An optimization method for improving rural photovoltaic power consumption capacity by using mobile energy storage

Abstract

The application relates to the technical field of new energy, and discloses an optimization method for improving rural photovoltaic consumption capacity by using mobile energy storage, which comprises the following steps: constructing a system architecture of rural distributed photovoltaic energy storage decentralized charging and storage and centralized discharging; setting a first constraint condition to construct a constant-volume model of a mobile energy storage vehicle and a mobile power generation vehicle, with the maximum benefit of a centralized aggregation type energy storage power station as a target; setting a second constraint condition to construct an optimization operation model of the mobile energy storage vehicle, with the sum of the total cost of the mobile energy storage vehicle cost, the transportation cost of a trailer for transporting the mobile energy storage vehicle and the distributed photovoltaic power station light abandonment penalty cost as a target; and iteratively solving the constant-volume model of the mobile energy storage vehicle and the mobile power generation vehicle and the optimization operation model of the mobile energy storage vehicle in sequence. The method not only effectively improves photovoltaic consumption efficiency, but also can serve as a backup power source for users, significantly enhances the reliability of a rural low-voltage power distribution network, improves economic benefits and shortens the investment return period.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to an optimized method for improving the rural photovoltaic absorption capacity using mobile energy storage. Background Technology

[0002] Currently, rural distributed photovoltaic (PV) power stations are generally characterized by small capacity, large number, and scattered configuration, which brings a series of problems. First, since each PV power station requires an independent inverter, the long idle time of the inverter capacity significantly increases system costs. Second, there is a mismatch between PV power generation and electricity load in time and space, making it difficult to effectively store and utilize excess electricity, easily leading to curtailment. Simultaneously, decentralized PV power stations struggle to participate in the electricity market on a large scale, face grid connection difficulties, and have a limited self-consumption revenue model, restricting economic benefits. Furthermore, rural low-voltage distribution networks are not only long but also radial, frequently experiencing low voltage on the low-voltage side of some distribution transformers during peak electricity consumption periods. This significant voltage quality problem prevents loads from operating normally, reducing power supply reliability.

[0003] In summary, existing technologies are insufficient to meet the demand for utilizing large amounts of rural photovoltaic (PV) resources. Although stationary energy storage can absorb daytime PV power, the weak rural power distribution network structure, low transmission capacity, and high investment costs for network upgrades, coupled with a lack of investment incentive from power grid companies, mean that the large amount of PV power absorbed by storage is difficult to sell to the grid and cannot be effectively consumed by local loads. Furthermore, many non-stationary power consumption scenarios exist in rural areas, such as agricultural harvesting, processing, and temporary irrigation. These locations often lack power lines and facilities, creating a significant demand for flexible, mobile power sources. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides an optimized method for enhancing rural photovoltaic (PV) absorption capacity using mobile energy storage. This method involves using energy storage vehicles to absorb distributed PV power, then forming a centralized aggregation and distribution model. A portion of the power is centrally connected to the grid for sale, while the remainder is uniformly dispatched to temporary power load points. This expands the scale of PV projects, increases the profitability of rural PV resources, and simultaneously solves the problems of some power points being without power due to lack of power lines or voltage quality issues, thus achieving the goals of increasing income and ensuring power supply through distributed PV.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0006] An optimized method for enhancing rural photovoltaic grid integration capacity using mobile energy storage includes the following steps:

[0007] S1. Construct a system architecture for distributed photovoltaic energy storage in rural areas, which includes mobile energy storage vehicles, mobile power generation vehicles, and centralized aggregated energy storage power stations.

[0008] S2. With the goal of maximizing the revenue of centralized aggregated energy storage power stations and setting the first constraint, construct a fixed-capacity model for mobile energy storage vehicles and mobile power generation vehicles.

[0009] S3. With the goal of minimizing the total cost of mobile energy storage vehicles, the transportation cost of towing and transporting mobile energy storage vehicles, and the cost of curtailment penalties for distributed photovoltaic power stations, and with a second constraint, construct an optimized operation model for mobile energy storage vehicles.

[0010] S4. Iteratively solve the constant-capacity model of the mobile energy storage vehicle and the optimized operation model of the mobile energy storage vehicle in sequence to achieve the optimized configuration of the mobile energy storage vehicle and the mobile power generation vehicle.

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

[0012] This invention proposes an optimized method for enhancing rural photovoltaic (PV) absorption capacity using mobile energy storage. By constructing a decentralized charging and storage architecture encompassing rural distributed PV systems, mobile energy storage vehicles, mobile power generation vehicles, centralized aggregated energy storage stations, and an energy monitoring and management system, and simultaneously building a two-layer model to optimize the energy storage system configuration, this 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 dispersed configuration of rural distributed PV power stations. It not only effectively improves PV absorption efficiency but also serves as a backup power source for users, significantly enhancing the reliability of rural low-voltage distribution networks, increasing economic benefits, and shortening the investment payback period. Furthermore, the constructed system architecture is also applicable to the planning and operation of rural distributed PV energy storage systems. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating an optimized method for enhancing rural photovoltaic absorption capacity using mobile energy storage, as proposed in this invention.

[0014] Figure 2 This is a schematic diagram of the system architecture for distributed photovoltaic energy storage in rural areas, showing decentralized charging and centralized discharging in the example.

[0015] Figure 3 This is a schematic diagram illustrating the distributed charging and centralized discharging process of the system architecture in this embodiment. Detailed Implementation

[0016] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

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

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

[0019] Specifically, the system architecture of rural distributed photovoltaic energy storage, which combines decentralized charging and centralized discharging, 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, one centralized aggregation energy storage power station, and one energy monitoring and management system. Among them, the rooftop photovoltaic power stations of each farmer are connected to the mobile energy storage vehicles through DC charging controllers. The mobile energy storage vehicles are transported to the centralized aggregation energy storage power station by tractor trailers. The centralized aggregation energy storage power station is responsible for centralized discharge and management of electrical energy at fixed connection points with the distribution network. The energy monitoring and management system monitors and schedules the entire system in real time. Specifically: (1) The mobile energy storage vehicles transport the photovoltaic power collected in a decentralized manner to the centralized aggregation energy storage power station. In the centralized aggregation energy storage power station, multiple mobile energy storage vehicles are connected in parallel. The DC power is converted to AC power by the inverter of the centralized aggregation energy storage power station and finally connected to the distribution network. The centralized aggregation energy storage power station transmits data to the mobile energy storage vehicles through the energy monitoring and management system. The transmitted data includes the power status, location information, charging and discharging status of the mobile energy storage vehicles, etc., so as to optimize scheduling and (2) According to user needs, a certain number of mobile energy storage vehicles are equipped with inverters to become mobile power generation vehicles, so as to convert the DC power in the mobile energy storage vehicles into AC power to provide power to users; the centralized aggregation energy storage station transmits data with the 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., for optimizing power supply and management; (3) Power conversion and grid connection: the centralized aggregation energy storage station connects multiple mobile energy storage vehicles in parallel, converts DC power into AC power through the inverter of the centralized aggregation energy storage station, and finally connects to the distribution network, thereby ensuring the quality and stability of power and making it conform to the grid standard; (4) Participation in the power market: the centralized aggregation energy storage station can participate in the power market through centralized discharge, supply power to the distribution network during peak power demand periods, and obtain additional economic benefits; (5) the energy monitoring and management system monitors the following data: 1) Rural distributed photovoltaic power station: power generation, power output, operating status, etc. 2) Mobile energy storage vehicle: power status, location information, charging and discharging status, etc. 3) Mobile generator: power status, location information, discharge status, user demand, etc. 4) Centralized aggregated energy storage station: power storage status, discharge status, operating efficiency, etc. 5) Road network and tractor-trailer: location information, transportation status, operating efficiency, etc.

[0021] In summary, this system architecture converts solar energy into direct current (DC) through rural distributed photovoltaic power stations. This DC power is then stored in mobile energy storage vehicles and transported to a centralized aggregated energy storage station. The centralized aggregated energy storage station then converts the DC power into alternating current (AC) via an inverter, which is then fed 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 power supply. This architecture not only enables decentralized energy collection and centralized management but also improves energy efficiency and system operational flexibility through the collaborative work of its components. In this system architecture, the consumption of electricity and the improvement of users' electricity experience are mainly reflected in two key processes: (1) the process of consumption of distributed photovoltaic power stations, which is completely different from the previous method of multiple distributed photovoltaic energy storage systems using inverters to connect to the grid independently. Under this architecture, the photovoltaic panels convert solar energy into DC power, which is then stored in mobile energy storage vehicles after being regulated by the charging controller. The towing trailer transports the fully charged mobile energy storage vehicles to the centralized aggregation energy storage station. After all the mobile energy storage vehicles are connected in parallel, the DC power is converted into AC power through the inverter at the connection point with the distribution network and finally connected to the distribution network, realizing the effective consumption and integration of electricity. This mode not only improves the efficiency of electricity consumption, but also effectively saves the cost of repeatedly investing in inverter equipment for multiple distributed photovoltaic energy storage systems. (2) For users to rent mobile energy storage vehicles, the centralized aggregation energy storage power station will configure a certain number of mobile energy storage vehicles with inverters according to user needs, and turn them into mobile power generation vehicles. The DC power in the mobile energy storage vehicles can be converted into AC power, providing a stable and reliable power supply for users to receive power during peak load periods, meeting the decentralized and flexible power demand in rural areas, improving users' power experience, and enhancing the reliability of rural low-voltage distribution networks.

[0022] Furthermore, an experiment was conducted in a rural area to verify the effectiveness of the system architecture proposed in this invention. Specifically, a distributed photovoltaic energy storage system was planned to be built in a rural area. Ten farmers in the area intended to invest in building rooftop photovoltaic power stations, each with an installed capacity of approximately 10kW. Simultaneously, a site for a centralized aggregated energy storage power station was available in the area, at a suitable distance from each farmer's rooftop photovoltaic power station. The system architecture of the rural distributed photovoltaic energy storage system, featuring decentralized charging and centralized discharging, is as follows: Figure 2 As shown, the system includes 10 rural distributed photovoltaic power stations, several mobile energy storage vehicles, several mobile power generation vehicles, one centralized aggregated energy storage power station, and one energy monitoring and management system; the system architecture's workflow for decentralized charging and storage and centralized discharging is as follows: Figure 3 As shown, from Figure 3It is understood that farmers use photovoltaic panels to convert solar energy into electricity, which is then stored locally by small mobile energy storage units. The fully charged storage units are then transported by trailer to a centralized aggregated energy storage power station. Here, multiple mobile energy storage vehicles are connected in parallel and connected to the low-voltage power distribution network via an inverter, supporting peak shaving and improving voltage stability. After discharging, the mobile energy storage vehicles can be towed to the photovoltaic station for charging during off-peak hours, or distributed to high-load farmers according to real-time demand, alleviating power shortages.

[0023] S2. With the goal of maximizing the revenue of centralized aggregated energy storage power stations and setting the first constraint, construct a fixed-capacity model for mobile energy storage vehicles and mobile power generation vehicles.

[0024] In this embodiment, the objective function is to maximize the revenue of the centralized aggregated energy storage power station. This revenue includes revenue from long-term contract electricity sales to the distribution network and revenue from mobile energy storage vehicles leased to users. Decision variables include the power and capacity of the mobile energy storage vehicles and the power of the mobile generator vehicles. The first constraints include node voltage constraints, power flow constraints, branch active power constraints, and load demand satisfaction constraints. A fixed-capacity model for the mobile energy storage vehicles and mobile generator vehicles is constructed. This model is used to achieve a reasonable configuration of the capacity of the mobile energy storage vehicles and mobile generator vehicles, ensuring that the system architecture achieves maximum economic benefits under various constraints. Simultaneously, it improves the asset utilization rate of the power distribution equipment and enhances the economy and reliability of the system. Specifically, in subsequent steps, a stochastic global search optimization method is used to solve the gene encoding problem, thereby obtaining the optimal values ​​of each decision variable to determine the configuration scheme of the mobile energy storage vehicles and mobile generator vehicles.

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

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

[0027]

[0028] Where max represents the maximum value, F represents the revenue of the centralized aggregated energy storage power station, and f DN f represents the revenue generated from selling electricity through the distribution network by a centralized aggregated energy storage power station. LD This indicates that centralized aggregated energy storage power stations are leased to users, and mobile energy storage vehicles provide users with revenue from selling electricity generated during discharge. impro This represents the benefit from improved utilization of power distribution equipment assets, where T represents the daily operating time, and λ represents the total operating time. DN,t P represents the transaction price between the centralized energy storage power station and the distribution network at time t. DN,t C represents the electricity traded between the centralized energy storage power station and the distribution network at time t. Inv P represents the cost of a single inverter, h represents the number of mobile energy storage vehicles, and P represents the cost of a single inverter. MES,x,tLet λ represent the electric power of mobile energy storage vehicle x at time t, M represent the number of users who need to rent the mobile energy storage vehicle, and λ represent the electric power of mobile energy storage vehicle x at time t. LD,t P represents the transaction price of electricity leased from the centralized energy storage power station to the user's mobile energy storage vehicle at time t. MEG,m,t This represents the rented power of user m at time t, which is the power of the mobile generator.

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

[0030]

[0031] Where A1 represents the node voltage constraint. U represents the minimum and maximum allowable voltage values ​​at node i in the distribution network, respectively. i δ represents the voltage at node i in the distribution network. i This represents the phase angle at node i in the distribution network. Let i represent the minimum and maximum phase angles at node i in the distribution network, respectively.

[0032] In this embodiment, each node of the distribution network refers to the electricity load of the rural distribution network connected to the system architecture, distributed photovoltaic power stations, and centralized aggregated energy storage power stations.

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

[0034]

[0035] Where A2 represents power flow constraint, P i Q i U represents the active power injection and reactive power injection at node i in the distribution network. j a represents the voltage at node j in the distribution network. ij G indicates whether there is a direct electrical connection between node i and node j. ij B ij Let θ represent the resistance and reactance of the branch between node i and node j, respectively. Let sin represent the sine function and cos represent the cosine function. ij This 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 If the value is 1, then the value is 0; otherwise, the value is 0.

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

[0038] A3:-P l,max ≤P l,t ≤P l,max

[0039] Where A3 represents the active power constraint of the branch, P l,max P represents the maximum active power of the l-th branch. l,t This represents the actual active power of the l-th branch at time t.

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

[0041] A4:P MEG,m,t ≥P LD,m,t

[0042] Where A4 represents the user load demand constraint, P LD,m,t This 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 objective of minimizing the total cost of mobile energy storage vehicles, the transportation cost of towing and transporting mobile energy storage vehicles, and the cost of curtailment penalties for distributed photovoltaic power stations, and with a second constraint, construct an optimized operation model for mobile energy storage vehicles.

[0046] In this embodiment, the objective function is to minimize the sum of the costs of the mobile energy storage vehicle, the transportation costs of the mobile energy storage vehicle towing the trailer, and the costs of curtailment penalties for the distributed photovoltaic power station. Second constraints are set, including charging and discharging power constraints and capacity constraints for the mobile energy storage vehicle. 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, and reducing curtailment, thus enhancing the economic and environmental benefits of the entire system. Specifically, in subsequent steps, the optimal operation strategy is obtained using the particle swarm optimization method to minimize the operating cost of the system architecture proposed in this invention under this energy storage configuration.

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

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

[0049]

[0050] Among them, C MES C represents the cost of the mobile energy storage vehicle. EES C represents the investment and construction cost of energy storage batteries. M C represents the operating and maintenance costs of energy storage batteries. PB C represents the cost of charging and discharging energy storage batteries. pP represents the unit power cost of energy storage batteries. MES,x C represents the electrical power of mobile energy storage vehicle x. E E represents the unit capacity cost of energy storage batteries. MES,x Let r represent the configured capacity of mobile energy storage vehicle x, r represent the discount rate, n represent the lifespan of the energy storage battery, and C represent the configuration capacity of the mobile energy storage vehicle x. m The operation and maintenance cost of the energy storage battery is represented by Q, which represents the annual power generation of the energy storage battery. buy,t p represents the electricity purchase price of the energy storage battery at time t. sell,t This represents the electricity price sold by the energy storage battery at time t. Let 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 C of the energy storage battery is... p Related to battery management systems, power conversion devices, and monitoring devices; cost per unit capacity of energy storage batteries C E Related to the scale of the battery system construction; Energy storage battery operation and maintenance costs C M This refers to the human, material, and financial resources invested in ensuring the safe and stable operation of energy storage batteries, and it is related to the annual power generation of the energy storage batteries.

[0052] S32. Calculate the transportation cost of transporting the mobile energy storage vehicle by towing a trailer, i.e.:

[0053]

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

[0055] S33. Calculate the cost of curtailment penalty for distributed photovoltaic power stations, i.e.:

[0056]

[0057] Among them, C QG P represents the cost of curtailment penalties for distributed photovoltaic (PV) power plants, β represents the curtailment penalty coefficient, and P represents the cost of curtailment penalties for distributed PV power plants. PV,t This represents the photovoltaic power generation at time t. This 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] Where B1 represents the charging and discharging power constraint of the mobile energy storage vehicle, Let represent the charging efficiency and discharging efficiency of the mobile energy storage vehicle at time t, respectively. This represents the discharge power of the mobile energy storage vehicle at time t. P represents the charging and discharging states of the mobile energy storage vehicle at time t, respectively. MES,ch,max P MES,dis,max These represent the maximum charging power and maximum discharging power of the mobile energy storage vehicle, respectively.

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

[0062] S35. Construct capacity constraints for mobile energy storage vehicles, namely:

[0063]

[0064] Where B2 represents the capacity constraint of the mobile energy storage vehicle, E MES,t E represents the configured capacity of the mobile energy storage vehicle at time t. MES,t-1 E represents the configured capacity of the mobile energy storage vehicle at time t-1. MES_min E MES_max These represent the minimum and maximum configuration capacities of the mobile energy storage vehicle, respectively. Let E represent the minimum and maximum energy storage factors of the mobile energy storage vehicle at time t, respectively. MES_batcap This indicates the capacity of the mobile energy storage vehicle.

[0065] S36. With the objective of minimizing the total cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle towing a trailer, and the cost of curtailment penalties for distributed photovoltaic power stations, and based on the capacity constraints and charging / discharging power constraints of the mobile energy storage vehicle, an optimized operation model for the mobile energy storage vehicle is constructed, namely:

[0066]

[0067] Wherein, min represents taking the minimum value, and F′ represents the sum of the costs of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle towing the trailer, and the cost of the curtailment penalty for the distributed photovoltaic power station.

[0068] S4. Iteratively solve the constant-capacity model of the mobile energy storage vehicle and the optimized operation model of the mobile energy storage vehicle in sequence to achieve the optimized configuration of the mobile energy storage vehicle and the mobile power generation vehicle.

[0069] In this embodiment, this step enables the rational 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 achieves maximum economic benefits under various constraints, while improving the asset utilization rate and energy utilization rate of the power distribution equipment, enhancing the economy and reliability of the system, and enhancing environmental benefits.

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

[0071] S41. Use the constant-capacity model of the mobile energy storage vehicle and the mobile power generation vehicle as the outer layer model, and the optimized operation model of the mobile energy storage vehicle as the inner layer model.

[0072] S42. Gene encoding is performed using a stochastic global search optimization method, and power flow calculation tools are used to optimize and solve the outer layer model.

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

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

[0075] In this embodiment, the photovoltaic power generation, user load demand, transaction price between the centralized aggregation energy storage power station and the distribution network, cost of a single inverter, unit transportation cost of tractor-trailer transport of mobile energy storage vehicles, and curtailment penalty coefficient are respectively parameters P. PV,t P LD,m,t , λ DN,t C Inv c Tran,t , β.

[0076] S422. Set the parameters for 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 configured capacity of the mobile energy storage vehicle, and the electric power of the mobile generator vehicle into chromosomes.

[0079] In this embodiment, the electric power of the mobile energy storage vehicle, the configured capacity of the mobile energy storage vehicle, and the electric power of the mobile generator vehicle are respectively parameter P. MES,x,t E MES,x P MEG,m,t .

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

[0081]

[0082]

[0083] In this embodiment, the objective function value F of the outer layer model is calculated so that the optimal objective function value of the outer layer model can be obtained through subsequent iterative solutions.

[0084] S425. Use the objective function value of the outer model as the fitness of the chromosome for iterative optimization.

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

[0086] S4251. Initialize the population and randomly generate multiple sets of candidate solutions;

[0087] S4252. Calculate the power flow parameters of the distribution network using a power flow calculation tool, and determine whether they meet the node voltage constraints, power flow constraints, branch active power constraints, and user load demand constraints. If yes, proceed to step S4253; otherwise, proceed to 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 branches at the nodes in the distribution network. Their 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 generator vehicle are obtained.

[0090] S43. The particle swarm optimization method is used to optimize and solve the inner layer model.

[0091] Specifically, step S43 includes:

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

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

[0094] S432. Set 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, that is:

[0097]

[0098] In this embodiment, the objective function value F of the inner layer model is calculated. ′ This allows for iterative optimization in subsequent steps to obtain the optimal objective function value for the inner layer model.

[0099] S434. Using the objective function value of the inner model as the cost value of each particle, perform iterative optimization, specifically as follows:

[0100] S4341. Initialize the particle swarm and randomly generate the charging power, discharging power, charging status, and discharging status 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 constraints and charging / discharging power constraints of the mobile energy storage vehicle. If yes, proceed to 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 constraints and the upper limit of the charging / discharging power constraints of the mobile energy storage vehicle before proceeding to 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 layer model in the current iteration and the previous iteration is less than the preset threshold, the objective function converges, and the optimal charging power, discharging power of the mobile energy storage vehicle and the optimal objective function value of the inner layer 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 distribution network transformation cost without considering energy storage logistics or whether the outer model has reached the maximum number of iterations for optimization. If so, output the optimal solution for the electric power of the mobile energy storage vehicle and the mobile generator vehicle, and the configuration capacity of the mobile energy storage vehicle. Otherwise, continue to execute step S42.

[0104] In summary, the proposed optimization method for enhancing rural photovoltaic (PV) absorption capacity using mobile energy storage addresses these issues by constructing a decentralized charging and storage architecture that integrates rural distributed PV systems, mobile energy storage vehicles, mobile power generation vehicles, centralized aggregated energy storage stations, and an energy monitoring and management system. Simultaneously, a two-layer model is built to optimize the configuration of the energy storage system. This method solves the problems of high grid connection costs, low absorption efficiency, insufficient economic benefits, and poor voltage quality in rural distributed PV power stations due to their small capacity and dispersed configuration. It not only effectively improves PV absorption efficiency but also serves as a backup power source for users, significantly enhancing the reliability of rural low-voltage distribution networks, increasing economic benefits, and shortening the investment payback period. Furthermore, the constructed system architecture is also applicable to the planning and operation of rural distributed PV energy storage systems, providing a novel approach and method for solving rural energy problems.

[0105] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0106] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. An optimized method for enhancing rural photovoltaic power absorption capacity using mobile energy storage, characterized in that, Includes the following steps: S1. Construct a system architecture for distributed photovoltaic energy storage in rural areas, which includes mobile energy storage vehicles, mobile power generation vehicles, and centralized aggregated energy storage power stations. S2. Taking the maximum benefit of centralized aggregated energy storage power station as the objective and setting the first constraint, construct the fixed-capacity model of mobile energy storage vehicle and mobile power generation vehicle; wherein, the first constraint includes node voltage constraint, power flow constraint, branch active power constraint, and user load demand constraint. S3. With the objective of minimizing the total cost of mobile energy storage vehicles, the transportation cost of towing and transporting mobile energy storage vehicles, and the cost of curtailment penalties for distributed photovoltaic power stations, and setting a second constraint, construct an optimized operation model for mobile energy storage vehicles; wherein, the second constraint includes the charging and discharging power constraint of mobile energy storage vehicles and the capacity constraint of mobile energy storage vehicles. S4. Iteratively solve the constant-capacity model of the mobile energy storage vehicle and the optimized operation model of the mobile energy storage vehicle in sequence to achieve the optimized configuration of the mobile energy storage vehicle and the mobile power generation vehicle.

2. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 1, characterized in that, The system architecture for distributed photovoltaic energy storage in rural areas, which combines decentralized charging and centralized discharging, also includes distributed photovoltaic power stations in rural areas and energy monitoring and management systems.

3. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 2, characterized in that, Step S2 specifically includes: S21. Construct the objective function for maximizing the revenue of a centralized aggregated energy storage power station, namely: in, This indicates taking the maximum value. This indicates the revenue of a centralized, aggregated energy storage power station. This refers to the revenue generated from selling electricity through the distribution network by a centralized aggregated energy storage power station. This refers to the leasing of centralized aggregated energy storage power stations to users via mobile energy storage vehicles, which then generate revenue for users through the sale of discharged electricity. This indicates the benefits derived from improving the utilization rate of power distribution equipment assets. It indicates the duration of a day's operation. Indicates the first The transaction price of electricity between centralized energy storage power stations and the distribution network in real time. Indicates the first The electricity traded between the centralized energy storage power station and the distribution network is constantly monitored. This indicates the cost of a single inverter. Indicates the number of mobile energy storage vehicles. Indicates the first Mobile energy storage vehicle electrical power, This indicates the number of users who need to rent mobile energy storage vehicles. Indicates the first The transaction price of electricity for mobile energy storage vehicles leased from centralized, aggregated energy storage power stations to users. Indicates the first Time users The rented power output, i.e., the power output of the mobile generator vehicle; S22. Construct node voltage constraints, namely: in, This indicates node voltage constraints. , These represent nodes in the distribution network. The minimum and maximum allowable voltage values ​​at the location. Represents nodes in a distribution network Voltage at that point Represents nodes in a distribution network The phase angle at the point, , These represent nodes in the distribution network. The minimum and maximum phase angles at the point; S23. Construct power flow constraints, namely: in, Indicates current constraint. , Represents nodes in a distribution network Active power injection and reactive power injection, Represents nodes in a distribution network Voltage at that point Represents a node With nodes Is there a direct electrical connection between them? , Representing nodes respectively With nodes The resistance and reactance of the branches between them Represents the sine function. Represents the cosine function. Represents a node With nodes The phase angle difference between them; S24. Construct active power constraints for the branch, namely: in, This indicates the active power constraint of the branch. Indicates the first The maximum active power of the branch circuit. Indicates the first Time of the first The actual active power of each branch circuit; S25. Construct user load demand constraints, namely: in, This indicates user load demand constraints. Indicates the first Time users Load demand; S26. Construct a fixed-capacity model for the mobile energy storage vehicle and the mobile power generation vehicle, namely: 。 4. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 3, characterized in that, Step S3 specifically includes: S31. Calculate the cost of the mobile energy storage vehicle, i.e.: in, This indicates the cost of mobile energy storage vehicles. This indicates the investment and construction costs of energy storage batteries. This indicates the operating and maintenance costs of energy storage batteries. This indicates the cost of charging and discharging energy storage batteries. This indicates the unit power cost of energy storage batteries. Indicates mobile energy storage vehicle electrical power, This indicates the unit capacity cost of energy storage batteries. Indicates mobile energy storage vehicle Configuration capacity, This represents the discount rate. This indicates the lifespan of the energy storage battery. This represents the operation and maintenance cost of the annual power generation of the energy storage battery. This indicates the annual power generation of the energy storage battery. Indicates the first The electricity purchase price for energy storage batteries Indicates the first The electricity price for energy storage batteries. , They represent the first Mobile energy storage vehicle Charging power and discharging power; S32. Calculate the transportation cost of transporting the mobile energy storage vehicle by towing a trailer, i.e.: in, This indicates the transportation cost of using a trailer to transport a mobile energy storage vehicle. Indicates the first The unit transportation cost of constantly towing trailers to transport mobile energy storage vehicles. Indicates the first The transportation distance at any given moment; S33. Calculate the cost of curtailment penalty for distributed photovoltaic power stations, i.e.: in, This indicates the cost of penalties for curtailment of distributed photovoltaic power stations. This represents the light-wasting penalty coefficient. Indicates the first Real-time photovoltaic power generation Indicates the first The charging power of the mobile energy storage vehicle at all times; S34. Construct charging and discharging power constraints for mobile energy storage vehicles, namely: in, This indicates the charging and discharging power constraints of mobile energy storage vehicles. , They represent the first The charging and discharging efficiency of mobile energy storage vehicles at all times. Indicates the first The discharge power of the mobile energy storage vehicle at all times. , They represent the first The charging and discharging status of the mobile energy storage vehicle is constantly monitored. , These represent the maximum charging power and maximum discharging power of the mobile energy storage vehicle, respectively. S35. Construct capacity constraints for mobile energy storage vehicles, namely: in, This indicates the capacity constraint of mobile energy storage vehicles. Indicates the first The configuration capacity of the mobile energy storage vehicle at any time. Indicates the first The configuration capacity of the mobile energy storage vehicle at any time. , These represent the minimum and maximum configuration capacities of the mobile energy storage vehicle, respectively. , They represent the first The minimum and maximum energy storage factors of mobile energy storage vehicles at all times. Indicates the capacity of the mobile energy storage vehicle; S36. With the objective of minimizing the total cost of the mobile energy storage vehicle, the transportation cost of the mobile energy storage vehicle towing a trailer, and the cost of curtailment penalties for distributed photovoltaic power stations, and based on the capacity constraints and charging / discharging power constraints of the mobile energy storage vehicle, an optimized operation model for the mobile energy storage vehicle is constructed, namely: in, This indicates taking the minimum value. This represents the sum of the costs of mobile energy storage vehicles, transportation costs for towing and transporting mobile energy storage vehicles, and the costs of curtailment penalties for distributed photovoltaic power stations.

5. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 4, characterized in that, Step S4 specifically includes: S41. Use the fixed-capacity model of the mobile energy storage vehicle and the mobile power generation vehicle as the outer layer model, and the optimized operation model of the mobile energy storage vehicle as the inner layer model. S42. Gene encoding is performed using a stochastic global search optimization method, and the outer layer model is optimized and solved using a power flow calculation tool. S43. The particle swarm optimization method is used to optimize and solve the inner layer 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 distribution network transformation cost without considering energy storage logistics or whether the outer model has reached the maximum number of iterations for optimization. If so, output the optimal solution for the electric power of the mobile energy storage vehicle and the mobile generator vehicle, and the configuration capacity of the mobile energy storage vehicle. Otherwise, continue to execute step S42.

6. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 5, characterized in that, Step S42 specifically includes: S421. Set the first input parameters, which include photovoltaic power generation, user load demand, transaction price between centralized aggregated energy storage power station and distribution network, cost of a single inverter, unit transportation cost of tractor-trailer transport of mobile energy storage vehicle, and curtailment penalty coefficient. S422. Set the parameters for the random global search optimization method, including population size, first maximum number of iterations, crossover rate, and mutation rate; S423. Encode the electric power of the mobile energy storage vehicle, the configured capacity of the mobile energy storage vehicle, and the electric power of the mobile generator vehicle into chromosomes; S424. Calculate the objective function value of the outer model, i.e.: ； S425. Use the objective function value of the outer model as the fitness of the chromosome for iterative optimization.

7. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 6, characterized in that, Step S425 specifically includes: S4251. Initialize the population and randomly generate multiple sets of candidate solutions; S4252. Use the power flow calculation tool to calculate the power flow parameters of the distribution network and determine whether they meet the node voltage constraints, power flow constraints, branch active power constraints, and user load demand constraints. If yes, proceed to step S4253; otherwise, proceed to 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 generator vehicle are obtained.

8. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 7, characterized in that, In step S4252, 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 branches at the nodes in the distribution network.

9. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 8, characterized in that, Step S43 specifically includes: S431. Set the second input parameters, which include the purchase price and sale price of the energy storage battery, the charging efficiency and discharge efficiency of the mobile energy storage vehicle, the optimal power of the mobile energy storage vehicle transmitted by the outer model, the configuration capacity of the mobile energy storage vehicle, and the power of the mobile generator vehicle. S432. Set 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. S433. Calculate the objective function value of the inner model, that is: ； S434. Use the objective function value of the inner model as the cost value of each particle and perform iterative optimization.

10. The optimized method for improving rural photovoltaic absorption capacity using mobile energy storage according to claim 9, characterized in that, Step S434 specifically includes: S4341. Initialize the particle swarm and randomly generate the charging power, discharging power, charging status, and discharging status 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 constraints and charging / discharging power constraints of the mobile energy storage vehicle. If yes, proceed to 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 constraints and the upper limit of the charging / discharging power constraints of the mobile energy storage vehicle before proceeding to 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 layer model in the current iteration and the previous iteration is less than the preset threshold, the objective function converges, and the optimal charging power, discharging power of the mobile energy storage vehicle and the optimal objective function value of the inner layer model are obtained.

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