A scheduling method and system of light storage and sale coordination intelligent control

Abstract

The present application relates to a kind of light storage sale collaborative intelligent control scheduling method and system, belong to light storage sale collaborative scheduling technical field, method includes the following steps: step 1: the double resource pool of fixed energy storage and mobile energy storage is built;Step 2: according to grid electricity price signal, fixed energy storage state of charge and load forecast, mobile energy storage scheduling task is generated;Step 3: the charge-discharge power of fixed energy storage is dynamically adjusted to suppress power gap;Step 4: based on credit mechanism, mobile energy storage is ordered priority ranking;Step 5: introduce the constraint of on-the-way service, allow adjustment to pass through node in the case where not exceed user tolerance deviation, determine the proportion of income sharing, and the income generated due to on-the-way scheduling is shared with user according to the proportion of income sharing.The collaborative scheduling of fixed energy storage and mobile energy storage, dynamic power suppression, credit priority ordering, on-the-way service constraint and income sharing are realized, and the overall income and resource utilization of light storage sale system are improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic-storage-sales collaborative scheduling technology, specifically a scheduling method and system for photovoltaic-storage-sales collaborative intelligent control. Background Technology

[0002] With the rapid development of integrated photovoltaic, energy storage, and electricity sales businesses, coordinated dispatch of photovoltaic, energy storage, and electricity sales has become a core technological direction for the integrated development of photovoltaic consumption, efficient energy storage utilization, and market-based electricity sales in new power systems. It achieves coordinated interaction between power generation, grid, load, and storage, optimized allocation of power resources, and maximized economic benefits by coordinating photovoltaic power generation, energy storage charging and discharging, and electricity sales. With the large-scale integration of distributed photovoltaic power and the advancement of power market reforms, the requirements for dispatch flexibility, resource utilization, and load fluctuation mitigation capabilities of photovoltaic-energy storage-electricity sales systems are continuously increasing. Traditional dispatch modes that rely solely on fixed energy storage are no longer suitable for complex scenarios.

[0003] In the evolution of photovoltaic-storage-electricity coordinated scheduling technology, the scheduling method of photovoltaic-storage-electricity coordinated intelligent control has gradually become a research and application focus, aiming to achieve dynamic matching of energy storage, load, and electricity price through intelligent algorithms. However, existing technical solutions have significant limitations: First, relying solely on fixed energy storage units results in insufficient scheduling flexibility, resource redundancy, and gaps, making it impossible to utilize massive mobile energy storage resources such as electric heavy trucks, battery swapping stations, and mobile power banks; second, a precise matching mechanism between the spatiotemporal characteristics of mobile energy storage and scheduling tasks has not been established, and there is a lack of usable quantifiable evaluation methods, resulting in insufficient scientific rigor in scheduling decisions.

[0004] Therefore, those skilled in the art provide a scheduling method and system for coordinated intelligent control of optical energy storage and sales to solve the problems mentioned in the background art. Summary of the Invention

[0005] The purpose of this invention is to provide a scheduling method and system for coordinated intelligent control of photovoltaic energy storage and sales, which realizes coordinated scheduling of fixed energy storage and mobile energy storage, dynamic power smoothing, credit score priority order assignment, on-the-way service constraints and revenue sharing, thereby improving the overall revenue and resource utilization of the photovoltaic energy storage and sales system and solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A scheduling method for coordinated intelligent control of optical energy storage and sales includes the following steps: Step 1: Construct a dual resource pool of fixed energy storage and mobile energy storage. Mobile energy storage includes electric heavy trucks, battery swapping stations, and mobile power banks. Users are authorized to access the system and upload real-time location, remaining power, travel route, and time window information. Step 2: Based on the grid electricity price signal, the state of charge of fixed energy storage and load forecast, generate a mobile energy storage dispatch task. The dispatch task includes the target node, expected arrival time, charging and discharging power and charging and discharging amount. Step 3: During the scheduling process, obtain the real-time location and estimated arrival time of mobile energy storage, and dynamically adjust the charging and discharging power of stationary energy storage to mitigate the power shortage. Step 4: Prioritize task assignments for mobile energy storage based on a credit score mechanism. The credit score is dynamically updated based on the on-time arrival rate and charging / discharging preparation efficiency of historical tasks. Step 5: Based on the user's original travel path, introduce a route service constraint, allowing adjustments to the nodes along the route without exceeding the user's tolerance deviation, determine the revenue sharing ratio, and share the revenue generated by the route scheduling with the user according to this revenue sharing ratio.

[0007] As a further aspect of the present invention: the generation process of the mobile energy storage scheduling task includes: constructing an objective function with the goal of maximizing the total system revenue, the objective function including electricity sales revenue, grid service revenue, fixed energy storage depreciation cost, mobile energy storage scheduling compensation cost, and user revenue sharing expenditure; and using a rolling time-domain optimization algorithm to solve for the optimal scheduling decision sequence in each scheduling cycle based on the current electricity price, load forecast, and mobile energy storage availability status.

[0008] As a further aspect of the present invention: the availability status of the mobile energy storage is characterized by a spatiotemporal availability index. The calculation process of the spatiotemporal availability index is as follows: First, the path distance between the current location of the mobile energy storage and the target node is obtained, and the theoretical travel time is calculated in combination with its average travel speed; then, the user-authorized time window margin is obtained, which represents the user's allowed range of arrival time flexibility. The ratio of the theoretical travel time to the time window margin is truncated to its maximum value to obtain the time availability factor; simultaneously, the current remaining power of the mobile energy storage is obtained, and the minimum guaranteed power required to complete its original journey is deducted to obtain the available discharge capacity; finally, the time availability factor is multiplied by the available discharge capacity to obtain the spatiotemporal availability; the system prioritizes scheduling mobile energy storage with a spatiotemporal availability higher than a preset threshold; the calculation formula for the spatiotemporal availability is: ; in, For the spatiotemporal availability of mobile energy storage, The theoretical travel time, Grant users a time window margin. This is the current remaining battery level. This is the minimum guaranteed power level.

[0009] As a further aspect of the present invention: the specific process of dynamically adjusting the charging and discharging power of the fixed energy storage includes: S1: Based on the expected arrival time of mobile energy storage, mobile energy storage is regarded as a source of injected or extracted power at a future moment; S2: Establish a power adjustment model for stationary energy storage. This model uses the time period from the current moment to the arrival time of mobile energy storage as the adjustment window, and uses the state-of-charge safety boundary of stationary energy storage as a constraint. The dynamic charging and discharging power of stationary energy storage is calculated in the following way: S201: Overlay the power curve expected to be provided by mobile energy storage with the load forecast curve to obtain the net load curve; S202: Fixed energy storage aims to smooth out net load fluctuations and uses a proportional-integral controller to adjust its charging and discharging power. The input of the proportional-integral controller is the deviation between the net load and the average load of the system, and the output is the power adjustment amount of the fixed energy storage. S203: When there is a deviation between the actual arrival time and the expected arrival time of mobile energy storage, recalculate the net load curve and update the power adjustment amount.

[0010] As a further aspect of this invention: the update rule of the credit score mechanism is as follows: for each scheduling task, the time deviation between the actual arrival time of the mobile energy storage at the charging / discharging node and the expected arrival time, and the preparation deviation between the actual time spent completing the charging / discharging preparation work and the standard preparation time are obtained; the time deviation and preparation deviation are mapped to the credit score increment interval respectively, and the credit score increment is obtained by calculating the deviation value using a piecewise linear function, with a larger increment for smaller deviations; simultaneously, for unauthorized task cancellation, a credit score deduction item is set; the final credit score is obtained by superimposing the weighted moving average of historical credit scores with the current task increment; the calculation formula for credit score update is: ; in, For the updated current credit score, Historical credit score, Historical weighting coefficients Due to time deviation, For the maximum permissible time deviation, To prepare for deviations, For the maximum permissible preparation deviation, and These are the weighting factors for time deviation and preparation deviation, respectively; mobile energy storage with higher credit scores receives higher order priority and a better revenue sharing ratio.

[0011] As a further aspect of the present invention: the relationship between the revenue sharing ratio and the credit score is as follows: the credit score is divided into several grade intervals, each grade interval corresponding to a basic revenue sharing coefficient; above the basic revenue sharing coefficient, a dynamic correction coefficient is further calculated based on the actual performance deviation of the mobile energy storage in this task. The dynamic correction coefficient is obtained by normalizing the time deviation and preparation deviation and then multiplying it by an adjustment factor; finally, the revenue sharing ratio of the mobile energy storage is equal to the sum of the basic revenue sharing coefficient and the dynamic correction coefficient, and does not exceed a preset upper limit value.

[0012] As a further aspect of the present invention, the method for implementing the route-following service constraint includes: obtaining the original sequence of nodes along the user's original travel path and the planned arrival time of each node; for candidate charging / discharging nodes, calculating the additional distance and additional time to detour from the original path to the candidate node; only when the additional distance is less than or equal to the user's tolerable detour distance and the additional time is less than or equal to the user's tolerable delay time, including the candidate node in the schedulable set; then, with the goal of minimizing the overall system cost or maximizing the overall benefit, selecting the optimal route-following charging / discharging node from the schedulable set, and binding the scheduling task to the user's travel; the user confirms whether to accept the route-following scheduling through a mobile APP, and the system records the travel binding information after acceptance.

[0013] As a further aspect of the present invention: the objective function for maximizing comprehensive revenue includes the user incentive cost generated by on-route dispatching, which is calculated as follows: the additional time and distance costs incurred due to detours are obtained and converted into user time value losses; simultaneously, the electricity arbitrage revenue or grid ancillary service revenue generated by on-route dispatching is obtained; the user incentive cost is set as a predetermined percentage of the net revenue from on-route dispatching, and this percentage is positively correlated with the user's historical participation level—the higher the historical participation level, the higher the user's share of the revenue; the formula for calculating the user incentive cost is: ; in, User incentive costs, The net revenue from on-route dispatching is the sum of electricity arbitrage revenue and grid service revenue, minus the additional costs incurred by detours. Based on the basic profit-sharing ratio, This is the participation adjustment factor. The number of times a user has accepted on-the-way scheduling in their history. This represents the total number of times a user has been pushed to a scheduled service along their route throughout history. When making scheduling decisions, the system incorporates user incentive costs as a deduction term in the objective function to maximize the system's net revenue.

[0014] As a further aspect of the present invention, the method further includes the calculation of coordinated charging and discharging power allocation between mobile energy storage and stationary energy storage: after the mobile energy storage arrives at the target node and is connected to the system, the system determines the charging and discharging power of the stationary energy storage and the mobile energy storage respectively based on the current state of charge of the stationary energy storage, the remaining power of the mobile energy storage, the real-time electricity price, and the load forecast for the next period, using a rule-based or optimization-based power allocation algorithm; wherein, when the electricity price is in a low period and the state of charge of the stationary energy storage is below a high threshold, the stationary energy storage is used for charging first, and when the state of charge of the stationary energy storage reaches a preset value of its rated capacity, the excess power is allocated to the mobile energy storage; when the electricity price is in a high period and the load demand is greater than the discharge capacity of the stationary energy storage, the mobile energy storage is scheduled to discharge first to make up for the gap, while the discharge capacity of the stationary energy storage is reserved to cope with subsequent load fluctuations; during this power allocation process, the charging and discharging depth of the mobile energy storage is limited by the minimum power requirement for its subsequent journey.

[0015] This application also discloses a scheduling system for collaborative intelligent control of photovoltaic energy storage and sales, employing a scheduling method for collaborative intelligent control of photovoltaic energy storage and sales, including: The dual resource pool management module is used to build and maintain dual resource pools of fixed energy storage and mobile energy storage. The mobile energy storage includes electric heavy trucks, battery swapping stations, and mobile power banks. This module receives real-time location, remaining power, travel path, and time window information uploaded by mobile energy storage devices authorized by the user. The scheduling task generation module is used to generate mobile energy storage scheduling tasks based on grid electricity price signals, fixed energy storage state of charge and load forecast. The scheduling task includes target node, expected arrival time, charging and discharging power and charging and discharging amount. The dynamic power adjustment module is used to obtain the real-time location and estimated arrival time of mobile energy storage during the scheduling process, and dynamically adjust the charging and discharging power of fixed energy storage to smooth out the power gap. The credit score management module is used to prioritize mobile energy storage task assignments based on a credit score mechanism. The credit score is dynamically updated based on the on-time arrival rate and charging / discharging preparation efficiency of historical tasks. The route service module is used to introduce route service constraints on the user's original route, allowing adjustments to the nodes along the route without exceeding the user's tolerance deviation, and sharing the revenue generated by the route scheduling with the user in proportion. The scheduling system achieves joint optimized scheduling of fixed energy storage and mobile energy storage through the coordinated operation of the above modules.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This application constructs a dual resource pool of fixed and mobile energy storage, fully integrating mobile energy storage resources such as electric heavy trucks, battery swapping stations, and mobile power banks. This overcomes the flexibility limitations of traditional single fixed energy storage scheduling, significantly improving the overall utilization rate and scheduling elasticity of energy storage resources in the photovoltaic-storage-sales system. It achieves precise quantitative assessment of the dispatchability of mobile energy storage based on spatiotemporal availability indicators, enabling efficient matching between mobile energy storage and scheduling tasks. Dynamically adjusting the charging and discharging power of fixed energy storage during scheduling effectively mitigates power gaps and net load fluctuations, ensuring system power supply and demand balance and operational stability. A credit score mechanism is used to prioritize mobile energy storage task assignments. It can significantly improve the on-time execution rate and charging / discharging preparation efficiency, ensure scheduling reliability and execution effect, introduce route service constraints to adjust the route without exceeding the user's tolerance deviation, take into account the user's original route rights, and combine with the revenue sharing mechanism to incentivize users to actively connect and participate in scheduling, achieving a win-win situation for the system and users. After the mobile energy storage is connected, the charging and discharging power allocation is carried out through dual energy storage collaboratively, and the charging and discharging strategy is optimized by combining real-time electricity price, state of charge and load forecast. While ensuring the power demand of the mobile energy storage in the subsequent journey, it maximizes the total system revenue, comprehensively improves the intelligence level, resource utilization efficiency and economic benefits of photovoltaic-storage-sales collaborative scheduling. Attached Figure Description

[0017] Figure 1 A flowchart of a scheduling method for collaborative intelligent control of optical energy storage and sales; Figure 2 This is a structural block diagram of a scheduling system for collaborative intelligent control of optical energy storage and sales. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] As mentioned in the background section of this application, research has revealed that existing technical solutions have significant limitations: First, relying solely on fixed energy storage units results in insufficient scheduling flexibility, resource redundancy, and resource gaps, making it impossible to utilize massive mobile energy storage resources such as electric heavy trucks, battery swapping stations, and mobile power banks; second, a precise matching mechanism between the spatiotemporal characteristics of mobile energy storage and scheduling tasks has not been established, and there is a lack of available quantifiable evaluation methods, resulting in insufficient scientific rigor in scheduling decisions.

[0020] To address the aforementioned shortcomings, this application discloses a scheduling method and system for coordinated intelligent control of photovoltaic energy storage and sales, which enables coordinated scheduling of fixed and mobile energy storage, dynamic power stabilization, priority order assignment based on credit score, on-the-way service constraints, and revenue sharing, thereby improving the overall revenue and resource utilization of the photovoltaic energy storage and sales system.

[0021] The following will describe in detail, with reference to the accompanying drawings, how the solution of this application solves the above-mentioned technical problems.

[0022] Please see Figure 1 In this embodiment of the invention, a scheduling method for coordinated intelligent control of photovoltaic energy storage and sales includes the following steps: Step 1: Constructing a dual resource pool of fixed energy storage and mobile energy storage, wherein the mobile energy storage includes electric heavy trucks, battery swapping stations, and mobile power banks, with users authorized to access the system and upload real-time location, remaining power, travel path, and time window information; Step 2: Generating mobile energy storage scheduling tasks based on grid electricity price signals, fixed energy storage state of charge, and load forecasts, the scheduling tasks include target nodes, expected arrival time, charging and discharging power, and charging and discharging quantity; Step 3: During the scheduling process, obtaining the real-time location and expected arrival time of the mobile energy storage, and dynamically adjusting the charging and discharging power of the fixed energy storage to mitigate power shortages; Step 4: Prioritizing mobile energy storage task assignments based on a credit score mechanism, with the credit score dynamically updated based on the on-time arrival rate and charging and discharging preparation efficiency in historical tasks; Step 5: Introducing a route-following service constraint based on the user's original travel path, allowing adjustments to the route nodes without exceeding the user's tolerance deviation, determining the revenue sharing ratio, and sharing the revenue generated from route-following scheduling with the user according to this revenue sharing ratio. By constructing a dual resource pool of fixed and mobile energy storage, integrating diverse mobile energy storage resources such as electric heavy trucks, battery swapping stations, and mobile power banks, the scale and flexibility of energy storage dispatch resources are significantly expanded. Precise dispatch tasks are generated by combining grid price signals, fixed energy storage state of charge, and load forecasts, improving the accuracy and efficiency of photovoltaic-energy storage-sales collaborative dispatch. During dispatch, the real-time location and estimated arrival time of mobile energy storage are obtained, and the charging and discharging power of fixed energy storage is dynamically adjusted, effectively mitigating power shortages and ensuring system power stability. A credit score mechanism optimizes the priority of mobile energy storage task assignment, improving the timeliness and reliability of dispatch execution. Simultaneously, based on the service constraint of following the route, the travel path is adjusted without exceeding the user's tolerance deviation. Revenue sharing incentivizes users to authorize access and participate in dispatch, both without interfering with users' original travel plans and expanding the application scenarios of energy storage dispatch. This achieves intelligent control of photovoltaic-energy storage-sales collaboration, efficient utilization of energy storage resources, stable grid operation, and a win-win situation for all parties.

[0023] In this embodiment, the process of generating mobile energy storage scheduling tasks includes: constructing an objective function aimed at maximizing the total system revenue, which includes electricity sales revenue, grid service revenue, fixed energy storage depreciation cost, mobile energy storage scheduling compensation cost, and user revenue sharing expenditure; and employing a rolling time-domain optimization algorithm to solve for the optimal scheduling decision sequence within each scheduling cycle based on the current electricity price, load forecast, and mobile energy storage availability. By constructing an objective function that maximizes the total system revenue covering electricity sales revenue, grid service revenue, fixed energy storage depreciation cost, mobile energy storage scheduling compensation cost, and user revenue sharing expenditure, a comprehensive balance between revenue and cost is achieved, avoiding scheduling imbalances driven by a single revenue orientation. Simultaneously, the rolling time-domain optimization algorithm dynamically solves for the optimal scheduling decision sequence in a periodic manner, combining real-time electricity price, load forecast, and mobile energy storage availability, ensuring that scheduling decisions align with real-time operating conditions, significantly improving decision accuracy and timeliness, maximizing the overall economic benefits of the photovoltaic-energy storage-sales collaborative system, and guaranteeing the economic viability and feasibility of the scheduling scheme.

[0024] Specific examples: Taking the light-storage-sales collaborative system of urban commercial complexes as an example, the scheduling cycle is set to 15 minutes, and the day is divided into 96 scheduling periods, with rolling time-domain optimized scheduling implemented: The objective function for maximizing the total system revenue is expressed by the following formula: ; in, The maximum total profit (in yuan). Revenue from electricity sales of the photovoltaic-storage system (RMB); Revenue from grid ancillary services (RMB); Fixed energy storage depreciation cost (RMB); Cost of mobile energy storage dispatch compensation (RMB); Revenue sharing expenses for mobile energy storage users (in yuan).

[0025] Optimized execution over time: The first scheduling cycle (00:00-00:15): The real-time electricity price is 0.28 yuan / kWh during off-peak hours, the predicted load of the complex is 400kW, the fixed energy storage is at 35% charge, and there is no available mobile energy storage. The optimal decision is to charge the fixed energy storage to full power.

[0026] Second scheduling cycle (00:15-00:30): Update electricity price, load, fixed energy storage charge status to 60%, connect one electric heavy truck mobile energy storage, re-roll the optimal decision, continue charging the fixed energy storage and dispatch scheduling tasks to the electric heavy truck.

[0027] By iterating in this cycle, the system continuously outputs the optimal scheduling sequence that fits the real-time operating conditions, thereby maximizing the total benefits of the system.

[0028] In this embodiment, the availability of mobile energy storage is characterized by a spatiotemporal availability index. The calculation process of the spatiotemporal availability index is as follows: First, the path distance between the current location of the mobile energy storage and the target node is obtained, and the theoretical travel time is calculated based on its average travel speed. Then, the user-authorized time window margin is obtained, which represents the user's allowed range of arrival time flexibility. The ratio of the theoretical travel time to the time window margin is truncated to its maximum value to obtain the time availability factor. Simultaneously, the current remaining power of the mobile energy storage is obtained, and the minimum guaranteed power required to complete its original journey is deducted to obtain the available discharge capacity. Finally, the time availability factor is multiplied by the available discharge capacity to obtain the spatiotemporal availability. The system prioritizes scheduling mobile energy storage with a spatiotemporal availability higher than a preset threshold. The calculation formula for spatiotemporal availability is: ; in, For the spatiotemporal availability of mobile energy storage, The theoretical travel time, Grant users a time window margin. This is the current remaining battery level. This ensures a minimum guaranteed power supply. By comprehensively measuring the time adaptability and power availability of mobile energy storage through spatiotemporal availability indicators, the dispatchable potential is accurately quantified, enabling priority dispatch of highly adaptable energy storage, reducing invalid dispatch orders and dispatch deviations, and improving the efficiency of energy storage resource matching and the reliability of dispatch execution.

[0029] Specific examples: Using the scenario of a photovoltaic-storage-sales collaborative system in an urban commercial complex, the spatiotemporal availability of one electric heavy-duty truck connected to the system is calculated: Obtain basic parameter values: theoretical driving time =20min, user-authorized time window margin =30 minutes, current remaining battery power =80%, minimum guaranteed battery capacity =30%; Spatiotemporal availability calculation: ; Substitute parameters for calculation: ; Scheduling Application: The system presets a time and space availability threshold of 0.25. The availability of this electric heavy truck is 0.165, which is less than the threshold, so it is not included in the priority scheduling queue to avoid scheduling failure due to insufficient time adaptability.

[0030] In this embodiment, the specific process of dynamically adjusting the charging and discharging power of stationary energy storage includes: S1: Based on the expected arrival time of mobile energy storage, the mobile energy storage is regarded as a power source to be injected or extracted at a future time; S2: A power adjustment model for stationary energy storage is established. This power adjustment model uses the time period between the current time and the arrival time of the mobile energy storage as the adjustment window, and the state of charge safety boundary of the stationary energy storage as a constraint. The dynamic charging and discharging power of the stationary energy storage is calculated in the following manner: S201: The power curve expected to be provided by the mobile energy storage is superimposed with the load prediction curve to obtain the net load curve; S202: The stationary energy storage aims to smooth out net load fluctuations and uses a proportional-integral controller to adjust its charging and discharging power. The input of the proportional-integral controller is the deviation between the net load and the average load of the system, and the output is the power adjustment amount of the stationary energy storage; S203: When there is a deviation between the actual arrival time and the expected arrival time of the mobile energy storage, the net load curve is recalculated and the power adjustment amount is updated. By treating mobile energy storage as a future power source and establishing a fixed energy storage power adjustment model, a PI controller is used to track net load deviations and dynamically adjust charging and discharging power. It can also correct deviations in real time based on the actual arrival of mobile energy storage, quickly smooth out power gaps, stabilize system operating conditions, and improve the power balance capability and power supply reliability of photovoltaic-storage-sales coordinated scheduling.

[0031] Specific examples: Using the scenario of a photovoltaic-storage-sales collaborative system in urban commercial complexes, and taking the participation of electric heavy-duty trucks in dispatch as an example: The parameter settings are as follows: Adjust window duration T: The time interval from the current moment to the expected arrival of the mobile energy storage, T=30min; System average load Average power consumption of commercial complexes during a given time period =500kW; Real-time load forecast The predicted power consumption of the complex at time t fluctuates between 450kW and 550kW. Mobile energy storage expected injection power The estimated power that the electric heavy-duty truck will deliver to the system upon arrival. =100kW; Stationary energy storage SOC safety lower limit 20%; Safety Limit 90%; PI controller proportional coefficient : 0.8; Integral coefficient : 0.2; Net load average : Average power of the net load curve =400kW.

[0032] Net load curve calculation, net load The formula is the difference between the load forecast and the projected power of mobile energy storage: Substitute into the calculation: The fluctuation range is 350kW to 450kW.

[0033] The PI controller power adjustment calculation is as follows: Load deviation signal: ; Fixed energy storage power adjustment amount: ; The system's net load fluctuation is controlled within ±20kW by dynamically adjusting the system based on the calculation results.

[0034] Real-time correction of arrival deviation; if the electric heavy truck arrives with a 5-minute delay, the system will re-collect the data. and ,renew And recalculate To continuously ensure system power balance.

[0035] In this embodiment, the credit score update rule is as follows: For each scheduling task, the time deviation between the actual arrival time of the mobile energy storage at the charging / discharging node and the expected arrival time, and the preparation deviation between the actual time spent completing the charging / discharging preparation work and the standard preparation time are obtained; the time deviation and preparation deviation are mapped to the credit score increment interval respectively, and the credit score increment is obtained by calculating the deviation value using a piecewise linear function, with a larger increment for smaller deviations; at the same time, a credit score deduction item is set for unauthorized task cancellation; finally, the credit score is obtained by superimposing the weighted moving average of historical credit scores with the current task increment; the calculation formula for credit score update is: ; in, For the updated current credit score, Historical credit score, Historical weighting coefficients Due to time deviation, For the maximum permissible time deviation, To prepare for deviations, For the maximum permissible preparation deviation, and These are the weighting factors for time deviation and preparation deviation, respectively. Mobile energy storage with higher credit scores receives higher order priority and a better revenue sharing ratio. The credit score increment is calculated piecewise linearly based on the time deviation and preparation deviation of task execution. Credit score deductions are set for unauthorized task cancellations. The credit score is dynamically updated by combining the weighted moving average of historical credit scores. The credit score is directly linked to order priority and revenue sharing, which effectively improves the timeliness of mobile energy storage in fulfilling its obligations and the efficiency of charging and discharging preparation, ensuring the stable implementation of dispatching tasks.

[0036] Specific examples: Using the scenario of a photovoltaic-storage-sales collaborative system in urban commercial complexes, let's take the dispatching task of mobile energy storage in electric heavy trucks as an example: The core parameters are defined as follows: The historical credit score before the update, with a value of 90 (out of 100). Historical credit score weighting coefficient, with a value of 0.8 (meaning that historical credit score accounts for 80% in the update); Task execution time deviation, which is the difference between the actual arrival time and the planned arrival time, is set to 2 minutes (2 minutes delay). The maximum time deviation threshold allowed by the system is 10 minutes (exceeding this threshold will result in a significant deduction of points). The weighting coefficient for the time deviation item is 5 (representing the weight of the impact of timeliness on credit score). Task preparation deviation, which is the difference between the actual preparation time and the standard preparation time, is set to 1 minute (the preparation process took 1 minute longer). The maximum allowable preparation deviation threshold for the system is 5 minutes. : The weighting coefficient for the preparation deviation item, with a value of 3 (representing the weight of the impact of preparation efficiency on credit score).

[0037] The credit score update formula is: ; Substitute the numerical values ​​into the calculation: ; The updated credit score for this electric heavy-duty truck is 73.28, a decrease from its historical score of 90. The system allocates order priority and revenue sharing based on the score. Unauthorized cancellation of tasks will trigger a deduction of credit score, directly reducing its subsequent dispatch eligibility and revenue sharing ratio. Mobile energy storage systems with a credit score of 80 or higher are eligible for priority order allocation and a 10% revenue sharing bonus. For vehicles scoring 70-80 points (such as 73.28 points in this example), the order priority is reduced, and the revenue sharing bonus is reduced to 5%. If the vehicle subsequently cancels its mission without a valid reason, it will trigger an additional 10 credit points deduction, further affecting its dispatch eligibility and earnings.

[0038] In this embodiment, the relationship between the revenue sharing ratio and the credit score is as follows: the credit score is divided into several level intervals, each level interval corresponding to a basic revenue sharing coefficient; above the basic revenue sharing coefficient, a dynamic correction coefficient is further calculated based on the actual performance deviation of the mobile energy storage in this task. The dynamic correction coefficient is obtained by normalizing the time deviation and preparation deviation and then multiplying it by an adjustment factor; finally, the revenue sharing ratio of the mobile energy storage is equal to the sum of the basic revenue sharing coefficient and the dynamic correction coefficient, and does not exceed a preset upper limit. Based on the real-time location, trip planning, and route service constraints of the mobile energy storage, the task dispatch and path are dynamically adjusted. Under the premise of not exceeding the user's tolerance deviation, the scheduling arrangement is optimized, which not only ensures the efficient execution of energy storage scheduling, but also minimizes the interference with the original trips of mobile energy storage users, achieving a balance between scheduling services and user travel needs, expanding application scenarios, and increasing user participation.

[0039] Specific examples: Using the scenario of a photovoltaic-storage-sales collaborative system in urban commercial complexes, let's take the dispatching task of mobile energy storage in electric heavy trucks as an example: The core parameters are defined as follows: The user's original planned route: from factory A (0,0) to warehouse B (10,0), the straight-line distance... .

[0040] The target point of the scheduling task is C(5,1), which is located near the original route.

[0041] The maximum acceptable trip deviation threshold for users: .

[0042] Deviation calculation formula: ,in This represents the total actual distance traveled.

[0043] Initial path evaluation (without optimization): Specifically, assume the initial dispatch path is A→C→B.

[0044] Distances per segment: ; ; Actual total distance: ; Deviation calculation: The deviation of the path is 2%, which is far less than the threshold of 15%, and meets the user's requirements.

[0045] Compare paths that do not meet the conditions. Specifically, if the task point is far away, such as D(5,3), the path A→D→B is: Actual total distance ; Deviation This path was automatically excluded by the system because it exceeded the user's tolerance level.

[0046] In its scheduling optimization and application, the system prioritizes the route A→C→B with the lowest deviation for order dispatch. Under this scheme, the electric heavy truck only needs to travel an additional 0.2km to complete the dispatch task, with almost no impact on the user's original journey. Simultaneously, the vehicle receives corresponding dispatch revenue, achieving a win-win situation.

[0047] In this embodiment, the method for implementing the route-following service constraint includes: obtaining the original sequence of nodes along the user's original travel path and the planned arrival time of each node; for candidate charging / discharging nodes, calculating the additional distance and additional time to detour from the original path to the candidate node; only when the additional distance is less than or equal to the user's tolerable detour distance and the additional time is less than or equal to the user's tolerable delay time, including the candidate node in the schedulable set; then, with the goal of minimizing the overall system cost or maximizing the overall benefit, selecting the optimal route-following charging / discharging node from the schedulable set, and binding the scheduling task to the user's trip; the user confirms whether to accept the route-following scheduling through a mobile APP, and the system records the trip binding information after acceptance. By obtaining the nodes along the user's original journey and the planned arrival time, the additional distance and time of detour candidate charging and discharging nodes are accurately calculated. Nodes are included in the schedulable set only when they do not exceed the user's tolerable detour distance and delay time. The optimal along-route node is selected to maximize system benefits or minimize costs and bound to the journey. After confirmation by the user's APP, the process is executed. This not only strictly controls the interference with the user's original journey, but also selects highly adaptable scheduling nodes, improves the scheduling implementation rate and user participation, and achieves efficient coordination between energy storage scheduling and user journey.

[0048] Specific examples: Using the scenario of a photovoltaic-storage-sales collaborative system in urban commercial complexes, let's take mobile energy storage in electric heavy-duty trucks as an example: The core parameters are defined as follows: Original route: Starting point S(0,0) → Ending point E(10,0), original travel distance Planned driving time ; Candidate charge / discharge node: Commercial complex C (5, 0.5); User tolerance detour distance User tolerance latency ; The electric heavy-duty truck travels at a speed of v=30km / h; Additional distance and additional time calculation, actual total path distance: ; Additional distance: ; Additional time: ; Path constraint judgment: If the constraints are satisfied, candidate node C is included in the schedulable set.

[0049] The system selects the optimal node and binds it to the trip. With the goal of maximizing overall benefits, it selects commercial complex C as the optimal charging and discharging node along the way, binds the scheduling task to the original trip of the electric heavy truck, and pushes it to the user's APP. After the user confirms, the scheduling is officially executed.

[0050] In this embodiment, the objective function for maximizing overall revenue includes the user incentive cost generated by on-route dispatching. This user incentive cost is calculated as follows: The additional time and distance costs incurred due to detours are obtained and converted into user time value loss; simultaneously, the electricity arbitrage revenue or grid ancillary service revenue generated by on-route dispatching is obtained; the user incentive cost is set as a predetermined percentage of the net revenue from on-route dispatching, and this percentage is positively correlated with the user's historical participation level—the higher the historical participation level, the higher the user's share of the revenue. The formula for calculating the user incentive cost is: ; in, User incentive costs, The net revenue from on-route dispatching is the sum of electricity arbitrage revenue and grid service revenue, minus the additional costs incurred by detours. Based on the basic profit-sharing ratio, This is the participation adjustment factor. The number of times a user has accepted on-the-way scheduling in their history. This represents the total number of times a user has been pushed for on-route scheduling in the past. When making scheduling decisions, the system incorporates user incentive costs as a subtraction term in the objective function to maximize the system's net revenue. Based on the net revenue from on-route scheduling, the system calculates user incentive costs by combining the basic revenue sharing ratio with the user's historical participation. This cost is then used as a subtraction term in the objective function for maximizing the system's net revenue. By positively adjusting the revenue sharing ratio based on historical participation, the system accurately balances system revenue and user incentives, continuously encouraging users to participate in on-route scheduling and improving the stability and long-term enthusiasm of mobile energy storage participants.

[0051] Specific examples: Using the scenario of a photovoltaic-storage-sales collaborative system in urban commercial complexes, let's take the participation of electric heavy-duty trucks in on-route dispatching as an example: The core parameters are defined as follows: Net revenue from on-route dispatching (electricity arbitrage revenue + power grid service revenue - additional detour costs), valued at 200 yuan; Base profit sharing ratio, with a value of 0.2; : Participation adjustment factor, with a value of 0.3; The number of times the user has accepted on-the-way dispatch in the past: 8 times; Total number of times the user was pushed along the route in history, 10 times.

[0052] User incentive cost formula: .

[0053] Substitute the numerical values ​​into the calculation: .

[0054] In the scheduling application, the system uses the 88 yuan user incentive cost as the objective function to calculate the system's net revenue. The electric heavy truck receives a higher share of the incentive due to its high historical participation, and continues to participate in scheduling. The system also obtains stable mobile energy storage resources, achieving a win-win situation for both scheduling revenue and user participation.

[0055] In this embodiment, the method further includes the calculation of coordinated charging and discharging power allocation between mobile energy storage and stationary energy storage: After the mobile energy storage arrives at the target node and is connected to the system, the system determines the charging and discharging power of the stationary energy storage and the mobile energy storage respectively based on the current state of charge of the stationary energy storage, the remaining power of the mobile energy storage, the real-time electricity price, and the load forecast for the next period, using a rule-based or optimization-based power allocation algorithm; wherein, when the electricity price is in a low period and the state of charge of the stationary energy storage is below a high threshold, the stationary energy storage is used for charging first, and when the state of charge of the stationary energy storage reaches a preset value of its rated capacity, the excess power is allocated to the mobile energy storage; when the electricity price is in a high period and the load demand is greater than the discharge capacity of the stationary energy storage, the mobile energy storage is scheduled to discharge first to make up for the gap, while the discharge capacity of the stationary energy storage is reserved to cope with subsequent load fluctuations; in this power allocation process, the charging and discharging depth of the mobile energy storage is limited by the minimum power requirement for its subsequent journey. By combining real-time electricity prices, the state of charge of fixed energy storage, the remaining capacity of mobile energy storage, and load forecasting, the system achieves coordinated charging and discharging power allocation between fixed and mobile energy storage. During off-peak hours, fixed energy storage is prioritized for charging, and power is then allocated to mobile energy storage after it is fully charged. During peak hours, mobile energy storage is prioritized for discharging to make up for load gaps while reserving fixed energy storage capacity to cope with subsequent fluctuations. At the same time, the charging and discharging depth of mobile energy storage is strictly constrained to not exceed the guaranteed capacity. This maximizes the benefits of electricity price arbitrage and grid support, improves the system's power guarantee capability, and does not interfere with the original travel plans of mobile energy storage users, thus achieving efficient utilization of dual energy storage resources and economical and stable system operation.

[0056] Specific examples: Using the scenario of a photovoltaic-storage-sales collaborative system for urban commercial complexes, and taking the collaborative scheduling of mobile energy storage and fixed energy storage in electric heavy-duty trucks as an example: The core parameters are defined as follows: Electricity pricing is divided into two periods: off-peak hours (00:00-06:00) and peak hours (10:00-14:00) with a price of 0.28 yuan / kWh. Stationary energy storage: rated charge / discharge power 500kW, SOC high threshold 90%, SOC low threshold 20%; Mobile energy storage (electric heavy truck): Battery capacity 100kWh, rated charge / discharge power 200kW, guaranteed minimum capacity. =30%, current remaining battery power =80%; Peak load forecast: .

[0057] During off-peak hours, with coordinated charging allocation, and the current SOC of the fixed energy storage being between 40% and 90%, the system prioritizes full-power charging, and the charging power of the fixed energy storage is [not specified]. Mobile energy storage will not be allocated charging power for the time being. Once the SOC of fixed energy storage reaches 90%, the excess grid power will be allocated to the charging of electric heavy trucks.

[0058] Coordinated discharge distribution during peak hours, fixed maximum discharge power of energy storage The system first calculates the power deficit: ; Prioritize the discharge of 100kW from electric heavy-duty trucks to fill the gap. After discharge, the remaining power of the electric heavy-duty trucks is: ; This ensures that the minimum power supply is met; fixed energy storage will only release 500kW of capacity, reserving the remaining power to cope with subsequent load fluctuations.

[0059] The scheduling application enables low-cost charging during off-peak hours and high-value discharging during peak hours, maximizing system arbitrage profits. At the same time, the remaining power of electric heavy trucks can meet the subsequent travel needs, and the dual energy storage works together to stably support the power consumption of commercial complexes.

[0060] like Figure 2 As shown, this application also discloses a photovoltaic-storage-sales collaborative intelligent control scheduling system, employing a photovoltaic-storage-sales collaborative intelligent control scheduling method, including: a dual resource pool management module, used to construct and maintain dual resource pools of fixed energy storage and mobile energy storage, wherein mobile energy storage includes electric heavy trucks, battery swapping stations, and mobile power banks, this module receives real-time location, remaining power, travel path, and time window information uploaded by user-authorized mobile energy storage; a scheduling task generation module, used to generate mobile energy storage scheduling tasks based on grid electricity price signals, fixed energy storage state of charge, and load forecasts, the scheduling task including target node, expected arrival time, charging and discharging power, and charging and discharging amount; and a dynamic power adjustment module, used to... During the scheduling process, the real-time location and estimated arrival time of mobile energy storage are acquired, and the charging and discharging power of fixed energy storage is dynamically adjusted to mitigate power shortages. The credit score management module prioritizes mobile energy storage tasks based on a credit score mechanism, with the credit score dynamically updated according to the on-time arrival rate and charging / discharging preparation efficiency of historical tasks. The route service module introduces route service constraints based on the user's original travel path, allowing adjustments to transit nodes within the user's tolerance deviation limit, and sharing the revenue generated from route scheduling with the user proportionally. Through the coordinated operation of the above modules, the scheduling system achieves joint optimized scheduling of fixed and mobile energy storage.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A scheduling method for coordinated intelligent control of photovoltaic energy storage and sales, characterized in that, Includes the following steps: Step 1: Construct a dual resource pool of fixed energy storage and mobile energy storage. Mobile energy storage includes electric heavy trucks, battery swapping stations, and mobile power banks. Users are authorized to access the system and upload real-time location, remaining power, travel route, and time window information. Step 2: Based on the grid electricity price signal, the state of charge of fixed energy storage and load forecast, generate a mobile energy storage dispatch task. The dispatch task includes the target node, expected arrival time, charging and discharging power and charging and discharging amount. Step 3: During the scheduling process, obtain the real-time location and estimated arrival time of mobile energy storage, and dynamically adjust the charging and discharging power of stationary energy storage to mitigate the power shortage. Step 4: Prioritize task assignments for mobile energy storage based on a credit score mechanism. The credit score is dynamically updated based on the on-time arrival rate and charging / discharging preparation efficiency of historical tasks. Step 5: Based on the user's original travel path, introduce a route service constraint, allowing adjustments to the nodes along the route without exceeding the user's tolerance deviation, determine the revenue sharing ratio, and share the revenue generated by the route scheduling with the user according to this revenue sharing ratio. The process of generating the mobile energy storage dispatch task includes: constructing an objective function with the goal of maximizing the total system revenue, the objective function including electricity sales revenue, grid service revenue, fixed energy storage depreciation cost, mobile energy storage dispatch compensation cost, and user revenue sharing expenditure; and using a rolling time-domain optimization algorithm to solve for the optimal dispatch decision sequence in each dispatch cycle based on the current electricity price, load forecast, and mobile energy storage availability. The availability status of the mobile energy storage is characterized by a spatiotemporal availability index. The calculation process of the spatiotemporal availability index is as follows: First, obtain the path distance between the current location of the mobile energy storage and the target node, and calculate the theoretical travel time based on its average travel speed; then, obtain the user-authorized time window margin, which represents the user's allowed range of arrival time flexibility. The ratio of the theoretical travel time to the time window margin is truncated to its maximum value to obtain the time availability factor; simultaneously, obtain the current remaining power of the mobile energy storage, and deduct the minimum guaranteed power required to complete its original journey to obtain the available discharge capacity; finally, multiply the time availability factor by the available discharge capacity to obtain the spatiotemporal availability; the system prioritizes scheduling mobile energy storage with a spatiotemporal availability higher than a preset threshold. The credit score mechanism updates according to the following rules: For each scheduling task, the time deviation between the actual arrival time of the mobile energy storage at the charging / discharging node and the expected arrival time, and the preparation deviation between the actual time spent completing the charging / discharging preparation and the standard preparation time are obtained; the time deviation and preparation deviation are mapped to the credit score increment range, and the credit score increment is calculated by performing a piecewise linear function on the deviation value, with a larger increment for smaller deviations; at the same time, a credit score deduction is set for unauthorized task cancellation; finally, the credit score is obtained by superimposing the weighted moving average of historical credit scores with the current task increment. The implementation method of the route-following service constraint includes: obtaining the original sequence of nodes along the user's original travel path and the planned arrival time of each node; for candidate charging / discharging nodes, calculating the additional distance and additional time to detour from the original path to the candidate node; only when the additional distance is less than or equal to the user's tolerable detour distance and the additional time is less than or equal to the user's tolerable delay time, the candidate node is included in the schedulable set; then, with the goal of minimizing the overall system cost or maximizing the overall benefit, the optimal route-following charging / discharging node is selected from the schedulable set, and the scheduling task is bound to the user's trip; the user confirms whether to accept the route-following scheduling through a mobile APP, and the system records the trip binding information after acceptance; The objective function for maximizing overall revenue includes the user incentive cost generated by on-route dispatching. This user incentive cost is calculated as follows: the additional time and distance costs incurred due to detours are obtained and converted into user time value loss; at the same time, the electricity arbitrage revenue or grid ancillary service revenue brought by on-route dispatching is obtained; the user incentive cost is set as a predetermined percentage of the net revenue from on-route dispatching, and this percentage is positively correlated with the user's historical participation. The higher the historical participation, the higher the percentage of revenue the user receives.

2. The scheduling method for collaborative intelligent control of optical storage and sales according to claim 1, characterized in that, The specific process of dynamically adjusting the charging and discharging power of the fixed energy storage includes: S1: Based on the expected arrival time of mobile energy storage, mobile energy storage is regarded as a source of injected or extracted power at a future moment. S2: Establish a power adjustment model for stationary energy storage. This model uses the time period from the current moment to the arrival time of mobile energy storage as the adjustment window, and uses the state-of-charge safety boundary of stationary energy storage as a constraint. The dynamic charging and discharging power of stationary energy storage is calculated in the following way: S201: Overlay the power curve expected to be provided by mobile energy storage with the load forecast curve to obtain the net load curve; S202: Fixed energy storage aims to smooth out net load fluctuations and uses a proportional-integral controller to adjust its charging and discharging power. The input of the proportional-integral controller is the deviation between the net load and the average load of the system, and the output is the power adjustment amount of the fixed energy storage. S203: When there is a deviation between the actual arrival time and the expected arrival time of mobile energy storage, recalculate the net load curve and update the power adjustment amount.

3. The scheduling method for collaborative intelligent control of optical storage and sales according to claim 2, characterized in that, The relationship between the revenue sharing ratio and the credit score is as follows: the credit score is divided into several level intervals, and each level interval corresponds to a basic revenue sharing coefficient; above the basic revenue sharing coefficient, a dynamic correction coefficient is further calculated based on the actual performance deviation of the mobile energy storage in this task. The dynamic correction coefficient is obtained by normalizing the time deviation and preparation deviation and then multiplying it by an adjustment factor; finally, the revenue sharing ratio of the mobile energy storage is equal to the sum of the basic revenue sharing coefficient and the dynamic correction coefficient, and does not exceed a preset upper limit.

4. The scheduling method for coordinated intelligent control of optical storage and sales according to claim 3, characterized in that, The method also includes the calculation of coordinated charging and discharging power allocation between mobile energy storage and stationary energy storage: After the mobile energy storage arrives at the target node and is connected to the system, the system determines the charging and discharging power of the stationary energy storage and the mobile energy storage respectively based on the current state of charge of the stationary energy storage, the remaining power of the mobile energy storage, the real-time electricity price, and the load forecast for the next period, using a rule-based or optimization-based power allocation algorithm; wherein, when the electricity price is in a low period and the state of charge of the stationary energy storage is below a high threshold, the stationary energy storage is used for charging first, and when the state of charge of the stationary energy storage reaches the preset value of its rated capacity, the excess power is allocated to the mobile energy storage; when the electricity price is in a high period and the load demand is greater than the discharge capacity of the stationary energy storage, the mobile energy storage is scheduled to discharge first to make up for the gap, while the discharge capacity of the stationary energy storage is reserved to cope with subsequent load fluctuations; in this power allocation process, the charging and discharging depth of the mobile energy storage is limited by the minimum power requirement for its subsequent journey.

5. A scheduling system for coordinated intelligent control of photovoltaic energy storage and sales, characterized in that, The scheduling method for collaborative intelligent control of optical storage and sales as described in any one of claims 1-4 includes: The dual resource pool management module is used to build and maintain dual resource pools of fixed energy storage and mobile energy storage. The mobile energy storage includes electric heavy trucks, battery swapping stations, and mobile power banks. This module receives real-time location, remaining power, travel path, and time window information uploaded by mobile energy storage devices authorized by the user. The scheduling task generation module is used to generate mobile energy storage scheduling tasks based on grid electricity price signals, fixed energy storage state of charge and load forecast. The scheduling task includes target node, expected arrival time, charging and discharging power and charging and discharging amount. The dynamic power adjustment module is used to obtain the real-time location and estimated arrival time of mobile energy storage during the scheduling process, and dynamically adjust the charging and discharging power of fixed energy storage to smooth out the power gap. The credit score management module is used to prioritize mobile energy storage task assignments based on a credit score mechanism. The credit score is dynamically updated based on the on-time arrival rate and charging / discharging preparation efficiency of historical tasks. The route service module introduces route service constraints on the user's original travel path, allowing adjustments to the nodes along the route without exceeding the user's tolerance deviation, and sharing the revenue generated from the route scheduling with the user proportionally.

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