Optimization method for improving rural photovoltaic absorption capability by using mobile energy storage

Abstract

The invention relates to the technical field of new energy, and discloses an optimization method for improving the rural photovoltaic absorption capability by using mobile energy storage, and the method comprises the steps: constructing a system architecture of distributed charging and storage and centralized discharging of rural distributed photovoltaic energy storage; setting a first constraint condition by taking the maximum benefit of the centralized aggregation type energy storage power station as a target, and constructing a constant volume model of the mobile energy storage vehicle and the mobile power generation vehicle; setting a second constraint condition by taking the minimum sum of the total cost of the cost of the mobile energy storage vehicle, the transportation cost of the traction trailer for transporting the mobile energy storage vehicle and the light abandoning punishment cost of the distributed photovoltaic power station as a target, and constructing an optimized operation model of the mobile energy storage vehicle; iteratively solving the constant volume models of the mobile energy storage vehicle and the mobile power generation vehicle and the optimized operation model of the mobile energy storage vehicle in sequence; the method not only effectively improves the photovoltaic consumption efficiency, but also can be used as a standby power supply of a user, remarkably enhances the reliability of the rural low-voltage power distribution network, improves the economic benefit and shortens the return on investment period.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to an optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage. Background Art

[0002] Currently, distributed photovoltaic power stations in rural areas are generally characterized by small capacity, large numbers, and dispersed configurations, which presents a series of problems. First, because each photovoltaic power station requires its own inverter, the long idle time of the inverter capacity significantly increases system costs. Second, there is a temporal and spatial mismatch between photovoltaic power generation and power load, making it difficult to effectively store and utilize excess electricity, which is prone to curtailment. At the same time, decentralized photovoltaic power stations struggle to participate in the electricity market on a large scale and have difficulty accessing the grid. Their self-generation and self-use revenue model is limited, and their economic benefits are limited. In addition, rural low-voltage distribution network lines are not only long but also radial. During peak power consumption periods, the low-voltage side of some distribution transformers often experiences low voltage. This prominent voltage quality problem prevents loads from operating normally, reducing power supply reliability.

[0003] In summary, existing technologies struggle to meet the demand for utilizing the vast amount of rural photovoltaic resources. While fixed energy storage can absorb daytime photovoltaic power, rural distribution networks are weak, transmission capacity is low, and the investment required to transform them is substantial. Grid companies lack the investment incentive to do so. Consequently, even if large amounts of photovoltaic power are difficult to sell online, it's difficult to absorb them locally. Furthermore, rural areas also have numerous non-stationary electricity usage scenarios, such as agricultural harvesting, processing, and temporary irrigation. These locations often lack power lines and infrastructure, creating a significant need for flexible, mobile power sources. Summary of the Invention

[0004] To address the aforementioned shortcomings of the existing technology, the present invention provides an optimized method for improving rural photovoltaic power consumption capacity through mobile energy storage. This method uses energy storage vehicles to absorb distributed photovoltaic power, then forms a centralized, aggregated distribution model. A portion of this power is connected to the grid and discharged for sale, while the remaining portion is dispatched to temporary load points. This expands the scale of photovoltaic power generation and increases the profitability of rural photovoltaic resources. It also addresses the issue of some power points being unable to use power due to a lack of power lines or voltage quality issues, ultimately achieving the goal of increasing revenue and ensuring power supply through distributed photovoltaics.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] An optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage comprises the following steps:

[0007] S1. Build a system architecture for distributed charging and discharging of rural photovoltaic energy storage, including mobile energy storage vehicles, mobile power generation vehicles, and centralized energy storage power stations;

[0008] S2. Targeting the maximum benefit of the centralized energy storage power station and setting the first constraint, a fixed-capacity model for the mobile energy storage vehicle and mobile power generation vehicle is constructed.

[0009] S3. Optimizing the sum of the cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle using a tractor trailer, and the penalty cost of abandoned solar power at the distributed photovoltaic power station is minimized, and a second constraint is set to construct an optimal operation model for the mobile energy storage vehicle.

[0010] S4. Iteratively solving the fixed capacity model of the mobile energy storage vehicle and the mobile power generation vehicle and the optimized operation model of the mobile energy storage vehicle in turn to achieve the optimal configuration of the mobile energy storage vehicle and the mobile power generation vehicle.

[0011] The present invention has the following beneficial effects:

[0012] The present invention proposes an optimization method for improving the rural photovoltaic absorption capacity by utilizing mobile energy storage. By constructing a decentralized charging and storage and centralized discharging architecture including rural distributed photovoltaics, mobile energy storage vehicles, mobile power generation vehicles, centralized aggregated energy storage power stations, and energy monitoring and management systems, and at the same time constructing a two-layer model to optimize the configuration method of the energy storage system, the method solves the problems of high grid connection costs, low absorption efficiency, insufficient economic benefits, and poor distribution network voltage quality caused by the small capacity and decentralized configuration of rural distributed photovoltaic power stations. It can not only effectively improve the photovoltaic absorption efficiency, but also serve as a backup power source for users, significantly enhancing the reliability of rural low-voltage distribution networks, improving economic benefits, and shortening the investment payback period. In addition, the constructed system architecture is also suitable for the planning and operation of rural distributed photovoltaic energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flow chart of an optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage, as proposed by the present invention;

[0014] Figure 2 Schematic diagram of the system architecture of distributed charging and centralized discharging of rural distributed photovoltaic energy storage in the embodiment;

[0015] Figure 3 Schematic diagram of the workflow of distributed charging and storage and centralized discharging of the system architecture in the embodiment. DETAILED DESCRIPTION

[0016] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0017] like Figure 1 As shown, an optimization method for improving rural photovoltaic absorption capacity by using mobile energy storage includes the following steps S1-S4:

[0018] S1. Construct a system architecture for distributed charging and discharging of rural photovoltaic energy storage, which includes mobile energy storage vehicles, mobile power generation vehicles and centralized aggregated energy storage power stations.

[0019] Specifically, the system architecture of distributed charging and centralized discharging of rural distributed photovoltaic energy storage also includes rural distributed photovoltaic power stations and energy monitoring and management systems.

[0020] In this embodiment, the system architecture includes several rural distributed photovoltaic power stations, several mobile energy storage vehicles, several mobile power generation vehicles, a centralized aggregated energy storage power station and an energy monitoring and management system; wherein, each farmer's rooftop photovoltaic power station is connected to the mobile energy storage vehicle through a DC charging controller, and the mobile energy storage vehicle is transported to the centralized aggregated energy storage power station by a tractor trailer. The centralized aggregated energy storage power station is responsible for centralized discharge and management of electric energy at a fixed connection point with the distribution network, and the energy monitoring and management system monitors and dispatches the entire system in real time, specifically: (1) the mobile energy storage vehicle transports the dispersed and collected photovoltaic power to the centralized aggregated energy storage power station; in the centralized aggregated energy storage power station, multiple mobile energy storage vehicles are connected in parallel, and the DC power is converted into AC power by the inverter of the centralized aggregated energy storage power station, and finally incorporated into the distribution network; the centralized aggregated energy storage power station transmits data with the mobile energy storage vehicle through the energy monitoring and management system, and the transmitted data includes the power status, location information, charge and discharge status, etc. of the mobile energy storage vehicle, so as to be used for optimizing scheduling and Management; (2) Centralized aggregated energy storage power stations equip a certain number of mobile energy storage vehicles with inverters according to user needs, turning them into mobile power generation vehicles to convert the DC power in the mobile energy storage vehicles into AC power to provide electricity to users; centralized aggregated energy storage power stations transmit data with mobile power generation vehicles through the energy monitoring and management system. The transmitted data includes user needs, the location and status of the mobile power generation vehicles, etc., which are used to optimize power supply and management; (3) Power conversion and grid connection: centralized aggregated energy storage power stations connect multiple mobile energy storage vehicles in parallel, convert DC power into AC power through the inverters of centralized aggregated energy storage power stations, and finally connect them to the distribution network, thereby ensuring the quality and stability of electricity and making it meet the standards of the power grid; (4) Participation in the electricity market: centralized aggregated energy storage power stations can participate in the electricity market through centralized discharge, supply power to the distribution network during peak electricity demand periods, and obtain additional economic benefits; (5) The energy monitoring and management system monitors the following data: 1) Rural distributed photovoltaic power stations: power generation power, power output, operating status, etc. 2) Mobile energy storage vehicles: power status, location information, charging and discharging status, etc. 3) Mobile power generation vehicles: power status, location information, discharge status, user needs, etc. 4) Centralized energy storage power stations: power storage status, discharge status, operating efficiency, etc. 5) Road network and tractor trailers: location information, transportation status, operating efficiency, etc.

[0021] In summary, this system architecture converts solar energy into direct current (DC) through distributed rural photovoltaic power stations. This is then stored and transported to a centralized energy storage power station via mobile energy storage vehicles. The centralized energy storage power station's inverter then converts the DC power into AC, which is then incorporated into the distribution network. Simultaneously, the mobile energy storage vehicles, equipped with inverters, become mobile generators, providing electricity to users. The energy monitoring and management system monitors and optimizes the operation of each component in real time, achieving efficient energy utilization and a stable supply. This architecture not only achieves decentralized energy collection and centralized management but also improves energy efficiency and system operational flexibility through the collaborative work of various components. Moreover, in this system architecture, the absorption of electric energy and the improvement of users' electricity consumption experience are mainly reflected in two key processes: (1) The absorption process of distributed photovoltaic power stations is completely different from the previous method in which multiple distributed photovoltaic energy storage systems are connected to the grid separately using inverters. Under this architecture, photovoltaic panels convert solar energy into direct current, which is stored in mobile energy storage vehicles after being regulated by charging controllers. Towing trailers transport fully charged mobile energy storage vehicles to centralized aggregated energy storage power stations. After all mobile energy storage vehicles are connected in parallel, they uniformly convert direct current into alternating current through inverters at the connection points with the distribution network, and are finally connected to the distribution network to achieve effective absorption and integration of electric energy. This model not only improves the efficiency of electric energy absorption, but also effectively saves the cost of repeated investment in inverter equipment for multiple distributed photovoltaic energy storage systems. (2) In the process of renting mobile energy storage vehicles for users, the centralized energy storage power station will equip a certain number of mobile energy storage vehicles with inverters according to user needs, turning them into mobile power generation vehicles. The DC power in the mobile energy storage vehicles can be converted into AC power, providing stable and reliable power supply for users receiving power during peak load periods, meeting the decentralized and flexible electricity demand in rural areas, improving users' electricity experience, and enhancing the reliability of rural low-voltage distribution networks.

[0022] In addition, a certain rural area was used as an experiment to verify the effectiveness of the system architecture proposed in this invention. Specifically, a certain rural area planned to build a distributed photovoltaic energy storage system. There were 10 farmers in the area who were interested in investing in the construction of rooftop photovoltaic power stations. The installed capacity of each rooftop photovoltaic power station was about 10kW. At the same time, there was a construction site for a centralized aggregated energy storage power station in the area, which was moderately far from the rooftop photovoltaic power stations of each farmer. The system architecture of the distributed photovoltaic energy storage in the rural area with decentralized charging and centralized discharging is as follows: Figure 2 As shown in the figure, it includes 10 rural distributed photovoltaic power stations, several mobile energy storage vehicles, several mobile power generation vehicles, 1 centralized aggregated energy storage power station and 1 energy monitoring and management system; and the system architecture performs the work flow of distributed charging and centralized discharging as shown in the figure. Figure 3 As shown, from Figure 3Farmers use photovoltaic panels to convert solar energy into electricity, which is then stored locally in small mobile energy storage units. The fully charged units are then transported to a centralized energy storage power station via trailers. Here, multiple mobile energy storage units are connected in parallel via inverters to the low-voltage distribution network, supporting peak load regulation and improving voltage stability. After discharge, the mobile energy storage units can be towed to the photovoltaic station for charging during low-price periods, or distributed to high-load farmers based on real-time demand, alleviating power shortages.

[0023] S2. Taking the maximum benefit of the centralized aggregated energy storage power station as the goal and setting the first constraint condition, a fixed capacity model of the mobile energy storage vehicle and the mobile power generation vehicle is constructed.

[0024] In this embodiment, the objective function is to maximize the revenue of the centralized aggregated energy storage power station. This revenue includes the revenue from long-term contractual electricity sales with the distribution network and the revenue from mobile energy storage vehicles leased to users. The decision variables include the power and capacity of the mobile energy storage vehicle and the power of the mobile power generation vehicle. The first constraint conditions include node voltage constraints, power flow constraints, branch active power constraints, and load demand constraints. A fixed-capacity model for the mobile energy storage vehicle and mobile power generation vehicle is constructed, and this model is used to achieve a reasonable configuration of the capacity of the mobile energy storage vehicle and mobile power generation vehicle, ensuring that the system architecture achieves maximum economic benefits while meeting various constraints. At the same time, the asset utilization rate of the distribution equipment is improved, and the economy and reliability of the system are enhanced. That is, in subsequent steps, a random global search optimization method is used to perform genetic coding to solve the optimal value of each decision variable to determine the configuration plan of the mobile energy storage vehicle and mobile power generation vehicle.

[0025] Specifically, step S2 includes S21-S26:

[0026] S21. Construct the objective function for maximizing the benefits of a centralized energy storage power station, namely:

[0027]

[0028] Among them, max represents the maximum value, F represents the income of the centralized aggregated energy storage power station, and f DN represents the electricity sales revenue obtained by the centralized energy storage power station through discharging from the distribution network, f LD represents the electricity sales revenue from the centralized energy storage power station renting mobile energy storage vehicles to users to provide discharge services, f impro represents the benefits of improving the asset utilization of distribution equipment, T represents the operating time of a day, and λ DN,t represents the transaction price between the centralized energy storage power station and the distribution network at time t, P DN,t represents the transaction power between the centralized energy storage power station and the distribution network at time t, C Inv represents the cost of a single inverter, h represents the number of mobile energy storage vehicles, P MES,x,trepresents the electric power of mobile energy storage vehicle x at time t, M represents the number of users who need to rent mobile energy storage vehicles, and λ LD,t P represents the transaction price of electricity rented by the centralized energy storage power station to the user's mobile energy storage vehicle at time t, MEG,m,t represents the rented electric power of user m at time t, that is, the electric power of the mobile generator.

[0029] S22. Construct node voltage constraints, namely:

[0030]

[0031] Among them, A1 represents the node voltage constraint, They represent the minimum and maximum voltage allowed at node i in the distribution network, U i represents the voltage at node i in the distribution network, δ i represents the phase angle at node i in the distribution network, They represent the minimum and maximum phase angles at node i in the distribution network, respectively.

[0032] In this embodiment, the nodes of the distribution network refer to the power load, distributed photovoltaic power station, and centralized energy storage power station of the rural distribution network connected under the system architecture.

[0033] S23. Construct power flow constraints, namely:

[0034]

[0035] Among them, A2 represents the power flow constraint, P i , Q i represents the active and reactive power injection of node i in the distribution network, U j represents the voltage at node j in the distribution network, a ij Indicates whether there is a direct electrical connection between node i and node j, G ij 、B ij Respectively represent the resistance and reactance of the branch between node i and node j, sin represents the sine function, cos represents the cosine function, θ ij Represents the phase angle difference between node i and node j.

[0036] In this embodiment, if there is a direct electrical connection between node i and node j, then a ij is 1 if yes, otherwise, it is 0.

[0037] S24. Construct branch active power constraints, namely:

[0038] A3:-P l,max ≤P l,t ≤P l,max

[0039] Among them, A3 represents the branch active power constraint, P l,max represents the maximum active power of the lth branch, P l,t It represents the actual active power of the lth branch at the tth moment.

[0040] S25. Construct user load demand constraints, namely:

[0041] A4:P MEG,m,t ≥P LD,m,t

[0042] Among them, A4 represents the user load demand constraint, P LD,m,t represents the load demand of user m at time t.

[0043] S26. Construct a fixed capacity model for the mobile energy storage vehicle and the mobile power generation vehicle, namely:

[0044]

[0045] S3. With the goal of minimizing the sum of the cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle using a tractor trailer, and the penalty cost of abandoned light in the distributed photovoltaic power station, and setting the second constraint condition, an optimal operation model for the mobile energy storage vehicle is constructed.

[0046] In this embodiment, the objective function is to minimize the sum of the cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle using a tractor trailer, and the penalty cost of curtailment of the distributed photovoltaic power station. Second constraints are set, including the mobile energy storage vehicle charging and discharging power constraint and the mobile energy storage vehicle capacity constraint. An optimized operation model for the mobile energy storage vehicle is constructed. This model aims to optimize the charging and discharging strategy of the mobile energy storage vehicle at each moment, thereby reducing the operating cost of the system architecture, improving energy utilization efficiency, reducing curtailment, and thus improving the economic and environmental benefits of the entire system. Specifically, in subsequent steps, the particle swarm method is used to solve the optimal operation strategy to minimize the operating cost of the system architecture proposed in the present invention under this energy storage configuration.

[0047] Specifically, step S3 includes S31-S36:

[0048] S31. Calculate the cost of the mobile energy storage vehicle, namely:

[0049]

[0050] Among them, C MES Represents the cost of mobile energy storage vehicle, C EES represents the investment and construction cost of energy storage batteries, C M Represents the operation and maintenance cost of the energy storage battery, C PB Represents the charging and discharging cost of the energy storage battery, C pRepresents the unit power cost of energy storage battery, P MES,x Represents the electric power of mobile energy storage vehicle x, C E Represents the unit capacity cost of energy storage battery, E MES,x represents the configuration capacity of the mobile energy storage vehicle x, r represents the discount rate, n represents the life of the energy storage battery, C m represents the operation and maintenance cost of the energy storage battery’s annual power generation, Q represents the energy storage battery’s annual power generation, and p buy,t represents the electricity purchase price of the energy storage battery at time t, p sell,t represents the electricity price of the energy storage battery at time t, They represent the charging power and discharging power of the mobile energy storage vehicle x at time t respectively.

[0051] In this embodiment, the unit power cost of the energy storage battery is C p Related to battery management system, power conversion device and monitoring device; unit capacity cost of energy storage battery C E Related to the construction scale of the battery system; the operation and maintenance cost of the energy storage battery C M , refers to the human, material and financial resources invested to ensure the safe and stable operation of energy storage batteries, which is related to the annual power generation of energy storage batteries.

[0052] S32. Calculate the transportation cost of the mobile energy storage vehicle using a tractor trailer, namely:

[0053]

[0054] Among them, C Tran represents the transportation cost of transporting mobile energy storage vehicle by tractor trailer, c Tran,t The unit transportation cost of the mobile energy storage vehicle transported by the tractor trailer at time t, d Tran,t represents the transportation distance at time t.

[0055] S33. Calculate the penalty cost for abandoned solar power in distributed photovoltaic power stations, namely:

[0056]

[0057] Among them, C QG represents the penalty cost of abandoned light in distributed photovoltaic power stations, β represents the penalty coefficient for abandoned light, and P PV,t represents the photovoltaic power generation at time t, Represents the charging power of the mobile energy storage vehicle at time t.

[0058] S34. Construct charging and discharging power constraints for mobile energy storage vehicles, namely:

[0059]

[0060] Among them, B1 represents the charging and discharging power constraint of the mobile energy storage vehicle. They represent the charging efficiency and discharging efficiency of the mobile energy storage vehicle at time t, represents the discharge power of the mobile energy storage vehicle at time t, They represent the charging state and discharging state of the mobile energy storage vehicle at time t, respectively. MES,ch,max 、P MES,dis,max They respectively represent the maximum charging power and maximum discharging power of the mobile energy storage vehicle.

[0061] In this embodiment, only charging or discharging is possible for any specified time interval.

[0062] S35. Construct the capacity constraint of mobile energy storage vehicles, namely:

[0063]

[0064] Among them, B2 represents the capacity constraint of the mobile energy storage vehicle, E MES,t represents the configuration capacity of the mobile energy storage vehicle at time t, E MES,t-1 represents the configuration capacity of the mobile energy storage vehicle at time t-1, E MES_min 、E MES_max They represent the minimum configuration capacity and maximum configuration capacity of the mobile energy storage vehicle respectively. They represent the minimum and maximum energy storage factors of the mobile energy storage vehicle at time t, E MES_batcap Indicates the capacity of the mobile energy storage vehicle.

[0065] S36. With the goal of minimizing the sum of the cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle using a tractor trailer, and the penalty cost of abandoned solar power in distributed photovoltaic power stations, and based on the capacity constraints and the charging and discharging power constraints of the mobile energy storage vehicle, an optimal operation model for the mobile energy storage vehicle is constructed, namely:

[0066]

[0067] Among them, min represents the minimum value, and F′ represents the sum of the cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle using a tractor trailer, and the penalty cost of the distributed photovoltaic power station for abandoning light.

[0068] S4. Iteratively solving the fixed capacity model of the mobile energy storage vehicle and the mobile power generation vehicle and the optimized operation model of the mobile energy storage vehicle in turn to achieve the optimal configuration of the mobile energy storage vehicle and the mobile power generation vehicle.

[0069] In this embodiment, this step realizes the reasonable configuration of the charging and discharging operation strategy and capacity of the mobile energy storage vehicle and the mobile power generation vehicle, ensuring that the system obtains the maximum economic benefit while meeting various constraints, while improving the asset utilization and energy utilization of the distribution equipment, enhancing the economy and reliability of the system, and enhancing environmental benefits.

[0070] Specifically, step S4 includes S41-S42:

[0071] S41. The fixed capacity models of the mobile energy storage vehicle and the mobile power generation vehicle are used as the outer model, and the optimized operation model of the mobile energy storage vehicle is used as the inner model.

[0072] S42. Use random global search optimization method for gene encoding, and combine with tidal calculation tools to optimize and solve the outer model.

[0073] Specifically, step S42 includes S421-S425:

[0074] S421. Set the first input parameters, which include photovoltaic power generation, user load demand, transaction electricity price between the centralized energy storage power station and the distribution network, single inverter cost, unit transportation cost of the mobile energy storage vehicle transported by tractor trailer, and curtailment penalty coefficient.

[0075] In this embodiment, the photovoltaic power generation, the user's load demand, the transaction price between the centralized energy storage power station and the distribution network, the cost of a single inverter, the unit transportation cost of the mobile energy storage vehicle transported by a tractor trailer, and the penalty coefficient for abandonment of light are parameters P respectively. PV,t 、P LD,m,t ,λ DN,t 、C Inv 、c Tran,t , β.

[0076] S422. Setting parameters of the random global search optimization method, including population size, first maximum number of iterations, crossover rate, and mutation rate.

[0077] In this embodiment, the population size is M=100; the first maximum number of iterations is I=200; the crossover rate is r_cross=0.8; and the mutation rate is r_mutation=0.05.

[0078] S423. Encode the electric power of the mobile energy storage vehicle, the configuration capacity of the mobile energy storage vehicle, and the electric power of the mobile power generation vehicle into chromosomes.

[0079] In this embodiment, the electric power of the mobile energy storage vehicle, the configuration capacity of the mobile energy storage vehicle, and the electric power of the mobile power generation vehicle are parameters P and MES,x,t 、E MES,x 、P MEG,m,t .

[0080] S424. Calculate the objective function value of the outer model, namely:

[0081]

[0082]

[0083] In this embodiment, the objective function value F of the outer layer model is calculated so as to obtain the optimal objective function value of the outer layer model through subsequent iterative solution.

[0084] S425. Using the objective function value of the outer model as the fitness of the chromosome, perform iterative optimization.

[0085] Specifically, step S425 includes S4251-S4254:

[0086] S4251, initialize the population and randomly generate multiple sets of candidate solutions;

[0087] S4252. Use the power flow calculation tool to calculate the power flow parameters of the distribution network and determine whether the node voltage constraint, power flow constraint, branch active power constraint, and user load demand constraint are met. If so, execute step S4253; otherwise, execute step S4251. The power flow parameters of the distribution network include the voltage, phase angle, active power injection, reactive power injection, and actual active power of the branch nodes in the distribution network. The corresponding parameters are: U i , δ i 、P i , Q i 、P l,t .

[0088] S4253. Calculate the objective function value of the outer model again and use it as the fitness of the chromosome. At the same time, select individuals with high fitness for crossover and mutation.

[0089] S4254. Repeat steps S4252-S4253. If the first maximum number of iterations is reached, the optimal electric power of the mobile energy storage vehicle, the configuration capacity of the mobile energy storage vehicle, and the electric power of the mobile power generation vehicle are obtained.

[0090] S43. Use particle swarm method to optimize and solve the inner model.

[0091] Specifically, step S43 includes:

[0092] S431. Set the second input parameters, which include the purchase price and sales price of the energy storage battery, the charging efficiency and discharge efficiency of the mobile energy storage vehicle, the optimal electric power of the mobile energy storage vehicle transmitted by the outer model, the configuration capacity of the mobile energy storage vehicle, and the electric power of the mobile power generation vehicle.

[0093] In this embodiment, the parameters corresponding to the purchase price and sales price of the energy storage battery, and the charging efficiency and discharge efficiency of the mobile energy storage vehicle are: buy,t 、p sell,t 、

[0094] S432. Setting the particle swarm method parameters, including the number of particles, the second maximum number of iterations, the first learning factor, and the second learning factor.

[0095] In this embodiment, the number of particles is L=50; the second maximum number of iterations is K=100; the first learning factor and the second learning factor have the same value and are: c1=c2=1.8.

[0096] S433. Calculate the objective function value of the inner model, namely:

[0097]

[0098] In this embodiment, by calculating the objective function value F of the inner layer model ′ , so that iterative optimization can be performed in subsequent steps to obtain the optimal objective function value of the inner model.

[0099] S434: The objective function value of the inner model is used as the cost value of each particle and iterative optimization is performed, specifically:

[0100] S4341. Initialize the particle swarm and randomly generate the charging power, discharging power, charging state, and discharging state of the mobile energy storage vehicle.

[0101] S4342. Determine whether the charging power, discharging power, charging state, and discharging state of the mobile energy storage vehicle meet the capacity constraint of the mobile energy storage vehicle and the charging and discharging power constraint of the mobile energy storage vehicle. If so, execute step S4343. Otherwise, use the penalty function to correct the objective function value of the inner model or directly correct the upper and lower limits of the capacity constraint of the mobile energy storage vehicle and the upper limit of the charging and discharging power constraint of the mobile energy storage vehicle, and then execute step S4343.

[0102] S4343. Calculate the cost value of each particle again, update the individual optimal and global optimal solutions, and adjust the charging power, discharging power, charging state, and discharging state of the mobile energy storage vehicle. If the change in the objective function value of the inner model between the current iteration and the previous iteration is less than the preset threshold, the objective function converges, and the optimal charging power and discharging power of the mobile energy storage vehicle and the optimal objective function value of the inner model are obtained.

[0103] S44. Calculate the difference between the objective functions of the outer model and the inner model, and determine whether the objective difference is less than the cost of transforming the distribution network without considering energy storage logistics or whether the outer model optimization solution reaches the maximum number of iterations. If so, output the optimal solution for the electric power of the mobile energy storage vehicle and the mobile power generation vehicle, and the configuration capacity of the mobile energy storage vehicle; otherwise, continue to execute step S42.

[0104] In summary, the present invention proposes an optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage. By constructing a decentralized charging and storage and centralized discharging architecture including rural distributed photovoltaics, mobile energy storage vehicles, mobile power generation vehicles, centralized aggregated energy storage power stations and energy monitoring and management systems, and at the same time constructing a two-layer model to optimize the configuration method of the energy storage system, it solves the problems of high grid connection cost, low absorption efficiency, insufficient economic benefits and poor distribution network voltage quality caused by small capacity and decentralized configuration of rural distributed photovoltaic power stations. It can not only effectively improve the photovoltaic absorption efficiency, but also serve as a backup power source for users, significantly enhance the reliability of rural low-voltage distribution networks, improve economic benefits and shorten the investment return cycle; in addition, the constructed system architecture is also suitable for the planning and operation of rural distributed photovoltaic energy storage systems, providing a new idea and method for solving rural energy problems.

[0105] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0106] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. An optimization method for improving rural photovoltaic absorption capacity by using mobile energy storage, characterized in that: The following steps are involved: S1. Build a system architecture for distributed charging and discharging of rural photovoltaic energy storage, including mobile energy storage vehicles, mobile power generation vehicles, and centralized energy storage power stations; S2. Targeting the maximum benefit of the centralized energy storage power station and setting the first constraint, a fixed-capacity model for the mobile energy storage vehicle and mobile power generation vehicle is constructed. S3. Optimizing the sum of the cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle using a tractor trailer, and the penalty cost of abandoned solar power at the distributed photovoltaic power station is minimized, and a second constraint is set to construct an optimal operation model for the mobile energy storage vehicle. S4. Iteratively solving the fixed capacity model of the mobile energy storage vehicle and the mobile power generation vehicle and the optimized operation model of the mobile energy storage vehicle in turn to achieve the optimal configuration of the mobile energy storage vehicle and the mobile power generation vehicle.

2. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 1 is characterized in that: The system architecture of distributed charging and centralized discharging of rural distributed photovoltaic energy storage also includes rural distributed photovoltaic power stations and energy monitoring and management systems.

3. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 2 is characterized in that: Step S2 specifically includes: S21. Construct the objective function for maximizing the benefits of a centralized energy storage power station, namely: Among them, max represents the maximum value, F represents the income of the centralized aggregated energy storage power station, and f DN represents the electricity sales revenue obtained by the centralized energy storage power station through discharging from the distribution network, f LD represents the electricity sales revenue from the centralized energy storage power station renting mobile energy storage vehicles to users to provide discharge services, f inpro represents the benefits of improving the asset utilization of distribution equipment, T represents the operating time of a day, and λ DN,t represents the transaction price between the centralized energy storage power station and the distribution network at time t, P DN,t represents the transaction power between the centralized energy storage power station and the distribution network at time t, C Inv represents the cost of a single inverter, h represents the number of mobile energy storage vehicles, P MES,x,t represents the electric power of the mobile energy storage vehicle x at time t, M represents the number of users who need to rent a mobile energy storage vehicle, and λ LD,t P represents the transaction price of electricity rented by the centralized energy storage power station to the user's mobile energy storage vehicle at time t, MEG,m,t represents the rented electric power of user m at time t, i.e. the electric power of the mobile generator; S22. Construct node voltage constraints, namely: A1: Among them, A1 represents the node voltage constraint, They represent the minimum and maximum voltage allowed at node i in the distribution network, U i represents the voltage at node i in the distribution network, δ i represents the phase angle at node i in the distribution network, They represent the minimum and maximum phase angles at node i in the distribution network respectively; S23. Construct power flow constraints, namely: A2: Among them, A2 represents the power flow constraint, P i , Q i represents the active and reactive power injection of node i in the distribution network, U j represents the voltage at node j in the distribution network, a ij Indicates whether there is a direct electrical connection between node i and node j, G ij 、B ij Respectively represent the resistance and reactance of the branch between node i and node j, sin represents the sine function, cos represents the cosine function, θ ij represents the phase angle difference between node i and node j; S24. Construct branch active power constraints, namely: <h2 style=";text-align:left;direction:ltr">A3:-P<h2 style=";text-align:left;direction:ltr"> l,max <h2 style=";text-align:left;direction:ltr"> ≤P<h2 style=";text-align:left;direction:ltr"> l,t <h2 style=";text-align:left;direction:ltr"> ≤P<h2 style=";text-align:left;direction:ltr"> l,max Among them, A3 represents the branch active power constraint, P l,max represents the maximum active power of the lth branch, P l,t represents the actual active power of the lth branch at the tth moment; S25. Construct user load demand constraints, namely: A4:P MEG,m,t ≥P LD,m,t Among them, A4 represents the user load demand constraint, P LD,m,t represents the load demand of user m at time t; S26. Construct a fixed capacity model for the mobile energy storage vehicle and the mobile power generation vehicle, namely:

4. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 3 is characterized in that: Step S3 specifically includes: S31. Calculate the cost of the mobile energy storage vehicle, namely: Among them, C MES Represents the cost of mobile energy storage vehicle, C EES represents the investment and construction cost of energy storage batteries, C M Represents the operation and maintenance cost of the energy storage battery, C PB Represents the charging and discharging cost of the energy storage battery, C p Represents the unit power cost of energy storage battery, P MES,x Represents the electric power of mobile energy storage vehicle x, C E Represents the unit capacity cost of energy storage battery, E MES,x represents the configuration capacity of the mobile energy storage vehicle x, r represents the discount rate, n represents the life of the energy storage battery, C m represents the operation and maintenance cost of the energy storage battery’s annual power generation, Q represents the energy storage battery’s annual power generation, and p buy,t represents the electricity purchase price of the energy storage battery at time t, p sell,t represents the electricity price of the energy storage battery at time t, They represent the charging power and discharging power of the mobile energy storage vehicle x at time t respectively; S32. Calculate the transportation cost of the mobile energy storage vehicle using a tractor trailer, namely: Among them, C Tran represents the transportation cost of transporting mobile energy storage vehicle by tractor trailer, c Tran,t The unit transportation cost of the mobile energy storage vehicle transported by the tractor trailer at time t, d Tran,t represents the transportation distance at time t; S33. Calculate the penalty cost for abandoned solar power in distributed photovoltaic power stations, namely: Among them, C QG represents the penalty cost of abandoned light in distributed photovoltaic power stations, β represents the penalty coefficient for abandoned light, and P PV,t represents the photovoltaic power generation at time t, represents the charging power of the mobile energy storage vehicle at time t; S34. Construct charging and discharging power constraints for mobile energy storage vehicles, namely: B1: Among them, B1 represents the charging and discharging power constraint of the mobile energy storage vehicle. They represent the charging efficiency and discharging efficiency of the mobile energy storage vehicle at time t, represents the discharge power of the mobile energy storage vehicle at time t, They represent the charging state and discharging state of the mobile energy storage vehicle at time t, respectively. MES,ch,max 、P MES,dis,max Respectively represent the maximum charging power and maximum discharging power of the mobile energy storage vehicle; S35. Construct the capacity constraint of mobile energy storage vehicles, namely: B2: Among them, B2 represents the capacity constraint of the mobile energy storage vehicle, E MES,t represents the configuration capacity of the mobile energy storage vehicle at time t, E MES,t-1 represents the configuration capacity of the mobile energy storage vehicle at time t-1, E MES_min 、E MES_max They represent the minimum configuration capacity and maximum configuration capacity of the mobile energy storage vehicle respectively. They represent the minimum and maximum energy storage factors of the mobile energy storage vehicle at time t, E MES_batcap Indicates the capacity of the mobile energy storage vehicle; S36. With the goal of minimizing the sum of the cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle using a tractor trailer, and the penalty cost of abandoned solar power in distributed photovoltaic power stations, and based on the capacity constraints and the charging and discharging power constraints of the mobile energy storage vehicle, an optimal operation model for the mobile energy storage vehicle is constructed, namely: Among them, min represents the minimum value, and F′ represents the sum of the cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle using a tractor trailer, and the penalty cost of the distributed photovoltaic power station for abandoning light.

5. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 4 is characterized in that: Step S4 specifically includes: S41, using the fixed capacity models of the mobile energy storage vehicle and the mobile power generation vehicle as the outer model, and the optimized operation model of the mobile energy storage vehicle as the inner model; S42, using random global search optimization method for gene encoding, while combining with the power flow calculation tool to optimize and solve the outer model; S43, using particle swarm optimization to solve the inner model; S44. Calculate the difference between the objective functions of the outer model and the inner model, and determine whether the objective difference is less than the cost of transforming the distribution network without considering energy storage logistics or whether the outer model optimization solution reaches the maximum number of iterations. If so, output the optimal solution for the electric power of the mobile energy storage vehicle and the mobile power generation vehicle, and the configuration capacity of the mobile energy storage vehicle; otherwise, continue to execute step S42.

6. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 5 is characterized in that: Step S42 specifically includes: S421. Setting first input parameters, including photovoltaic power generation, user load demand, transaction price between the centralized energy storage power station and the distribution network, cost of a single inverter, unit transportation cost of a mobile energy storage vehicle transported by a tractor trailer, and a penalty coefficient for curtailment of solar power. S422, setting parameters of the random global search optimization method, which include population size, first maximum number of iterations, crossover rate, and mutation rate; S423, encoding the electric power of the mobile energy storage vehicle, the configuration capacity of the mobile energy storage vehicle, and the electric power of the mobile power generation vehicle into chromosomes; S424. Calculate the objective function value of the outer model, namely: S425. Using the objective function value of the outer model as the fitness of the chromosome, perform iterative optimization.

7. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 6 is characterized in that: Step S425 specifically includes: S4251, initialize the population and randomly generate multiple sets of candidate solutions; S4252. Calculate the distribution network flow parameters using a flow calculation tool to determine whether the node voltage constraint, flow constraint, branch active power constraint, and user load demand constraint are met. If so, execute step S4253; otherwise, execute step S4251. S4253. Calculate the objective function value of the outer model again and use it as the fitness of the chromosome. At the same time, select individuals with high fitness for crossover and mutation. S4254. Repeat steps S4252-S4253. If the first maximum number of iterations is reached, the optimal electric power of the mobile energy storage vehicle, the configuration capacity of the mobile energy storage vehicle, and the electric power of the mobile power generation vehicle are obtained.

8. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 7 is characterized in that: The distribution network flow parameters in step S4252 include the voltage, phase angle, active power injection, reactive power injection and actual active power of the branches at the nodes in the distribution network.

9. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 8 is characterized in that: Step S43 specifically includes: S431. Setting second input parameters, which include the purchase price and sales price of the energy storage battery, the charging efficiency and discharge efficiency of the mobile energy storage vehicle, the optimal electric power of the mobile energy storage vehicle transmitted by the outer model, the configuration capacity of the mobile energy storage vehicle, and the electric power of the mobile power generation vehicle; S432, setting the particle swarm method parameters, which include the number of particles, the second maximum number of iterations, the first learning factor, and the second learning factor; S433. Calculate the objective function value of the inner model, namely: S434. Using the objective function value of the inner model as the cost value of each particle, perform iterative optimization.

10. The optimization method for improving rural photovoltaic absorption capacity by utilizing mobile energy storage according to claim 9 is characterized in that: Step S434 specifically includes: S4341. Initialize the particle swarm and randomly generate the charging power, discharging power, charging state, and discharging state of the mobile energy storage vehicle; S4342: Determine whether the charging power, discharging power, charging state, and discharging state of the mobile energy storage vehicle meet the capacity constraint and the charging and discharging power constraint of the mobile energy storage vehicle. If so, execute step S4343; otherwise, use the penalty function to correct the objective function value of the inner model or directly correct the upper and lower limits of the capacity constraint and the upper limit of the charging and discharging power constraint of the mobile energy storage vehicle, and then execute step S4343; S4343. Calculate the cost value of each particle again, update the individual optimal and global optimal solutions, and adjust the charging power, discharging power, charging state, and discharging state of the mobile energy storage vehicle. If the change in the objective function value of the inner model between the current iteration and the previous iteration is less than the preset threshold, the objective function converges, and the optimal charging power and discharging power of the mobile energy storage vehicle and the optimal objective function value of the inner model are obtained.

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