Multi-objective optimization scheduling system for optical storage direct-flexible micro-grid

Abstract

The invention relates to the technical field of power grid dispatching, and discloses an optical storage direct-current flexible micro-grid multi-target optimization dispatching system, which comprises a photovoltaic power generation module, an energy storage module, a direct-current power supply module, a flexible power supply module and a multi-target optimization dispatching module, and is characterized in that the multi-target optimization dispatching module comprises a monitoring unit used for monitoring each load unit in real time, acquiring basic power utilization information; the prediction unit is used for carrying out load prediction on the load unit at each moment every day according to the power utilization basic information; and the optimal scheduling unit is used for establishing a multi-target model by adopting a multi-target optimization algorithm and by taking the lowest daily operation cost and the maximum photovoltaic consumption rate of the optical storage direct-flexible micro-grid as targets, and solving the multi-target model based on a load prediction result to obtain an energy scheduling scheme of the optical storage direct-flexible micro-grid. According to the invention, the operation state of the micro-grid can be predicted and scheduled more accurately, so that an energy scheduling scheme with a better effect is obtained, and the energy utilization efficiency and the system operation stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a multi-objective optimization dispatching system for a photovoltaic-storage-direct-flexible microgrid. Background Art

[0002] With the profound adjustment of the global energy structure and the increasing awareness of environmental protection, the development and utilization of renewable energy has become an important issue in today's world. As an important form of renewable energy utilization, microgrids, with their distributed, modular and flexible characteristics, have gradually become one of the key technologies for energy transformation. Especially with the rapid development of photovoltaic power generation technology, the photovoltaic storage direct and flexible microgrid, as an advanced system combining photovoltaic power generation, energy storage technology, DC power supply and flexible power consumption, is gradually showing its unique advantages and application prospects in the energy field. However, the operation and management of the photovoltaic storage direct and flexible microgrid faces multiple challenges. On the one hand, due to the intermittent and instability of renewable energy, the energy supply of microgrids is highly volatile, and effective scheduling strategies are needed to balance the supply and demand relationship. On the other hand, with the expansion of the scale and increase of complexity of microgrids, how to minimize costs, stabilize operations and maximize environmental benefits while meeting energy demand has become an urgent problem to be solved.

[0003] Under the dual challenges of energy utilization and environmental protection, the multi-objective optimization and dispatching system of photovoltaic, storage, direct and flexible microgrid came into being. With advanced technology and innovative concepts, it provides strong support for efficient utilization and sustainable development of energy. However, the traditional multi-objective optimization and dispatching system of photovoltaic, storage, direct and flexible microgrid is still not perfect in use. The singleness of the multi-objective optimization algorithm leads to low computing efficiency, which affects the optimization and dispatching efficiency of the microgrid and affects user use. Summary of the invention

[0004] In view of this, the present invention provides a multi-objective optimization and scheduling system for a photovoltaic, energy-storage, direct-current and flexible microgrid to solve the technical problems of low optimization and scheduling efficiency of the multi-objective optimization and scheduling system for a photovoltaic, energy-storage, direct-current and flexible microgrid.

[0005] The present invention provides a multi-objective optimization scheduling system for a photovoltaic, storage, direct current and flexible microgrid, comprising: a photovoltaic power generation module, used to provide core energy for the photovoltaic, storage, direct current and flexible microgrid; an energy storage module, used to balance the energy supply and demand of the microgrid; a DC power supply module, used to transmit the DC power generated by the photovoltaic power generation module and the energy storage module to each load unit; a flexible power supply module, used to adjust the operation mode and power of the load unit according to real-time energy demand and electricity price information; a multi-objective optimization scheduling module, comprising: a monitoring unit, used to monitor each load unit in real time and obtain basic electricity consumption information; a prediction unit, used to predict the load of the load unit at each time of the day according to the basic electricity consumption information; an optimization scheduling unit, used to adopt a multi-objective optimization algorithm, establish a multi-objective model with the objectives of minimizing the daily operation cost of the photovoltaic, storage, direct current and flexible microgrid and maximizing the photovoltaic absorption rate, and solve the multi-objective model based on the load prediction result to obtain an energy scheduling plan of the photovoltaic, storage, direct current and flexible microgrid, wherein the energy scheduling plan is used to schedule the photovoltaic power generation module, the energy storage module, the DC power supply module and the flexible power supply module.

[0006] Optionally, the optimization scheduling unit includes: a learning algorithm unit, which is used to use machine learning technology to predict photovoltaic power generation and energy demand, and provide data support for scheduling decisions; an optimization algorithm unit, which is used to adopt a multi-objective optimization algorithm to establish a multi-objective model with the goals of minimizing the daily operating cost of the microgrid and maximizing the photovoltaic absorption rate, and solve the multi-objective model based on the load forecast result to obtain the energy scheduling plan of the photovoltaic storage direct flexible microgrid; a cloud computing unit, which is used to use the cloud computing platform to centrally store and process microgrid data, and optimize the energy scheduling plan in conjunction with big data.

[0007] Optionally, the multi-objective model includes a first objective function with the goal of minimizing the daily operating cost of the photovoltaic storage direct flexible microgrid, a second objective function with the goal of maximizing the photovoltaic absorption rate, and constraints, and the first objective function f1 is:

[0008] f1=C inv +C om +C g

[0009] Where: C inv is the daily initial investment cost of the photovoltaic-storage direct-flexible microgrid; C om is the maintenance cost of the photovoltaic-storage direct-flexible microgrid; C g The cost of purchasing electricity from the grid;

[0010] The second objective function f2 is:

[0011]

[0012] Where P a，v(t) is the photovoltaic power consumption at time t; P v (t) is the actual output power of the photovoltaic power plant at time t, and n is the number of selected moments;

[0013] The constraints include power balance constraints, energy storage battery constraints and distribution network interaction power constraints. The power balance constraints are:

[0014] P g (t)+P v (t) = P load (t)+P ev (t)+k1P ch (t)

[0015] P g (t)+P v (t)+k2P disch (t) = P load (t)+P ev (t), t = 0, 1, ..., 24

[0016] Where P ch (t), P disch (t) are the charging power and discharging power of the battery at time t; P g (t) is the power grid output at time t; P v (t) is the photovoltaic power generation power; P ev (t) is the charging power of the electric vehicle; P load (t) is the basic power consumption; k1 is the charging state parameter, k2 is the discharging state parameter, k1+k2∈[0,1], k1=1 means the battery is in the charging state; k2=1 means the battery is in the discharging state; k1=0, k2=0 means neither charging nor discharging;

[0017] The energy storage battery constraints are:

[0018] S OC，min ≤S OC (t)≤S OC，max

[0019] P bat，min ≤P bat (t)≤P bat，max

[0020] In the formula, S OC，max and S OC，min are the upper and lower limits of the battery state of charge respectively; P bat，max and P bat，min are the upper and lower limits of the battery charging and discharging power, S OC (t) and P bat (t) is the state of charge and charge / discharge power of the battery at time t;

[0021] The distribution network interaction power constraint is:

[0022]

[0023] In the formula, It is the upper limit power of the grid side output.

[0024] Optionally, the optimization scheduling unit is also used to substitute the load forecast results as known parameters into the multi-objective model, and use the initial energy scheduling plan as initial particles, and solve the multi-objective model through a particle swarm optimization algorithm based on elite reverse learning strategy, worst particle exclusion pair method and chaotic perturbation to obtain the energy scheduling plan of the photovoltaic storage direct flexible microgrid.

[0025] Optionally, the multi-objective optimization dispatching system of the photovoltaic, storage, direct and flexible microgrid also includes: a data monitoring and management module, which is responsible for collecting, analyzing and displaying the operating data of the photovoltaic, storage, direct and flexible microgrid, and performing operation and maintenance management of the photovoltaic, storage, direct and flexible microgrid.

[0026] Optionally, the data monitoring and management module includes: a data collection unit, which is used to collect the power generation, power consumption, energy storage status, and environmental parameters in the photovoltaic, storage, direct and flexible microgrid; a data analysis unit, which is used to analyze and evaluate the data collected by the data collection unit in combination with the big data system, and obtain the usage and operating status of the load units in the photovoltaic, storage, direct and flexible microgrid as well as the operating status and fault conditions of each device; a microgrid display unit, which is used to visualize the usage and operating status of the load units in the photovoltaic, storage, direct and flexible microgrid; a data storage unit, which is used to record and store the data collected by the data collection unit and to back up them; an operation and maintenance management unit, which is used to supervise and maintain the operating status and fault conditions of each device in the photovoltaic, storage, direct and flexible microgrid.

[0027] Optionally, the operation and maintenance management unit includes: an online operation and maintenance unit, which is used to perform interventional debugging of a first type of early warning device in the photovoltaic and storage direct and flexible microgrid through remote control; and an offline operation and maintenance unit, which is used to perform interventional debugging of a second type of early warning device in the photovoltaic and storage direct and flexible microgrid through manual means, wherein the failure degree of the second type of early warning device is greater than that of the first type of early warning device.

[0028] Optionally, the online operation and maintenance unit includes: an online supervision reminder unit, which is used to initiate a supervision reminder notification to the handheld terminal of the operation and maintenance personnel through the operation and maintenance management client or mini program when an equipment failure is detected; a remote access unit, which is used to access the operation and maintenance management backend through the operation and maintenance management client or mini program, and the operation and maintenance management backend debugs and operates the photovoltaic storage direct-flexible microgrid through the remote access unit.

[0029] Optionally, the offline operation and maintenance unit includes: a machine maintenance selection unit, which is used to select the corresponding responsible machine maintenance personnel according to the failure of the equipment in the photovoltaic storage direct and flexible microgrid when an equipment failure is detected; an offline supervision reminder unit, which is used to initiate an operation and maintenance notification to the handheld terminal of the recommended machine maintenance personnel; a route optimization unit, which is used to send route information to the handheld terminal of the machine maintenance personnel through the positioning system; a plan formulation unit, which is used to combine the big data system to formulate a maintenance plan according to the problem of the equipment to be maintained in the photovoltaic storage direct and flexible microgrid, wherein the equipment to be maintained is the equipment with a detected fault; an operation and maintenance tool listing unit, which is used to send the items required for the maintenance plan to the recommended machine maintenance personnel according to the problem of the equipment to be maintained in the photovoltaic storage direct and flexible microgrid.

[0030] Optionally, the load unit is an electric vehicle, and the prediction unit includes: a probability model construction unit, which is used to construct a probability model of the starting charging time, a probability model of the ending charging time, a probability model of the daily mileage, and a probability model function for the initial state of charge of the electric vehicle according to the basic electricity consumption information; a Monte Carlo unit, which is used to use the Monte Carlo method to predict the load of the load unit at each time of the day based on the probability model of the starting charging time, the probability model of the ending charging time, the probability model of the daily mileage, and the probability model function for the initial state of charge.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] A multi-objective optimization and scheduling system for a photovoltaic, energy storage, direct current and flexible microgrid of the present invention monitors each load unit in real time through a monitoring unit in a multi-objective optimization and scheduling module to obtain basic electricity consumption information. A prediction unit predicts the load of the load units at various times of the day based on the basic electricity consumption information. The optimization and scheduling unit adopts a multi-objective optimization algorithm to establish a multi-objective model with the goals of minimizing the daily operating cost of the photovoltaic, energy storage, direct current and flexible microgrid and maximizing the photovoltaic absorption rate. The multi-objective model is solved based on the load prediction result to obtain an energy scheduling plan for the photovoltaic, energy storage, direct current and flexible microgrid. Then, the photovoltaic power generation module, energy storage module, DC power supply module and flexible power supply module can be scheduled according to the energy scheduling plan. Compared with traditional methods, the optimization and scheduling strategy can more accurately predict and schedule the operating status of the microgrid, thereby obtaining a more effective energy scheduling plan and improving energy utilization efficiency and system operation stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 It is a structural schematic diagram of the multi-objective optimization dispatching system of the photovoltaic-storage-direct-flexible microgrid in the present invention;

[0035] Figure 2 It is a schematic diagram of the system modules composed of the multi-objective optimization scheduling module in the present invention;

[0036] Figure 3 This is a schematic diagram of the system modules composed of the data monitoring and management modules in the present invention;

[0037] Figure 4 This is a schematic diagram of the system modules composed of the operation and maintenance management unit in the present invention;

[0038] Figure 5 This is a schematic diagram of the photovoltaic power generation module system in the present invention;

[0039] Figure 6 It is a schematic diagram of the energy storage module system module in the present invention;

[0040] Figure 7 It is a flow chart of the implementation of the improved particle swarm algorithm in the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0042] At present, the traditional multi-objective optimization and dispatching system of a photovoltaic and energy-storage direct-current flexible microgrid is still not perfect in use. For example, the singleness of the multi-objective optimization algorithm and the low computational efficiency affect the optimization and dispatching efficiency of the microgrid, as well as the lack of system operation and maintenance management. It relies heavily on on-site platform management, and the operation and maintenance efficiency cannot be improved, affecting user use.

[0043] To this end, the present invention proposes a multi-objective optimization scheduling system for a photovoltaic-storage-direct-flexible microgrid to solve at least one of the above problems.

[0044] According to an embodiment of the present invention, an embodiment of a multi-objective optimization scheduling system for a photovoltaic, energy-storage, direct-current and flexible microgrid is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] In this embodiment, a multi-objective optimization scheduling system for a photovoltaic-storage-direct-flexible microgrid is provided. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown, the system includes:

[0046] Photovoltaic power generation module is used to provide core energy for the photovoltaic storage direct flexible microgrid. Specifically, the photovoltaic power generation module includes:

[0047] Photovoltaic matrix units, which convert solar energy into DC electricity;

[0048] The inverter system unit is used to convert direct current into alternating current, and output the alternating current into the power grid through the grid-connected inverter device;

[0049] The off-grid control unit is used to obtain electricity from the grid when the electricity generated by photovoltaic power generation cannot meet the user's needs.

[0050] When the grid is running, the photovoltaic matrix unit converts solar energy into DC power, and the inverter converts DC power into AC power for use in the microgrid or to be connected to the mains. The inverter system unit directly outputs AC power into the grid through the grid-connected inverter device. When the power of the renewable energy system cannot meet the user's needs, the off-grid control unit will obtain enough power from the grid to meet the user's needs.

[0051] Energy storage module, used to balance the energy supply and demand of the microgrid. Specifically, the energy storage module includes:

[0052] A battery pack unit for storing surplus electric energy generated by the photovoltaic power generation module;

[0053] A charging and discharging control unit is used to release electric energy to balance the energy supply and demand of the microgrid when energy demand peaks or the photovoltaic power generation module has insufficient production capacity;

[0054] The battery management unit is used to monitor, evaluate, protect and balance the battery cells.

[0055] When the power grid is running, the battery pack unit stores the surplus electric energy generated by the photovoltaic power generation module. The charge and discharge control unit releases electric energy when the energy demand peaks or the photovoltaic power generation module has insufficient production capacity, balancing the energy supply and demand of the microgrid. The battery pack management unit monitors, evaluates, protects and balances the battery pack.

[0056] The remaining capacity of a battery pack unit during the charge and discharge process can be expressed as:

[0057]

[0058] Where: E(t) and E(t-Δt) are the remaining power at time t and t-Δt respectively; Δt is the time interval; P d (t), P c (t) are the discharge power and the charging power respectively; η d , η c are the discharge efficiency and the charge efficiency respectively.

[0059] The remaining power is monitored according to the above formula.

[0060] The DC power supply module is used to transmit the DC power generated by the photovoltaic power generation module and the energy storage module to each load unit.

[0061] The load unit is the electrical equipment, such as key charging piles, factory equipment, and household appliances. The DC power supply module can safely and efficiently transmit the DC power generated by the photovoltaic power generation module and the energy storage module to each load unit. When the demand of the load unit is small, the output energy can be stored through energy storage.

[0062] Flexible power supply module, used to adjust the operation mode and power of load units according to real-time energy demand and electricity price information.

[0063] Specifically, the flexible power supply module obtains real-time energy demand and electricity price information from the Internet or internal information of the power grid, and then intelligently adjusts the operating mode and power of the power-consuming equipment.

[0064] Multi-objective optimization scheduling module, including:

[0065] The monitoring unit is used to monitor each load unit in real time and obtain basic information on electricity consumption. Specifically, the basic information on electricity consumption includes historical daily electricity consumption, hourly electricity consumption, etc.

[0066] The prediction unit is used to perform load prediction on the load units at each moment of the day according to the basic power consumption information. Specifically, the load units at each moment of the day in the future are predicted by the collected basic power consumption information to obtain the power load at each moment.

[0067] In one example, the load unit is an electric vehicle, and the prediction unit includes:

[0068] A probability model building unit, used to build a probability model of the start charging time, a probability model of the end charging time, a probability model of the daily mileage and a probability model function for the initial charge state of the electric vehicle according to the basic electricity consumption information;

[0069] The Monte Carlo unit is used to use the Monte Carlo method to predict the load of the load unit at each time of the day using a probability model function based on the probability model of the starting charging time, the probability model of the ending charging time, the probability model of the daily mileage and the initial state of charge.

[0070] The load unit is an electric vehicle. The prediction unit uses the Monte Carlo method to predict the charging load of the electric vehicle. The charging law of the electric vehicle is mainly related to the user's starting charging time and daily mileage. The starting charging time of the electric vehicle is t start and the end of charging time t end The probability model formula is:

[0071]

[0072] In the formula, μ s , μ e are the mean values ​​of the starting charging time and the ending charging time respectively; σ s , σ e are the standard deviations of the start and end charging times, respectively. s (t start ), f e (t end ) are the starting charging time t start The charging end time is t end The mean of the start charging time and the end charging time, the standard deviation of the start charging time and the end charging time are calculated through historical data, and then the probability model of the start charging time and the end charging time is obtained according to the above formula.

[0073] The daily mileage probability model is:

[0074]

[0075] Where x is the daily mileage; μ d is the distribution mean of daily mileage; σ d is the standard deviation, f d (x) represents the probability corresponding to the daily mileage being x.

[0076] The initial state of charge of the electric vehicle is expressed by the probability model function expression as follows:

[0077]

[0078] Where: μ0 is the average value of the initial charging state of the electric vehicle battery; σ o is the standard deviation, f o (S oc ) is the corresponding probability.

[0079] After obtaining the above probability model, the Monte Carlo method can be used to predict the power load of electric vehicles at various times of the day, such as the charging load for 24 hours a day.

[0080] The optimization scheduling unit is used to adopt a multi-objective optimization algorithm to establish a multi-objective model with the goals of minimizing the daily operating cost of the photovoltaic storage direct-current flexible microgrid and maximizing the photovoltaic absorption rate. The multi-objective model is solved based on the load forecast results to obtain the energy scheduling plan of the photovoltaic storage direct-current flexible microgrid. Among them, the energy scheduling plan is used to schedule photovoltaic power generation modules, energy storage modules, DC power supply modules and flexible power supply modules.

[0081] Specifically, a multi-objective model with the goals of minimizing the daily operating cost of the photovoltaic storage direct-flexible microgrid and maximizing the photovoltaic absorption rate comprehensively considers multiple objectives such as energy utilization, cost, and stability, and solves them to obtain the optimal energy scheduling plan.

[0082] The energy scheduling plan includes the power generation power of the photovoltaic power generation module, the number of operating photovoltaic units, the number of photovoltaic units connected to the grid, the charging and discharging status of each battery group in the energy storage module, etc. The energy scheduling plan is obtained according to the optimization to schedule each component in the photovoltaic storage direct flexible microgrid.

[0083] A multi-objective optimization and scheduling system for a photovoltaic, energy storage, direct current and flexible microgrid in an embodiment of the present invention monitors each load unit in real time through a monitoring unit in a multi-objective optimization and scheduling module to obtain basic electricity consumption information. A prediction unit predicts the load of the load units at various times of the day based on the basic electricity consumption information. The optimization and scheduling unit adopts a multi-objective optimization algorithm to establish a multi-objective model with the goals of minimizing the daily operating cost of the photovoltaic, energy storage, direct current and flexible microgrid and maximizing the photovoltaic absorption rate. The multi-objective model is solved based on the load prediction result to obtain an energy scheduling plan for the photovoltaic, energy storage, direct current and flexible microgrid. Then, the photovoltaic power generation module, energy storage module, DC power supply module and flexible power supply module can be scheduled according to the energy scheduling plan. Compared with traditional methods, the optimization and scheduling strategy can more accurately predict and schedule the operating status of the microgrid, thereby obtaining a more effective energy scheduling plan and improving energy utilization efficiency and system operation stability.

[0084] In some embodiments, the optimization scheduling unit includes:

[0085] Learning algorithm unit, used to use machine learning technology to predict photovoltaic power generation and energy demand, and provide data support for scheduling decisions;

[0086] The optimization algorithm unit is used to adopt a multi-objective optimization algorithm to establish a multi-objective model with the goal of minimizing the daily operating cost of the microgrid and maximizing the photovoltaic absorption rate, and to solve the multi-objective model based on the load forecast results to obtain the energy dispatch plan of the photovoltaic storage direct-flexible microgrid;

[0087] The cloud computing unit is used to centrally store and process microgrid data using the cloud computing platform, and to optimize energy scheduling solutions in conjunction with big data.

[0088] Specifically, the optimization scheduling unit includes an intelligent algorithm unit, which provides data and information support for the optimization scheduling of the microgrid through advanced optimization algorithms and artificial intelligence technologies.

[0089] The intelligent algorithm unit includes the learning algorithm unit, the optimization algorithm unit and the cloud computing unit. The learning algorithm unit uses machine learning technology to predict photovoltaic power generation, energy demand, etc., and provides data support for scheduling decisions. The optimization algorithm unit adopts a multi-objective optimization algorithm, with the goal of minimizing the daily operating cost of the microgrid and maximizing the photovoltaic absorption rate, comprehensively considering multiple goals such as energy utilization, cost, and stability, and formulating the optimal scheduling plan. The cloud computing unit can access the cloud computing platform, use the cloud computing platform to centrally store and process microgrid data, and cooperate with big data analysis to explore potential value and optimize scheduling strategies.

[0090] In the learning algorithm unit, the daily photovoltaic power generation can be expressed as:

[0091] P=E×A×η×P R

[0092] Where P is the daily power generation of photovoltaic power; E is the radiation energy per unit area; A is the area of ​​the photovoltaic array installed in the park; η is the conversion efficiency; P R It is a correction coefficient, which is related to factors such as the attenuation of the device operation, inverter efficiency, and correction of power drop caused by dust blocking components. The correction coefficient and conversion efficiency can be optimized through a machine learning model.

[0093] In some embodiments, the multi-objective model includes a first objective function with the goal of minimizing the daily operating cost of the photovoltaic storage direct flexible microgrid, a second objective function with the goal of maximizing the photovoltaic absorption rate, and constraints.

[0094] The daily operating cost of the microgrid includes the daily initial investment cost of the microgrid system, operation and maintenance costs, and the cost of purchasing electricity from the grid. That is, the first objective function f1 is:

[0095] f1=C inv +C om +C g

[0096] Where: C inv is the daily initial investment cost of the photovoltaic-storage direct-flexible microgrid; C om is the maintenance cost of the photovoltaic-storage direct-flexible microgrid; C g The cost of purchasing electricity from the grid.

[0097] The second objective function aims to maximize the photovoltaic absorption rate, that is, minimize the abandoned light rate. The second objective function f2 is:

[0098]

[0099] Where P a，v (t) is the photovoltaic power consumption at time t; P v (t) is the actual output power of the photovoltaic power plant at time t, and n is the number of selected moments;

[0100] In order to achieve the multi-objective optimization of minimizing the daily operating cost of the microgrid and maximizing the photovoltaic absorption rate proposed in the present invention, the constraints that need to be met are:

[0101] 1. Power balance constraint: Maintaining the balance between energy consumption and production capacity of each device is a necessary condition for the normal operation of the microgrid, which can be expressed as:

[0102] P g (t)+P v (t) = P load (t)+P ev (t)+k1P ch (t)

[0103] P g (t)+P v (t)+k2P disch (t) = P load (t)+P ev (t), t = 0, 1, ..., 24

[0104] Where P ch (t), P disch (t) are the charging power and discharging power of the battery at time t; P g (t) is the power grid output at time t; P v (t) is the photovoltaic power generation power; P ev (t) is the charging power of the electric vehicle; P load (t) is the basic power consumption; k1 is the charging state parameter, k2 is the discharging state parameter, k1+k2∈[0,1], k1=1 means the battery is in the charging state; k2=1 means the battery is in the discharging state; k1=0, k2=0 means neither charging nor discharging;

[0105] 2. Energy storage battery constraints: To ensure that the energy storage battery always works within the safe range, its state of charge value must flow out of the buffer zone. The constraints are expressed as:

[0106] S OC，min ≤S OC (t)≤S OC，max

[0107] P bat，min ≤P bat (t)≤P bat，max

[0108] In the formula, S OC，maxand S OC，min are the upper and lower limits of the battery state of charge respectively; P bat，max and P bat，min are the upper and lower limits of the battery charging and discharging power, S OC (t) and P bat (t) is the state of charge and charge / discharge power of the battery at time t;

[0109] 3. Distribution network interactive power constraint. To ensure the stable, safe and economical operation of the microgrid, the output power of the grid should be within the range allowed by the system operation. The constraint is expressed as:

[0110]

[0111] In the formula, It is the upper limit power of the grid side output.

[0112] In some embodiments, the optimization scheduling unit is also used to substitute the load forecast results as known parameters into the multi-objective model, and use the initial energy scheduling plan as the initial particles, and solve the multi-objective model through a particle swarm optimization algorithm based on elite reverse learning strategy, worst particle exclusion pair method and chaotic perturbation to obtain the energy scheduling plan of the photovoltaic storage direct flexible microgrid.

[0113] Specifically, the load forecast result includes the power load at each moment, which is substituted into the first objective function and the second objective function. When the energy scheduling plan is clear, the values ​​of the first objective function and the second objective function can be calculated.

[0114] like Figure 7 As shown, in order to obtain the optimal energy scheduling solution, the improved particle swarm algorithm adopted in the embodiment of the present invention is divided into two stages: the first stage is the optimization stage of the improved particle swarm algorithm, including the early exploration stage with the worst particle repulsion effect and the later mining stage with the disappearance of the worst particle repulsion effect; the second stage is a small-scale fine optimization stage with hidden chaotic interference based on the optimization results of the particle swarm algorithm.

[0115] First, generate several initial energy scheduling plans, including the power generation of photovoltaic power generation modules, the number of photovoltaic units in operation, the number of photovoltaic units connected to the grid, the charge and discharge status of each battery group in the energy storage module, etc., which are equivalent to a point in D-dimensional space, namely the initial particle, and then calculate the fitness of the initial position. In the early stage of the search of the particle swarm algorithm, the elite reverse learning strategy is introduced to construct the reverse solution of the elite individuals to increase the diversity of the population, expand the search range of the population, and avoid the particle swarm from falling into the local optimum. Let X i =(x i，1 , x i，2 , …, x i，D ), is a point in D-dimensional space, x i，j∈[a j , b j ], the corresponding reverse point is set to X′ i =(x′ i，1 , x′ i，2 , …, x′ i，D ), the relationship between the dimensions of the origin and the reverse point is:

[0116] x′ i，j = k(a j +b j )-x i，j

[0117] In the formula, k is a random number between 0 and 1.

[0118] The embodiment of the present invention introduces the worst particle exclusion method in the early stage of the search of the particle swarm algorithm, and applies the worst particle exclusion effect only to the early search process for the optimal area. The worst particles gradually weaken as the algorithm is updated, and completely disappear in the later mining stage. Assume that the i-th particle after the basic particle swarm algorithm is updated is X i =(x i，1 , x i，2 , …, x i，D ), the current worst particle is X worst =(x1, x2, ..., x D ), under the worst particle repulsion, the particle moves to X” i =(x” i，1 , x” i，2 ,…,x” i，D ), the moving formula is expressed as:

[0119]

[0120]

[0121] Where δ is the attenuation factor, which gradually decreases with the increase of the update number t. When t is large enough, δ≈0; k is the repulsive displacement weight value, k=2, and R is the Euclidean distance between the i-th particle and the current worst particle. The larger the Euclidean distance, the smaller the k / R value, and the smaller the repulsive effect, and vice versa; rand j is a random variable between [0, 1]. The particle position is updated according to the above movement formula, and the optimal solution of the first stage is obtained after several iterations.

[0122] The embodiment of the present invention introduces chaotic disturbance in the second stage of the search of the particle swarm algorithm to enhance the local exploration capability and obtain the global optimal solution faster and better. The Logistic chaos model is used to make the dimensions of the optimal particle in a chaotic state in the set feasible domain space. Assume that the optimal particle calculated by the improved particle swarm algorithm is Xbest =(x1, x2, ..., x D ), the subsequent optimization process starts from this optimal particle, and the form of the Logistic chaos model is as follows:

[0123] φ n+1 =μφ n (1-φ n ), n = 0, 1, ...

[0124] In the formula, μ = 4, μ n ∈[0, 1], then the sequence μ determined by the Logistic chaos model n In a completely chaotic state; the value range of each dimension of the particle is x i，j ∈[a j , b j ], the particle after one disturbance is:

[0125] X"' i =(x'' i，1 , x"' i，2 ,…,x'' i，D ), j = 1, 2, ..., D

[0126] The relationship between the particles before and after the disturbance is:

[0127] X"' j =x j +5%×(b j -a j )(2φ n -1)

[0128] The above formula indicates that the particle will fluctuate ±5% in each dimension centered on the optimal solution (b j -a j ) moves randomly within a small range. If a better solution is found, the original optimal solution is replaced. After a limited number of searches, the optimization process ends and the optimal solution is obtained.

[0129] Through the above algorithm optimization, the best solution is obtained and the best energy scheduling plan is obtained. Through the improved particle swarm algorithm, the obtained energy scheduling plan can have better effects in reducing operating costs and improving photovoltaic payout ratio.

[0130] In some embodiments, the photovoltaic storage direct flexible microgrid multi-objective optimization scheduling system further includes:

[0131] The data monitoring and management module is responsible for collecting, analyzing and displaying the operating data of the photovoltaic storage direct flexible microgrid, and performing operation and maintenance management of the photovoltaic storage direct flexible microgrid. The overall functions of the data monitoring and management module include collecting, analyzing and displaying the operating data of the photovoltaic storage direct flexible microgrid, and performing operation and maintenance management of the microgrid, which involves real-time monitoring and long-term maintenance of the microgrid.

[0132] By centrally managing the operating data of microgrids, the transparency and predictability of the system can be improved, thereby optimizing resource allocation and scheduling strategies.

[0133] Specifically, the data monitoring and management module includes:

[0134] A data collection unit is used to collect the power generation, power consumption, energy storage status, and environmental parameters in the photovoltaic and energy storage direct flexible microgrid;

[0135] The data analysis unit is used to analyze and evaluate the data collected by the data collection unit in combination with the big data system, and obtain the usage and operating status of the load units in the photovoltaic storage direct-flexible microgrid as well as the operating status and fault conditions of each device;

[0136] Microgrid display unit, used to visualize the usage and operation status of load units in the photovoltaic storage direct-flexible microgrid;

[0137] A data storage unit, used for recording and storing the data collected by the data collection unit and performing backup;

[0138] The operation and maintenance management unit is used to monitor and maintain the operating status and fault conditions of each device in the photovoltaic storage direct flexible microgrid.

[0139] The data collection unit, data analysis unit, microgrid display unit, data storage unit and operation and maintenance management unit are responsible for data collection, analysis, display, storage and operation and maintenance respectively. This modular design allows each subunit to focus on its specific task, improving the efficiency and accuracy of data processing. At the same time, the visual display helps the operation and maintenance personnel to quickly understand the status of the microgrid.

[0140] Furthermore, the operation and maintenance management unit includes:

[0141] The online operation and maintenance unit is used to perform interventional adjustments to the first type of early warning equipment in the photovoltaic storage direct flexible microgrid through remote control.

[0142] The offline operation and maintenance unit is used to manually perform interventional debugging on the second type of early warning equipment in the photovoltaic storage direct flexible microgrid, where the failure degree of the second type of early warning equipment is greater than that of the first type of early warning equipment.

[0143] Specifically, the data analysis unit classifies the warning according to the fault situation. If the fault is minor, it is classified as the first type of warning equipment, otherwise, it is classified as the second type of warning equipment. The online operation and maintenance unit intervenes in some minor situations of the relevant equipment that has been warned in the microgrid and the equipment system. Different maintenance strategies are adopted according to the degree of equipment failure, which improves the flexibility and response speed of maintenance work.

[0144] Furthermore, the online operation and maintenance unit includes:

[0145] The online supervision reminder unit is used to send supervision reminder notifications to the handheld terminal of the operation and maintenance personnel through the operation and maintenance management client or applet when a device failure is detected;

[0146] The remote access unit is used to access the operation and maintenance management backend through the operation and maintenance management client or applet. The operation and maintenance management backend debugs and operates the photovoltaic storage direct-flexible microgrid through the remote access unit.

[0147] The online supervision reminder unit is responsible for reminding the operation and maintenance personnel when the equipment fails, while the remote access unit allows the operation and maintenance personnel to remotely debug and operate the microgrid. Through remote monitoring and operation, the operation and maintenance personnel can quickly respond to equipment failures, reduce downtime, and improve system reliability.

[0148] Furthermore, the offline operation and maintenance unit includes:

[0149] The machine maintenance selection unit is used to select the corresponding responsible machine maintenance personnel according to the failure of the equipment in the solar-storage direct-flexible microgrid when a device failure is detected;

[0150] Offline supervision reminder unit, used to initiate operation and maintenance notifications to the handheld terminals of the selected maintenance personnel;

[0151] The route optimization unit is used to send route information to the maintenance point to the handheld terminal of the machine maintenance personnel through the positioning system;

[0152] A plan formulation unit is used to formulate a maintenance plan based on the problem of the equipment to be maintained in the photovoltaic storage direct flexible microgrid in combination with the big data system, where the equipment to be maintained is the equipment where the fault is detected;

[0153] The operation and maintenance tool listing unit is used to send the problems of the equipment to be maintained in the photovoltaic storage direct flexible microgrid and the items required for the maintenance plan to the selected maintenance personnel.

[0154] The machine maintenance recommendation unit, offline supervision reminder unit, route optimization unit, plan formulation unit and operation and maintenance tool listing unit are respectively responsible for recommending machine maintenance personnel, sending operation and maintenance notifications, optimizing routes to maintenance points, formulating maintenance plans and tool requirement notifications. This detailed offline operation and maintenance process design can ensure that when manual intervention is required, the operation and maintenance personnel can arrive at the site quickly and accurately and perform maintenance work, thereby minimizing the impact of faults on the operation of the microgrid.

[0155] A multi-objective optimization scheduling system for a photovoltaic-storage-direct-flexible microgrid according to an embodiment of the present invention has the following advantages:

[0156] Through the monitoring unit in the multi-objective optimization scheduling module, each load unit is monitored in real time to obtain basic electricity consumption information. The prediction unit predicts the load of the load units at various times of the day based on the basic electricity consumption information. The optimization scheduling unit adopts a multi-objective optimization algorithm to establish a multi-objective model with the goal of minimizing the daily operating cost of the photovoltaic storage direct-current flexible microgrid and maximizing the photovoltaic absorption rate. The multi-objective model is solved based on the load prediction results to obtain the energy scheduling plan of the photovoltaic storage direct-current flexible microgrid. Then, the photovoltaic power generation module, energy storage module, DC power supply module and flexible power supply module can be scheduled according to the energy scheduling plan. Compared with traditional methods, the optimization scheduling strategy can more accurately predict and schedule the operating status of the microgrid, thereby obtaining a better energy scheduling plan and improving energy utilization efficiency and system operation stability.

[0157] On the basis of the traditional microgrid on-site platform supervision, by combining online and offline methods in the operation and maintenance management unit, the fault point can be quickly located and effective measures can be taken to repair it. Secondly, the application of big data technology makes the power grid maintenance more accurate and efficient. By collecting and analyzing massive data, a scientific basis is provided for the formulation of targeted maintenance strategies, making the maintenance of the microgrid system more intelligent, humane and scientific. At the same time, it is more convenient for users to meet their electricity needs, which is also of great significance for promoting the development of renewable energy.

[0158] Use cloud computing platforms to centrally store and process microgrid data, and use big data to optimize energy scheduling plans and save local resources;

[0159] The Monte Carlo method is used to predict the load units at each time of the day based on the probability model of the starting charging time, the probability model of the ending charging time, the probability model of the daily mileage and the initial state of charge. The load prediction results are more accurate.

[0160] The energy dispatching scheme of the photovoltaic-storage direct-flexible microgrid is obtained by solving the multi-objective model based on the elite reverse learning strategy, the worst particle exclusion pair method and the chaotic perturbation particle swarm optimization algorithm, so that the optimized scheme is more in line with the needs of the power grid and more efficient.

[0161] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A multi-objective optimization scheduling system for a photovoltaic and energy-storage direct-flexible microgrid, characterized in that: include: Photovoltaic power generation module, used to provide core energy for the photovoltaic storage direct flexible microgrid; Energy storage modules, used to balance the energy supply and demand of the microgrid; A DC power supply module, used to transmit the DC power generated by the photovoltaic power generation module and the energy storage module to each load unit; Flexible power supply module, used to adjust the operation mode and power of load units according to real-time energy demand and electricity price information; Multi-objective optimization scheduling module, including: The monitoring unit is used to monitor each load unit in real time and obtain basic information on electricity consumption; A prediction unit, used for predicting the load of the load unit at each time of the day according to the basic electricity consumption information; The optimization scheduling unit is used to adopt a multi-objective optimization algorithm to establish a multi-objective model with the goals of minimizing the daily operating cost of the photovoltaic-storage-direct-flexible microgrid and maximizing the photovoltaic absorption rate, and solve the multi-objective model based on the load forecast result to obtain an energy scheduling plan for the photovoltaic-storage-direct-flexible microgrid, wherein the energy scheduling plan is used to schedule the photovoltaic power generation module, the energy storage module, the DC power supply module and the flexible power supply module.

2. The multi-objective optimization dispatching system of the photovoltaic storage direct flexible microgrid according to claim 1 is characterized in that: The optimization scheduling unit comprises: Learning algorithm unit, used to use machine learning technology to predict photovoltaic power generation and energy demand, and provide data support for scheduling decisions; An optimization algorithm unit is used to adopt a multi-objective optimization algorithm to establish a multi-objective model with the goal of minimizing the daily operation cost of the microgrid and maximizing the photovoltaic absorption rate, and solve the multi-objective model based on the load forecast result to obtain an energy dispatching plan for the photovoltaic storage direct-flexible microgrid; The cloud computing unit is used to centrally store and process microgrid data using the cloud computing platform, and to optimize energy scheduling solutions in conjunction with big data.

3. The multi-objective optimization dispatching system of the photovoltaic storage direct flexible microgrid according to claim 1 is characterized in that: The multi-objective model includes a first objective function with the goal of minimizing the daily operating cost of the photovoltaic storage direct flexible microgrid, a second objective function with the goal of maximizing the photovoltaic absorption rate, and constraints. The first objective function f1 is: f1=C inv +C om +C g Where: C inv is the daily initial investment cost of the photovoltaic-storage direct-flexible microgrid; C om is the maintenance cost of the photovoltaic-storage direct-flexible microgrid; C g The cost of purchasing electricity from the grid; The second objective function f2 is: Where P a，v (t) is the photovoltaic power consumption at time t; P v (t) is the actual output power of the photovoltaic power plant at time t, and n is the number of selected moments; The constraints include power balance constraints, energy storage battery constraints and distribution network interaction power constraints. The power balance constraints are: P g (t)+P v (t)=P load (t)+P ev (t)+k1P ch (t) P g (t)+P v (t)+k2P disch (t)=P load (t)+P ev (t),t=0,1,…,24 Where P ch (t), P disch (t) are the charging power and discharging power of the battery at time t; P g (t) is the power grid output at time t; P v (t) is the photovoltaic power generation power; P ev (t) is the charging power of the electric vehicle; P load (t) is the basic power consumption; k1 is the charging state parameter, k2 is the discharging state parameter, k1+k2∈[0,1], k1=1 means the battery is in the charging state; k2=1 means the battery is in the discharging state; k1=0, k2=0 means neither charging nor discharging; The energy storage battery constraints are: S OC，min ≤S OC (t)≤S OC，max P bat，min ≤P bat (t)≤P bat，max In the formula, S OC，max and S OC，min They are the upper and lower limits of the battery state of charge respectively; P bat，max and P bat，min are the upper and lower limits of the battery charging and discharging power, S OC (t) and P bat (t) is the state of charge and charge / discharge power of the battery at time t; The distribution network interaction power constraint is: 0≤P g (t)≤P gmax Where P gmax It is the upper limit power of the grid side output.

4. The multi-objective optimization dispatching system of the photovoltaic and energy storage direct-flexible microgrid according to claim 3 is characterized in that: The optimization scheduling unit is also used to substitute the load forecast results as known parameters into the multi-objective model, and use the initial energy scheduling plan as the initial particles, and solve the multi-objective model through a particle swarm optimization algorithm based on elite reverse learning strategy, worst particle exclusion pair method and chaotic perturbation to obtain the energy scheduling plan of the photovoltaic storage direct-flexible microgrid.

5. The multi-objective optimization dispatching system of the photovoltaic and energy storage direct-flexible microgrid according to claim 1 is characterized in that: Also includes: The data monitoring and management module is responsible for collecting, analyzing and displaying the operating data of the photovoltaic, storage, direct and flexible microgrid, and for carrying out operation and maintenance management of the photovoltaic, storage, direct and flexible microgrid.

6. The multi-objective optimization dispatching system of the photovoltaic and energy storage direct-flexible microgrid according to claim 5 is characterized in that: The data monitoring and management module includes: A data collection unit is used to collect the power generation, power consumption, energy storage status, and environmental parameters in the photovoltaic and energy storage direct flexible microgrid; The data analysis unit is used to analyze and evaluate the data collected by the data collection unit in combination with the big data system, and obtain the usage and operating status of the load units in the photovoltaic storage direct-flexible microgrid as well as the operating status and fault conditions of each device; Microgrid display unit, used to visualize the usage and operation status of load units in the photovoltaic storage direct-flexible microgrid; A data storage unit, used for recording and storing the data collected by the data collection unit and performing backup; The operation and maintenance management unit is used to monitor and maintain the operating status and fault conditions of each device in the photovoltaic storage direct flexible microgrid.

7. The multi-objective optimization dispatching system of the photovoltaic and energy storage direct-flexible microgrid according to claim 6 is characterized in that: The operation and maintenance management unit includes: The online operation and maintenance unit is used to perform interventional adjustment of the first type of early warning equipment in the photovoltaic storage direct flexible microgrid through remote control; The offline operation and maintenance unit is used to manually perform interventional debugging on the second type of early warning equipment in the photovoltaic storage direct flexible microgrid, where the failure degree of the second type of early warning equipment is greater than that of the first type of early warning equipment.

8. The multi-objective optimization dispatching system of the photovoltaic and energy storage direct-flexible microgrid according to claim 7 is characterized in that: The online operation and maintenance unit includes: The online supervision reminder unit is used to send supervision reminder notifications to the handheld terminal of the operation and maintenance personnel through the operation and maintenance management client or applet when a device failure is detected; The remote access unit is used to access the operation and maintenance management backend through the operation and maintenance management client or applet. The operation and maintenance management backend debugs and operates the photovoltaic storage direct-flexible microgrid through the remote access unit.

9. The multi-objective optimization dispatching system of the photovoltaic and energy storage direct-flexible microgrid according to claim 7 is characterized in that: The offline operation and maintenance unit includes: The machine maintenance selection unit is used to select the corresponding responsible machine maintenance personnel according to the failure of the equipment in the solar-storage direct-flexible microgrid when a device failure is detected; Offline supervision reminder unit, used to initiate operation and maintenance notifications to the handheld terminals of the selected maintenance personnel; The route optimization unit is used to send route information to the maintenance point to the handheld terminal of the machine maintenance personnel through the positioning system; A plan formulation unit is used to formulate a maintenance plan based on the problem of the equipment to be maintained in the photovoltaic storage direct flexible microgrid in combination with the big data system, where the equipment to be maintained is the equipment where the fault is detected; The operation and maintenance tool listing unit is used to send the problems of the equipment to be maintained in the photovoltaic storage direct flexible microgrid and the items required for the maintenance plan to the selected maintenance personnel.

10. The multi-objective optimization dispatching system of the photovoltaic and energy storage direct-flexible microgrid according to claim 1 is characterized in that: The load unit is an electric vehicle, and the prediction unit includes: A probability model building unit, used to build a probability model of the start charging time, a probability model of the end charging time, a probability model of the daily mileage and a probability model function for the initial state of charge of the electric vehicle according to the basic electricity consumption information; The Monte Carlo unit is used to use the Monte Carlo method to predict the load of the load unit at each time of the day using a probability model function based on the probability model of the starting charging time, the probability model of the ending charging time, the probability model of the daily mileage and the initial state of charge.

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