Operation method and system of cloud energy storage and sustainable distributed energy system

Abstract

The invention discloses an operation method and system for a cloud energy storage and sustainable distributed energy system, and relates to the technical field of energy scheduling, and the method comprises the steps: carrying out the cooperative management of the cloud energy storage and sustainable distributed energy system for electric energy storage through multi-stage dynamic configuration and dual-scene cooperative scheduling according to the charging power demand; according to a charging power demand, maximization of a comprehensive net present value for measuring investment profit is taken as a target to establish a target function, flexibility is added to the target function, and a design constraint, an energy balance constraint, a charging pile optimal scheduling constraint and a scheduling constraint of a battery energy storage system BESS and a transformer are taken as constraint conditions. Constructing an optimal scheduling model for measuring economic benefits and environmental benefits; the cloud energy storage and sustainable distributed energy system is subjected to optimization management through a collaborative management and optimization scheduling model and addition of an aggregator for energy coordinated scheduling. According to the invention, the stability of power supply can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling, and in particular to an operation method and system of a cloud energy storage and sustainable distributed energy system. Background Art

[0002] Photovoltaics (PV) power generation, as a clean and renewable form of energy, has developed rapidly in recent years. However, the intermittent and volatile nature of PV power generation poses a great challenge to the power grid. At the same time, the popularity of electric vehicles (EV) has put forward higher requirements for charging infrastructure. Traditional electric vehicle charging stations mainly rely on power grids for power supply, resulting in a sharp increase in grid load, especially during peak charging periods, which may cause power shortages and grid instability. To solve the above problems, electric vehicle charging stations integrating photovoltaic and energy storage systems have emerged. Such charging stations can not only reduce dependence on the power grid, but also improve the utilization rate of renewable energy.

[0003] In the prior art, traditional electric vehicle charging stations mainly rely on power supply from the power grid. Electric vehicle charging stations are usually directly connected to the power grid and adjust the grid voltage to a voltage and current level suitable for electric vehicle charging through a transformer.

[0004] However, the above technology will cause a sharp increase in grid load during charging peak periods, leading to power shortages and unstable power supply. Summary of the invention

[0005] The embodiments of the present invention provide a method and system for operating a cloud energy storage and sustainable distributed energy system, which can solve the problem of power grid instability caused by power shortage in the prior art.

[0006] The embodiment of the present invention provides an operation method of a cloud energy storage and sustainable distributed energy system, comprising the following steps: integrating a photovoltaic system and a battery energy storage system BESS into an electric vehicle charging station as a sustainable distributed energy system; obtaining the charging power demand of the electric vehicle charging pile by simulating the charging behavior of the electric vehicle at the charging pile; according to the charging power demand, collaboratively managing the cloud energy storage and sustainable distributed energy system for electric energy storage through multi-stage dynamic configuration and dual-scenario collaborative scheduling; the multi-stage dynamic configuration represents the adjustment of storage capacity and power service contracts in different power consumption stages, and the dual-scenario collaborative scheduling represents the management of the energy balance between various system components and system loads of the cloud energy storage and sustainable distributed energy system in different scenarios; according to the charging power demand, maximizing the comprehensive net present value measuring investment profit is used as the goal to establish an objective function, and flexibility is added to the objective function, and the design constraint, energy balance constraint, charging pile optimal scheduling constraint, and battery energy storage system BESS and transformer scheduling constraints are used as constraint conditions to construct an optimization scheduling model for measuring economic benefits and environmental benefits; through collaborative management and optimization scheduling model, and adding an aggregator for energy coordinated scheduling to the cloud energy storage and sustainable distributed energy system, the cloud energy storage and sustainable distributed energy system is optimized and managed.

[0007] Furthermore, the charging power demand of the electric vehicle charging pile is obtained by simulating the charging behavior of the electric vehicle at the charging pile, and the specific steps include: setting the electric vehicle arrival time interval to obey the exponential distribution, and setting the charging demand and the additional stay time to obey the normal distribution; using the Monte Carlo simulation method to simulate the electric vehicle information arriving at the charging station throughout the year; according to the simulation results, the electric vehicle is assigned to the corresponding charging pile, so that each charging pile can only charge one electric vehicle; and the charging power demand of the electric vehicle charging pile is obtained.

[0008] Furthermore, the cloud energy storage and sustainable distributed energy system for electric energy storage are collaboratively managed through multi-stage dynamic configuration and dual-scenario collaborative scheduling, and the specific steps include: constructing a multi-stage dynamic configuration, including: the cloud energy storage and the sustainable distributed energy system sign a service contract with the cloud energy storage CES; the service contract includes storage power and capacity subscription, and the service request of the distributed energy system DES to the cloud energy storage CES is within the capacity and power range subscribed by the distributed energy system DES; determining whether the distributed energy system DES needs to change the storage service contract or is in a contract adjustment transition period; obtaining the storage service price according to the service contract and the storage service contract, and adjusting the energy storage capacity and price according to the actual operating conditions; constructing dual-scenario collaborative scheduling, including: formulating active maintenance and scheduling strategies for the cloud energy storage CES and the distributed energy system DES according to different operating scenarios; conducting a year-round operation evaluation of the cloud energy storage CES and the sustainable distributed energy system DES; and collaboratively managing the cloud energy storage and the sustainable distributed energy system through the constructed multi-stage dynamic configuration and dual-scenario collaborative scheduling.

[0009] Furthermore, the optimization scheduling model is solved by a multi-objective non-dominant classification genetic algorithm NSGA-II.

[0010] Furthermore, the construction of the optimization scheduling model for measuring economic benefits and environmental benefits includes the following specific steps:

[0011] Taking the maximization of the comprehensive net present value of cloud energy storage and sustainable distributed energy system during construction and operation as the objective function, the comprehensive net present value NPV is as follows:

[0012]

[0013] Among them, AOP represents the annual operating profit of cloud energy storage and sustainable distributed energy systems; CRF l,i represents the capital recovery coefficient, and the capital recovery coefficient is a function of the discount rate i and the system life cycle l; and TCI represents the total construction investment of cloud energy storage and sustainable distributed energy systems, including the economic cost and equivalent carbon emission cost in the construction investment process;

[0014] By adjusting the form and parameters of the objective function, the flexibility of the established comprehensive net present value maximization objective is increased to adapt to different constraints and different scenarios;

[0015] The flexibility FI is formulated as:

[0016]

[0017] in, is the total amount of electricity purchased from the power grid, is the total amount of electricity sold by the power grid, is the energy value of the battery energy storage system BESS without energy storage, For cloud energy storage, CES has no energy value for storage;

[0018] The equipment design parameters are used as design constraints, the charge and discharge balance in each time period is used as the energy balance constraint, the charging pile can meet the charging demand as the optimal scheduling constraint of the charging pile, and the stability of charging and discharging is used as the scheduling constraint of the battery energy storage system BESS and the transformer;

[0019] Construct an optimization scheduling model based on the objective function and constraints.

[0020] Furthermore, the aggregator adopts a charging and discharging power scheduling algorithm.

[0021] Furthermore, the design constraints specifically include: the design capacity of all equipment in the electric vehicle charging station is limited to a fixed range due to the limitation of system scale and occupied physical space, as a design constraint.

[0022] Furthermore, the energy balance constraint specifically includes: within any time period, the input power of the bus should be equal to the output power. As the energy balance constraint, the input power includes the power purchased from the main grid, the power generated by photovoltaics and the power discharged by the battery energy storage system BESS. The output power includes the power sold to the main grid and the power charged for electric vehicles and the battery energy storage system BESS. The bus is a power bus, which represents the flow and distribution point of electric energy between different parts.

[0023] Furthermore, the optimal scheduling constraint of the charging pile includes the following specific steps: obtaining the charging power demand of the electric vehicle charging pile according to the simulated charging behavior of the electric vehicle at the charging pile, and obtaining the arrival and departure time of the electric vehicle; optimizing the output power of the charging pile at a moment through a synchronous model as the optimal scheduling constraint of the charging pile.

[0024] Furthermore, the photovoltaic system needs to obtain real-time output power, and the steps of obtaining the real-time output power include: collecting weather-related data and collecting the temperature of photovoltaic cells; training a radial basis function-based neural network model RBFNN with weather-related data to predict the temperature of photovoltaic cells and obtain a prediction model; inputting future weather-related data into the prediction model to obtain predicted photovoltaic cell temperature; using the predicted photovoltaic cell temperature and solar irradiance, inputting a circuit parameter model to obtain circuit parameters, and solving a single diode model based on the circuit parameters to obtain the output power of the photovoltaic system.

[0025] The embodiment of the present invention provides an operation system of a cloud energy storage and sustainable distributed energy system, including: a system building module, which is used to integrate a photovoltaic system and a battery energy storage system BESS into an electric vehicle charging station as a sustainable distributed energy system; obtain the charging power demand of the electric vehicle charging pile by simulating the charging behavior of the electric vehicle at the charging pile; a collaborative management building module, which is used to collaboratively manage the cloud energy storage and sustainable distributed energy system for electric energy storage through multi-stage dynamic configuration and dual-scenario collaborative scheduling according to the charging power demand; the multi-stage dynamic configuration means adjusting the storage capacity and the power service contract in different power consumption stages, and the dual-scenario collaborative scheduling means managing the cloud energy storage and the sustainable distributed energy system in different scenarios. Energy balance between various system components and system loads of sustainable distributed energy systems; a scheduling model construction module, which is used to establish an objective function based on the charging power demand, taking the maximization of the comprehensive net present value of measuring investment profits as the goal, and adding flexibility to the objective function, using design constraints, energy balance constraints, charging pile optimal scheduling constraints, and battery energy storage system BESS and transformer scheduling constraints as constraints to build an optimization scheduling model for measuring economic and environmental benefits; a system management module, which is used to optimize the management of cloud energy storage and sustainable distributed energy systems by collaboratively managing and optimizing scheduling models, and adding an aggregator for energy coordinated scheduling to cloud energy storage and sustainable distributed energy systems.

[0026] The embodiment of the present invention provides a method and system for operating a cloud energy storage and sustainable distributed energy system. Compared with the prior art, the beneficial effects thereof are as follows:

[0027] According to the charging power demand, cloud energy storage and sustainable distributed energy systems used for electric energy storage are managed collaboratively through multi-stage dynamic configuration and dual-scenario collaborative scheduling; according to the charging power demand, the objective function is established with the maximization of the comprehensive net present value that measures investment profits as the goal, and flexibility is added to the objective function. With design constraints, energy balance constraints, optimal scheduling constraints of charging piles, and scheduling constraints of battery energy storage systems BESS and transformers as constraints, an optimization scheduling model for measuring economic and environmental benefits is constructed.

[0028] Among them, the collaborative management and optimization scheduling models of multi-stage dynamic configuration and dual-scenario collaborative scheduling are all for coordinating and scheduling cloud energy storage and sustainable distributed energy systems during the charging process of electric vehicles, so as to balance the power supply and ensure stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of a comprehensive electric vehicle charging station integrating a photovoltaic system and a battery energy storage system provided by an embodiment of the present invention;

[0030] Figure 2A schematic diagram of a cloud storage and distributed energy system provided by an embodiment of the present invention;

[0031] Figure 3 A flowchart of the implementation of the method for synchronous capacity configuration and scheduling optimization of an integrated electric vehicle charging station integrating a photovoltaic system and a battery energy storage system provided in an embodiment of the present invention;

[0032] Figure 4 This is a flowchart of the overall implementation of the cloud storage and distributed energy system provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0034] See also Figure 1 to Figure 4 The embodiment of the present invention provides a method for operating a cloud energy storage and sustainable distributed energy system, comprising the following steps:

[0035] Step 1: Integrate the photovoltaic system and the battery energy storage system BESS (Battery Energy Storage System) into the electric vehicle charging station as a sustainable distributed energy system; obtain the charging power demand of the electric vehicle charging pile by simulating the charging behavior of the electric vehicle at the charging pile.

[0036] Step 2: Based on the charging power demand, the cloud energy storage and sustainable distributed energy system used for electric energy storage are collaboratively managed through multi-stage dynamic configuration and dual-scenario collaborative scheduling; multi-stage dynamic configuration means adjusting the storage capacity and power service contract in different power consumption stages, and dual-scenario collaborative scheduling means managing the energy balance between various system components and system loads of the cloud energy storage and sustainable distributed energy system in different scenarios.

[0037] Step 3: According to the charging power demand, the objective function is established with the goal of maximizing the comprehensive net present value of investment profit, and flexibility is added to the objective function. With design constraints, energy balance constraints, optimal scheduling constraints of charging piles, and scheduling constraints of battery energy storage system BESS and transformers as constraints, an optimization scheduling model for measuring economic and environmental benefits is constructed.

[0038] Step 4: Optimize the management of cloud energy storage and sustainable distributed energy systems by collaboratively managing and optimizing the scheduling model and adding an aggregator for energy coordination and scheduling to the cloud energy storage and sustainable distributed energy systems.

[0039] 1. Hybrid modeling of photovoltaic systems.

[0040] PV output power is a nonlinear function of solar irradiance and battery temperature, and battery temperature is affected by weather conditions, including solar irradiance (G), ambient temperature (Tamb), and wind speed (vwind). Linear models cannot accurately capture the power characteristics of PV, resulting in suboptimal capacity configuration and scheduling of integrated charging stations. Therefore, a hybrid modeling approach is adopted to accurately predict PV cell temperature and output power using a radial basis function-based neural network RBFNN (Radial Basis Function Neural Network). First, based on weather condition parameters such as solar irradiance (G), ambient temperature (Tamb), and wind speed (vwind), the PV cell temperature is calculated using the RBFNN in formula (1) below and the empirical equation in formula (2). Then, using solar irradiance and the calculated battery temperature, I is calculated according to formula (3): PV ,I S ,a,R S and R P Finally, by solving the single diode R in equation (4), P Model, and get the photovoltaic output power P.

[0041]

[0042] Among them, x T,m and x T,m represents the input layer of the RBFNN used to predict the battery temperature and output power model. y0 represents the output layer of the battery temperature model RBFNN, which represents the heat transfer characteristics of photovoltaics. y1 and y2 are the output layers of the RBFNN used for the output power prediction model, which affect the circuit parameters in the single diode RP model. T , σ T , They represent the number of hidden neurons, kernel width, hidden neuron center, output layer and constant bias of RBFNN in the battery temperature model. PV ,I S ,a,R S and R P are the circuit parameters of the model, representing the photocurrent, reverse saturation current, series resistance, shunt resistance and ideal factor of the diode respectively; I PV ,STC,I S , STC, a STC , RS,STC , R P,STC represents the circuit parameters under standard test conditions. μ is the electrical parameter. y1 and y2 represent the output layer neurons of RBFNN. P,m and n,m They are the input layer and the nth output layer of the RBF (Radial basis function network) neural network for the mth group of historical data. n,P , σ n,P , and are the parameters of RBFNN in the output power prediction model. I and V are the photovoltaic output current and voltage respectively. B is the Boltzmann constant, and q is the absolute value of the electron's charge.

[0043] The method has good generalizability across different photovoltaic types and a wide range of weather conditions.

[0044] 2. Random simulation method for electric vehicle charging behavior.

[0045] The key prerequisite for achieving optimal scheduling of charging piles is to build a correct modeling method for electric vehicle charging demand. For an integrated charging station, the most important parameters describing the charging behavior of electric vehicles include the arrival time t of each electric vehicle. a , departure time t l and charging power demand P EV However, predicting these parameters can be challenging due to strong uncertainties in the differences in charging habits between different regions. In previous studies, these parameters were always simulated using probability distribution functions, such as normal, uniform, and exponential distribution functions, which were evaluated from available historical data. This paper constructs a stochastic simulation method for EV charging behavior, which is described in detail as follows:

[0046] ① First, some reasonable assumptions about the significant parameter distribution function. For example, assuming that the EV arrival time interval Δt EV It follows an exponential distribution, and P EV and the additional residence time Δt after the EV completes the charging process extra It obeys normal distribution. The specific formula is as follows:

[0047]

[0048] Among them, λ h represents the expected EV arrival time interval at hour h of the day. μ EV and σ EV P EV The mean and standard deviation of P EV,min and P EV,max PEV The minimum and maximum bounds of μ extra and σ extra They are Δt extra The mean and standard deviation of Δt extra,min and Δt extra,max They are Δt extra minimum and maximum bounds of .

[0049] ② Then, a random simulation method based on Monte Carlo simulation is used to generate the information of electric vehicles arriving at the integrated charging station throughout the year. a By Δt EV The distribution simulation of leaving time t l The above simulation is performed iteratively until the current time t a More than 8760h. The mathematical equation is described as follows:

[0050]

[0051] Among them, n and N a Respectively represent the serial number and total number of electric vehicles arriving at the integrated charging station. CP,max Indicates the maximum output power of the charging pile.

[0052] ③Finally, the electric vehicles are assigned to appropriate charging piles according to the time intervals occupied by different electric vehicles. A charging pile can only charge electric vehicles at any given time. Therefore, we use a binary variable z c,t To describe the occupied state of pile c at time t, it can be described as:

[0053]

[0054] in, are the arrival time and departure time of the c-pile EV respectively.

[0055] Using the above simulation method, we can obtain the total number of electric vehicles N that can be served by the comprehensive charging station throughout the year: s , as well as entry and exit times, charging power requirements and designated charging stations for each electric vehicle.

[0056] 3. Collaborative management strategy of cloud energy storage and sustainable distributed energy systems.

[0057] Distributed energy systems composed of photovoltaic energy storage and cloud energy storage are combined with cloud energy storage. Cloud energy storage CES (Cloud Energy Storage) is an independent electric energy storage service provider and makes profits from leasing storage services. CES can provide services for a large range of grids or a large number of distributed energy systems DESs (Distributed Energy System) at the same time, and communicate with CES users in real time through information technology. When a DES charges / discharges it, it sends / extracts energy to the grid in the cloud storage in CES. Then, the CES provider charges / discharges its storage facilities to compensate the DES load on the grid accordingly. Compared with traditional DES that interact directly with the grid, the interaction between DES and CES is self-balancing and self-compensating, with less impact on the grid. It should be noted that CES is based on the grid, and when the grid line between CES and DES is congested due to grid capacity constraints, CES services may sometimes be unavailable. In order to avoid grid imbalance, CES may go offline. This work assumes that DES selects a closed CES provider to obtain reliable storage services. In addition, since DES itself provides energy, it does not need to pay real-time grid prices. There are two advantages to connecting DES to CES. First, the storage cost of DES will be lower because the storage service (from CES) is cheap, because the storage facility / pool of CES can be built cost-effectively. Second, the storage service (from CES) is the virtual energy storage of DES, which can be adjusted dynamically by changing the service agreement with CES. DES-CES includes multiple mature and available technologies, such as photovoltaic PV (Photovoltaics), wind turbine WT (Wind Turbine), gas turbine GT (Gas Turbine), ground source heat pump GSHP (Ground Source HeatPump), absorption heat pump AHP (Absorption Heat Pump), gas boiler GB (Gas Boiler), thermal energy storage tank TES (Thermal Energy Storage) and CES storage service. It mixes renewable geothermal, solar and wind energy for joint cooling, heating and power generation. DES-CES is driven by heat from GT and GB, and electricity from GT, PV and WT. GB and GT are both consuming natural gas. GSHP consumes electricity to collect stable geothermal energy to provide heat or cold energy, while AHP converts heat into cold energy. Finally, TES and energy storage services (from CES) can consume excess heat and electricity, respectively.

[0058] With the introduction of CES, DES can dynamically adjust the configuration of its energy storage system, and a multi-stage dual-scenario collaborative management strategy is developed to explore these advantages. In this work, its starting power factor is 20%. PV and WT are combined with user demand to form a net load. The scenario definition based on the DES operating domain and net load distribution is as follows:

[0059] The operational scenario is defined as the normal scenario: with the help of auxiliary equipment, the diverse net load distribution can be met. The operational scenario is defined as the robust scenario: the situation exceeds the capacity of energy conversion flexibility. CES, TES, GB, GSHP and the grid must work hard to move the net load to the normal scenario.

[0060] The energy storage system has a higher priority and dominates energy management. Sufficient TES and CES discharge allows the capacity of the energy storage system to meet larger net loads in robust scenarios. Sufficient CES charging capacity can absorb excess renewable energy to cope with smaller net loads under strong load conditions. Under normal circumstances, sufficient TES charging capacity can absorb excess energy. However, the charging and discharging capabilities of TES and CES depend on their state of charge SOC (State of Charge). Since TES and CES are not energy generation units, the present invention introduces an active energy reserve mechanism for active maintenance in normal scenarios, so it has stronger regulation capabilities in robust scenarios. Therefore, the robust and normal scenarios are connected to form a dual-scenario collaboration.

[0061] The collaborative management strategy mainly consists of multi-stage dynamic configuration and dual-scenario collaborative scheduling. The former refers to adjusting storage capacity and power contracts at different stages. The latter manages the energy balance between various components and loads in normal and robust scenarios. The specific steps are as follows:

[0062] ① Sign a service contract with CES. The contract includes storage power and capacity subscription, and DES's service requests to CES should be within the capacity and power range of its subscription.

[0063] ② Check whether DES needs to change the storage service contract or is in the transition period of contract adjustment. If so, set the delay to 24 in the first case, or subtract 1 from the delay in the second case, and then go to ③. If not, transfer the excess power (S p ) is set to 0, and then go to ④. The contract transition period is the buffer time (24h) allowed by BESS when BESS changes the storage contract.

[0064] ③ Calculate the price. When DES switches the storage service subscription at hour t, the remaining energy in CES may exceed the subscription capacity in the new contract phase. This remaining energy is calculated and allocated to S p.

[0065] ④Determine the adjustment range of CES.

[0066] ⑤ Determine the running scenario. If it is normal, go to ⑥. Otherwise, go to ⑦.

[0067] ⑥ Actively maintain CES and DES. But the behavior of TES and CES must be determined first. When the remaining energy of DES in CES is lower than (exceeds) the optimal state of charge, CES should be charged (discharged) to bring SOC close to the optimal state of charge. When the remaining thermal energy in TES exceeds the optimal state of charge, TES releases the stored thermal energy to bring SOC close to the optimal state of charge. Continue to ⑧.

[0068] ⑦CES and TES can work at full load. CES and TES will move the net load to the normal scenario at full load. Active maintenance under normal circumstances prevents the purchase and sale of the grid and the operation of GB. Therefore, underutilized fossil fuels and high costs are avoided. The discharge power from CES can drive GSHP to meet the thermal load.

[0069] ⑧ Check if all data has been evaluated. If not, return to ②. Otherwise, the annual operation results will be obtained.

[0070] 4. Synchronize capacity configuration and scheduling optimization model.

[0071] 4.1 Objective function

[0072] The objective function of the optimization model is to maximize the comprehensive net present value during construction and operation, taking into account both economic and environmental benefits. Its mathematical equation is described as:

[0073]

[0074] Among them, AOP and TCI represent the annual operating profit and total construction investment of the system respectively. l,i represents the capital recovery factor, which is a function of the discount rate i and the system life cycle l. The mathematical equation is shown as:

[0075]

[0076] TCI includes the economic cost and equivalent carbon emission cost during the construction investment process, as shown below:

[0077]

[0078] Among them C I and E Iare the economic cost and equivalent carbon emission cost in the construction investment process. r is the transaction price of each carbon. q represents the equipment type, including photovoltaic, BESS and transformer. W q is the capacity of device q, and are the basic constant cost and capacity unit cost of equipment q in the construction investment process. q is a binary variable indicating whether device q is constructed. CP and They are the number of charging piles and the investment cost respectively. and are the basic constant of capacity and unit equivalent carbon emission during the construction process of equipment q, respectively.

[0079] The annual operating profit AOP is described as:

[0080] AOP=RC P -C D -C M -rE O (11)

[0081] Where R represents the annual revenue of the integrated charging station, which includes the benefits of selling electricity to the grid and charging electric vehicles. The mathematical equation is described as:

[0082]

[0083] Where Δt is the time step of the scheduling process. sell and β load These are the prices for selling electricity to the grid and for charging electric vehicles. and are the power sold to the grid and the power charged to the electric vehicle at time t. Note that if the generated power is only self-produced and the remaining power is prohibited from being connected to the main grid, then β sell is zero, Indicates the wasted power of the system.

[0084] C P Represents the annual cost of purchasing electricity, which includes the base rate and time-of-use rates. The mathematical equation is:

[0085]

[0086] Among them, α buy It is the basic price of electricity purchased from the main grid every month. and They are the time-of-use electricity price and the power of the purchased electricity.

[0087] C Drepresents the annual cost of equipment degradation. In this study, the PV system is assumed to degrade linearly with operating time, while the aging of the BESS is assumed to depend on the cycle life. Therefore, C D It can be calculated as:

[0088]

[0089] in, is the degradation cost of BESS per kWh capacity. and are the discharge power and charging power of BESS at time t respectively. cycle is the total number of charge and discharge cycles of the BESS during its entire life cycle. PV is the annual degradation cost per kilowatt of capacity of the PV system.

[0090] C M Represents the annual equipment maintenance cost, which can be described as:

[0091]

[0092] in, Annual maintenance cost for each charging station. is the annual maintenance cost of equipment q during the power generation process.

[0093] E O It represents the annual equivalent carbon emissions during operation, including the carbon emissions generated by purchasing electricity and recycling power generation equipment. The mathematical equation is shown as:

[0094]

[0095] Among them, μ buy , and Represents the unit equivalent carbon emissions of operating BESS and PV respectively when purchasing electricity from the main grid.

[0096] When cloud energy storage and sustainable distributed energy systems are managed in a coordinated manner, flexibility FI is added to the objective function:

[0097]

[0098] in, and They are respectively the sum of electricity purchased from (sold to) the power grid. and is the corresponding value without energy storage (BESS and CES storage services). Energy storage that GT actively charges and maintains under normal circumstances can effectively cope with variable renewable energy and loads. It reduces the buying and selling of the grid and fuel consumption. FI makes sense because the former reduces the independence of BESS and increases the energy bill, while the latter does not make full use of fossil fuels. When the energy storage system with cloud energy storage can completely avoid these two parts, FI will reach 100%. By adding a flexibility model to the objective function, the energy distribution of the battery energy storage system BESS and the cloud energy storage CES can be optimized when the cloud energy storage is managed in coordination with the sustainable distributed energy system.

[0099] 4.2 Constraints.

[0100] The capacity configuration and scheduling of photovoltaic energy storage integrated electric vehicle charging stations should be subject to five equations, including design constraints, energy balance constraints and scheduling constraints. These constraints are introduced in detail as follows.

[0101] (1) Design constraints.

[0102] The design capacity of all equipment in a comprehensive electric vehicle charging station is limited to a specific range due to the system size and the physical space occupied. The mathematical equation is:

[0103]

[0104] in, and are the minimum and maximum capacities of device q, respectively.

[0105] (2) Energy balance constraint.

[0106] At any time period t, the input power of the bus should be equal to the output power. The input power includes the power purchased from the main grid, the power generated by PV, and the power discharged by BESS. The output power includes the power sold to the main grid, the power for charging electric vehicles and BESS. Therefore, the balance constraint of the bus can be described as:

[0107]

[0108] Among them, η dch and η ch are the discharge efficiency and charging efficiency of BESS respectively.

[0109] The energy storage St of BESS at time t is equal to the energy storage S at the previous time t-1 t-1 The sum of the net charging power Δt during this period is shown in formula (20).

[0110]

[0111] (3) Constraints for optimal scheduling of charging piles.

[0112] Traditional scheduling methods give priority to meeting the charging needs of electric vehicles in a timely manner. Once the electric vehicle arrives, the charging pile operates at maximum power. This method ignores the differences in PV capacity and time-of-use electricity prices in different time periods, which may lead to unsatisfactory comprehensive charging station scheduling results. The present invention develops an optimal scheduling method for charging piles and combines it with a parallel capacity configuration and scheduling optimization model. The method first assigns all electric vehicles to suitable charging piles based on arrival and departure times. The arrival and departure times of the charging piles are obtained by a random simulation method. Then the output power of the charging pile c at time t is optimized through a synchronous model To meet the power requirements of all electric vehicles. The mathematical equation for the optimal scheduling constraint of charging piles is:

[0113]

[0114]

[0115] in, Represents the power demand of the tth charging pile at the moment. represents the power demand of the vth electric vehicle at the cth charging station. CP,min and P CP,max They are the minimum and maximum output power of the charging pile respectively.

[0116] (4) Dispatch constraints of BESS and transformers.

[0117] In order to improve the safety and stability of the main power grid, the power transaction between the integrated charging station and the main power grid should be constrained as follows:

[0118]

[0119] Among them, P buy,min and P buy,max are the minimum and maximum power that can be purchased from the main grid. sell,min and P sell ,max are respectively the minimum and maximum power that can be supplied to the main grid. and are binary variables that determine the purchase or sale of electricity at time t. Assume that the purchase and sale of electricity from the grid cannot occur at the same time.

[0120]

[0121] Note that P buy,max Equal to the active power of the transformer, which is the capacity W T and transformer efficiency η T function.

[0122] P buy,max =η T W T (27)

[0123] The constraints in equation (24) are nonlinear because and P buy,max are all optimization variables. Therefore, the optimization model is a mixed integer nonlinear optimization programming (MINLP) problem with high computational difficulty. Therefore, the large M linearization method is used to linearize the nonlinear constraints and equation (24) to equivalently convert it into:

[0124]

[0125] The discharge and charge power of BESS are affected by both capacity and c-rate, as shown in equations (29) and (30):

[0126]

[0127] in, and are binary variables, which determine whether the BESS is in the discharge or charging state at time t. dch,min and γ dch,max are the minimum and maximum discharge c rates, respectively. ch,min and γ ch,max are the minimum and maximum charging c rates, respectively. For BESS, discharge and charging can be performed simultaneously, which is described as:

[0128]

[0129] Formulas (29) and (30) are nonlinear constraints and are linearized using the large M method. The equivalent equations are:

[0130]

[0131]

[0132] In addition, the state of energy (SOE) is introduced to represent the current energy storage state of BESS, and its calculation formula is:

[0133] SOE t =S t / W BESS (34)

[0134] To prevent BESS from overcharging or overdischarging, S t Stay within the proper range:

[0135]

[0136] Among them, SOEmin and SOEmax are the minimum and maximum SOE of BESS respectively.

[0137] 4.3 Optimization algorithm.

[0138] In terms of algorithms, in the patent, the proposed optimization model is solved by the well-known multi-objective non-dominant classification genetic algorithm-II (NSGA-II). The specific optimization steps are described as follows:

[0139] Step 1: Randomly initialize the population. Randomly initialize several groups of decision variables according to the number of individuals in the population. Then, set the evolution time to 1.

[0140] Step 2: Fitness value evaluation. Import the decision variables, simulate the annual operation of the model, and then analyze the results through equations.

[0141] Step 3: Population update. According to the individual performance obtained in step 2, the individuals are sorted by non-dominance. Then the dominant individuals are selected for crossover and mutation operations to update the population.

[0142] Step 4: Determine the termination condition. When the population evolution time reaches the maximum value, output the optimal solution and exit. Otherwise, increase the evolution time by 1 and return to step 2.

[0143] 5. Bidirectional charging station.

[0144] In addition to the EV charging piles, photovoltaic PV and battery energy storage system BESS systems, an aggregator is added, which also determines the energy management decision. The proposed charging and discharging power scheduling algorithm is executed in the aggregator. It is assumed that the charging station can automatically detect the vehicle arrival time, initial charge state SOC and battery capacity of the EV through a unified communication protocol. The EV user provides the departure time and the final required SOC through the user machine interface before charging.

[0145] The EV charging behavior is completed by Section 2, which fully captures the behavior of the EV fleet. The PV forecast is completed by Section 1. The load and PV data are collected and fitted to a normal probability distribution function. The obtained normal probability distribution function is then used to generate sample load data for further use.

[0146] Aggregators coordinate dispatch operations. EVs and BESS are first charged from the grid. In addition, the grid provides energy for system construction loads that are primarily served by BESS and PV. During periods of high electricity prices, power requests from the grid lead the aggregator to develop a strategy to maximize trading profits with the grid. This trading strategy stipulates that PV and BESS are the first suppliers to sell electricity to the grid. If the demand on the grid exceeds the power that both can provide, EVs are used to reduce the shortage while ensuring that they get the required state of charge on time.

[0147] The present invention proposes a synchronous capacity configuration and scheduling optimization model for an electric vehicle charging station that integrates photovoltaic and energy storage systems. When establishing the system model, the radial basis function neural network model is firstly used to accurately predict the temperature and output power of photovoltaic cells by combining historical weather data and real-time monitoring data. This method can effectively solve the problem that the traditional linear model cannot accurately capture the power characteristics of the photovoltaic system and provide more accurate power generation prediction. Secondly, in order to achieve optimal scheduling of charging piles, a non-random simulation method is used to establish a correct electric vehicle EV charging demand model. Finally, a multi-objective optimization algorithm that comprehensively considers photovoltaic power generation capacity, charging and discharging status of energy storage system, time segment electricity price and electric vehicle charging demand is used to formulate the optimal charging scheduling plan to maximize the use of photovoltaic power generation and reduce peak charging load. Cloud energy storage technology is also introduced, and the hybrid renewable energy distributed energy system DES is connected with cloud energy storage (shared energy storage provider CES) to build a DES storage system in a subscription mode. Using the subscription mode, DES-CES can dynamically adjust storage capacity and power consumption by changing the storage service contract. Then, a multi-stage dual-scenario collaborative management strategy based on active energy reserves is designed to schedule DES-CES.

[0148] The present invention also has the following beneficial effects:

[0149] 1. Improve the utilization rate of renewable energy: By integrating photovoltaic power generation and battery energy storage systems into electric vehicle charging stations, the present invention can maximize the use of solar energy. The photovoltaic system generates electricity during the day, and the excess electricity can be stored in the battery for use at night or on cloudy days, thereby reducing dependence on traditional fossil energy.

[0150] 2. Reduce carbon emissions: The use of clean energy and energy storage technology effectively reduces carbon emissions generated during the charging process of electric vehicles. Photovoltaic systems do not produce greenhouse gases when generating electricity. By increasing the utilization rate of clean energy, the carbon footprint of the power system can be significantly reduced.

[0151] 3. Enhance grid stability: The present invention improves grid stability and power supply reliability through the coordinated work of the energy storage system. The energy storage system can release energy during peak load periods, relieve grid pressure, and reduce grid fluctuations.

[0152] 4. Optimize energy management: The proposed multi-objective optimization model can comprehensively consider multiple factors such as photovoltaic power generation capacity, energy storage system status, electric vehicle charging needs, etc., to achieve efficient management and optimal allocation of energy.

[0153] 5. Significant economic benefits: By optimizing the charging and discharging strategies, the cost of charging electric vehicles can be reduced and the economic benefits of charging stations can be improved. By using the time-segmented electricity price strategy, charging is done when electricity prices are low and discharging is done when electricity prices are high, further reducing the overall operating costs.

[0154] 6. Promote the popularization of electric vehicles: It improves the efficiency and reliability of electric vehicle charging infrastructure, solves the concerns of electric vehicle users about long charging time and high charging costs, and thus promotes the popularization and application of electric vehicles.

[0155] 7. Promote the transformation of the energy system: Integrating photovoltaic and energy storage technologies into electric vehicle charging stations has promoted the transformation of the energy system towards a clean, efficient and sustainable direction, and helped to achieve energy structure adjustment and energy revolution.

[0156] 8. Environmental protection and sustainable development: By reducing the use of fossil energy and lowering carbon emissions, the present invention is of great significance to environmental protection and sustainable development. It can not only alleviate the problem of climate change, but also improve air quality and protect the ecological environment.

[0157] 9. Improve energy utilization efficiency: The application of multi-objective optimization algorithms and real-time scheduling strategies has improved energy utilization efficiency, reduced energy waste, and achieved maximum utilization of energy resources.

[0158] 10. Innovative charging station capacity configuration and scheduling optimization model: A hybrid modeling method for photovoltaic systems based on radial basis function neural networks is proposed to improve the accuracy of photovoltaic power generation prediction. The electric vehicle charging demand model is constructed through a non-random simulation method, the scheduling strategy of charging piles is optimized, and the charging efficiency is improved. Multi-objective optimization algorithm: This invention introduces a multi-objective optimization algorithm, which comprehensively considers photovoltaic power generation, energy storage system status, electric vehicle charging demand and time-segmented electricity prices, realizes the optimal charging scheduling plan, and maximizes the economic and environmental benefits of the system. Comprehensive energy management strategy: A comprehensive energy management strategy is proposed, including real-time monitoring, data transmission, prediction models and scheduling algorithms, to ensure efficient operation and continuous optimization of the system under various operating conditions.

[0159] The present invention proposes a synchronous capacity configuration and scheduling optimization method for an integrated electric vehicle charging station based on a photovoltaic and battery energy storage system, combined with a multi-objective optimization collaborative management strategy for a sustainable distributed energy system with cloud energy storage, and improves the energy utilization efficiency and reliability of electric vehicle charging stations by utilizing photovoltaic power generation and energy storage technology. Through the multi-objective optimization algorithm, the system operating costs and carbon emissions are reduced, achieving a win-win situation in economic and environmental benefits. This patent not only promotes the popularization and application of electric vehicles, but also provides strong support for the clean, efficient and sustainable development of energy systems, and is of great significance to environmental protection and energy revolution.

[0160] The embodiment of the present invention provides an operation system of a cloud energy storage and sustainable distributed energy system, including: a system construction module, which is used to integrate a photovoltaic system and a battery energy storage system BESS into an electric vehicle charging station as a sustainable distributed energy system; and obtain the charging power demand of the electric vehicle charging pile by simulating the charging behavior of the electric vehicle at the charging pile. A collaborative management construction module, which is used to collaboratively manage the cloud energy storage and sustainable distributed energy system for electric energy storage through multi-stage dynamic configuration and dual-scenario collaborative scheduling according to the charging power demand; the multi-stage dynamic configuration means adjusting the storage capacity and power service contract in different power consumption stages, and the dual-scenario collaborative scheduling means managing the energy balance between various system components and system loads of the cloud energy storage and sustainable distributed energy system in different scenarios. A scheduling model construction module, which is used to establish an objective function based on the charging power demand, taking the maximization of the comprehensive net present value measuring the investment profit as the goal, and adding flexibility to the objective function, and taking the design constraints, energy balance constraints, charging pile optimal scheduling constraints, and battery energy storage system BESS and transformer scheduling constraints as constraints to construct an optimization scheduling model for measuring economic benefits and environmental benefits. The system management module is used to optimize the management of cloud energy storage and sustainable distributed energy systems through collaborative management and optimization of scheduling models, and to add an aggregator for energy coordination and scheduling to cloud energy storage and sustainable distributed energy systems.

[0161] A specific embodiment is as follows:

[0162] 1. Synchronous capacity configuration and scheduling optimization of integrated electric vehicle charging stations based on photovoltaic and battery energy storage systems.

[0163] ① Hybrid modeling of photovoltaic systems.

[0164] Step 1: Collect and prepare data. Collect local weather data such as solar irradiance, ambient temperature, and wind speed. The data can come from weather stations, satellite data, or local sensor networks.

[0165] Step 2: Build and train the RBF neural network model. Use the collected historical weather data to train the radial basis function based neural network (RBFNN) model. The goal is to predict the temperature of the photovoltaic cell based on the input weather data.

[0166] Step 3: Predict the temperature of photovoltaic cells. Input the real-time solar irradiance, ambient temperature and wind speed data into the trained RBFNN model to predict the temperature of photovoltaic cells.

[0167] Step 4: Calculate the photovoltaic output power parameters. Use the predicted photovoltaic cell temperature and solar irradiance to calculate the key parameters of photovoltaic output power.

[0168] Step 5: Solve the single diode model. Use the parameters calculated in the previous step to solve the single diode model and obtain the real-time output power of the photovoltaic system.

[0169] ② Random simulation method of electric vehicle charging behavior.

[0170] Step 1: Define the distribution of electric vehicle arrival and charging demand. Assume that the electric vehicle arrival time follows an exponential distribution. Assume that the charging demand and additional stay time follow a normal distribution.

[0171] Step 2: Monte Carlo simulation method. The Monte Carlo simulation method is used to generate information about electric vehicles arriving at charging stations throughout the year, including the arrival time, departure time, and charging demand of each vehicle.

[0172] Step 3: Assign charging piles. According to the data generated by the simulation, electric vehicles are assigned to appropriate charging piles. Ensure that each charging pile can only charge one electric vehicle at any time.

[0173] ③Synchronous capacity configuration and scheduling optimization model.

[0174] Step 1: Define the optimization objective function, the goal is to maximize the comprehensive net present value (NPV), taking into account both economic and environmental benefits.

[0175] Step 2: Calculate the system’s annual operating profit and total construction investment, including the economic cost and carbon emission cost of equipment such as the photovoltaic system, battery energy storage system, and transformer.

[0176] Step 3: Calculate the annual revenue of the comprehensive charging station, including the revenue from selling electricity to the grid and charging electric vehicles.

[0177] Step 4: Optimize configuration and dispatch strategy. Using the optimization algorithm, the PV system, BESS and charging station are synchronously configured and dispatched. Ensure that the system's operating costs and carbon emissions are minimized while meeting demand. At the same time, an aggregator is added, which also determines the energy management decision. The proposed charging and discharging power dispatch algorithm is executed in the aggregator. It is assumed that the charging station can automatically detect the vehicle arrival time, initial charge state SOC and battery capacity of the electric vehicle through a unified communication protocol. The electric vehicle user provides the departure time and the final required SOC through the user interface before charging. The aggregator coordinates the dispatch operation. The electric vehicle and BESS are first charged from the grid. In addition, the grid provides energy for the system construction load that is mainly served by BESS and PV. During high electricity prices, power requests from the grid cause the integrator to formulate a strategy to maximize the trading profit with the grid. The trading strategy stipulates that PV and BESS are the first suppliers to sell electricity to the grid. If the demand of the grid exceeds the power that both can provide, electric vehicles are used to reduce the shortage while ensuring that they get the required charge state on time.

[0178] 2. Multi-objective optimization collaborative management strategy for sustainable distributed energy systems considering cloud energy storage.

[0179] ① Collaborative management of cloud energy storage and distributed energy systems.

[0180] Step 1: Identify energy storage service needs and providers: Identify distributed energy systems (DES) that require energy storage services and cloud energy storage service providers (CES) that provide energy storage services.

[0181] Step 2: Establish a real-time communication mechanism: DES communicates with CES in real time through information technology (such as the Internet of Things platform). When DES needs energy storage services, it sends energy storage or discharge requests to CES.

[0182] Step 3: Providing energy storage services: CES provides energy storage services according to the contract and responds to the needs of DES. It dynamically adjusts the energy storage capacity and power output to compensate for the grid load.

[0183] ②Multi-stage dual-scenario collaborative management strategy.

[0184] Step 1: Sign an energy storage service contract. DES signs a service contract with CES to define the storage power and capacity subscription scope.

[0185] Step 2: Regularly check and adjust. Regularly check and adjust storage service contracts, and dynamically adjust energy storage capacity based on changes in demand.

[0186] Step 3: Calculate and adjust prices. Calculate the storage service prices at each stage to ensure economic rationality. Adjust the storage capacity and price according to the actual operation to ensure the optimal state.

[0187] Step 4: Develop operation and scheduling strategies. According to different operation scenarios (normal and robust), develop active maintenance and energy scheduling strategies for CES and DES to ensure the stability and efficiency of the system under various load conditions.

[0188] Step 5: Full-year operation evaluation. Evaluate the full-year operation data and optimize the storage service contract and scheduling strategy to ensure efficient operation and continuous optimization of the system under various operating conditions.

[0189] Through these detailed steps and methods, the integration of photovoltaic systems, battery energy storage systems and cloud energy storage technologies can significantly improve the efficiency, stability and sustainability of electric vehicle charging stations and distributed energy systems.

[0190] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for operating a cloud energy storage and sustainable distributed energy system, characterized in that: The following steps are involved: Integrate photovoltaic systems and battery energy storage systems (BESS) into electric vehicle charging stations as sustainable distributed energy systems; obtain the charging power demand of electric vehicle charging piles by simulating the charging behavior of electric vehicles at charging piles; According to the charging power demand, the cloud energy storage and sustainable distributed energy system for electric energy storage are collaboratively managed through multi-stage dynamic configuration and dual-scenario collaborative scheduling; the multi-stage dynamic configuration means adjusting the storage capacity and power service contract in different power consumption stages, and the dual-scenario collaborative scheduling means managing the energy balance between various system components and system loads of the cloud energy storage and sustainable distributed energy system in different scenarios; According to the charging power demand, the objective function is established with the goal of maximizing the comprehensive net present value of investment profit. Flexibility is added to the objective function. The optimal scheduling model for measuring economic and environmental benefits is constructed with design constraints, energy balance constraints, optimal scheduling constraints for charging piles, and scheduling constraints for battery energy storage systems (BESS) and transformers as constraints. By collaboratively managing and optimizing the scheduling model and adding an aggregator for energy coordination and scheduling to cloud energy storage and sustainable distributed energy systems, cloud energy storage and sustainable distributed energy systems can be optimized and managed.

2. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The specific steps of obtaining the charging power demand of the electric vehicle charging pile by simulating the charging behavior of the electric vehicle at the charging pile include: The time interval between electric vehicle arrivals is set to follow an exponential distribution, and the charging demand and additional stay time are set to follow a normal distribution; Monte Carlo simulation method is used to simulate the information of electric vehicles arriving at charging stations throughout the year; According to the simulation results, electric vehicles are assigned to corresponding charging piles so that each charging pile can only charge one electric vehicle; Get the charging power demand of electric vehicle charging piles.

3. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The cloud energy storage for electric energy storage and the sustainable distributed energy system are collaboratively managed through multi-stage dynamic configuration and dual-scenario collaborative scheduling, and the specific steps include: Constructing a multi-stage dynamic configuration, including: cloud energy storage and sustainable distributed energy system and cloud energy storage CES sign a service contract; the service contract includes storage power and capacity subscription, and the distributed energy system DES's service request to the cloud energy storage CES is within the capacity and power range subscribed by the distributed energy system DES; determining whether the distributed energy system DES needs to change the storage service contract or is in a contract adjustment transition period; obtaining the storage service price according to the service contract and storage service contract, and adjusting the energy storage capacity and price according to the actual operation situation; Construct dual-scenario coordinated dispatch, including: formulating active maintenance and dispatch strategies for cloud energy storage CES and distributed energy system DES according to different operation scenarios; conducting year-round operation evaluation of cloud energy storage CES and sustainable distributed energy system DES; Cloud energy storage and sustainable distributed energy systems are managed collaboratively through the construction of multi-stage dynamic configuration and dual-scenario collaborative scheduling.

4. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The optimization scheduling model is solved by a multi-objective non-dominant classification genetic algorithm NSGA-II.

5. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The construction of the optimization scheduling model for measuring economic benefits and environmental benefits includes the following specific steps: Taking the maximization of the comprehensive net present value of cloud energy storage and sustainable distributed energy system during construction and operation as the objective function, the comprehensive net present value NPV is as follows: Among them, AOP represents the annual operating profit of cloud energy storage and sustainable distributed energy systems; CRF l,i represents the capital recovery coefficient, and the capital recovery coefficient is a function of the discount rate i and the system life cycle l; and TCI represents the total construction investment of cloud energy storage and sustainable distributed energy systems, including the economic cost and equivalent carbon emission cost in the construction investment process; By adjusting the form and parameters of the objective function, the flexibility of the established comprehensive net present value maximization objective is increased to adapt to different constraints and different scenarios; The flexibility FI is formulated as: in, is the total amount of electricity purchased from the power grid, is the total amount of electricity sold by the power grid, is the energy value of the battery energy storage system BESS without energy storage, For cloud energy storage, CES has no energy value for storage; The equipment design parameters are used as design constraints, the charge and discharge balance in each time period is used as the energy balance constraint, the charging pile can meet the charging demand as the optimal scheduling constraint of the charging pile, and the stability of charging and discharging is used as the scheduling constraint of the battery energy storage system BESS and the transformer; Construct an optimization scheduling model based on the objective function and constraints.

6. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The aggregator adopts a charging and discharging power scheduling algorithm.

7. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The design constraints specifically include: The design capacity of all equipment in an EV charging station is limited to a fixed range as a design constraint due to the limitation of system scale and occupied physical space.

8. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The energy balance constraints specifically include: In any time period, the input power of the bus should be equal to the output power as an energy balance constraint. The input power includes power purchased from the main grid, power generated by photovoltaics and discharged by the battery energy storage system BESS. The output power includes power sold to the main grid and power charged for electric vehicles and the battery energy storage system BESS. The bus is an electric bus, which represents the flow and distribution point of electric energy between different parts.

9. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The optimal scheduling constraint of the charging pile includes the following specific steps: The charging power demand of the electric vehicle charging pile is obtained based on the simulated charging behavior of the electric vehicle at the charging pile, and the arrival and departure time of the electric vehicle is obtained; The output power of the charging pile at a certain moment is optimized through the synchronization model as the optimal scheduling constraint of the charging pile.

10. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, characterized in that: The photovoltaic system needs to obtain real-time output power, and the steps of obtaining the real-time output power include: Collect weather-related data and collect the temperature of photovoltaic cells; The temperature of photovoltaic cells is predicted by training the radial basis function-based neural network model RBFNN with weather-related data to obtain a prediction model; Input future weather-related data into the prediction model to obtain the predicted photovoltaic cell temperature; The predicted photovoltaic cell temperature and solar irradiance are used to input the circuit parameter model to obtain the circuit parameters, and the single diode model is solved according to the circuit parameters to obtain the output power of the photovoltaic system.

11. An operating system for a cloud energy storage and sustainable distributed energy system, characterized in that: include: System building module, used to integrate photovoltaic system and battery energy storage system BESS into electric vehicle charging station as a sustainable distributed energy system; obtain the charging power demand of electric vehicle charging pile by simulating the charging behavior of electric vehicles at charging piles; A collaborative management building module is used to collaboratively manage cloud energy storage and sustainable distributed energy systems for electric energy storage according to charging power demand through multi-stage dynamic configuration and dual-scenario collaborative scheduling; the multi-stage dynamic configuration means adjusting storage capacity and power service contracts at different power consumption stages, and the dual-scenario collaborative scheduling means managing the energy balance between various system components and system loads of cloud energy storage and sustainable distributed energy systems under different scenarios; The scheduling model building module is used to establish an objective function based on the charging power demand and to maximize the comprehensive net present value of investment profit. It also adds flexibility to the objective function and uses design constraints, energy balance constraints, optimal scheduling constraints of charging piles, and scheduling constraints of battery energy storage systems (BESS) and transformers as constraints to build an optimization scheduling model for measuring economic and environmental benefits. The system management module is used to optimize the management of cloud energy storage and sustainable distributed energy systems through collaborative management and optimization of scheduling models, and to add an aggregator for energy coordination and scheduling to cloud energy storage and sustainable distributed energy systems.

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