Energy storage configuration and scheduling method and device considering section power flow power fluctuation optimization and source network load storage cooperation

Abstract

The invention discloses an energy storage configuration and scheduling method and device considering section power flow power fluctuation optimization and source network load storage coordination, and belongs to the technical field of energy storage configuration and scheduling. The method comprises the steps that after a new energy output curve, a load curve and a power grid topological structure are obtained, a constructed double-layer mixed integer programming model is solved, and a new energy output curve is obtained; obtaining an energy storage rated capacity and an energy storage intra-day optimization scheduling curve; and then calculating a standard deviation of daily cross-section tide power fluctuation, if the standard deviation of the daily cross-section tide power fluctuation is greater than a preset threshold value, solving the double-layer mixed integer programming again until a preset convergence condition is met, obtaining an optimal energy storage rated capacity and an optimal intra-day optimal scheduling curve of energy storage, and performing energy storage configuration and scheduling according to the optimal energy storage rated capacity and the optimal intra-day optimal scheduling curve. According to the method, the reasonable configuration scheme is ensured by designing the optimization target and the constraint, the reasonable energy storage configuration capacity is ensured by reducing the economic weight of the energy storage capacity, and the redundancy or insufficiency of the capacity is avoided.

Description

Technical Field

[0001] The present invention relates to an energy storage configuration and scheduling method and device that considers cross-section tidal power fluctuation optimization and source-grid-load-storage coordination, belonging to the technical field of energy storage configuration and scheduling. Background Art

[0002] Photovoltaic and wind power generation are highly random and intermittent. Large-scale grid integration leads to increased peak-shaving pressure, node voltage offsets, load fluctuations, and difficulty accommodating renewable energy. However, the spatial and temporal mismatch between renewable energy output and load results in significant fluctuations in grid cross-section power flow, threatening grid security. Energy storage, with its ability to continuously adjust from -100% to 100% of rated power, can effectively enhance system peak-shaving capabilities. Following renewable energy output, it can smooth power fluctuations and track scheduled output, improving the certainty and predictability of renewable energy generation. In conjunction with system operations, it can implement peak-shaving and valley-filling, load tracking, frequency and voltage regulation, and power quality management, enhancing the system's own regulation capabilities. Deploying a certain scale of energy storage systems in areas with high concentrations of renewable energy can also effectively promote local renewable energy consumption and alleviate grid congestion. However, traditional energy storage deployment methods often focus on single scenarios (such as peak-shaving and valley-filling) without deep integration with grid power flow, resulting in irrational deployment plans. Furthermore, energy storage capacity planning is separated from scheduling strategies, lacking a closed loop of "configuration, dispatch, and verification." This makes it easy for capacity to be redundant or insufficient in actual projects. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and device for energy storage configuration and scheduling that takes into account the optimization of cross-section tidal power fluctuations and the coordination of source, grid, load and storage, so as to solve the problems of unreasonable configuration schemes, redundant and insufficient capacity in the prior art.

[0004] To achieve the above objectives, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides an energy storage configuration and scheduling method that considers cross-section power flow fluctuation optimization and source-grid-load-storage coordination, including:

[0006] Obtain new energy output curves, load curves and grid topology;

[0007] A two-level mixed integer programming model is constructed based on the acquired data to obtain the energy storage rated capacity and the daily optimization scheduling curve of the energy storage. The two-level mixed integer programming model includes an upper model and a lower model. The upper model is constructed with minimizing the power fluctuation of the power flow in the key sections of the power grid as the core goal, controlling the energy storage investment cost as the auxiliary goal, and the physical constraints of the energy storage system, the power grid security constraints, and the balance between new energy and load as constraints. The lower model is constructed with minimizing the standard deviation of the power flow fluctuation of the daily section as the goal, and the energy storage rated capacity constraint and the state of charge final value constraint as constraints.

[0008] Performing a power flow calculation based on the energy storage rated capacity and the energy storage's intraday optimization scheduling curve to obtain the standard deviation of the daily cross-sectional power fluctuation. If the standard deviation of the daily cross-sectional power fluctuation is greater than a preset threshold, reducing the energy storage capacity economic weight in the objective function of the upper-level model, and resolving the bi-level mixed integer programming until the preset convergence condition is met, thereby obtaining the optimal energy storage rated capacity and the optimal intraday optimization scheduling curve for energy storage;

[0009] Energy storage configuration and scheduling are carried out based on the optimal energy storage rated capacity and the optimal intraday optimization scheduling curve of energy storage.

[0010] Furthermore, the objective function of the upper model is expressed as:

[0011] ;

[0012] Among them, min means minimization, P line,t It represents the actual power flow of the section at time t, represents the average power of the section flow, λ represents the economic weight of the energy storage capacity, E ESS It represents the rated capacity of energy storage, and T represents the maximum time.

[0013] Furthermore, the physical constraints of the energy storage system include charge and discharge power limits, state of charge continuity constraints, and state of charge upper and lower limit constraints;

[0014] The charge and discharge power limit is expressed as:

[0015] −P discharge,max ≤P ESS,t ≤P charge,max ;

[0016] Among them, P ESS,t Represents the energy storage power at time t, P ESS,t A positive value indicates that the energy storage is being charged, P ESS,t A negative value indicates that the energy storage is discharging. discharge,max Indicates the maximum discharge power, P charge,max Indicates the maximum charging power;

[0017] The state of charge continuity constraint is expressed as:

[0018] ;

[0019] Among them, SOC t Indicates the state of charge at time t, SOC t-1 represents the state of charge at time t-1, η charge Represents the charging efficiency, η discharge Indicates discharge efficiency, P charge,t represents the charging power at time t, P discharge,t Indicates the discharge power at time t, E ESS represents the rated capacity of energy storage, and Δt represents the time interval;

[0020] The upper and lower limit constraints of the state of charge are expressed as:

[0021] SOC min ≤SOC t ≤SOC max ;

[0022] Among them, SOC min Indicates the minimum state of charge, SOC max Indicates the highest state of charge.

[0023] Furthermore, the power grid security constraints include a section power flow constraint not exceeding a thermal stability limit constraint and a node voltage deviation constraint;

[0024] The constraint that the cross-section power flow does not exceed the thermal stability limit is expressed as:

[0025] ∣P line,t ∣≤P linemax ;

[0026] Among them, P line,t represents the actual power flow of the section at time t, |*| represents the absolute value of *, P linemax Indicates the thermal stability limit power of the section;

[0027] The node voltage deviation constraint is expressed as:

[0028] V imin ≤V i,t ≤V imax ;

[0029] Among them, V i,t represents the voltage of key node i at time t, V imin Indicates the lower voltage limit of key node i, V imax Represents the upper voltage limit of key node i.

[0030] Furthermore, the balance between new energy and load is expressed as:

[0031] P PV,t +P Wind,t +P ESS,t =P Load,t +P loss,t +P grid,t ;

[0032] Among them, P PV,t represents the power generated by photovoltaic power at time t, P Wind,t represents the power generated by wind power at time t, P ESS,t Represents the energy storage power at time t, P Load,t represents the power load at time t, P loss,t represents the network loss at time t, P grid,t Indicates the power exchanged with the main network at time t.

[0033] Furthermore, the objective function of the lower layer model is expressed as:

[0034] ;

[0035] Among them, min means minimization, P line,t P represents the actual power flow of the section at time t, line,τ Indicates the actual power flow of the section at time τ.

[0036] Furthermore, the energy storage rated capacity constraint is expressed as:

[0037] 10h⋅∣P ESS,t ∣≥E ESS ≥4h⋅∣P ESS,t ∣;

[0038] Among them, E ESS Indicates the rated capacity of energy storage, P ESS,t It represents the energy storage power at time t, |*| represents the absolute value of *, and h represents hours.

[0039] Furthermore, the state of charge final value constraint is expressed as:

[0040] SOC 24 =SOC0;

[0041] Among them, SOC 24 It indicates the state of charge at the 24th hour of the day, and SOC0 indicates the state of charge at the 0th hour of the day.

[0042] Furthermore, the threshold is calculated by the following formula:

[0043] Threshold = k⋅P linemax ;

[0044] Where k represents the safety factor, Plinemax Indicates the thermal stability limit power of the section;

[0045] The convergence condition is that the reduction ratio of the daily cross-sectional tidal power fluctuation after a preset number of consecutive iterations is less than 1%.

[0046] In a second aspect, the present invention provides an energy storage configuration and scheduling method that considers cross-section power fluctuation optimization and source-grid-load-storage coordination, including:

[0047] The power grid data acquisition module is configured to: acquire the new energy output curve, load curve and power grid topology;

[0048] The two-level mixed integer programming model solving module is configured to solve the constructed two-level mixed integer programming model based on the acquired data to obtain the energy storage rated capacity and the daily optimal dispatch curve of the energy storage. The two-level mixed integer programming model includes an upper model and a lower model. The upper model is constructed with minimizing the power fluctuation of the power flow at the key section of the power grid as the core goal, controlling the energy storage investment cost as the auxiliary goal, and physical constraints of the energy storage system, power grid security constraints, and the balance between new energy and load as constraints. The lower model is constructed with minimizing the standard deviation of the power flow fluctuation of the daily section as the goal, and constraints on the energy storage rated capacity and the final value of the state of charge.

[0049] The iterative module is configured to: perform power flow calculation based on the rated capacity of the energy storage and the daily optimized dispatch curve of the energy storage to obtain the standard deviation of the daily cross-sectional power fluctuation; if the standard deviation of the daily cross-sectional power fluctuation is greater than a preset threshold, reduce the economic weight of the energy storage capacity in the objective function of the upper-level model, and re-solve the bi-level mixed integer programming until the preset convergence condition is met, thereby obtaining the optimal rated capacity of the energy storage and the optimal daily optimized dispatch curve of the energy storage;

[0050] The energy storage configuration and scheduling module is configured to: perform energy storage configuration and scheduling based on the optimal energy storage rated capacity and the optimal intra-day optimization scheduling curve of energy storage.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention provides a method and device for energy storage configuration and scheduling that considers optimization of cross-section power flow fluctuations and coordination of source, grid, load and storage. By designing the optimization objectives and constraints of the upper and lower models, the method improves grid security, reduces the risk of overload of transmission equipment, and ensures the rationality of the configuration plan. By verifying the standard deviation of daily cross-section power flow fluctuations, it achieves two-way verification of strategy-optimized grid simulation. If the verification fails, the economic weight of the energy storage capacity is reduced to ensure the rationality of the energy storage configuration capacity, avoiding capacity redundancy or shortage. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for energy storage configuration and scheduling that considers cross-section power fluctuation optimization and source-grid-load-storage coordination, as provided in Example 1;

[0054] Figure 2 This is a simulation diagram of the IEEE 9-node system provided in Example 2;

[0055] Figure 3 This is a schematic diagram of the energy storage charging and discharging optimization scheduling curve and the cross-section power flow fluctuation curve provided in Example 2. DETAILED DESCRIPTION

[0056] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] Example 1

[0058] like Figure 1 As shown, the present invention provides an energy storage configuration and scheduling method that considers cross-section power fluctuation optimization and source-grid-load-storage coordination, including:

[0059] Obtain new energy output curves, load curves and grid topology;

[0060] A two-level mixed integer programming model is constructed based on the acquired data to obtain the energy storage rated capacity and the daily optimization scheduling curve of the energy storage. The two-level mixed integer programming model includes an upper model and a lower model. The upper model is constructed with minimizing the power fluctuation of the power flow in the key sections of the power grid as the core goal, controlling the energy storage investment cost as the auxiliary goal, and the physical constraints of the energy storage system, the power grid security constraints, and the balance between new energy and load as constraints. The lower model is constructed with minimizing the standard deviation of the power flow fluctuation of the daily section as the goal, and the energy storage rated capacity constraint and the state of charge final value constraint as constraints.

[0061] Performing a power flow calculation based on the energy storage rated capacity and the energy storage's intraday optimization scheduling curve to obtain the standard deviation of the daily cross-sectional power fluctuation. If the standard deviation of the daily cross-sectional power fluctuation is greater than a preset threshold, reducing the energy storage capacity economic weight in the objective function of the upper-level model, and resolving the bi-level mixed integer programming until the preset convergence condition is met, thereby obtaining the optimal energy storage rated capacity and the optimal intraday optimization scheduling curve for energy storage;

[0062] Energy storage configuration and scheduling are carried out based on the optimal energy storage rated capacity and the optimal intraday optimization scheduling curve of energy storage.

[0063] The present invention improves grid security, reduces the risk of overload of transmission equipment, and ensures the rationality of configuration schemes by designing the optimization objectives and constraints of upper and lower models. By verifying the standard deviation of daily cross-section power fluctuations, the present invention realizes two-way verification of strategy-optimized grid simulation. If the verification fails, the economic weight of energy storage capacity is reduced to ensure the rationality of energy storage configuration capacity and avoid capacity redundancy or shortage.

[0064] Example 2

[0065] This embodiment provides an energy storage configuration and scheduling method that considers cross-section power flow fluctuation optimization and source-grid-load-storage coordination, including the following four stages:

[0066] Phase 1: Mathematical modeling of the problem.

[0067] First, the renewable energy output curve, load curve, grid topology, and section flow power constraints are input to build a grid benchmark flow model based on the power system simulation software BPA (Bonneville Power Administration), and key sections (such as transmission corridors and hub nodes) are marked.

[0068] Then, the objective function is defined: minimizing power fluctuations in key sections of the power grid is the core objective, controlling energy storage investment costs is the auxiliary objective, and the physical constraints of the energy storage system, grid security constraints, and the balance between new energy and load are the constraints. A multi-timescale comprehensive optimization model is constructed:

[0069] ;

[0070] Among them, min means minimization, P line,t It represents the actual power flow of the section at time t, represents the average power of the section flow, λ represents the economic weight of the energy storage capacity, C ESS Represents the investment cost of the energy storage system, that is, E ESS × energy storage unit price, E ESS represents the rated capacity of energy storage in MWh, T represents the maximum time, g(u,x) represents the solution constraints (physical constraints of energy storage system, grid security constraints and balance between new energy and load), u represents the uncertainty of the corresponding flexible resource in the system, and U represents the set of uncertainties.

[0071] The physical constraints of the energy storage system include charging and discharging power limits, state of charge continuity constraints, and state of charge upper and lower limit constraints.

[0072] The charge and discharge power limits are expressed as:

[0073] −P discharge,max ≤P ESS,t ≤P charge,max ;

[0074] Among them, P ESS,t Represents the energy storage power at time t, P ESS,t A positive value indicates that the energy storage is being charged, P ESS,t A negative value indicates that the energy storage is discharging. discharge,max Indicates the maximum discharge power in MW, P charge,max Indicates the maximum charging power in MW;

[0075] The state of charge continuity constraint is expressed as:

[0076] ;

[0077] Among them, SOC t Indicates the state of charge at time t, in %, SOC t-1 represents the state of charge at time t-1, η charge represents the charging efficiency, η discharge represents the discharge efficiency, η charge and η discharge The value range of P is 90%~95%. charge,t Indicates the charging power at time t, in MW, P discharge,t Indicates the discharge power at time t, in MW, E ESS It represents the rated capacity of energy storage in MWh, and Δt represents the time interval in hours;

[0078] The upper and lower limit constraints of the state of charge are expressed as:

[0079] SOC min ≤SOC t ≤SOC max ;

[0080] Among them, SOC min Indicates the minimum state of charge, SOC max Indicates the highest state of charge.

[0081] Grid security constraints include the section power flow not exceeding the thermal stability limit constraint and the node voltage deviation constraint;

[0082] The constraint that the cross-section power flow does not exceed the thermal stability limit is expressed as:

[0083] ∣P line,t ∣≤P linemax ;

[0084] Among them, P line,t It represents the actual power of the section at time t, in MW, |*| represents the absolute value of *, P linemax Indicates the thermal stability limit power of the section, in MW;

[0085] The node voltage deviation constraint is expressed as:

[0086] V imin ≤V i,t ≤V imax ;

[0087] Among them, V i,t Represents the voltage of key node i at time t, in kV, V imin Indicates the lower voltage limit of key node i, in kV, V imax Indicates the upper voltage limit of key node i, in kV.

[0088] The balance between new energy and load is expressed as:

[0089] P PV,t +P Wind,t +P ESS,t =P Load,t +P loss,t +P grid,t ;

[0090] Among them, P PV,t Indicates the power generated by photovoltaic power at time t, in MW, P Wind,t Indicates the power generated by wind power at time t, in MW, P ESS,t Indicates the energy storage power at time t, in MW, P Load,t Indicates the power load at time t, in MW, P loss,t Indicates the grid loss at time t, in MW, P grid,t Indicates the power exchanged with the main grid at time t, which can be positive or negative and is expressed in MW.

[0091] Phase 2: Optimize the model and use particle swarm optimization to solve it.

[0092] In order to take into account both long-time scale capacity planning and short-time scale scheduling, a two-level mixed integer programming model is adopted.

[0093] The multi-time-scale integrated optimization model constructed in Phase 1 is used as the upper-level model for optimizing the energy storage rated capacity. The energy storage rated capacity is used as the decision variable, the core objective is to minimize the power fluctuations at key sections of the power grid, and the auxiliary objective is to control the energy storage investment cost. The objective function is constructed based on the physical constraints of the energy storage system, the security constraints of the power grid, and the balance between new energy and load.

[0094] ;

[0095] After normalizing the energy storage system investment cost in the objective function, the objective function of the upper model is expressed as:

[0096] ;

[0097] Among them, min means minimization, P line,t It represents the actual power flow of the section at time t, represents the average power of the section flow, λ represents the economic weight of the energy storage capacity, E ESS It represents the rated capacity of energy storage, and T represents the maximum time, which is 24 hours.

[0098] Based on the upper-level model, the lower-level model for optimizing the energy storage intraday scheduling strategy uses the energy storage charging and discharging power as the decision variable, minimizes the standard deviation of the daily cross-sectional power fluctuation as the goal, and uses the energy storage rated capacity constraint and the state of charge terminal value constraint as constraints. The following objective function is constructed:

[0099] ;

[0100] Among them, min means minimization, P line,t P represents the actual power flow of the section at time t, line,τ Indicates the actual power flow of the section at time τ.

[0101] The energy storage rated capacity constraint is expressed as:

[0102] 10h⋅∣P ESS,t ∣≥E ESS ≥4h⋅∣P ESS,t ∣;

[0103] Among them, E ESS Indicates the rated capacity of energy storage, P ESS,t It represents the energy storage power at time t, |*| represents the absolute value of *, and h represents hours.

[0104] The energy storage rated capacity constraint can also be expressed as:

[0105] ∣P ESS,t ∣∈[0.1E ESS ,0.25E ESS ].

[0106] The final state of charge constraint is expressed as:

[0107] SOC 24 =SOC0;

[0108] Among them, SOC 24 It indicates the state of charge at the 24th hour of the day, and SOC0 indicates the state of charge at the 0th hour of the day.

[0109] In this embodiment, a particle swarm optimization algorithm is used to solve the constructed two-level mixed integer programming model to obtain the energy storage rated capacity and the energy storage daily optimization scheduling curve (a curve composed of the charge and discharge power of the energy storage at each moment of the day).

[0110] Phase 3: Closed-loop verification.

[0111] The energy storage rated capacity and the daily optimal dispatch curve of energy storage obtained by solving the two-level mixed integer programming model in stage two are embedded in the grid benchmark power flow model in stage one to perform power flow calculations and obtain the daily cross-sectional power flow fluctuation and its standard deviation.

[0112] A power flow calculation is performed based on the rated capacity of the energy storage and the intraday optimization scheduling curve of the energy storage to obtain the standard deviation of the daily cross-sectional power fluctuation. If the standard deviation of the daily cross-sectional power fluctuation is greater than a preset threshold, the economic weight of the energy storage capacity in the objective function of the upper-level model is reduced, and the two-level mixed integer programming is re-solved until the preset convergence conditions are met, thereby obtaining the optimal energy storage rated capacity and the optimal intraday optimization scheduling curve of the energy storage.

[0113] The threshold is calculated using the following formula:

[0114] Threshold = k⋅P linemax ;

[0115] Wherein, k represents the safety factor, which is 0.15 in this embodiment, that is, the allowable fluctuation range is 15% of the limit value, P linemax Indicates the thermal stability limit power of the section, in MW;

[0116] The convergence condition is that the reduction ratio of the daily cross-sectional tidal power fluctuation is less than 1% for three consecutive iterations.

[0117] Phase 4: Energy storage configuration and scheduling are carried out based on the optimal energy storage rated capacity and the optimal intraday optimization scheduling curve of energy storage.

[0118] In order to verify the technical effect of this embodiment, Figure 2 Taking the system shown as an example, Figure 2 The network topology of the IEEE 9-node system is as follows: generator nodes: G1 (node 1), G2 (node 2), G3 (node 3); load nodes: Load A (node 5), Load B (node 6), Load C (node 8); key section: select line 5 to line 7 (connecting the main grid and the load center); Figure 2 The numbers 1 to 7 represent lines 1 to 7 respectively.

[0119] Figure 2 The parameter settings of the IEEE 9-bus system are shown in Table 1, and the energy storage parameter settings are shown in Table 2.

[0120] Table 1 - IEEE 9-bus system parameters

[0121]

[0122] Table 2-Energy storage parameters

[0123]

[0124] Optimize the model and solve:

[0125] With the core objective of minimizing power fluctuations along the critical section of the power grid, Lines 5 to 7, a multi-timescale integrated optimization model was constructed. A two-level mixed integer programming model was used to preliminarily plan energy storage capacity and intraday scheduling strategies.

[0126] The objective function of the upper model is optimized as follows:

[0127] ;

[0128] Among them, P line5-7,t Indicates the actual power flow of the section from line 5 to line 7 at time t, Indicates the average power of the section flow from line 5 to line 7.

[0129] The objective function of the lower model is optimized as:

[0130] ;

[0131] Where, represents P line5-7,τ Indicates the actual power flow of the section from line 5 to line 7 at time τ.

[0132] A particle swarm optimization algorithm was used to solve the optimized two-level mixed integer programming model, resulting in the energy storage rated capacity and the optimal daily dispatch curve. This was then embedded into the IEEE 9-bus grid benchmark power flow model for power flow calculations, yielding actual fluctuation data (daily cross-sectional power flow fluctuation and its standard deviation) for lines 5 to 7. The standard deviation of the daily cross-sectional power flow fluctuation was calculated to be 19.8 MW.

[0133] The threshold value of the standard deviation of the daily cross-sectional tidal power fluctuation is k⋅P linemax =0.15×150=22.5MW. At this point, the standard deviation of the daily cross-sectional power fluctuation meets the threshold. After iteration, the power flow simulation results meet the convergence conditions. The energy storage rated capacity and the energy storage's optimal daily dispatch curve at this point are taken as the optimal energy storage rated capacity and the optimal daily dispatch curve, respectively.

[0134] The optimized configuration results are shown in Table 3:

[0135] Table 3 - Optimization configuration results

[0136]

[0137] At this time, the energy storage charging and discharging optimization scheduling curve and the cross-section power flow fluctuation curve are as follows: Figure 3 shown.

[0138] Example 3

[0139] Based on the same technical concept as Example 1, this embodiment provides an energy storage configuration and scheduling method that considers cross-section power flow fluctuation optimization and source-grid-load-storage coordination, including:

[0140] The power grid data acquisition module is configured to: acquire the new energy output curve, load curve and power grid topology;

[0141] The two-level mixed integer programming model solving module is configured to solve the constructed two-level mixed integer programming model based on the acquired data to obtain the energy storage rated capacity and the daily optimal dispatch curve of the energy storage. The two-level mixed integer programming model includes an upper model and a lower model. The upper model is constructed with minimizing the power fluctuation of the power flow at the key section of the power grid as the core goal, controlling the energy storage investment cost as the auxiliary goal, and physical constraints of the energy storage system, power grid security constraints, and the balance between new energy and load as constraints. The lower model is constructed with minimizing the standard deviation of the power flow fluctuation of the daily section as the goal, and constraints on the energy storage rated capacity and the final value of the state of charge.

[0142] The iterative module is configured to: perform power flow calculation based on the rated capacity of the energy storage and the daily optimized dispatch curve of the energy storage to obtain the standard deviation of the daily cross-sectional power fluctuation; if the standard deviation of the daily cross-sectional power fluctuation is greater than a preset threshold, reduce the economic weight of the energy storage capacity in the objective function of the upper-level model, and re-solve the bi-level mixed integer programming until the preset convergence condition is met, thereby obtaining the optimal rated capacity of the energy storage and the optimal daily optimized dispatch curve of the energy storage;

[0143] The energy storage configuration and scheduling module is configured to: perform energy storage configuration and scheduling based on the optimal energy storage rated capacity and the optimal intra-day optimization scheduling curve of energy storage.

[0144] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for energy storage configuration and scheduling that considers cross-section power fluctuation optimization and source-grid-load-storage coordination, characterized in that: include: Obtain new energy output curves, load curves and grid topology; A two-level mixed integer programming model is constructed based on the acquired data to obtain the energy storage rated capacity and the daily optimization scheduling curve of the energy storage. The two-level mixed integer programming model includes an upper model and a lower model. The upper model is constructed with minimizing the power fluctuation of the power flow in the key sections of the power grid as the core goal, controlling the energy storage investment cost as the auxiliary goal, and the physical constraints of the energy storage system, the power grid security constraints, and the balance between new energy and load as constraints. The lower model is constructed with minimizing the standard deviation of the power flow fluctuation of the daily section as the goal, and the energy storage rated capacity constraint and the state of charge final value constraint as constraints. Performing a power flow calculation based on the energy storage rated capacity and the energy storage's intraday optimization scheduling curve to obtain the standard deviation of the daily cross-sectional power fluctuation. If the standard deviation of the daily cross-sectional power fluctuation is greater than a preset threshold, reducing the energy storage capacity economic weight in the objective function of the upper-level model, and resolving the bi-level mixed integer programming until the preset convergence condition is met, thereby obtaining the optimal energy storage rated capacity and the optimal intraday optimization scheduling curve for energy storage; Energy storage configuration and scheduling are carried out based on the optimal energy storage rated capacity and the optimal intraday optimization scheduling curve of energy storage.

2. The energy storage configuration and scheduling method considering cross-section power fluctuation optimization and source-grid-load-storage coordination according to claim 1 is characterized in that: The objective function of the upper model is expressed as: ； Among them, min means minimization, P line,t It represents the actual power flow of the section at time t, represents the average power of the section flow, λ represents the economic weight of the energy storage capacity, E ESS It represents the rated capacity of energy storage, and T represents the maximum time.

3. The energy storage configuration and scheduling method considering cross-section power fluctuation optimization and source-grid-load-storage coordination according to claim 1 is characterized in that: The physical constraints of the energy storage system include charge and discharge power limits, state of charge continuity constraints, and state of charge upper and lower limit constraints; The charge and discharge power limit is expressed as: −P discharge,max ≤P ESS,t ≤P charge,max ; Among them, P ESS,t Represents the energy storage power at time t, P ESS,t A positive value indicates that the energy storage is being charged, P ESS,t A negative value indicates that the energy storage is discharging. discharge,max Indicates the maximum discharge power, P charge,max Indicates the maximum charging power; The state of charge continuity constraint is expressed as: ； Among them, SOC t Indicates the state of charge at time t, SOC t-1 represents the state of charge at time t-1, η charge represents the charging efficiency, η discharge Indicates discharge efficiency, P charge,t represents the charging power at time t, P discharge,t Indicates the discharge power at time t, E ESS represents the rated capacity of energy storage, and Δt represents the time interval; The upper and lower limit constraints of the state of charge are expressed as: SOC min ≤SOC t ≤SOC max ; Among them, SOC min Indicates the minimum state of charge, SOC max Indicates the highest state of charge.

4. The energy storage configuration and scheduling method considering cross-section power fluctuation optimization and source-grid-load-storage coordination according to claim 1 is characterized in that: The power grid security constraints include a section power flow constraint not exceeding a thermal stability limit constraint and a node voltage deviation constraint; The constraint that the cross-section power flow does not exceed the thermal stability limit is expressed as: ∣P line,t ∣≤P linemax ; Among them, P line,t represents the actual power flow of the section at time t, |*| represents the absolute value of *, P linemax Indicates the thermal stability limit power of the section; The node voltage deviation constraint is expressed as: In imin ≤V i,t ≤V imax ; Among them, V i,t represents the voltage of key node i at time t, V imin Indicates the lower voltage limit of key node i, V imax Represents the upper voltage limit of key node i.

5. The energy storage configuration and scheduling method considering cross-section power fluctuation optimization and source-grid-load-storage coordination according to claim 1 is characterized in that: The new energy and load balance is expressed as: P PV,t +P Wind,t +P ESS,t =P Load,t +P loss,t +P grid,t ; Among them, P PV,t represents the power generated by photovoltaic power at time t, P Wind,t represents the power generated by wind power at time t, P ESS,t Represents the energy storage power at time t, P Load,t represents the power load at time t, P loss,t represents the network loss at time t, P grid,t Indicates the power exchanged with the main network at time t.

6. The energy storage configuration and scheduling method considering cross-section power fluctuation optimization and source-grid-load-storage coordination according to claim 1 is characterized in that: The objective function of the lower model is expressed as: ； Among them, min means minimization, P line,t P represents the actual power flow of the section at time t, line,τ Indicates the actual power flow of the section at time τ.

7. The energy storage configuration and scheduling method considering cross-section power fluctuation optimization and source-grid-load-storage coordination according to claim 1 is characterized in that: The energy storage rated capacity constraint is expressed as: 10h⋅∣P ESS,t ∣≥E ESS ≥4h⋅∣P ESS,t ∣; Among them, E ESS Indicates the rated capacity of energy storage, P ESS,t It represents the energy storage power at time t, |*| represents the absolute value of *, and h represents hours.

8. The energy storage configuration and scheduling method considering cross-section power fluctuation optimization and source-grid-load-storage coordination according to claim 1 is characterized in that: The final state of charge constraint is expressed as: SOC 24 =SOC0; Among them, SOC 24 It indicates the state of charge at the 24th hour of the day, and SOC0 indicates the state of charge at the 0th hour of the day.

9. The energy storage configuration and scheduling method considering cross-section power fluctuation optimization and source-grid-load-storage coordination according to claim 1 is characterized in that: The threshold is calculated by the following formula: Threshold = k⋅P linemax ; Where k represents the safety factor, P linemax Indicates the thermal stability limit power of the section; The convergence condition is that the reduction ratio of the daily cross-sectional tidal power fluctuation after a preset number of consecutive iterations is less than 1%.

10. A method for energy storage configuration and scheduling that considers cross-section power fluctuation optimization and source-grid-load-storage coordination, characterized in that: include: The power grid data acquisition module is configured to: acquire the new energy output curve, load curve and power grid topology; The two-level mixed integer programming model solving module is configured to solve the constructed two-level mixed integer programming model based on the acquired data to obtain the energy storage rated capacity and the daily optimal dispatch curve of the energy storage. The two-level mixed integer programming model includes an upper model and a lower model. The upper model is constructed with minimizing the power fluctuation of the power flow at the key section of the power grid as the core goal, controlling the energy storage investment cost as the auxiliary goal, and physical constraints of the energy storage system, power grid security constraints, and the balance between new energy and load as constraints. The lower model is constructed with minimizing the standard deviation of the power flow fluctuation of the daily section as the goal, and constraints on the energy storage rated capacity and the final value of the state of charge. The iterative module is configured to: perform power flow calculation based on the rated capacity of the energy storage and the daily optimized dispatch curve of the energy storage to obtain the standard deviation of the daily cross-sectional power fluctuation; if the standard deviation of the daily cross-sectional power fluctuation is greater than a preset threshold, reduce the economic weight of the energy storage capacity in the objective function of the upper-level model, and re-solve the bi-level mixed integer programming until the preset convergence condition is met, thereby obtaining the optimal rated capacity of the energy storage and the optimal daily optimized dispatch curve of the energy storage; The energy storage configuration and scheduling module is configured to: perform energy storage configuration and scheduling based on the optimal energy storage rated capacity and the optimal intra-day optimization scheduling curve of energy storage.

Images