Micro-grid energy-carbon unified scheduling method based on SoC and OS platforms

Abstract

The invention discloses a micro-grid energy-carbon unified scheduling method based on SoC and OS platforms, and the method comprises the steps: carrying out the regional deployment of each micro-grid node according to the position and type, and enabling all micro-grid nodes in each region to be connected with a regional scheduling center, all micro-grid nodes in each area and the corresponding area dispatching center are connected into a looped network structure through SoC, and each area dispatching center is managed and controlled by a micro-grid energy carbon intelligent management and control center in a unified manner; each regional dispatching center is connected with the micro-grid energy carbon intelligent management and control center through an SoC to form a ring network structure; the micro-grid energy carbon intelligent management and control center collects information of each micro-grid node in the corresponding area in real time; the micro-grid energy-carbon intelligent management and control center carries out micro-grid energy-carbon unified scheduling on each micro-grid node through a load prediction model and an improved particle swarm algorithm; and after the micro-grid node receives the comprehensive dispatching instruction, an equipment control module in the OS is utilized to automatically send an equipment adjusting instruction, so that energy output and load adjustment are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-carbon coordinated scheduling, and in particular to a microgrid energy-carbon unified scheduling method based on SoC and OS platforms. Background Art

[0002] With the transformation of the global energy structure and the increase in the demand for sustainable development, the pace of construction of energy Internet and smart grid is accelerating. In this context, microgrids, as an important part of distributed energy systems, play an increasingly important role in energy supply and distribution with their flexibility, reliability and economy. However, with the expansion of microgrid scale and the increase in complexity, its energy management and carbon emission control issues have become increasingly prominent, becoming a key factor restricting the efficient, safe and stable operation of microgrids.

[0003] In order to achieve refined management and optimized operation of microgrids, the industry has begun to explore solutions based on advanced information technology. In microgrid energy and carbon management, SoC (System on Chip) technology can realize the integration and coordinated control of various sensors, controllers and actuators in the microgrid, thereby realizing accurate monitoring and real-time regulation of energy and carbon emissions. The OS (Operating System) platform can achieve unified management and scheduling of various devices and systems in the microgrid to ensure that they can operate according to predetermined strategies and rules. At the same time, the OS platform can also provide a wealth of application programming interfaces (APIs) to support developers to develop various energy-carbon coordination and control applications according to actual needs, and further enrich and improve the energy management and carbon emission control functions of the microgrid. Summary of the invention

[0004] Technical purpose: In view of the defects in the prior art, the present invention discloses a microgrid energy and carbon unified scheduling method based on SoC and OS platform, and constructs a ring network structure of microgrid nodes, regional scheduling center, and microgrid energy and carbon intelligent management and control center on the basis of SoC and OS. Through the load prediction model and improved particle swarm algorithm, the microgrid energy and carbon are uniformly scheduled for each microgrid node to achieve energy output and load regulation.

[0005] Technical solution: In order to achieve the above technical objectives, the present invention adopts the following technical solution.

[0006] A microgrid energy and carbon unified scheduling method based on SoC and OS platform includes the following steps:

[0007] (1) Each microgrid node is deployed in different regions according to its location and type. All microgrid nodes in each region are connected to a regional dispatching center. All microgrid nodes in each region and the corresponding regional dispatching center are connected to form a ring network structure through SoC. Each regional dispatching center is then uniformly controlled by a microgrid energy carbon intelligent management and control center. Each regional dispatching center and the microgrid energy carbon intelligent management and control center are connected to form a ring network structure through SoC.

[0008] (2) The microgrid energy and carbon intelligent management and control center collects information of each microgrid node in the corresponding area in real time;

[0009] (3) The microgrid energy and carbon intelligent management and control center performs unified microgrid energy and carbon dispatching on each microgrid node through a load prediction model and an improved particle swarm algorithm;

[0010] (4) After receiving the comprehensive dispatching instruction, the microgrid node automatically issues equipment adjustment instructions using the equipment control module in the OS to achieve energy output and load adjustment.

[0011] Beneficial effects:

[0012] 1. Based on SoC and OS, the present invention constructs a ring network structure of each microgrid node, regional dispatch center, and microgrid energy carbon intelligent management and control center, and performs microgrid energy carbon unified dispatch on each microgrid node through load prediction model and improved particle swarm algorithm to achieve energy output and load regulation;

[0013] 2. The present invention connects each microgrid node to a network for monitoring, establishes a regional dispatching center and an intelligent management and control center, collects information such as the power generation power and electricity, grid-connected status, fault and alarm information, energy storage capacity and electricity, charging and discharging power, temperature and environmental information, power consumption and electricity, electricity demand, control instructions, etc. of each microgrid node, realizes the sharing of energy output and power load information of each microgrid node, and through the construction of a 1:1 simulation prediction model, can simulate and issue comprehensive dispatching instructions, send them to each microgrid node, perform dispatch in advance, and realize stable operation and efficient utilization of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the method of the present invention;

[0015] Figure 2 It is a diagram of the improved particle swarm algorithm method of the present invention;

[0016] Figure 3 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] As attached Figure 1 As shown, a microgrid energy and carbon unified scheduling method based on SoC and OS platform in this embodiment includes the following steps:

[0019] (1) Each microgrid node is deployed in different regions according to its location and type. All microgrid nodes in each region are connected to a regional dispatching center. All microgrid nodes in each region and the corresponding regional dispatching center are connected to form a ring network structure through SoC (System on Chip). Each regional dispatching center is then uniformly controlled by a microgrid energy-carbon intelligent management and control center. Each regional dispatching center is also connected to the microgrid energy-carbon intelligent management and control center through SoC (System on Chip) to form a ring network structure. The number of regional dispatching centers is an integer greater than or equal to 2, and the number of microgrid nodes is also an integer greater than or equal to 2.

[0020] Among them, microgrid nodes include distributed energy, energy storage devices, controllable loads, etc.; according to the type of equipment, microgrid nodes are divided into primary equipment and secondary equipment, the primary equipment includes various sensors, generators, valves, current controllers, compressors, etc.; the secondary equipment includes control stations, local operator stations, network cameras and switches; wherein the control station automatically controls the field equipment, collects real-time information of field valve opening sensors, voltage sensors, power monitoring sensors and other equipment, and outputs control instructions to control the start and stop of the generator and the opening of the valve after control logic calculation; the local operator station is used for real-time monitoring and control of the current microgrid node during system early debugging and regular inspection and maintenance;

[0021] As attached Figure 3As shown, the microgrid energy carbon intelligent control center and each regional dispatch center, as well as each regional dispatch center and the corresponding microgrid nodes are connected and communicated through optical fiber; the connection between each microgrid node is connected through an industrial-grade switch, and PLC is used as the device controller, OS platform is used as the upper operation platform, and SoC (System on Chip) data acquisition module is used to connect the on-site intelligent instrument to collect data, so as to build an on-site DCS control platform; the basic principle of the data acquisition module is to integrate a set of communication drivers in SoC (System on Chip), and obtain data from different equipment sources by calling the software communication interface API provided by various equipment manufacturers. At the same time, the computing engine embedded in the memory of SoC (System on Chip) is used to set up a corresponding node for each connected device (each node is distinguished by the node number), forming a one-to-one data mirror, so that the SoC (System on Chip) and the terminal device can synchronize data. Among them, each node can be configured with a computing engine, corresponding to a communication device respectively. The controlled device of the microgrid node accepts the regulation and control of the local PLC controller, and reserves the control signals of the regional dispatch center and the intelligent control center;

[0022] All microgrid nodes in each area and the corresponding regional dispatching center are connected to form a ring network structure through a SoC (System on Chip) data acquisition module, and each regional dispatching center and the intelligent management and control center are also connected to form a ring network structure through a SoC (System on Chip) data acquisition module.

[0023] (2) The microgrid energy carbon intelligent management and control center collects relevant information of each microgrid node in the corresponding area in real time; the relevant information of each microgrid node includes power generation power and electricity, grid connection status, fault and alarm information, temperature and environmental information, power consumption and electricity, electricity demand, and control instructions.

[0024] (3) The microgrid energy and carbon intelligent management and control center performs unified microgrid energy and carbon dispatching on each microgrid node through a load forecasting model and an improved particle swarm algorithm, including:

[0025] The microgrid energy and carbon intelligent management and control center combines the relevant information and historical energy and carbon data of each microgrid node, and calculates and allocates the energy output and power load forecast that each region should provide through load forecasting model and improved particle swarm algorithm simulation analysis, and sends control instructions to each regional dispatch center. After receiving the control instructions, each regional dispatch center analyzes and calculates the output and load demand that each microgrid node should perform, notifies each microgrid node, and controls each microgrid node to perform unified regulation;

[0026] Or: the microgrid energy carbon intelligent management and control center combines the relevant information and historical energy carbon data of each microgrid node, calculates and distributes the new energy output and load conditions that each region should provide through load forecasting model and improved particle swarm algorithm simulation analysis, that is, the power generation P of the i-th distributed power source G，i , and issue dispatching instructions to each of the microgrid nodes, which are then controlled in advance;

[0027] (4) After receiving the comprehensive dispatching instruction, the microgrid node uses the device control module in the OS (operating system) to automatically issue device adjustment instructions to achieve energy output and load adjustment.

[0028] The intelligent control center uses the OS platform as the upper operating platform for simulation modeling and unified control strategy configuration. The intelligent control center collects information such as power generation and electricity, grid-connected status, fault and alarm information, temperature and environmental information, power and electricity consumption, electricity demand, control instructions, etc. transmitted by each microgrid node, and builds a 1:1 simulation model of each microgrid node. Based on the collected historical energy-carbon data, it simulates, analyzes, calculates and allocates the distributed power output and power load that each region should provide, and sends it to each regional dispatch center through optical fiber communication;

[0029] Each regional dispatch center receives the dispatch instructions issued by the intelligent control center, analyzes and calculates the energy output and power load of each microgrid node; after receiving the dispatch instructions from the superior, each regional dispatch center controls the start and stop of the on-site equipment through the PLC controller to adjust the energy output and power load; the intelligent control center or regional dispatch center can also directly set the energy output or power load value of each microgrid node. According to the above control process, the unified control method of each microgrid node can be optimized, and each microgrid node can be comprehensively dispatched and controlled.

[0030] In step (3), the unified energy and carbon scheduling of the microgrid utilizes a load forecasting model and an improved particle swarm algorithm to adjust the input power of the distribution network and the output power of the existing distributed power sources in the microgrid, so that on the basis of satisfying power balance, the constraints of distributed power source output, node voltage, and line capacity are met at the same time, so that the power generation cost and greenhouse gas emission cost of the entire system are minimized, thereby realizing low-carbon power generation scheduling.

[0031] The objective function of the load forecasting model is:

[0032] min C=min(C G +C E )

[0033] Among them, min C is the objective function of the load forecasting model, CG is the power generation cost of distributed energy in the microgrid, and CE is the carbon emission cost of the distribution network;

[0034]

[0035] Among them, K G，i is the power generation coefficient of the i-th distributed power source in the distribution network, that is, the cost per kWh of power generation; P G，i is the power generation of the i-th distributed generation, 1≤i≤N, N is the number of distributed generation in the distribution network;

[0036]

[0037] Among them, K E，i is the emission coefficient of the i-th distributed generation in the distribution network, that is, the weight of greenhouse gases emitted for every 1 kWh of electricity generated; P E，i is the power generation of the i-th distributed generation unit, R E The greenhouse gas emission price refers to the emission fee required to be paid for each kg of greenhouse gas emitted;

[0038] When performing unified dispatch of microgrid energy and carbon, the power balance constraints must be met:

[0039]

[0040] Among them, P load is the total load in the entire system, obtained by statistically averaging the historical load data of the distribution network at that moment in the historical data; P loss is the loss of all lines in the whole system. loss This can be obtained by statistically analyzing the historical data of the microgrid.

[0041] In order to ensure the stable operation of distributed power sources, the power generation of each distributed power source must meet its upper and lower limits:

[0042]

[0043] in, They are the upper and lower limits of power generation of each distributed generation unit;

[0044] Subsequently, an improved particle swarm algorithm based on simulated annealing and local mutation is used to calculate the optimal solution, thereby obtaining the output of each distributed energy source with the lowest operating cost and the lowest carbon emission, and sending the corresponding output comprehensive control instructions to each microgrid node.

[0045] The improved particle swarm algorithm based on simulated annealing and local mutation is:

[0046] v i (t) = ωv i(t-1)+c1r1[pbest(t-1)-x i ]+c2r2[gebest(t-1)-x i ]

[0047] x i (t) = x i (t-1)+v i (t)

[0048]

[0049] T t+1 =λT t

[0050] Among them, v i (t) is the velocity vector of the ith particle in the tth iteration, v i (t-1) is the velocity vector of the ith particle in the t-1th iteration, c1 and c2 are learning factors, which enable the particle to have the ability to self-summarize and learn from the excellent individuals in the group, so as to move closer to the optimal position of the individual and the group; r1 and r2 are random numbers between [0, 1], which are used to maintain the diversity of the group; pbest(t-1) is the optimal position currently searched by the particle, that is, the optimal position of the particle in the t-1th iteration; gebest(t-1) is the optimal position currently searched by the entire group; x i (t) is the position vector of the ith particle at the tth iteration, x i (t-1) is the position vector of the ith particle at the t-1th iteration, pv(i, t) is the evaluation value of the ith particle in the t-th iteration, that is, the ratio of the total amount of historical optimal fitness value of the ith particle in the t-th iteration in the previous t-1 iterations to the total amount of improvement of the global optimal particle, is the total amount of historical optimal fitness value, is the total amount of global optimal particle improvement, t-n+1≤j≤t, f() is the evaluation value calculation function, T t+1 is the temperature after the t+1th iteration, λ is a constant, λ∈(0,1), T t is the temperature after the tth iteration, t is the current iteration number; ω is the inertia weight, which plays a role in weighing the local optimal ability and the global optimal ability, and adopts the phenomenon of premature solution and oscillation near the global optimal solution in the later stage of the solution algorithm that changes linearly with the number of iterations;

[0051]

[0052] Among them, ω min is the minimum value of the inertia weight, ω max is the maximum value of the inertia weight, iter maxis the maximum number of iterations, iter is the current number of iterations;

[0053] When pv(i, t) is 0, it indicates that the individual's historical optimal position has not been improved, and a mutation operation can be performed to update the individual's latest position to the current global optimal position. Set the maximum allowable particle speed V max , when the particle velocity exceeds the threshold, it is taken as V max ;

[0054] As attached Figure 2 As shown in the figure, the specific solution steps of the improved particle swarm algorithm are as follows:

[0055] (1) Initialize particles, including setting the initial inertia weight ω, group size, number of particle iterations, and maximum allowable particle speed V max , give the initial particle a random velocity V i 0 ;

[0056] (2) Calculate the fitness value (x) of each particle according to the objective function of the preset load forecasting model i ), evaluate the fitness value of each particle, and update the global optimal position after initialization, and update the position of each particle after initialization to the historical optimal position. The fitness function is F(minc)=1 / min(C G +C E ).

[0057] (3) Set the initial temperature to the global optimal solution for initializing particles. The initial temperature is the preset initial value.

[0058] (4) Update the velocity V of each particle according to the velocity update formula and position update formula i and position x i .

[0059] (5) Calculate the fitness value of each particle (x i ) and the difference Δf from the previous generation’s fitness value, if Or if Δf<0, then accept the new solution;

[0060] (6) Calculate the particle evaluation value pv. If pv(i, t) = 0, perform a mutation operation on the particle and update the position of the particle to the global optimal position.

[0061] (7) Update the annealing temperature and inertia weight ω.

[0062] (8) Determine whether the iteration stop condition is met. If so, stop the iteration and output the global optimal solution. Otherwise, return to step (4).

[0063] The present invention also discloses an electronic device, which includes: a memory for storing a computer program; and a processor for executing the computer program so that the device executes the aforementioned microgrid energy and carbon unified scheduling method based on SoC and OS platform.

[0064] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed, the device running the computer program implements the aforementioned microgrid energy and carbon unified scheduling method based on SoC and OS platform.

[0065] The "first" and "second" in the names such as "first" and "second" (if any) mentioned in the embodiments of the present application are only used as name identifiers and do not represent the first or second in order.

[0066] It can be known from the description of the above implementation mode that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, and the memory can be various types of memory, which can be a random access memory, a read-only memory, a flash memory, etc., such as a read-only memory (English: read-only memory, ROM) / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0067] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A microgrid energy and carbon unified scheduling method based on SoC and OS platform, characterized by: The following steps are involved: (1) Each microgrid node is deployed in different regions according to its location and type. All microgrid nodes in each region are connected to a regional dispatching center. All microgrid nodes in each region and the corresponding regional dispatching center are connected to form a ring network structure through SoC. Each regional dispatching center is then uniformly controlled by a microgrid energy carbon intelligent management and control center. Each regional dispatching center and the microgrid energy carbon intelligent management and control center are connected to form a ring network structure through SoC. (2) The microgrid energy and carbon intelligent management and control center collects information of each microgrid node in the corresponding area in real time; (3) The microgrid energy and carbon intelligent management and control center performs unified microgrid energy and carbon dispatching on each microgrid node through a load prediction model and an improved particle swarm algorithm; (4) After receiving the comprehensive dispatching instruction, the microgrid node automatically issues equipment adjustment instructions using the equipment control module in the OS to achieve energy output and load adjustment.

2. According to claim 1, a microgrid energy and carbon unified scheduling method based on SoC and OS platform is characterized by: The microgrid energy and carbon intelligent management and control center performs unified microgrid energy and carbon dispatching on each microgrid node through a load forecasting model and an improved particle swarm algorithm, including: The microgrid energy and carbon intelligent management and control center combines the relevant information and historical energy and carbon data of each microgrid node, and calculates and allocates the energy output and power load forecast that each region should provide through load forecasting model and improved particle swarm algorithm simulation analysis, and sends control instructions to each regional dispatch center. After receiving the control instructions, each regional dispatch center analyzes and calculates the output and load demand that each microgrid node should perform, notifies each microgrid node, and controls each microgrid node to perform unified regulation; Or: the microgrid energy-carbon intelligent management and control center combines the relevant information and historical energy-carbon data of each microgrid node, calculates and allocates the new energy output and load conditions that each region should provide through load forecasting model and improved particle swarm algorithm simulation analysis, and issues dispatch instructions to each microgrid node, which is then adjusted in advance.

3. According to claim 2, a microgrid energy and carbon unified scheduling method based on SoC and OS platform is characterized by: The objective function of the load forecasting model is: min C=min(C G +C E ) Among them, min C is the objective function of the load forecasting model, CG is the power generation cost of distributed energy in the microgrid, and C E is the carbon emission cost of the distribution network; Among them, K G，i is the power generation coefficient of the i-th distributed power source in the distribution network, that is, the cost per kWh of power generation; P G，i is the power generation of the i-th distributed generation, 1≤i≤N, N is the number of distributed generation in the distribution network; Among them, K E，i is the emission coefficient of the i-th distributed generation in the distribution network, that is, the weight of greenhouse gases emitted for every 1 kWh of electricity generated; P E，i is the power generation of the i-th distributed generation unit, R E The greenhouse gas emission price refers to the emission fee required to pay for every kg of greenhouse gas emitted.

4. According to claim 2, a microgrid energy and carbon unified scheduling method based on SoC and OS platform is characterized by: The power balance constraint of the load forecasting model is: Among them, P load is the total load in the whole system; P loss is the loss of all lines in the whole system, K G，i is the power generation coefficient of the i-th distributed power source in the distribution network, that is, the cost per kWh of power generation; P G，i is the power generation of the i-th distributed generation, 1≤i≤N, N is the number of distributed generation in the distribution network; The power generation of each distributed power source must meet its upper and lower limits: in, They are the upper and lower limits of power generation of each distributed generation unit.

5. According to claim 1, a microgrid energy and carbon unified scheduling method based on SoC and OS platform is characterized by: The improved particle swarm algorithm based on simulated annealing and local mutation is: v i (t)=ωv i (t-1)+c1r1[pbest(t-1)-x i ]+c2r2[gebest(t-1)-x i ] x i (t)=x i (t-1)+v i (t) T t+1 =λT t Among them, v i (t) is the velocity vector of the ith particle in the tth iteration, v i (t-1) is the velocity vector of the ith particle in the t-1th iteration, c1 and c2 are learning factors; r1 and r2 are random numbers between [0, 1]; pbest(t-1) is the optimal position currently searched by the particle; gebest(t-1) is the optimal position currently searched by the entire group; x i (t) is the position vector of the ith particle at the tth iteration, x i (t-1) is the position vector of the ith particle at the t-1th iteration, pv(i, t) is the evaluation value of the ith particle at the tth iteration, is the total amount of historical optimal fitness value, is the total amount of global optimal particle improvement, t-n+1≤j≤t, f() is the evaluation value calculation function, T t+1 is the temperature after the t+1th iteration, λ is a constant, λ∈(0,1), T t is the temperature after the tth iteration, t is the current iteration number; ω is the inertia weight.

6. According to claim 5, a microgrid energy and carbon unified scheduling method based on SoC and OS platform is characterized in that: The calculation formula of inertia weight is: Among them, ω min is the minimum value of the inertia weight, ω max is the maximum value of the inertia weight, iter max is the maximum number of iterations, and iter is the current number of iterations.

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