A multi-period optimal scheduling method and system based on a vertical stack block tower gravity energy storage system

Abstract

The application discloses a kind of based on vertical stack block tower gravity energy storage system Multi-period optimization scheduling method and system related to energy storage and power system operation optimization technical field, to solve the problem of mismatch between supply and demand caused by renewable energy fluctuation and the lack of energy storage compensation mechanism.The application includes obtaining photovoltaic output and load demand time series data, divided into several time periods according to the preset step length and calculate the net load of each time period;Determine the block tower structure parameters and operating parameters based on the target energy storage capacity and site constraints and establish the block tower energy-state model;With the net load of each time period as input scenario, a linear programming optimization scheduling model is constructed with the maximum unsatisfied load as the minimization target and solved to obtain the charge and discharge power scheduling scheme of each time period, and the stack block handling instructions and operation plan are generated according to the charge and discharge power scheduling scheme.The application effectively improves the power supply reliability and operation efficiency of the system without changing the hardware facilities.

Description

Technical Field

[0001] This invention relates to a multi-period optimization scheduling method and system based on a vertically stacked block tower gravity energy storage system, belonging to the field of energy storage and power system operation optimization technology. Background Technology

[0002] Traditional power systems use the power grid to plan and allocate power generation and consumption. However, load demand fluctuates over time (e.g., by the hour) and is affected by factors such as the type of electricity used and the number of users, resulting in dynamic changes in the supply and demand relationship over time.

[0003] As the pressure of carbon emissions from fossil fuel combustion increases, the proportion of renewable energy sources such as solar and wind power continues to rise. The output and load curves of these energy sources are often inconsistent and are affected by natural conditions such as sunshine, temperature, wind speed, and cloud cover, exhibiting intermittent characteristics and being difficult to control precisely. When the grid cannot fully absorb their output, it is often necessary to reduce the output (curtailment of electricity / solar power), resulting in energy loss.

[0004] Therefore, in scenarios with a high proportion of renewable energy access, supply and demand are often mismatched within the same cycle, and there is an urgent need to transfer surplus electricity to subsequent high-demand periods. Energy storage systems (ESS) have become an important supporting means.

[0005] Among existing energy storage technologies, chemical battery solutions can convert electrical energy into chemical energy for later use. However, their energy storage / output is highly dependent on the material system and faces constraints such as cost and engineering complexity in terms of large-scale and long-term energy storage, making it difficult to directly expand into grid-scale long-term energy storage solutions.

[0006] Gravity-related energy storage methods, such as pumped hydro storage, can utilize surplus electrical energy to raise the medium to a high level, and then use gravity to release it to drive generators when electricity is needed, demonstrating potential for long-term and large-capacity generation. Meanwhile, hydrogen energy, flywheel energy, and other energy storage pathways are also being researched for energy transfer on different time scales. However, these technologies are typically constrained by factors such as energy density, construction cost, scalability, and system efficiency, requiring a trade-off between cost and output capacity.

[0007] To reduce reliance on specific terrain and water resources and expand deployable scenarios, dry gravity energy storage, which utilizes the "lifting and lowering of solid heavy objects" to store potential energy, has emerged in recent years. Among them, the vertically stacked block energy storage scheme uses a crane / motor system to lift blocks and stack them in a tower structure to store gravitational potential energy. When demand exceeds supply, the blocks are lowered and electrical energy is recovered through regenerative braking and other methods to make up for the supply and demand gap.

[0008] However, such block tower energy storage systems are not only constrained by structural stability, accessibility, and transportation paths during engineering operation, but also require a series of discrete decisions to be made under the conditions of supply and demand fluctuations over multiple time periods, such as when to lift, to which layer / location to lift, when to release, and which block to release. If only simple rules or local decisions are relied upon, there may be situations where surplus energy is not effectively stored or cannot be released in time during critical periods to meet the load. Summary of the Invention

[0009] The purpose of this invention is to propose a multi-period optimization scheduling method and system based on a vertically stacked block tower gravity energy storage system, which aims to solve the problems of supply and demand mismatch caused by the power output fluctuation of renewable energy such as photovoltaics, the difficulty in effectively storing surplus electricity, and the inability to compensate in a timely manner during periods of power shortage.

[0010] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0011] In a first aspect, this invention proposes a multi-period optimized scheduling method based on a vertically stacked block tower gravity energy storage system, comprising:

[0012] Acquire time-series data of photovoltaic output and load demand, divide the time-series data into several time periods according to a preset step size, and calculate the net load of each time period.

[0013] Based on the target energy storage capacity and site constraints, the structural parameters and operating parameters of the block tower are determined, and an energy-state model of the block tower is established based on the structural parameters and operating parameters.

[0014] Based on the energy-state model, taking the net load of each time period as the input scenario, a mixed integer linear programming optimization scheduling model with the goal of minimizing the maximum unmet load during the planning period is constructed and solved to obtain the charging and discharging power scheduling scheme for each time period.

[0015] Based on the charging and discharging power scheduling scheme, stack block handling instructions and tower stacking plans are generated.

[0016] Furthermore, the net load is calculated using the following formula:

[0017]

[0018] In the formula, Let t be the net load for the t-th time period. Let t be the load demand in the t-th time period. For the photovoltaic output in the t-th time period; when The time indicates that there is a power shortage during that period. The time indicates that there is surplus electrical energy during that period.

[0019] Furthermore, the block tower structure parameters and operating parameters include the number of bottom grid points, the maximum number of layers, the block mass, the lifting efficiency, the lowering efficiency, and the initial stacking height; the number of bottom grid points is determined by dividing the bottom plane of the tower into a regular grid array, and the maximum number of layers is determined by discretizing the height direction into several layers.

[0020] Furthermore, the establishment of the block tower energy-state model based on the block tower structural parameters and operating parameters includes:

[0021] Establish state variables for the number of blocks in each layer, and decision variables for the lifting and lowering of blocks in each layer; by pre-calculating the energy required to lift a single block to each height layer and the energy that can be released when lowering it from each height layer, establish the constraint relationship between the number of block operations and the corresponding charging and discharging energy.

[0022] Establish value constraints, structural stability constraints, and operability constraints for the state variables and decision variables.

[0023] Furthermore, the value constraints of the state variables and decision variables represent the application of integer constraints to variables related to the number of blocks and non-negativity constraints to energy variables; the structural stability constraint represents that the number of blocks in any layer at any time does not exceed the number of blocks in the adjacent layer below it; the operability constraint represents that before performing a block take-out or block move operation on the k-th layer, the (k+1)-th layer needs to reserve empty spaces to meet the reconstruction requirements.

[0024] Furthermore, the construction and solution of the mixed-integer linear programming optimization scheduling model with the objective of minimizing the maximum unmet load includes:

[0025] By introducing variables of unmet load and curtailed light, a time-period supply-demand balance constraint is established. The objective function is constructed using the maximum value of unmet load in all time periods within the planning period. A mixed-integer linear programming solver is used to solve the objective function to obtain the optimal value for each time period.

[0026] Furthermore, the objective function introduces a scalar variable to represent the maximum unmet load in each time period during the planning period, and imposes a constraint on each time period that the unmet load is not greater than the scalar variable, thereby minimizing the maximum unmet load during the planning period.

[0027] Furthermore, the supply and demand balance constraints include:

[0028] Within each time period, the load demand, photovoltaic output, energy storage discharge, unmet load and curtailment amount satisfy the energy conservation relationship, and the unmet load and curtailment amount are non-negative.

[0029] Furthermore, the method also includes structural parameter optimization, which involves repeatedly constructing and solving the mixed integer linear programming optimization scheduling model by adjusting at least one of the structural parameters of the block tower, and determining the optimal scheme based on the optimal values ​​of the maximum unsatisfied load and the amount of abandoned light obtained from multiple solutions.

[0030] Secondly, this invention proposes a multi-period optimization scheduling system based on a vertically stacked block tower gravity energy storage system, including a data acquisition module, a parameter determination module, a modeling module, an optimization solution module, and an instruction generation module;

[0031] The data acquisition module is used to acquire time-series data of photovoltaic output and load demand, and divide the time-series data into several time periods according to a preset step size and calculate the net load of each time period.

[0032] The parameter determination module is used to determine the structural parameters and operating parameters of the block tower based on the target energy storage capacity and site constraints.

[0033] The modeling module is used to establish an energy-state model of the block tower based on the block tower structural parameters and operating parameters;

[0034] The optimization solution module is used to construct and solve a mixed integer linear programming optimization scheduling model based on the energy-state model, with the net load of each time period as the input scenario, aiming to minimize the maximum unmet load, so as to obtain the charging and discharging power scheduling scheme for each time period.

[0035] The instruction generation module is used to generate stack block handling instructions and tower stacking plans according to the charging and discharging power scheduling scheme.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] Compared to not configuring energy storage or simply using stacking / removal rules, this invention establishes a bulk energy-state model in the discrete time-discrete height layer dimension. It constructs an optimized scheduling model with the goal of minimizing the maximum unmet load during the planning period, transforming energy storage charging and discharging behavior from empirical rules to full-cycle global optimization. Given photovoltaic output and load curves, this method can significantly compress peak power shortages (reducing them to zero in some scenarios), while simultaneously improving bulk potential energy utilization, reducing curtailment, and extending the effective support time of energy storage, achieving a better match between photovoltaic output and load on the time scale.

[0038] This method, by introducing discrete structural constraints and global optimization objectives, overcomes the limitations of the simple stacking rules of traditional gravity energy storage. It can effectively reduce the peak load gap of off-grid or microgrid systems and improve power supply reliability and operating efficiency without changing the hardware facilities. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the vertically stacked block tower gravity energy storage system proposed in an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of the bridge crane and block tower arrangement proposed in an embodiment of the present invention;

[0041] Figure 3 This is a flowchart of a multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system proposed in an embodiment of the present invention;

[0042] Figure 4 This is a flowchart illustrating the multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system proposed in an embodiment of the present invention.

[0043] Figure 5 This is a schematic diagram of the load comparison curves under different scheduling strategies in embodiments of the present invention;

[0044] Figure 6 This is a schematic diagram illustrating the sensitivity analysis of the impact of the initial stacking height on the scheduling effect in an embodiment of the present invention.

[0045] Figure 1 The meanings of the reference numerals in the attached diagram are as follows: 1. Block tower; 2. Stacked blocks; 3. Bridge crane; 4. Photovoltaic array; 5. Power interface; 6. User load; 7. Control unit; 8. Ground / foundation. Detailed Implementation

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use.

[0047] Example 1:

[0048] This embodiment proposes a vertically stacked block tower gravity energy storage system, such as Figure 1 As shown, the system includes a block tower 1, stacked blocks 2, a bridge crane 3, a photovoltaic array 4, a power interface 5, a user load 6, a control unit 7, and a ground foundation 8.

[0049] The block tower 1 is a vertical frame structure fixed to the ground foundation 8. Its interior is divided into multiple storage compartments according to a grid to support the stacked blocks 2. The stacked blocks 2 are stacked vertically according to the grid at the bottom of the tower, and the mutual conversion of electrical energy and gravitational potential energy is achieved through changes in height.

[0050] A bridge crane 3 is positioned above the block tower 1 and is used to lift and stack blocks 2 between the multiple layers of the block tower 1. When lifting and lowering blocks, the motor generates electricity through regenerative braking; when lifting and raising blocks, it consumes electrical energy.

[0051] The photovoltaic array 4 is connected to the user load 6 and the bridge crane 3 via the control unit 7 and the power interface 5. The power interface 5 enables power conversion between the DC photovoltaic array, the AC load, and the crane drive.

[0052] Control unit 7 is a PLC controller, which is communicatively connected to bridge crane 3, power interface 5, and photovoltaic array 4. Control unit 7 monitors the power generation of photovoltaic array 4 and the power demand of user load 6 in real time: when the photovoltaic output exceeds the load demand, it controls bridge crane 3 to lift the block, storing the excess electrical energy as gravitational potential energy; when the photovoltaic output is insufficient, it controls bridge crane 3 to lower the block, converting the stored gravitational potential energy into electrical energy to supplement the power supply gap. This embodiment achieves the mutual conversion between electrical energy and gravitational potential energy through changes in block height and operates in coordination with the off-grid photovoltaic power supply system.

[0053] In this embodiment, as Figure 2 As shown, a bridge crane 3 is arranged above the block tower 1: it moves along two orthogonal directions to cover the grid at the bottom of the tower, the trolley moves in the plane, and the lifting device lifts or lowers the block in the vertical direction, thereby realizing access to the block of any tower column at any height, providing a physical basis for the modeling and execution of "blocks of any layer and any position can be lifted or lowered under the premise of satisfying structural constraints".

[0054] Example 2:

[0055] This embodiment, based on Embodiment 1, proposes a multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system, such as... Figure 3 and Figure 4 As shown, it includes:

[0056] S1: Data Acquisition and Structural Parameter Determination

[0057] In the target scenario, determine the scheduling cycle and time discretization method, obtain the photovoltaic output sequence and load demand sequence for each time period, and calculate the net load:

[0058] (1)

[0059] In the formula, Let t be the net load for the t-th time period. Let t be the load demand in the t-th time period. For the photovoltaic output in the t-th time period; when This indicates that there is a power shortage during that period, which needs to be compensated by the release of energy storage. The time indicates that there is surplus electrical energy available during that period, which can be used to charge the block.

[0060] Based on the target energy storage capacity, a basic model of gravitational potential energy and tower parameters is established:

[0061]

[0062]

[0063]

[0064] In the formula, Let be the gravitational potential energy of a single block at layer h, m be the mass of the block, and g be the gravitational acceleration. Single-story height; This represents the maximum number of stackable layers available for the tower. Maximum permissible building height; This represents the maximum number of blocks at the bottom layer. and These represent the number of grid points in the two horizontal directions at the bottom of the tower.

[0065] S2: Establishing an energy-state model for the block tower

[0066] Using "the number of blocks lifted / lowered at each level per time period" as the core decision variable, a constraint relationship is established between the number of block operations and the corresponding charging / discharging energy:

[0067]

[0068]

[0069] In the formula, To charge energy, This refers to the energy released during discharge. and These represent the number of blocks promoted and demoted at layer h, respectively, during time period t. and These are the energy parameters required to lift a single block at layer h and the energy released when it is lowered, respectively.

[0070] By establishing the energy relationship between photovoltaic output, direct power supply, energy storage discharge, and load demand, a supply-demand and energy storage interaction constraint is established:

[0071]

[0072] In the formula, and These represent the number of blocks at the current time and the previous time, respectively; to ensure structural stability, the number of blocks in the upper layer cannot exceed the number of blocks in the lower layer.

[0073]

[0074] Establish operability constraints: before performing block fetching or block shifting operations on the k-th layer, the (k+1)-th layer must reserve space to meet the reconstruction requirements and avoid "locking out":

[0075]

[0076] Constraint rules are established for variable values. By imposing integer constraints on variables related to the quantity of blocks and non-negativity constraints on energy variables, effective restrictions on variable values ​​are achieved.

[0077] S3: Optimizing the Scheduling Model Construction

[0078] Introducing the maximum unmet load scalar during the planning period And use it as the objective function to construct a minimization model:

[0079]

[0080]

[0081] In the formula, To minimize peak power shortages during different time periods and ensure supply-demand balance, a variable for the amount of curtailed solar power is introduced. Establish a balanced and interconnected energy relationship:

[0082]

[0083] In the formula This represents a time period. The total energy supply within the system. Through a non-negative trade-off between the two, a mixed-integer linear programming optimization scheduling model is formed with the objective of "minimizing the maximum unmet load".

[0084] S4: Model Solving, Instruction Generation, and Configuration Optimization

[0085] The mixed-integer linear programming optimization scheduling model described above is solved using a mixed-integer linear programming solver to obtain the optimal values ​​for each time period. and .

[0086] Based on the optimal solution, calculate the operational performance evaluation indicators for the planning period:

[0087]

[0088]

[0089] In the formula and These represent the cumulative unmet load and cumulative curtailed solar power during the planning period, respectively. and This is the sequence of optimal solutions; For time step.

[0090] Subsequently, the above sequence is converted into specific pick-up and drop-off operation instructions for the crane in chronological order. Further changes are made to structural parameters such as the number of grid points on the bottom of the tower and the maximum number of layers. Steps S1 to S4 are repeated to complete the system collaborative selection and optimization verification.

[0091] S5: Instruction Generation

[0092] Based on the optimal configuration of S4, stack block handling instructions and tower stacking plans are generated, and the handling instructions are converted into crane pick-up and drop operation instructions according to the time sequence.

[0093] Example 3:

[0094] Based on Example 2, this embodiment provides a specific implementation scheme using an off-grid photovoltaic-block gravity energy storage power supply system as an example:

[0095] The system is configured with a 5kVA inverter and 19 photovoltaic modules with a rated power of approximately 210Wp. Using hourly power generation and consumption data of a household with a 1-hour resolution and scaled proportionally, the total photovoltaic power generation and total load power consumption within 24 hours are both approximately 30kWh. Among them, there is a surplus of approximately 1855Wh of electricity during the 14 hours of daytime and a shortfall of approximately 1262Wh of electricity during the 20 hours of nighttime, which reflects the typical characteristics of "total balance but time mismatch".

[0096] In this scenario, a vertically stacked block energy storage tower is configured: the tower base has a 5×5 regular grid, the maximum stacking height is 50 layers, the block mass is about 1000kg, the crane lifting efficiency is taken as 0.8, and the lowering and recovery efficiency is taken as 0.7. Assuming that the initial full-layer height is 21 layers, the parameters are substituted into the model in steps S1 to S4 and solved to obtain the optimal lifting / lowering sequence for 24 time periods. The simulation results show that the unmet load in each time period of the day can be compressed to 0, achieving 24-hour power-free operation.

[0097] To verify the advantages over long timescales, the tower was enlarged to 10×10×50 mm, and the scheduling cycle was extended to one week (168 hours). When only simple stacking / removal rules were used for control, the load fluctuation within one week could reach approximately 2000 Wh, and from the 121st hour onwards, it gradually approached a "no storage" scenario. Figure 5 As shown; by employing the optimization scheduling model of this invention with the goal of "minimizing the maximum unmet load" and reasonably setting the initial full-floor height I, the maximum unmet load within a week can be compressed to 0; sensitivity analysis shows that for a 10×10×50 tower, a zero unmet load effect can be achieved when I ≥ 22 floors, as shown. Figure 6 As shown.

[0098] Example 4:

[0099] Based on Embodiment 2, this embodiment proposes a multi-period optimization scheduling system for a vertically stacked block tower gravity energy storage system, including a data acquisition module, a parameter determination module, a modeling module, an optimization solution module, and an instruction generation module.

[0100] The data acquisition module is used to acquire time-series data of photovoltaic power output and load demand, and divide the time-series data into several time periods according to a preset step size and calculate the net load of each time period.

[0101] The parameter determination module is used to determine the structural and operational parameters of the bulk tower based on the target energy storage capacity and site constraints.

[0102] The modeling module is used to establish an energy-state model of a block tower based on its structural and operational parameters.

[0103] The optimization solution module is used to construct and solve a mixed integer linear programming optimization scheduling model based on the energy-state model, with the net load of each time period as the input scenario, and the maximum unmet load as the minimization objective, to obtain the charging and discharging power scheduling scheme for each time period.

[0104] The instruction generation module is used to generate stack block handling instructions and tower stacking plans based on the charging and discharging power scheduling scheme.

[0105] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only used to explain the relative positional relationship and movement between components in a specific orientation. If the specific orientation changes, the directional indication will also change accordingly. These terms are used only for the convenience of describing this application and for simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0106] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0107] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art will understand the specific meaning of the above terms in this application based on the specific circumstances.

[0108] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A multi-period optimization scheduling method based on a vertically stacked block tower gravity energy storage system, characterized in that, include: Acquire time-series data of photovoltaic output and load demand, divide the time-series data into several time periods according to a preset step size, and calculate the net load of each time period. Based on the target energy storage capacity and site constraints, the structural parameters and operating parameters of the block tower are determined, and an energy-state model of the block tower is established based on the structural parameters and operating parameters. Based on the energy-state model, taking the net load of each time period as the input scenario, a mixed integer linear programming optimization scheduling model with the goal of minimizing the maximum unmet load during the planning period is constructed and solved to obtain the charging and discharging power scheduling scheme for each time period. Based on the charging and discharging power scheduling scheme, stack block handling instructions and tower stacking plans are generated.

2. The multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system according to claim 1, characterized in that, The net load is calculated using the following formula: In the formula, Let t be the net load for the t-th time period. Let t be the load demand in the t-th time period. For the photovoltaic output in the t-th time period; when The time indicates that there is a power shortage during that period. The time indicates that there is surplus electrical energy during that period.

3. The multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system according to claim 1, characterized in that, The block tower structure parameters and operating parameters include the number of grid points on the bottom surface, the maximum number of layers, the block mass, the lifting efficiency, the lowering efficiency, and the initial stacking height; the number of grid points on the bottom surface is determined by dividing the bottom plane of the tower into a regular grid array, and the maximum number of layers is determined by discretizing the height direction into several layers.

4. The multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system according to claim 1, characterized in that, The establishment of the block tower energy-state model based on the block tower structural parameters and operating parameters includes: Establish state variables for the number of blocks in each layer, and decision variables for the lifting and lowering of blocks in each layer; establish the constraint relationship between the number of blocks to be operated and the corresponding charging and discharging energy by pre-calculating the energy required to lift a single block to each height layer and the energy that can be released when lowering it from each height layer; establish the value constraints, structural stability constraints and operability constraints for the state variables and decision variables.

5. The multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system according to claim 4, characterized in that, The constraints on the values ​​of the state variables and decision variables represent the application of integer constraints to variables related to the number of blocks and non-negativity constraints to energy variables; the structural stability constraint represents that the number of blocks in any layer at any time does not exceed the number of blocks in the adjacent layer below it; the operability constraint represents that before performing a block take-out or block move operation on the k-th layer, the (k+1)-th layer must reserve empty spaces to meet the reconstruction requirements.

6. The multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system according to claim 1, characterized in that, The construction and solution of the mixed-integer linear programming optimization scheduling model with the objective of minimizing the maximum unsatisfied load includes: By introducing variables of unmet load and curtailed light, a time-period supply-demand balance constraint is established. The objective function is constructed using the maximum value of unmet load in all time periods within the planning period. A mixed-integer linear programming solver is used to solve the objective function to obtain the optimal value for each time period.

7. The multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system according to claim 6, characterized in that, The objective function introduces a scalar variable to represent the maximum unmet load in each time period during the planning period, and imposes a constraint on each time period that the unmet load is not greater than the scalar variable, thereby minimizing the maximum unmet load during the planning period.

8. The multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system according to claim 6, characterized in that, The supply and demand balance constraints include: Within each time period, the load demand, photovoltaic output, energy storage discharge, unmet load and curtailment amount satisfy the energy conservation relationship, and the unmet load and curtailment amount are non-negative.

9. The multi-period optimization scheduling method for a vertically stacked block tower gravity energy storage system according to claim 6, characterized in that, The method also includes structural parameter optimization, which involves repeatedly constructing and solving the mixed integer linear programming optimization scheduling model by adjusting at least one of the structural parameters of the block tower, and determining the optimal scheme based on the optimal values ​​of the maximum unmet load and the amount of abandoned light obtained from multiple solutions.

10. A multi-period optimized scheduling system based on a vertically stacked block tower gravity energy storage system, characterized in that, The system is configured to perform the method described in any one of claims 1 to 9, comprising a data acquisition module, a parameter determination module, a modeling module, an optimization solution module, and an instruction generation module; The data acquisition module is used to acquire time-series data of photovoltaic output and load demand, and divide the time-series data into several time periods according to a preset step size and calculate the net load of each time period. The parameter determination module is used to determine the structural parameters and operating parameters of the block tower based on the target energy storage capacity and site constraints. The modeling module is used to establish an energy-state model of the block tower based on the block tower structural parameters and operating parameters; The optimization solution module is used to construct and solve a mixed integer linear programming optimization scheduling model based on the energy-state model, with the net load of each time period as the input scenario, aiming to minimize the maximum unmet load, so as to obtain the charging and discharging power scheduling scheme for each time period. The instruction generation module is used to generate stack block handling instructions and tower stacking plans according to the charging and discharging power scheduling scheme.

Images