Multi-energy complementary optimization scheduling system and method based on intelligent micro-grid

Abstract

The invention discloses a multi-energy complementary optimization scheduling system and method based on an intelligent micro-grid, and relates to the field of power grid energy management, the system comprises a distributed power supply, an energy storage device, a load module, a communication module, a server and a monitoring terminal, the distributed power supply, the energy storage device and the load module are all connected with the micro-grid, and the communication module is connected with the server. The distributed power supply is connected with the communication module through the controller, the communication module, the energy storage equipment, the distributed power supply and the load module are all connected with the acquisition module, and the acquisition module comprises a power sensor, a voltage sensor, a current sensor, a temperature sensor and an SOC sensor; through a multi-energy complementary mode, the characteristics and advantages of different energy sources are fully utilized, and collaborative utilization of the energy sources is achieved; due to the combination of multiple energy sources and the addition of the energy storage equipment, the microgrid can keep stable power supply when facing fluctuation or failure of a single energy source; and real-time energy demands and supply conditions can be determined.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid energy management, and in particular to a multi-energy complementary optimization scheduling system and method based on a smart microgrid. Background Art

[0002] The power grid dispatching system is the core component of power grid operation, and is mainly responsible for the safe, stable and economical operation of the power grid. Its basic functions include organizing, commanding, guiding and coordinating the operation of the power grid to ensure the continuity and reliability of power supply.

[0003] However, the existing technology still has the following shortcomings:

[0004] Some existing technologies are not perfect in the configuration of multi-energy complementary systems and may over-rely on a certain energy source or equipment, which is not conducive to the environmental protection and economy of the system. For example, some systems may only configure gas turbines and ignore the use of other renewable energy sources.

[0005] Traditional scheduling strategies are usually relatively fixed and difficult to flexibly adjust according to real-time changes in energy supply and demand and equipment operating status. This may result in the system being unable to fully utilize energy in some cases, or even wasting energy.

[0006] Some existing technologies still rely on manual experience or simple rules for scheduling and lack the application of intelligent optimization algorithms, resulting in low scheduling efficiency and accuracy and difficulty in finding the optimal scheduling solution.

[0007] To this end, we provide a multi-energy complementary optimization scheduling system and method based on smart microgrid to solve the above problems. Summary of the invention

[0008] In view of the problems existing in the above-mentioned prior art, the present invention provides a multi-energy complementary optimization scheduling system and method based on a smart microgrid, which solves the problems of imperfect configuration of the multi-energy complementary system, poor flexibility of traditional scheduling strategies and lack of application of intelligent optimization algorithms.

[0009] In order to achieve the above object, the present invention adopts a multi-energy complementary optimization dispatching system based on a smart microgrid, comprising:

[0010] Distributed power supply, energy storage equipment, load module, communication module, server and monitoring terminal. Distributed power supply, energy storage equipment and load module are all connected to the microgrid. The distributed power supply is connected to the communication module through the controller. The communication module, energy storage equipment, distributed power supply and load module are all connected to the acquisition module. The acquisition module includes power sensor, voltage sensor, current sensor, temperature sensor and SOC sensor.

[0011] As a further optimization of the above solution, the distributed power source includes a photovoltaic power generation system, a wind power generation system and a micro gas turbine;

[0012] The energy storage device includes a lithium battery pack and a supercapacitor;

[0013] The load module includes residential loads, industrial loads and commercial loads, and the residential loads, industrial loads and commercial loads are all connected to the AC bus of the microgrid in parallel;

[0014] The communication module includes a wired communication module and a wireless communication module.

[0015] The present invention also discloses a multi-energy complementary optimization scheduling method based on a smart microgrid, comprising the following steps:

[0016] S1: Data acquisition and processing: various sensors in the acquisition module are used to collect parameter information on distributed power sources, energy storage devices and loads, and the communication module is used to receive the operating status of distributed power sources and energy storage devices, and the collected data is pre-processed and fused;

[0017] S2: Establish a mathematical model, set the objective function by minimizing the operating cost, and determine the constraints. By taking the operating cost as the objective function, the system is guided to find the optimal scheduling strategy.

[0018] S3: Swarm intelligence algorithm, which uses the particle swarm optimization algorithm to calculate the fitness according to the objective function in step S2, and finds a scheduling solution that satisfies the constraints and makes the objective function better, driving the entire particle group to continuously optimize the scheduling solution;

[0019] S4: Extraction and evaluation of the optimal scheduling plan. After the particle swarm optimization algorithm is completed, the corresponding optimal scheduling plan is extracted from the optimal position of the group, and the extracted optimal scheduling plan is evaluated in detail;

[0020] S5: System operation detection and feedback adjustment. According to the optimal scheduling plan, the control center generates corresponding control instructions. After the system runs according to the optimal scheduling plan, it continuously monitors the system's operating status through sensors and communication modules, obtains real-time data on the power generation of distributed power sources, the SOC of energy storage equipment, the charging and discharging current and voltage, and the power demand data of the load, and observes whether the system runs as expected.

[0021] As a further optimization of the above solution, the sensor in the acquisition module performs sampling at a frequency of every 1 minute or every 5 minutes;

[0022] Digital filtering technology is used to remove noise from the collected data, and the collected data is calibrated according to the characteristics of the sensor and calibration parameters.

[0023] As a further optimization of the above scheme, the power generation cost function of the distributed power source is set as C in S2 DG (P DG ), the energy storage equipment cost function is C ESS (P ESS , SOC), the electricity purchase cost function is C grid (P grid ), then the expression of the objective function is as follows:

[0024]

[0025] Among them, t0 and t1 are the start and end time of the scheduling period, P DG , P ESS , P grid They are the power generation power of distributed power sources, the charging and discharging power of energy storage equipment, and the exchange power with the external power grid.

[0026] As a further optimization of the above scheme, the constraints include power balance constraint, energy storage device constraint, and distributed power supply constraint. The power balance constraint is that at any time t, the sum of the power generation power of all distributed power supplies in the microgrid, the charging and discharging power of the energy storage device, and the exchange power with the external power grid is equal to the power demand of the load, and its expression is as follows:

[0027] P DG (t)+P ESS (t)+P grid (t) = P L (t)

[0028] Among them, P L (t) is the load power at time t.

[0029] As a further optimization of the above solution, the energy storage device constraints are as follows:

[0030] SOC upper and lower limit constraints: SOC min ≤SOC(t)≤SOC max ;

[0031] Charge and discharge power limit: P ESS,min ≤P ESS (t)≤P ESS,max ;

[0032] The constraints of distributed power sources include photovoltaic power generation constraints, wind power generation constraints and micro gas turbine constraints, among which photovoltaic power generation constraints include power output range constraints and photovoltaic inverter capacity constraints; wind power generation constraints include power output range constraints, wind speed correlation constraints and pitch angle and speed constraints; the micro gas turbine constraints also include power output range constraints:

[0033] The output power of the photovoltaic power generation system in the power output range constraint is affected by the light intensity and temperature, and its output power has an upper limit P PV,max and the lower limit P PV,min , denoted as P PV,min ≤P PV (t)≤P PV,max ; Under ideal conditions of the strongest sunlight and suitable temperature, the photovoltaic power generation system can reach its rated power output, close to P PV,max At night or when the light is very weak, the output power is close to P PV,min or 0;

[0034] In the photovoltaic inverter capacity constraint, the DC power generated by the photovoltaic panel must be converted into AC power by the inverter before it can be connected to the microgrid. The inverter has a rated capacity S INV When the output power of the photovoltaic power generation system passes through the inverter, it must meet P PV (t)≤S INV , otherwise the inverter will be damaged;

[0035] The power output range constraint in the wind power generation constraint is: the output power of the wind power generation system is closely related to the wind speed, and its power upper limit P WT,max and the lower limit P WT,min , denoted as P WT,min ≤P WT (t)≤P WT,max ; When the wind speed is lower than the cut-in wind speed, the wind turbine does not generate electricity and the output power is P WT,min , usually 0; when the wind speed is within the rated wind speed range, the relationship between the output power and wind speed is approximately linear; when the wind speed exceeds the cut-out wind speed, in order to protect the generator, the output power will be limited to P WT,max ;

[0036] The wind speed correlation constraint in the wind power generation constraint is: output power P WT (t) is a function of wind speed v(t) and is expressed as:

[0037]

[0038] Among them, v in is the cut-in wind speed, v rated is the rated wind speed, a and b are coefficients related to the characteristics of the wind turbine;

[0039] The pitch angle and speed constraints: To control the output power of the wind turbine and prevent overspeed, the pitch angle β(t) and the speed ω(t) are subject to the following constraints: the pitch angle must satisfy β min ≤β(t)≤β max , the speed must satisfy ω min ≤ω(t)≤ωmax , by adjusting the pitch angle and speed, the wind turbine can operate stably and output appropriate power at different wind speeds;

[0040] Power output range constraint in micro gas turbine constraints: Micro gas turbine has its rated power output range, power lower limit P MT,min and power cap P MT,min , to satisfy P MT,min ≤P MT (t)≤P MT,max , its minimum output power is limited by the stable combustion in the combustion chamber, and its maximum output power is limited by the capacity of the turbine and generator.

[0041] As a further optimization of the above solution, in S3, the specific process of the particle swarm optimization algorithm is as follows:

[0042] Each scheduling scheme is encoded as a particle, and the particle position vector is Xi = (xi1, xi2, ..., xin), which represents the decision variables of the distributed power generation distribution and the charging and discharging strategy of the energy storage device, where xi1 represents the power setting value of the solar photovoltaic system at a certain moment, and xi2 represents the discharge power of the battery energy storage system;

[0043] For the power generation element, its initial value is randomly selected within the range of the minimum and maximum power generation of the distributed generation, and the speed vector V i =(v i1 ,v i2 ,…,v in ) is set within a suitable interval according to certain rules;

[0044] The fitness value of each particle is calculated according to the objective function. For the scheduling scheme represented by particle i, the corresponding distributed generation power P DG 、Charging and discharging power of energy storage equipment P ESS Exchange power with the grid P grid Substitute into the objective function to calculate the operating cost;

[0045] The fitness value fitness i Set it as the inverse of cost J. The lower the cost, the higher the fitness value;

[0046] The speed update formula is: v ij (t+1)=wv ij (t)+c1r1(p ij -x ij (t))+c2r2(g ij -x ij (t));

[0047] Among them, w is the inertia weight, c1 and c2 are learning factors, c1=c2=2, r1 and r2 are random numbers between 0 and 1, t is the number of iterations, p ij is the individual optimal position P of particle i best,i The jth component, g ij is the optimal position of the group G best The jth component of

[0048] The position update formula is: ij (t+1)=x ij (t)+v ij (t+1);

[0049] By continuously iteratively updating the speed and position of the particles until the stopping criterion is met, the optimal position of the group G best The represented scheduling scheme is the optimized scheduling scheme.

[0050] As a further optimization of the above scheme, in S5, if the system operating status is monitored to deviate from the optimal scheduling plan, the feedback information is sent back to the server and the monitoring terminal. The server and the monitoring terminal re-evaluate the system status based on the feedback information and make local adjustments to the scheduling plan.

[0051] The multi-energy complementary optimization dispatching system and method based on smart microgrid of the present invention has the following beneficial effects:

[0052] The multi-energy complementary optimization scheduling system and method based on the smart microgrid of the present invention fully utilizes the characteristics and advantages of different energy sources through multi-energy complementarity to achieve coordinated utilization of energy.

[0053] The present invention provides a multi-energy complementary optimization scheduling system and method based on a smart microgrid. The combination of multiple energy sources and the addition of energy storage devices enable the microgrid to maintain stable power supply when facing fluctuations or failures of a single energy source.

[0054] The multi-energy complementary optimization scheduling system and method based on the smart microgrid of the present invention can flexibly adjust the output power of each energy source and the charging and discharging strategy of the energy storage device according to the real-time energy demand and supply situation to achieve supply and demand balance.

[0055] The multi-energy complementary optimization scheduling system and method based on the intelligent microgrid of the present invention, and the application of intelligent algorithms such as particle swarm optimization algorithm, can quickly find the optimal solution among many possible scheduling schemes, and realize efficient scheduling of distributed power sources, energy storage equipment, etc.

[0056] The multi-energy complementary optimization scheduling system and method based on the smart microgrid of the present invention promotes the utilization of renewable energy (such as solar energy and wind energy), reduces dependence on traditional fossil energy, reduces pollutant emissions and greenhouse gas emissions in the energy production process, and has positive significance for environmental protection.

[0057] With reference to the following description and drawings, a specific embodiment of the present invention is disclosed in detail, indicating the manner in which the principles of the present invention can be adopted. It should be understood that the scope of the embodiments of the present invention is not limited thereby, and within the spirit and scope of the appended claims, the embodiments of the present invention include many changes, modifications and equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a diagram of a multi-energy complementary optimization dispatching system based on a smart microgrid according to the present invention;

[0059] Figure 2 This is a diagram of the multi-energy complementary optimization scheduling method based on smart microgrid of the present invention. DETAILED DESCRIPTION

[0060] Please refer to the instruction manual Figure 1-2 The present invention provides a multi-energy complementary optimization scheduling system based on a smart microgrid, including a distributed power supply, an energy storage device, a load module, a communication module, a server and a monitoring terminal. The distributed power supply, the energy storage device and the load module are all connected to the microgrid. The distributed power supply is connected to the communication module through a controller. The communication module, the energy storage device, the distributed power supply and the load module are all connected to the acquisition module. The acquisition module includes a power sensor, a voltage sensor, a current sensor, a temperature sensor and a SOC sensor.

[0061] The server is the core device of the control center, which is used to store and process large amounts of data. It receives data from distributed power sources, energy storage devices, and load modules, runs optimization scheduling algorithms, and generates control instructions. The server has high performance requirements and needs to have strong computing power and storage capacity to cope with real-time data processing and complex scheduling tasks of the microgrid. The monitoring terminal is used to display the operating status of the microgrid, such as the power generation power of each distributed power source, the SOC of the energy storage device (the current remaining power of the energy storage device), the power demand of the load, and other information. At the same time, it can also receive user operation instructions, such as manually adjusting the operating mode of the distributed power source or viewing historical data. The monitoring terminal can be a computer monitor, touch screen, and other devices.

[0062] The server is connected to the controller and monitoring terminal through a network cable to form an internal LAN. Data transmission and command sending are carried out between the server and the controller, and the server sends the processed operation data to the monitoring terminal for display. The server is connected to the distributed power supply, energy storage equipment and load module through a communication module (wired communication or wireless communication). It receives data from the distributed power supply, energy storage equipment and load, and sends control instructions to the controller to realize the monitoring and optimization scheduling of the entire microgrid.

[0063] Distributed power sources include photovoltaic power generation systems, wind power generation systems and micro gas turbines;

[0064] Photovoltaic power generation systems include photovoltaic panels and photovoltaic inverters, wind power generation systems include wind turbines and converters, and micro gas turbines include compressors, combustion chambers, turbines, and generators. Photovoltaic panels convert solar energy into direct current, and photovoltaic inverters convert direct current into alternating current so that it can be connected to the microgrid. Wind turbines convert wind energy into mechanical energy, which is then converted into electrical energy through generators. The converter processes the electrical energy to meet the access requirements of the microgrid. After being compressed by the compressor, the air enters the combustion chamber and mixes with the fuel to burn. The high-temperature and high-pressure gas generated drives the turbine to rotate, which in turn drives the generator to generate electricity. Micro gas turbines can be used as emergency power sources or to supplement electricity during peak loads.

[0065] Energy storage devices include lithium battery packs and supercapacitors, which are composed of electrodes, electrolytes and separators.

[0066] The load modules include residential loads, industrial loads and commercial loads. Residential loads, industrial loads and commercial loads are all connected to the AC bus of the microgrid in parallel to ensure that each load works independently under the operating voltage of the microgrid without affecting each other.

[0067] The communication module includes a wired communication module and a wireless communication module, wherein the wired communication module includes a fiber optic transceiver and an Ethernet switch. The fiber optic transceiver connects the distributed power supply, energy storage equipment, controller and server through a fiber optic cable, and the Ethernet switch connects each device into a local area network through a network cable to realize internal transmission and sharing of data;

[0068] The wireless communication module includes Zigbee module, Wi-Fi router and 4G / 5G communication module. The Zigbee module forms a wireless sensor network among distributed power supply, energy storage equipment and load modules, and transmits data through wireless signals. The Wi-Fi router establishes a wireless connection with the controller by transmitting Wi-Fi signals. The 4G / 5G communication module communicates with the server and monitoring terminal through the base station of the mobile network operator to realize remote transmission of data.

[0069] Zigbee modules are mainly used for short-distance, low-rate wireless communications. In distributed power stations, such as between solar photovoltaic panels or wind turbines, the transmission of status monitoring data can be achieved through Zigbee modules. It has the characteristics of low power consumption and low cost, and is suitable for building small wireless sensor networks.

[0070] A multi-energy complementary optimization scheduling method based on a smart microgrid comprises the following steps:

[0071] S1. Data acquisition and processing: The parameter information of distributed power sources, energy storage devices and loads is collected through various sensors in the acquisition module. The operation status of distributed power sources and energy storage devices is received by the communication module, and the collected data is pre-processed and fused. The sensor samples at a frequency of 1 minute or 5 minutes. Digital filtering technology is used to remove noise from the collected data, and the collected data is calibrated according to the characteristics of the sensor and calibration parameters.

[0072] S2. Establish a mathematical model, set the objective function by minimizing the operating cost, and determine the constraints. By taking the operating cost as the objective function, the system can be guided to find the optimal scheduling strategy;

[0073] Taking the minimization of operating cost as the objective function provides a clear optimization direction for the multi-energy complementary optimization scheduling system of the smart microgrid. In this complex system, there are multiple energy generation, storage and consumption processes. By taking the operating cost as the objective function, the system can be guided to find the optimal scheduling strategy, so that the energy interaction between distributed power sources and energy storage devices can meet the load demand while reducing the cost as much as possible.

[0074] Assume that the power generation cost function of distributed generation is C DG (P DG ), which is related to factors such as fuel consumption and equipment maintenance of distributed power sources. The cost function of energy storage equipment is C ESS (P ESS , SOC), which is related to the number of charge and discharge times of energy storage equipment, battery life, etc. The more charge and discharge times, the faster the battery ages, and the higher the cost. The electricity purchase cost function is C grid (P grid ), then the expression of the objective function is as follows:

[0075]

[0076] Among them, t0 and t1 are the start and end time of the scheduling period, P DG , P ESS , P grid They are the power generated by distributed power sources, the charging and discharging power of energy storage equipment, and the exchange power with the external power grid;

[0077] The power balance constraint is that at any time t, the sum of the power generation power of all distributed power sources in the microgrid, the charging and discharging power of the energy storage equipment, and the exchange power with the external power grid is equal to the power demand of the load. The expression is as follows:

[0078] P DG (t)+P ESS (t)+P grid (t) = P L (t)

[0079] Among them, P L (t) is the load power at time t;

[0080] The energy storage device constraints are as follows

[0081] SOC upper and lower limit constraints: SOC min ≤SOC(t)≤SOC max , ensuring that the energy storage equipment will not be overcharged or over-discharged, ensuring its safety and lifespan;

[0082] Charge and discharge power limit: P ESS,min ≤P ESS (t)≤P ESS,max , which depends on the capacity and power characteristics of the energy storage device;

[0083] The constraints of distributed power sources include photovoltaic power generation constraints, wind power generation constraints and micro gas turbine constraints, among which the photovoltaic power generation constraints are as follows:

[0084] Power output range constraints

[0085] The output power of the photovoltaic power generation system is affected by the light intensity and temperature, and its output power has an upper limit P PV,max and the lower limit P PV,min , denoted as P PV,min ≤P PV (t)≤P PV,max ; Under ideal conditions of maximum sunlight and suitable temperature, the photovoltaic power generation system can reach its rated power output, close to P PV,max At night or when the light is very weak, the output power is close to P PV,min or 0;

[0086] PV inverter capacity constraints

[0087] The DC power generated by the photovoltaic panel is converted into AC power by the inverter before it can be connected to the microgrid. The inverter has a rated capacity S INV When the output power of the photovoltaic power generation system passes through the inverter, it must meet P PV (t)≤S INV , otherwise the inverter will be damaged;

[0088] The wind power generation constraints are as follows:

[0089] Power output range constraints

[0090] The output power of a wind power generation system is closely related to the wind speed, and its upper power limit P WT,max and the lower limit P WT,min , denoted as P WT,min ≤P WT (t)≤P WT,max ; When the wind speed is lower than the cut-in wind speed, the wind turbine does not generate electricity and the output power is P WT,min , usually 0; when the wind speed is within the rated wind speed range, the relationship between the output power and wind speed is approximately linear; when the wind speed exceeds the cut-out wind speed, in order to protect the generator, the output power will be limited to P WT,max ;

[0091] Wind speed dependency constraints

[0092] Output power P WT (t) is a function of wind speed v(t) and is expressed as:

[0093]

[0094] Among them, v in is the cut-in wind speed, v rated is the rated wind speed, a and b are coefficients related to the characteristics of the wind turbine;

[0095] Pitch angle and speed constraints:

[0096] In order to control the output power of the wind turbine and prevent overspeed, the pitch angle β(t) and the speed ω(t) are subject to the following restrictions: the pitch angle must satisfy β min ≤β(t)≤β max , the speed must satisfy ω min ≤ω(t)≤ω max , by adjusting the pitch angle and speed, the wind turbine can operate stably and output appropriate power at different wind speeds;

[0097] The microturbine constraints are as follows:

[0098] Power output range constraints

[0099] Micro gas turbine has its rated power output range, the power lower limit P MT,min and power cap P MT,min , to satisfy P MT,min ≤P MT (t)≤P MT,max , its minimum output power is limited by the stable combustion in the combustion chamber, and its maximum output power is limited by the capacity of the turbine and generator.

[0100] S3, swarm intelligence algorithm, uses particle swarm optimization algorithm to calculate the fitness according to the objective function in step S2, finds a scheduling solution that satisfies the constraints and makes the objective function better, and drives the entire particle group to continuously optimize the scheduling solution;

[0101] The specific process of particle swarm optimization algorithm is as follows:

[0102] Each possible scheduling scheme is encoded as a particle, and the particle position vector is Xi = (xi1, xi2, ..., xin), which represents the decision variables of the distributed power generation distribution and the charging and discharging strategy of the energy storage device, where xi1 represents the set value of the power generation of the solar photovoltaic system at a certain moment, and xi2 represents the discharge power of the battery energy storage system;

[0103] For the power generation element, its initial value is randomly selected within the range of the minimum and maximum power generation of the distributed generation, and the speed vector V i =(v i1 ,v i2 ,…,v in ) is set within a suitable interval according to certain rules;

[0104] The fitness value of each particle is calculated according to the objective function. For the scheduling scheme represented by particle i, the corresponding distributed generation power P DG 、Charging and discharging power of energy storage equipment P ESS Exchange power with the grid P grid Substitute into the objective function to calculate the operating cost;

[0105] The fitness value fitness i Set it as the inverse of cost J. The lower the cost, the higher the fitness value;

[0106] The speed update formula is: v ij (t+1)=wv ij (t)+c1r1(p ij -x ij (t))+c2r2(g ij -x ij (t));

[0107] Among them, w is the inertia weight, c1 and c2 are learning factors, c1=c2=2, r1 and r2 are random numbers between 0 and 1, t is the number of iterations, p ij is the individual optimal position P of particle i best,i The jth component, g ij is the optimal position of the group G best The jth component of

[0108] The position update formula is: ij (t+1)=x ij (t)+v ij (t+1);

[0109] By continuously iteratively updating the speed and position of the particles until the stopping criterion is met, the optimal position of the group G best The represented scheduling scheme is the optimized scheduling scheme.

[0110] S4. Extraction and evaluation of the optimal scheduling plan. After the particle swarm optimization algorithm is completed, the corresponding optimal scheduling plan is extracted from the optimal position of the group, and the extracted optimal scheduling plan is evaluated in detail; check whether all constraints are met. If it is found that the constraints are not met, further analysis of the reasons is required.

[0111] S5. System operation detection and feedback adjustment. According to the optimal scheduling plan, the control center generates corresponding control instructions. After the system runs according to the optimal scheduling plan, it continuously monitors the system's operating status through sensors and communication modules, and obtains real-time data such as the power generation power of distributed power sources, the SOC of energy storage equipment, the charging and discharging current and voltage, and the power demand of the load to observe whether the system operates as expected. If the system operating status is monitored to deviate from the optimal scheduling plan, the feedback information will be sent back to the server and monitoring terminal. The server and monitoring terminal will re-evaluate the system status based on the feedback information and make local adjustments to the scheduling plan.

[0112] Therefore, the present invention adopts the above-mentioned multi-energy complementary optimization scheduling system and method based on smart microgrid, and makes full use of the characteristics and advantages of different energy sources through multi-energy complementary mode to achieve the coordinated utilization of energy. For example, photovoltaic power generation generates electricity when there is sufficient sunlight during the day, wind power generation works when the wind speed is appropriate, micro gas turbines can be used as a stable backup power supply, and energy storage equipment can store energy when there is excess energy and release energy when there is insufficient energy, thereby greatly improving the comprehensive utilization efficiency of energy.

[0113] The combination of multiple energy sources and the addition of energy storage devices enable the microgrid to maintain a stable power supply when facing fluctuations or failures of a single energy source. For example, when wind or photovoltaic power generation becomes unstable due to weather conditions, micro gas turbines and energy storage devices can replenish power in a timely manner to ensure the stable operation of the system.

[0114] It can flexibly adjust the output power of each energy source and the charging and discharging strategy of energy storage equipment according to the real-time energy demand and supply situation to achieve supply and demand balance. For example, during peak power consumption period, energy storage equipment can be called to discharge and the power output of micro gas turbine can be increased; during low power consumption period, energy storage equipment can be charged and the operation of micro gas turbine can be reduced, so as to better meet the power demand of users and reduce operating costs.

[0115] The application of intelligent algorithms such as particle swarm optimization algorithm can quickly find the optimal solution among many possible scheduling schemes and realize efficient scheduling of distributed power sources, energy storage equipment, etc. Compared with traditional scheduling methods, this intelligent optimization scheduling can better adapt to complex energy systems and changing energy demands, and improve the operating performance and economic benefits of the system.

[0116] It has promoted the use of renewable energy (such as solar energy and wind energy), reduced dependence on traditional fossil energy, and reduced pollutant emissions and greenhouse gas emissions in the energy production process, which has positive significance for environmental protection.

Claims

1. A multi-energy complementary optimization dispatching system based on smart microgrid, characterized in that: include: Distributed power supply, energy storage equipment, load module, communication module, server and monitoring terminal, distributed power supply, energy storage equipment and load module are all connected to the microgrid, the distributed power supply is connected to the communication module through the controller, the communication module, energy storage equipment, distributed power supply and load module are all connected to the acquisition module, and the acquisition module includes power sensor, voltage sensor, current sensor, temperature sensor and SOC sensor.

2. A multi-energy complementary optimization dispatching system and method based on a smart microgrid according to claim 1, characterized in that: The distributed power source includes a photovoltaic power generation system, a wind power generation system and a micro gas turbine; The energy storage device includes a lithium battery pack and a supercapacitor; The load module includes residential loads, industrial loads and commercial loads, and the residential loads, industrial loads and commercial loads are all connected to the AC bus of the microgrid in parallel; The communication module includes a wired communication module and a wireless communication module.

3. A multi-energy complementary optimization scheduling method based on smart microgrid, characterized by: The multi-energy complementary optimization dispatching system based on the smart microgrid according to any one of claims 1 to 2 further comprises the following steps: S1: Data acquisition and processing: various sensors in the acquisition module are used to collect parameter information on distributed power sources, energy storage devices and loads, and the communication module is used to receive the operating status of distributed power sources and energy storage devices, and the collected data is pre-processed and fused; S2: Establish a mathematical model, set the objective function by minimizing the operating cost, and determine the constraints. By taking the operating cost as the objective function, the system is guided to find the optimal scheduling strategy. S3: Swarm intelligence algorithm, which uses the particle swarm optimization algorithm to calculate the fitness according to the objective function in step S2, and finds a scheduling solution that satisfies the constraints and makes the objective function better, driving the entire particle group to continuously optimize the scheduling solution; S4: Extraction and evaluation of the optimal scheduling plan. After the particle swarm optimization algorithm is completed, the corresponding optimal scheduling plan is extracted from the optimal position of the group, and the extracted optimal scheduling plan is evaluated in detail; S5: System operation detection and feedback adjustment. According to the optimal scheduling plan, the control center generates corresponding control instructions. After the system runs according to the optimal scheduling plan, it continuously monitors the system's operating status through sensors and communication modules, obtains real-time data on the power generation of distributed power sources, the SOC of energy storage equipment, the charging and discharging current and voltage, and the power demand data of the load, and observes whether the system runs as expected.

4. The multi-energy complementary optimization scheduling method based on smart microgrid according to claim 3 is characterized by: The sensor in the acquisition module performs sampling at a frequency of once every 1 minute or once every 5 minutes; Digital filtering technology is used to remove noise from the collected data, and the collected data is calibrated according to the characteristics of the sensor and calibration parameters.

5. The multi-energy complementary optimization scheduling method based on smart microgrid according to claim 3 is characterized by: In S2, the power generation cost function of the distributed power source is assumed to be C DG (P DG ), the energy storage equipment cost function is C ESS (P ESS , SOC), the electricity purchase cost function is C grid (P grid ), then the expression of the objective function is as follows: Among them, t0 and t1 are the start and end time of the scheduling period, P DG , P ESS , P grid They are the power generation power of distributed power sources, the charging and discharging power of energy storage equipment, and the exchange power with the external power grid.

6. The multi-energy complementary optimization scheduling method based on smart microgrid according to claim 3 is characterized by: The constraints include power balance constraint, energy storage device constraint, and distributed power supply constraint. The power balance constraint is that at any time t, the sum of the power generation power of all distributed power supplies in the microgrid, the charging and discharging power of the energy storage device, and the exchange power with the external power grid is equal to the power demand of the load, and its expression is as follows: P DG (t)+P ESS (t)+P grid (t)=P L (t) Among them, P L (t) is the load power at time t.

7. The multi-energy complementary optimization scheduling method based on smart microgrid according to claim 6 is characterized by: The energy storage device constraints are as follows: SOC upper and lower limit constraints: SOC min ≤SOC(t)≤SOC max ; Charge and discharge power limit: P ESS,min ≤P ESS (t)≤P ESS,max ; The constraints of distributed power sources include photovoltaic power generation constraints, wind power generation constraints and micro gas turbine constraints, among which photovoltaic power generation constraints include power output range constraints and photovoltaic inverter capacity constraints; wind power generation constraints include power output range constraints, wind speed correlation constraints and pitch angle and speed constraints; the micro gas turbine constraints also include power output range constraints: The output power of the photovoltaic power generation system in the power output range constraint is affected by the light intensity and temperature, and its output power has an upper limit P PV,max and the lower limit P PV,min , denoted as P PV,min ≤P PV (t)≤P PV,max ; Under ideal conditions of the strongest sunlight and suitable temperature, the photovoltaic power generation system can reach its rated power output, close to P PV,max At night or when the light is very weak, the output power is close to P PV,min or 0; In the photovoltaic inverter capacity constraint, the DC power generated by the photovoltaic panel must be converted into AC power by the inverter before it can be connected to the microgrid. The inverter has a rated capacity S INV When the output power of the photovoltaic power generation system passes through the inverter, it must meet P PV (t)≤S INV , otherwise the inverter will be damaged; The power output range constraint in the wind power generation constraint is: the output power of the wind power generation system is closely related to the wind speed, and its power upper limit P WT,max and the lower limit P WT,min , denoted as P WT,min ≤P WT (t)≤P WT,max ; When the wind speed is lower than the cut-in wind speed, the wind turbine does not generate electricity and the output power is P WT,min , usually 0; when the wind speed is within the rated wind speed range, the relationship between the output power and wind speed is approximately linear; when the wind speed exceeds the cut-out wind speed, in order to protect the generator, the output power will be limited to P WT,max ; The wind speed correlation constraint in the wind power generation constraint is: output power P WT (t) is a function of wind speed v(t) and is expressed as: Among them, v in is the cut-in wind speed, v rated is the rated wind speed, a and b are coefficients related to the characteristics of the wind turbine; The pitch angle and speed constraints: To control the output power of the wind turbine and prevent overspeed, the pitch angle β(t) and the speed ω(t) are subject to the following constraints: the pitch angle must satisfy β min ≤β(t)≤β max , the speed must satisfy ω min ≤ω(t)≤ω max , by adjusting the pitch angle and speed, the wind turbine can operate stably and output appropriate power at different wind speeds; Power output range constraint in micro gas turbine constraints: Micro gas turbine has its rated power output range, power lower limit P MT,min and power cap P MT,min , to satisfy P MT,min ≤P MT (t)≤P MT,max , its minimum output power is limited by the stable combustion in the combustion chamber, and its maximum output power is limited by the capacity of the turbine and generator.

8. The multi-energy complementary optimization scheduling method based on smart microgrid according to claim 3 is characterized by: In S3, the specific process of the particle swarm optimization algorithm is as follows: Each scheduling scheme is encoded as a particle, and the particle position vector is Xi = (xi1, xi2, ..., xin), which represents the decision variables of the distributed power generation distribution and the charging and discharging strategy of the energy storage device, where xi1 represents the power setting value of the solar photovoltaic system at a certain moment, and xi2 represents the discharge power of the battery energy storage system; For the power generation element, its initial value is randomly selected within the range of the minimum and maximum power generation of the distributed generation, and the speed vector V i =(v i1 ,v i2 ,…,v in ) is set within a suitable interval according to certain rules; The fitness value of each particle is calculated according to the objective function. For the scheduling scheme represented by particle i, the corresponding distributed generation power P DG 、Charging and discharging power of energy storage equipment P ESS Exchange power with the grid P grid Substitute into the objective function to calculate the operating cost; The fitness value fitness i Set it as the inverse of cost J. The lower the cost, the higher the fitness value; The speed update formula is: v ij (t+1)=wv ij (t)+c1r1(p ij -x ij (t))+c2r2(g ij -x ij (t)); Among them, w is the inertia weight, c1 and c2 are learning factors, c1=c2=2, r1 and r2 are random numbers between 0 and 1, t is the number of iterations, p ij is the individual optimal position P of particle i best,i The jth component, g ij is the optimal position of the group G best The jth component of The position update formula is: ij (t+1)=x ij (t)+v ij (t+1); By continuously iteratively updating the speed and position of the particles until the stopping criterion is met, the optimal position of the group G best The represented scheduling scheme is the optimized scheduling scheme.

9. The multi-energy complementary optimization scheduling method based on smart microgrid according to claim 3 is characterized by: In S5, if the system operation status is monitored to deviate from the optimal scheduling plan, the feedback information is sent back to the server and the monitoring terminal. The server and the monitoring terminal re-evaluate the system status based on the feedback information and make local adjustments to the scheduling plan.

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