding to each rural comprehensive energy system in the iteration is judged to be converged and the accumulated iteration number is not less than the preset maximum iteration number, taking the interactive power corresponding to each rural comprehensive energy system in the iteration as the target interactive power corresponding to each rural comprehensive energy system.
Specifically, when each iteration is finished, whether the interactive power corresponding to each rural comprehensive energy system in the iteration is converged or not can be judged.
If the interactive power convergence corresponding to each rural comprehensive energy system of the iteration is judged and obtained, whether the completed accumulated iteration times are larger than or equal to the preset maximum iteration times can be judged.
If the accumulated iteration number which is judged to be completed is larger than or equal to the preset maximum iteration number, the method indicates that the target interaction power corresponding to each rural integrated energy system can be found, wherein the target interaction power can be achieved simultaneously, and the target interaction power can be minimized, the running cost of the rural power distribution network can be maximized, the voltage safety margin of the rural power distribution network can be maximized, the integrated cost of each rural integrated energy system can be minimized, and the user satisfaction degree of each rural integrated energy system can be maximized. The target interaction power corresponding to each rural comprehensive energy system is the interaction power corresponding to each rural comprehensive energy system of the iteration.
It should be noted that the preset maximum iteration number may be determined according to the actual situation. The specific value of the preset maximum iteration number is not specifically limited in the embodiment of the present invention.
It should be noted that, if the deviation of the interaction power corresponding to each rural integrated energy system obtained by the preset number of iterations before the present iteration is within the allowable range, determining that the interaction power corresponding to each rural integrated energy system in the present iteration is converged. The preset number can be determined according to actual conditions, but in order to avoid misjudging the convergence of the interactive power corresponding to each rural comprehensive energy system in the iteration, the value of the preset number cannot be too small. The specific value of the preset number is not specifically limited in the embodiment of the present invention.
When each iteration is performed, the second optimization model can be solved based on optimization methods such as a multi-objective particle swarm algorithm and the like, and the fluctuation power corresponding to each rural comprehensive energy system of the iteration is obtained.
Step 104, the target interaction power corresponding to each rural integrated energy system is respectively sent to the first scheduling end corresponding to each rural integrated energy system, so that each first scheduling end schedules each rural integrated energy system based on the target interaction power corresponding to each rural integrated energy system.
Specifically, after determining the target interaction power corresponding to each rural integrated energy system, the target interaction power corresponding to each rural integrated energy system may be sent to the first scheduling end corresponding to each rural integrated energy system.
For any first scheduling end, after receiving the corresponding target interaction power, determining an energy scheduling scheme of a rural integrated energy system corresponding to the first scheduling end according to the target interaction power, and scheduling the rural integrated energy system according to the energy scheduling scheme.
The energy scheduling scheme of the rural integrated energy system may include output power of each energy end at each time, power of a flexible power load at a demand side, power of a flexible thermal load, and the like at each energy end at each time, and output power of equipment for realizing energy conversion, and the like.
According to the embodiment of the invention, based on the first optimization model, the second optimization model and dynamic games between the rural power distribution network and each rural comprehensive energy system, interaction power corresponding to each rural comprehensive energy system is used as a coupling variable, after multiple iterations are carried out, an optimal scheduling scheme is obtained, and the rural power distribution network and each rural comprehensive energy system are optimally scheduled according to the optimal scheduling scheme, so that the rural comprehensive energy configuration is more reasonable, the waste of resources can be reduced in the operation process of the rural power distribution network and each rural comprehensive energy system, the load of the rural power distribution network can be reduced, the operation stability of the rural power distribution network can be improved, and the consumption of clean energy can be improved.
Based on the foregoing embodiments, if it is determined that the interaction power corresponding to each rural integrated energy system in the present iteration converges and the accumulated iteration number is not less than the preset maximum iteration number, before taking the interaction power corresponding to each rural integrated energy system in the present iteration as the target interaction power corresponding to each rural integrated energy system, the method further includes: if the interactive power corresponding to each rural comprehensive energy system of the iteration is judged to be converged and the accumulated iteration times are smaller than the preset maximum iteration times, updating the interactive power corresponding to each rural comprehensive energy system of the iteration based on the interactive power corresponding to each rural comprehensive energy system of the iteration, the fluctuation power corresponding to each rural comprehensive energy system of the previous iteration and the first optimization model, and carrying out the next iteration based on the updated interactive power corresponding to each rural comprehensive energy system of the iteration.
Specifically, in multiple iterations, the interaction power corresponding to each rural integrated energy system obtained by solving the first optimization model and each second optimization model through the multi-objective particle swarm algorithm is related to the spatial distribution of particles in the first optimization model and each second optimization model, so that the local optimal solution is easily trapped. In the embodiment of the invention, after the converged iteration result is obtained each time, the iterative computation is continuously carried out based on the converged iteration result, so that the optimizing capability of the first optimizing model and each second optimizing model in the whole solution set space can be improved, and the local optimal solution is prevented from being used as a final result.
In the iteration, if the fact that the interactive power corresponding to each rural comprehensive energy system in the iteration is converged but the accumulated iteration times are smaller than the preset maximum iteration times is judged, relevant parameters in a first optimization model are initialized based on the interactive power corresponding to each rural comprehensive energy system in the iteration and the fluctuation power corresponding to each rural comprehensive energy system in the last iteration, the first optimization model after the relevant parameters are initialized is solved based on a multi-objective particle swarm algorithm, and a result obtained through solving is used as the interactive power corresponding to each rural comprehensive energy system in the iteration.
After the interactive power corresponding to each rural integrated energy system of the iteration is updated, the updated interactive power corresponding to each rural integrated energy system of the iteration is sent to a second scheduling end, so that the second scheduling can acquire the fluctuation power corresponding to each rural integrated energy system of the iteration according to the updated interactive power corresponding to each rural integrated energy system of the iteration, the iteration is ended, and the next iteration is started.
According to the method and the device for optimizing the rural comprehensive energy system, after the interactive power corresponding to each rural comprehensive energy system of the iteration is obtained through judgment, the interactive power corresponding to each rural comprehensive energy system of the iteration is updated after the accumulated iteration times are smaller than the preset maximum iteration times, and the next iteration is carried out based on the updated interactive power corresponding to each rural comprehensive energy system of the iteration, so that the situation that a local optimal solution is used as a final result can be avoided, the optimizing capability of a first optimizing model and each second optimizing model can be improved, and the configuration of rural comprehensive energy can be further optimized.
Based on the foregoing embodiments, if it is determined that the interaction power corresponding to each rural integrated energy system in the present iteration converges and the accumulated iteration number is not less than the preset maximum iteration number, before taking the interaction power corresponding to each rural integrated energy system in the present iteration as the target interaction power corresponding to each rural integrated energy system, the method further includes: and if the interactive power corresponding to each rural comprehensive energy system in the iteration is judged to be not converged, carrying out the next iteration.
Specifically, in the iteration, if it is judged that the interactive power corresponding to each rural integrated energy system in the iteration is not converged, the interactive power corresponding to each rural integrated energy system in the iteration is sent to a first scheduling end.
The first scheduling end can input the interaction power corresponding to each rural comprehensive energy system of the iteration into each second optimization model. Solving each second optimization model to obtain the fluctuation power corresponding to each rural comprehensive energy system of the iteration, ending the iteration and starting the next iteration.
According to the embodiment of the invention, the next iteration is carried out by judging and knowing that the interaction power corresponding to each rural comprehensive energy system in the iteration is not converged, the optimal scheduling scheme can be obtained through multiple iterations, and the rural power distribution network and each rural comprehensive energy system are optimally scheduled according to the optimal scheduling scheme, so that the rural comprehensive energy configuration is more reasonable.
Based on the foregoing embodiments, updating the interaction power corresponding to each rural integrated energy system of the present iteration based on the interaction power corresponding to each rural integrated energy system of the present iteration, the fluctuation power corresponding to each rural integrated energy system of the last iteration, and the first optimization model, specifically includes: updating the value of each particle.
Wherein the particles are randomly generated based on a multi-target particle swarm algorithm.
Specifically, in the iteration, if it is determined that the interactive power corresponding to each rural integrated energy system in the iteration converges but the accumulated iteration number is smaller than the preset maximum iteration number, after the fluctuating power corresponding to each rural integrated energy system in the iteration is obtained according to the interactive power corresponding to each rural integrated energy system in the iteration, new values of each particle in the target range can be randomly obtained.
The interactive power corresponding to each rural comprehensive energy system in the iteration is used as a new value of any particle.
It should be noted that, in the embodiment of the present invention, each particle corresponding to the first optimization model may represent a random solution of the interaction power corresponding to each rural integrated energy system.
And after the interactive power corresponding to each rural integrated energy system in the iteration and the fluctuation power of each rural integrated energy system in the iteration are input into the first optimization model, calculating the updated matching value corresponding to each particle.
Specifically, the interactive power corresponding to each rural integrated energy system in the iteration is used as the active power of each electric load node connected with each rural integrated energy system in the rural power distribution network, the parameters in the first optimization model are initialized, and the fluctuation power of each rural integrated energy system corresponding to the iteration is input into the first optimization model.
When the first optimization model is solved, if a conventional system weighting method is adopted, multiple targets are converted into single targets, the weight ratio needs to depend on subjective judgment, and a certain deviation exists. Therefore, in the embodiment of the invention, the interactive power of each rural integrated energy system in the iteration is updated by calculating the updated matching value corresponding to each particle and based on the updated matching value corresponding to each particle, and the specific calculation formula is as follows:
wherein,is the firstsFirst of particlesxMatching values of the objective functions; />For all the firstxOptimal values of the individual objective functions; for example, a->Is the firstsSystem integrated cost value of individual particles +.>The minimum comprehensive cost value generated in the iteration of the particle swarm algorithm is obtained.
The updated matching value corresponding to each particle may be expressed as a relational expression between the sub-objective function matching value and the objective optimal function matching value of each particle, where the objective optimal function matching value is 100%, and the calculation formula of the updated matching value corresponding to each particle is as follows:
wherein,F(s)a match value representing the s-th particle; x is the number of targets. If the matching value of the s-th particle is higher, the combination of the values of the s-th particle is better.
And taking the updated particle value with the highest corresponding matching value as the interactive power corresponding to each rural comprehensive energy system of the updated iteration.
Specifically, the updated particles may be sorted according to the order of the highest matching value of the updated particles, so as to obtain the updated particles with the highest corresponding matching value.
After the updated particles with the highest corresponding matching values are obtained, the updated particles with the highest corresponding matching values can be valued to replace the interactive power corresponding to the original rural comprehensive energy systems in the iteration, and the interactive power corresponding to the rural comprehensive energy systems in the iteration after updating can be used as the interactive power corresponding to the rural comprehensive energy systems in the iteration after updating.
According to the embodiment of the invention, the interactive power corresponding to each rural comprehensive energy system in the iteration is updated according to the updated matching value corresponding to each particle, so that the local optimal solution can be prevented from being used as a final result, the optimizing capability of the first optimizing model and each second optimizing model can be improved, the deviation caused by subjective judgment of the weight ratio when the first optimizing model and the second optimizing model are solved can be avoided, and the rural comprehensive energy configuration can be further optimized.
Based on the content of each embodiment, based on the fluctuation power and the first optimization model corresponding to each rural integrated energy system in the previous iteration, the interactive power corresponding to each rural integrated energy system in the current iteration is obtained, which specifically comprises the following steps: and after the fluctuation power corresponding to each rural comprehensive energy system iterated last time is input into the first optimization model, calculating the matching value corresponding to each particle.
Wherein the particles are randomly generated based on a multi-target particle swarm algorithm.
Specifically, after the fluctuation power corresponding to each rural integrated energy system in the previous iteration is input into the first optimization model, the method for calculating the matching value corresponding to each particle is the same as the method for calculating the matching value corresponding to each particle after the interaction power corresponding to each rural integrated energy system in the current iteration and the fluctuation power corresponding to each rural integrated energy system in the current iteration are input into the first optimization model, and details are omitted in the embodiment of the present invention.
It should be noted that, after the interaction power of each rural integrated energy system corresponding to the current iteration is input into each second optimization model, the method for calculating the matching value corresponding to each particle corresponding to each second optimization model is the same as the method for calculating the matching value corresponding to each updated particle after the interaction power of each rural integrated energy system corresponding to the current iteration and the fluctuation power of each rural integrated energy system corresponding to the current iteration are input into the first optimization model, which is not described in detail in the embodiment of the present invention.
It should be noted that, each particle corresponding to the second optimization model corresponding to any rural integrated energy system in the embodiment of the present invention may represent an energy scheduling scheme of the rural integrated energy system and a random solution of the fluctuating power corresponding to the rural integrated energy system.
And taking the value of the particle with the highest corresponding matching value as the interaction power corresponding to each rural comprehensive energy system of the iteration.
Specifically, the particles corresponding to the first optimization model may be ordered according to the order of the matching values of the particles corresponding to the first optimization model from high to low, so as to obtain the particle with the highest matching value in the particles corresponding to the first optimization model. After the particle with the highest corresponding matching value is obtained, the value of the particle with the highest corresponding matching value can be used as the interaction power corresponding to each rural comprehensive energy system of the iteration.
The particles corresponding to the second optimization model may be sorted according to the order of the matching values of the particles corresponding to the second optimization model from high to low, so as to obtain the particles with the highest matching values in the particles corresponding to the second optimization model. After the particle with the highest corresponding matching value is obtained, the value of the particle with the highest corresponding matching value can be used as the fluctuation power corresponding to each rural comprehensive energy system of the iteration.
According to the embodiment of the invention, the interactive power corresponding to each rural comprehensive energy system in the iteration is obtained according to the matching value corresponding to each particle, so that the deviation caused by subjective judgment of the weight ratio when the first optimization model and the second optimization model are solved can be avoided, and the configuration of rural comprehensive energy can be further optimized.
FIG. 2 is a second flow chart of the energy scheduling method according to the present invention. The energy scheduling method of the present invention is described below with reference to fig. 2. As shown in fig. 2, the method includes: step 201, sending fluctuation power corresponding to the last iteration to a second scheduling end corresponding to the rural power distribution network, so that the second scheduling end obtains interaction power corresponding to the current iteration based on the fluctuation power corresponding to the last iteration and the first optimization model, and if the interaction power corresponding to the current iteration is judged to be converged and the accumulated iteration number is not less than the preset maximum iteration number, taking the interaction power corresponding to the current iteration as target interaction power, and sending the target interaction power.
The fluctuation power corresponding to the previous iteration is obtained based on the interaction power corresponding to the previous iteration and the second optimization model; the optimization targets of the second optimization model comprise the minimization punishment cost of the rural comprehensive energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system; the optimization objectives of the first optimization model include minimizing the running cost of the rural power distribution network and maximizing the voltage safety margin of the rural power distribution network.
It should be noted that, the execution body of the embodiment of the present invention is a first scheduling end corresponding to any rural integrated energy system.
And when each iteration starts, the first scheduling end can send the fluctuation power corresponding to the rural comprehensive energy system in the last iteration to the second scheduling end corresponding to the rural power distribution network.
After the second scheduling end receives the fluctuation power corresponding to the rural integrated energy system in the previous iteration, the interaction power corresponding to the rural integrated energy system in the current iteration can be obtained according to the fluctuation power corresponding to the rural integrated energy system in the previous iteration.
If the interactive power corresponding to the rural comprehensive energy system in the iteration is judged to be converged and the accumulated iteration number is not less than the preset maximum iteration number, the method indicates that the target interactive power corresponding to the rural comprehensive energy system, which can simultaneously achieve the purposes of minimizing the running cost of the rural power distribution network, maximizing the voltage safety margin of the rural power distribution network, minimizing the comprehensive cost of each rural comprehensive energy system and maximizing the user satisfaction of each rural comprehensive energy system, is found. The target interaction power corresponding to the rural comprehensive energy system is the interaction power corresponding to each rural comprehensive energy system of the iteration.
The second scheduling end can send the target interaction power corresponding to the rural comprehensive energy system to the first scheduling end.
And 202, after receiving the target interaction power, determining an energy scheduling scheme according to the target interaction power, and scheduling the rural comprehensive energy system according to the energy scheduling scheme.
Specifically, after the first scheduling end receives the target interaction power corresponding to the rural integrated energy system, the target interaction power corresponding to the rural integrated energy system can be input into the second optimization model.
Based on the second optimization model solving method in the above embodiment, the second optimization model is solved, and the value of the particle with the highest corresponding matching value can be obtained. And determining the energy scheduling scheme of the rural comprehensive energy system according to the value of the particle with the highest corresponding matching value.
After the energy scheduling scheme of the rural comprehensive energy system is determined, the power of each energy side of the energy sides, the power of the flexible power load, the flexible thermal load and the like of the demand side and the power of the equipment for realizing energy conversion in the rural comprehensive energy system can be scheduled according to the energy scheduling scheme.
According to the embodiment of the invention, based on the first optimization model, the second optimization model and dynamic games between the rural power distribution network and each rural comprehensive energy system, interaction power corresponding to each rural comprehensive energy system is used as a coupling variable, after multiple iterations are carried out, an optimal scheduling scheme is obtained, and the rural power distribution network and each rural comprehensive energy system are optimally scheduled according to the optimal scheduling scheme, so that the rural comprehensive energy configuration is more reasonable, the waste of resources can be reduced in the operation process of the rural power distribution network and each rural comprehensive energy system, the load of the rural power distribution network can be reduced, the operation stability of the rural power distribution network can be improved, and the consumption of clean energy can be improved.
In order to facilitate understanding of the above embodiments of the present invention, an energy scheduling method will be described by way of an example.
IEEE33 with the voltage class of 12.66kV and the voltage limit value of [0.95,1.05] is used as a net rack of a rural power distribution network. The rural power distribution network comprises 32 electric load nodes, and the electric load nodes accessed to each rural comprehensive energy system are 14, 18, 25 and 32 respectively.
The first optimization model optimization control total target can be expressed by the following formula:
wherein,representation->Multi-target comprehensive optimal value of rural power distribution network at moment; />A multi-objective solution function representing the first optimization model in the above embodiment; /> 、/>The operation cost and the voltage safety margin of the rural power distribution network are respectively.
Rural power distribution network running costThe expression can be represented by the following formula:
wherein,the time-sharing electricity price and the electricity exchange cost are represented; />Representing intermittent power fluctuation penalty costs; />Representing the net cost.
Time-of-use electricity price and electricity exchange costThe expression can be represented by the following formula:
wherein,is an electrical load nodeiActive power of (2); />The time-sharing electricity price at the moment t of the power grid; />For the billing period.
Intermittent power fluctuation penalty costThe expression can be represented by the following formula:
Wherein,、/>、/>the power is respectively adjusted for the photovoltaic clean energy end, the wind power clean energy end and the unit of the distributed energy storage equipment; />Cost is penalized for intermittent power.
Cost of net lossThe expression can be represented by the following formula:
wherein,for line->At the position oftThe line loses power at that moment.
Voltage safety marginThe expression can be represented by the following formula: />
Wherein,and->Respectively istVoltage safety margin when the system operates normally and under preset faults at any time; />And->Respectively attSystem normal operation and predictive failure power down negativesLotus node->Is set, the voltage amplitude of (a) is set.
The second optimization model optimization control total target corresponding to any rural integrated energy system can be expressed by the following formula:
wherein,representation oftThe multi-target comprehensive optimal value of the rural comprehensive energy system at any moment; />A multi-objective solution function representing any of the second optimization models of the above embodiments; /> 、/>The comprehensive cost of the rural comprehensive energy system and the user satisfaction of the rural comprehensive energy system are respectively represented.
Comprehensive cost of any rural comprehensive energy systemThe expression can be represented by the following formula:
wherein,representing the time-sharing electricity price and the electricity cost; />Representing fuel costs; />Representing the operation and maintenance cost of the rural comprehensive energy system; / >Representing the demand side response regulation cost in the rural comprehensive energy system; />And represents the punishment cost of the rural comprehensive energy system.
Time-of-use electricity price and electricity costThe expression can be represented by the following formula:
wherein,for electrical load node->Active power of (2); />The time-sharing electricity price at the moment t of the power grid;t isCharging period.
Cost of fuelThe expression can be represented by the following formula:
wherein,representation->At the electrical load nodetBurning at the moment of timeThe unit gas consumption for converting the gas into heat energy; />Representation oftGenerating power of the gas generator at the moment; η (eta) G2P Representing the power generation efficiency of the gas generator; />Representing the cost of a unit gas; LHV represents the lower heating value of natural gas.
Operation and maintenance cost of any rural comprehensive energy systemThe expression can be represented by the following formula: />
Wherein delta 1 、δ 2 、δ 3 、δ 4 The unit power operation and maintenance cost of the gas generator, the photovoltaic clean energy end, the wind power clean energy end and the distributed energy storage equipment is represented by yuan/kW respectively;K 1 、K 2 、K 3 、K 4 respectively representing the number of the gas generators, the photovoltaic clean energy ends, the wind power clean energy ends and the distributed energy storage devices;representation oftGenerating power of the photovoltaic clean energy source end at any time; />Representation oftGenerating power of wind power clean energy source end at any time; / >Representation oftAnd generating power of the time distributed energy storage device.
Demand side response regulation and control cost in any rural comprehensive energy systemThe expression can be represented by the following formula:
wherein,、/>and->Respectively representing distributed energy storage equipment, flexible thermodynamic load and flexible electric load adjustment quantity in unit time; epsilon 1 、ε 2 And epsilon 3 The unit adjustment cost of the distributed energy storage equipment, the flexible thermodynamic load and the flexible electric load is respectively represented;N 1 、N 2 、N 3 the number of distributed energy storage devices, flexible thermal loads and flexible electrical loads are represented, respectively.
Punishment cost of any rural comprehensive energy systemThe expression can be represented by the following formula:
wherein,、/>the light and wind discarding power are respectively carried out; />、/>Punishment cost for light and wind discarding is respectively carried out; m1 and M2 are respectively abandoningNumber of optical wind curtailment devices.
User satisfaction of any rural integrated energy systemThe expression can be represented by the following formula:
wherein,、/>respectively represent the comfort and the economy of the demand side in any rural comprehensive energy system. />，/>Closer to 1 means higher comfort. If->The requirement side economy after the optimized operation is better; if->Indicating a poorer economy after optimization. Thus (S)>The larger the value, the higher the user satisfaction of the rural integrated energy system, the +. >The smaller the value, the worse the user satisfaction of the rural integrated energy system.
It should be noted that, each time the first optimization model and the second optimization model are solved, the obtained iteration result still needs to satisfy the constraint condition. The constraint conditions of the first optimization model can be rural power distribution network power balance constraint, grid inequality constraint, gas flow balance constraint, heat generation constraint of gas equipment and the like. Constraints of the second optimization model may be distributed energy storage device energy and timing constraints, user economy and comfort constraints, and the like.
Fig. 3 is a schematic structural diagram of an energy scheduling device provided by the invention. The energy scheduling device provided by the invention is described below with reference to fig. 3, and the energy scheduling device described below and the energy scheduling method described above can be referred to correspondingly. As shown in fig. 3, the apparatus includes: a first communication module 301, an iteration module 302, a judgment module 303 and a first scheduling module 304.
The first communication module 301 is configured to receive the fluctuating power corresponding to each rural integrated energy system in the previous iteration.
The iteration module 302 is configured to obtain, based on the fluctuation power corresponding to each rural integrated energy system in the previous iteration and the first optimization model, the interaction power corresponding to each rural integrated energy system in the current iteration.
And the judging module 303 is configured to, if it is judged that the interactive power corresponding to each rural integrated energy system in the current iteration converges and the accumulated iteration number is not less than the preset maximum iteration number, take the interactive power corresponding to each rural integrated energy system in the current iteration as the target interactive power corresponding to each rural integrated energy system.
The first scheduling module 304 is configured to send the target interaction power corresponding to each rural integrated energy system to a first scheduling end corresponding to each rural integrated energy system, so that each first scheduling end schedules each rural integrated energy system based on the target interaction power corresponding to each rural integrated energy system.
The optimization targets of the first optimization model comprise the minimization of the running cost of the rural power distribution network and the maximization of the voltage safety margin of the rural power distribution network; the fluctuation power corresponding to each rural comprehensive energy system in the previous iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in the previous iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system.
Specifically, the first communication module 301, the iteration module 302, the judgment module 303, and the first scheduling module 304 are electrically connected.
It should be noted that, the energy scheduling device in the embodiment of the present invention is a second scheduling end corresponding to the rural power distribution network.
And when each iteration starts, each first scheduling end can send the fluctuation power corresponding to each rural comprehensive energy system in the previous iteration to the second scheduling end.
The first communication module 301 may receive the fluctuating power corresponding to each rural integrated energy system in the last iteration sent by each first scheduling end.
When each iteration is performed, after the iteration module 302 receives the fluctuation power corresponding to each rural integrated energy system in the previous iteration, the fluctuation power corresponding to each rural integrated energy system in the previous iteration may be input into the first optimization model. And obtaining the interactive power corresponding to each rural comprehensive energy system of the iteration by solving the first optimization model.
It should be noted that, when each iteration is performed, the first optimization model may be solved based on the multi-target particle swarm algorithm, so as to obtain the interaction power corresponding to each rural integrated energy system in this iteration.
At the end of each iteration, the judging module 303 may judge whether the interactive power corresponding to each rural integrated energy system in the iteration converges.
If the judging module 303 judges that the interactive power corresponding to each rural integrated energy system in the iteration is converged, it can judge whether the completed accumulated iteration number is greater than or equal to the preset maximum iteration number.
If the judging module 303 judges that the number of completed accumulated iterations is greater than or equal to the preset maximum number of iterations, it indicates that the target interaction power corresponding to each rural integrated energy system can be found, which can simultaneously achieve the purposes of minimizing the operation cost of the rural power distribution network, maximizing the voltage safety margin of the rural power distribution network, minimizing the integrated cost of each rural integrated energy system and maximizing the user satisfaction of each rural integrated energy system. The target interaction power corresponding to each rural comprehensive energy system is the interaction power corresponding to each rural comprehensive energy system of the iteration.
After determining the target interaction power corresponding to each rural integrated energy system, the first scheduling module 304 may send the target interaction power corresponding to each rural integrated energy system to the first scheduling end corresponding to each rural integrated energy system.
According to the embodiment of the invention, based on the first optimization model, the second optimization model and dynamic games between the rural power distribution network and each rural comprehensive energy system, interaction power corresponding to each rural comprehensive energy system is used as a coupling variable, after multiple iterations are carried out, an optimal scheduling scheme is obtained, and the rural power distribution network and each rural comprehensive energy system are optimally scheduled according to the optimal scheduling scheme, so that the rural comprehensive energy configuration is more reasonable, the waste of resources can be reduced in the operation process of the rural power distribution network and each rural comprehensive energy system, the load of the rural power distribution network can be reduced, the operation stability of the rural power distribution network can be improved, and the consumption of clean energy can be improved.
Fig. 4 is a second schematic structural diagram of the energy scheduling device provided by the present invention. The energy scheduling device provided by the invention is described below with reference to fig. 4, and the energy scheduling device described below and the energy scheduling method described above can be referred to correspondingly. As shown in fig. 4, the apparatus includes: a second communication module 401 and a second scheduling module 402.
The second communication module 401 is configured to send, to a second scheduling end corresponding to the rural power distribution network, the fluctuating power corresponding to the previous iteration, so that the second scheduling end obtains the interactive power corresponding to the current iteration based on the fluctuating power corresponding to the previous iteration and the first optimization model, and if it is determined that the interactive power corresponding to the current iteration converges and the accumulated iteration number is not less than the preset maximum iteration number, the interactive power corresponding to the current iteration is taken as the target interactive power, and the target interactive power is sent.
The second scheduling module 402 is configured to send, to a second scheduling end corresponding to the rural power distribution network, the fluctuating power corresponding to the previous iteration, so that the second scheduling end obtains the interactive power corresponding to the current iteration based on the fluctuating power corresponding to the previous iteration and the first optimization model, and if it is determined that the interactive power corresponding to the current iteration converges and the accumulated iteration number is not less than the preset maximum iteration number, the interactive power corresponding to the current iteration is taken as the target interactive power, and the target interactive power is sent.
The fluctuation power corresponding to the previous iteration is obtained based on the interaction power corresponding to the previous iteration and the second optimization model; the optimization targets of the second optimization model comprise the minimization punishment cost of the rural comprehensive energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system; the optimization objectives of the first optimization model include minimizing the running cost of the rural power distribution network and maximizing the voltage safety margin of the rural power distribution network.
Specifically, the second communication module 401 and the second scheduling module 402 are electrically connected.
It should be noted that, the energy scheduling device in the embodiment of the present invention is a first scheduling end corresponding to any rural integrated energy system.
When each iteration starts, the second communication module 401 may send the fluctuating power corresponding to the rural comprehensive energy system in the previous iteration to the second scheduling end corresponding to the rural power distribution network.
After the second scheduling end receives the fluctuation power corresponding to the rural integrated energy system in the last iteration sent by the second communication module 401, the interaction power corresponding to the rural integrated energy system in the current iteration can be obtained according to the fluctuation power corresponding to the rural integrated energy system in the last iteration.
If the interactive power corresponding to the rural comprehensive energy system in the iteration is judged to be converged and the accumulated iteration number is not less than the preset maximum iteration number, the method indicates that the target interactive power corresponding to the rural comprehensive energy system, which can simultaneously achieve the purposes of minimizing the running cost of the rural power distribution network, maximizing the voltage safety margin of the rural power distribution network, minimizing the comprehensive cost of each rural comprehensive energy system and maximizing the user satisfaction of each rural comprehensive energy system, is found. The target interaction power corresponding to the rural comprehensive energy system is the interaction power corresponding to each rural comprehensive energy system of the iteration.
The second scheduling end may send the target interaction power corresponding to the rural integrated energy system to the second scheduling module 402.
After receiving the target interaction power corresponding to the rural integrated energy system, the second scheduling module 402 may input the target interaction power corresponding to the rural integrated energy system into the second optimization model.
Based on the second optimization model solving method in the above embodiment, the second optimization model is solved, and the value of the particle with the highest corresponding matching value can be obtained. And determining the energy scheduling scheme of the rural comprehensive energy system according to the value of the particle with the highest corresponding matching value.
After determining the energy scheduling scheme of the rural integrated energy system, the second scheduling module 402 may schedule the output power of each energy side of the energy sides, the power of the flexible power load and the flexible thermal load of the demand side, and the power of the device for implementing energy conversion in the rural integrated energy system according to the energy scheduling scheme.
According to the embodiment of the invention, based on the first optimization model, the second optimization model and dynamic games between the rural power distribution network and each rural comprehensive energy system, interaction power corresponding to each rural comprehensive energy system is used as a coupling variable, after multiple iterations are carried out, an optimal scheduling scheme is obtained, and the rural power distribution network and each rural comprehensive energy system are optimally scheduled according to the optimal scheduling scheme, so that the rural comprehensive energy configuration is more reasonable, the waste of resources can be reduced in the operation process of the rural power distribution network and each rural comprehensive energy system, the load of the rural power distribution network can be reduced, the operation stability of the rural power distribution network can be improved, and the consumption of clean energy can be improved.
Fig. 5 illustrates a physical schematic diagram of an electronic device, as shown in fig. 5, which may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform an energy scheduling method comprising: receiving fluctuation power corresponding to each rural comprehensive energy system in the last iteration; acquiring interaction power corresponding to each rural integrated energy system of the iteration based on the fluctuation power corresponding to each rural integrated energy system of the previous iteration and the first optimization model; if the interactive power corresponding to each rural comprehensive energy system of the iteration is judged to be converged and the accumulated iteration times are not less than the preset maximum iteration times, the interactive power corresponding to each rural comprehensive energy system of the iteration is taken as the target interactive power corresponding to each rural comprehensive energy system; the method comprises the steps that target interaction power corresponding to each rural comprehensive energy system is respectively sent to a first scheduling end corresponding to each rural comprehensive energy system, so that each first scheduling end schedules each rural comprehensive energy system based on the target interaction power corresponding to each rural comprehensive energy system; the optimization targets of the first optimization model comprise the minimization of the running cost of the rural power distribution network and the maximization of the voltage safety margin of the rural power distribution network; the fluctuation power corresponding to each rural comprehensive energy system in the previous iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in the previous iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the energy scheduling method provided by the above methods, the method comprising: receiving fluctuation power corresponding to each rural comprehensive energy system in the last iteration; acquiring interaction power corresponding to each rural integrated energy system of the iteration based on the fluctuation power corresponding to each rural integrated energy system of the previous iteration and the first optimization model; if the interactive power corresponding to each rural comprehensive energy system of the iteration is judged to be converged and the accumulated iteration times are not less than the preset maximum iteration times, the interactive power corresponding to each rural comprehensive energy system of the iteration is taken as the target interactive power corresponding to each rural comprehensive energy system; the method comprises the steps that target interaction power corresponding to each rural comprehensive energy system is respectively sent to a first scheduling end corresponding to each rural comprehensive energy system, so that each first scheduling end schedules each rural comprehensive energy system based on the target interaction power corresponding to each rural comprehensive energy system; the optimization targets of the first optimization model comprise the minimization of the running cost of the rural power distribution network and the maximization of the voltage safety margin of the rural power distribution network; the fluctuation power corresponding to each rural comprehensive energy system in the previous iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in the previous iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the energy scheduling methods provided above, the method comprising: receiving fluctuation power corresponding to each rural comprehensive energy system in the last iteration; acquiring interaction power corresponding to each rural integrated energy system of the iteration based on the fluctuation power corresponding to each rural integrated energy system of the previous iteration and the first optimization model; if the interactive power corresponding to each rural comprehensive energy system of the iteration is judged to be converged and the accumulated iteration times are not less than the preset maximum iteration times, the interactive power corresponding to each rural comprehensive energy system of the iteration is taken as the target interactive power corresponding to each rural comprehensive energy system; the method comprises the steps that target interaction power corresponding to each rural comprehensive energy system is respectively sent to a first scheduling end corresponding to each rural comprehensive energy system, so that each first scheduling end schedules each rural comprehensive energy system based on the target interaction power corresponding to each rural comprehensive energy system; the optimization targets of the first optimization model comprise the minimization of the running cost of the rural power distribution network and the maximization of the voltage safety margin of the rural power distribution network; the fluctuation power corresponding to each rural comprehensive energy system in the previous iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in the previous iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power consumption and the actual power consumption of clean energy in the rural comprehensive energy system.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (9)

1. An energy scheduling method, comprising:
receiving fluctuation power corresponding to each rural comprehensive energy system in the last iteration;
acquiring interaction power corresponding to each rural integrated energy system in the iteration based on the fluctuation power corresponding to each rural integrated energy system in the previous iteration and a first optimization model;
if the interactive power corresponding to each rural comprehensive energy system in the iteration is judged to be converged and the accumulated iteration number is not less than the preset maximum iteration number, the interactive power corresponding to each rural comprehensive energy system in the iteration is taken as the target interactive power corresponding to each rural comprehensive energy system;
The target interaction power corresponding to each rural integrated energy system is respectively sent to a first scheduling end corresponding to each rural integrated energy system, so that each first scheduling end schedules each rural integrated energy system based on the target interaction power corresponding to each rural integrated energy system;
the optimization targets of the first optimization model comprise the minimization of the running cost of the rural power distribution network and the maximization of the voltage safety margin of the rural power distribution network; the fluctuation power corresponding to each rural comprehensive energy system in the last iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in each generation in the last iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing the punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power and the actual power of the clean energy in the rural comprehensive energy system; the original interaction power corresponding to each rural comprehensive energy system and the original fluctuation power corresponding to each rural comprehensive energy system are initial values of the iteration;
the obtaining the interactive power corresponding to each rural integrated energy system of the iteration based on the fluctuating power corresponding to each rural integrated energy system of the previous iteration and the first optimization model specifically comprises the following steps:
After the fluctuation power corresponding to each rural comprehensive energy system in the last iteration is input into the first optimization model, calculating a matching value corresponding to each particle;
taking the value of the particle with the highest corresponding matching value as the interaction power corresponding to each rural comprehensive energy system of the iteration;
wherein the particles are randomly generated based on a multi-target particle swarm algorithm;
the original wave band power corresponding to each rural comprehensive energy system is obtained by respectively sending the original interaction power corresponding to each rural comprehensive energy system to a first scheduling end corresponding to each rural comprehensive energy system, so that the first scheduling end corresponding to each rural comprehensive energy system inputs the original interaction power corresponding to each rural comprehensive energy system into a second optimization model corresponding to each rural comprehensive energy system, and solving the second optimization model corresponding to each rural comprehensive energy system.
2. The energy scheduling method according to claim 1, wherein if it is determined that the interactive power corresponding to each rural integrated energy system in the current iteration converges and the accumulated iteration number is not less than the preset maximum iteration number, before using the interactive power corresponding to each rural integrated energy system in the current iteration as the target interactive power corresponding to each rural integrated energy system, the method further comprises:
If it is judged that the interactive power corresponding to each rural integrated energy system in the iteration is converged but the accumulated iteration times are smaller than the preset maximum iteration times, updating the interactive power corresponding to each rural integrated energy system in the iteration based on the interactive power corresponding to each rural integrated energy system in the iteration, the fluctuation power corresponding to each rural integrated energy system in the previous iteration and the first optimization model, and performing the next iteration based on the updated interactive power corresponding to each rural integrated energy system in the iteration.
3. The energy scheduling method according to claim 1, wherein if it is determined that the interactive power corresponding to each rural integrated energy system in the current iteration converges and the accumulated iteration number is not less than the preset maximum iteration number, before using the interactive power corresponding to each rural integrated energy system in the current iteration as the target interactive power corresponding to each rural integrated energy system, the method further comprises:
and if the interactive power corresponding to each rural comprehensive energy system in the iteration is judged to be not converged, performing the next iteration.
4. The energy scheduling method according to claim 2, wherein the updating the interactive power corresponding to each rural integrated energy system of the present iteration based on the interactive power corresponding to each rural integrated energy system of the present iteration, the fluctuating power corresponding to each rural integrated energy system of the last iteration, and the first optimization model specifically includes:
Updating the value of each particle;
after the interactive power corresponding to each rural integrated energy system of the iteration and the fluctuation power of each rural integrated energy system corresponding to the iteration are input into the first optimization model, calculating the updated matching value corresponding to each particle;
taking the value of the updated particle with the highest corresponding matching value as the interaction power corresponding to each rural comprehensive energy system of the updated iteration;
wherein the particles are randomly generated based on a multi-target particle swarm algorithm.
5. An energy scheduling method, comprising:
the method comprises the steps that fluctuation power corresponding to the last iteration is sent to a second scheduling end corresponding to a rural power distribution network, so that the second scheduling end obtains interaction power corresponding to the current iteration based on the fluctuation power corresponding to the last iteration and a first optimization model, and if the interaction power corresponding to the current iteration is judged to be converged and the accumulated iteration number is not smaller than the preset maximum iteration number, the interaction power corresponding to the current iteration is used as target interaction power, and the target interaction power is sent;
after receiving the target interaction power, determining an energy scheduling scheme according to the target interaction power, and scheduling a rural comprehensive energy system according to the energy scheduling scheme;
The fluctuation power corresponding to the previous iteration is obtained based on the interaction power corresponding to the previous iteration and a second optimization model; the optimization targets of the second optimization model comprise the minimum punishment cost of the rural comprehensive energy system; the punishment cost is determined according to the theoretical power and the actual power of the clean energy in the rural comprehensive energy system; the optimization targets of the first optimization model comprise minimizing the running cost of the rural power distribution network and maximizing the voltage safety margin of the rural power distribution network; the original interaction power corresponding to the rural comprehensive energy system and the original fluctuation power corresponding to the rural comprehensive energy system are initial values of the iteration;
the second scheduling end obtains the interaction power corresponding to each rural integrated energy system of the iteration based on the fluctuation power corresponding to each rural integrated energy system of the previous iteration and the first optimization model, and specifically comprises the following steps:
the second scheduling end inputs the fluctuation power corresponding to each rural comprehensive energy system in the last iteration into the first optimization model, and then calculates a matching value corresponding to each particle;
The second scheduling end takes the value of the particle with the highest corresponding matching value as the interaction power corresponding to each rural comprehensive energy system of the iteration;
wherein the particles are randomly generated based on a multi-target particle swarm algorithm;
the original wave band power corresponding to each rural comprehensive energy system is obtained by respectively sending the original interaction power corresponding to each rural comprehensive energy system to a first scheduling end corresponding to each rural comprehensive energy system, so that the first scheduling end corresponding to each rural comprehensive energy system inputs the original interaction power corresponding to each rural comprehensive energy system into a second optimization model corresponding to each rural comprehensive energy system, and solving the second optimization model corresponding to each rural comprehensive energy system.
6. An energy scheduling apparatus, comprising:
the first communication module is used for receiving the fluctuation power corresponding to each rural comprehensive energy system in the last iteration;
the iteration module is used for acquiring the interaction power corresponding to each rural integrated energy system in the iteration based on the fluctuation power corresponding to each rural integrated energy system in the previous iteration and the first optimization model;
The judging module is used for judging that the interactive power corresponding to each rural comprehensive energy system in the iteration is converged and the accumulated iteration number is not less than the preset maximum iteration number, and taking the interactive power corresponding to each rural comprehensive energy system in the iteration as the target interactive power corresponding to each rural comprehensive energy system;
the first scheduling module is used for respectively sending the target interaction power corresponding to each rural integrated energy system to a first scheduling end corresponding to each rural integrated energy system, so that each first scheduling end schedules each rural integrated energy system based on the target interaction power corresponding to each rural integrated energy system;
the optimization targets of the first optimization model comprise the minimization of the running cost of the rural power distribution network and the maximization of the voltage safety margin of the rural power distribution network; the fluctuation power corresponding to each rural comprehensive energy system in the last iteration is obtained based on the interaction power corresponding to each rural comprehensive energy system in each generation in the last iteration and each second optimization model; the optimization objective of the second optimization model comprises minimizing the punishment cost of the rural integrated energy system; the punishment cost is determined according to the theoretical power and the actual power of the clean energy in the rural comprehensive energy system; the original interaction power corresponding to each rural comprehensive energy system and the original fluctuation power corresponding to each rural comprehensive energy system are initial values of the iteration;
The obtaining the interactive power corresponding to each rural integrated energy system of the iteration based on the fluctuating power corresponding to each rural integrated energy system of the previous iteration and the first optimization model specifically comprises the following steps:
after the fluctuation power corresponding to each rural comprehensive energy system in the last iteration is input into the first optimization model, calculating a matching value corresponding to each particle;
taking the value of the particle with the highest corresponding matching value as the interaction power corresponding to each rural comprehensive energy system of the iteration;
wherein the particles are randomly generated based on a multi-target particle swarm algorithm;
the original wave band power corresponding to each rural comprehensive energy system is obtained by respectively sending the original interaction power corresponding to each rural comprehensive energy system to a first scheduling end corresponding to each rural comprehensive energy system, so that the first scheduling end corresponding to each rural comprehensive energy system inputs the original interaction power corresponding to each rural comprehensive energy system into a second optimization model corresponding to each rural comprehensive energy system, and solving the second optimization model corresponding to each rural comprehensive energy system.
7. An energy scheduling apparatus, comprising:
the second communication module is used for sending fluctuation power corresponding to the last iteration to a second scheduling end corresponding to the rural power distribution network, so that the second scheduling end obtains interaction power corresponding to the current iteration based on the fluctuation power corresponding to the last iteration and a first optimization model, and if the interaction power corresponding to the current iteration is judged to be converged and the accumulated iteration number is not less than the preset maximum iteration number, the interaction power corresponding to the current iteration is used as target interaction power, and the target interaction power is sent;
the second scheduling module is used for determining an energy scheduling scheme according to the target interaction power after receiving the target interaction power, and scheduling a rural comprehensive energy system according to the energy scheduling scheme;
the fluctuation power corresponding to the previous iteration is obtained based on the interaction power corresponding to the previous iteration and a second optimization model; the optimization targets of the second optimization model comprise the minimum punishment cost of the rural comprehensive energy system; the punishment cost is determined according to the theoretical power and the actual power of the clean energy in the rural comprehensive energy system; the optimization targets of the first optimization model comprise minimizing the running cost of the rural power distribution network and maximizing the voltage safety margin of the rural power distribution network; the original interaction power corresponding to the rural comprehensive energy system and the original fluctuation power corresponding to the rural comprehensive energy system are initial values of the iteration;
The second scheduling end obtains the interaction power corresponding to each rural integrated energy system of the iteration based on the fluctuation power corresponding to each rural integrated energy system of the previous iteration and the first optimization model, and specifically comprises the following steps:
the second scheduling end inputs the fluctuation power corresponding to each rural comprehensive energy system in the last iteration into the first optimization model, and then calculates a matching value corresponding to each particle;
the second scheduling end takes the value of the particle with the highest corresponding matching value as the interaction power corresponding to each rural comprehensive energy system of the iteration;
wherein the particles are randomly generated based on a multi-target particle swarm algorithm;
the original wave band power corresponding to each rural comprehensive energy system is obtained by respectively sending the original interaction power corresponding to each rural comprehensive energy system to a first scheduling end corresponding to each rural comprehensive energy system, so that the first scheduling end corresponding to each rural comprehensive energy system inputs the original interaction power corresponding to each rural comprehensive energy system into a second optimization model corresponding to each rural comprehensive energy system, and solving the second optimization model corresponding to each rural comprehensive energy system.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the energy scheduling method of any one of claims 1 to 4 when the program is executed.
9. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the energy scheduling method according to any of claims 1 to 4.
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