Multi-time scale source network load storage scheduling method and scheduling system containing all-vanadium-lithium battery hybrid energy storage

Abstract

The invention provides a multi-time scale source network load storage coordination day-ahead and intra-day scheduling method considering participation of a hybrid energy storage system composed of an all-vanadium redox flow battery and a lithium ion battery. The method comprises the following steps: determining a conventional unit start-stop plan, a time-of-use electricity price and a class-A IDR load calling plan in day-ahead scheduling; using the determined conventional unit start-stop plan, PDR response quantity and A-class IDR load response quantity as input quantities for intra-day scheduling, and determining an output plan of each unit, lithium ion energy storage charge-discharge quantity, all-vanadium liquid flow energy storage charge-discharge quantity, a calling plan of B-class IDR load and a final spinning reserve plan; and finally, through source network load storage multi-time scale coordination, in combination with prediction information obtained from day-ahead and intra-day scheduling models and characteristics related to the time scale, a more accurate scheduling plan is made, and the plan comprises determination of the spinning reserve capacity used by the system. According to the invention, a better method of source network load storage coordinated scheduling in day-ahead and intra-day scheduling is further studied, and finer modeling is carried out on the hybrid energy storage system.

Description

Technical Field

[0001] This invention relates to day-ahead and day-intraday scheduling, and more particularly to a day-ahead and day-intraday scheduling method for multi-timescale source-grid-load-storage coordination involving a hybrid energy storage system composed of vanadium redox flow batteries and lithium-ion batteries. Background Technology

[0002] Hybrid energy storage systems have attracted widespread attention and in-depth research due to their advantages such as good performance under varying operating conditions and ease of control. Among them, hybrid energy storage systems composed of vanadium redox flow batteries and lithium-ion batteries, especially those with complementary characteristics of the two types of batteries, show great potential. However, there is still a lack of hybrid energy storage system models that can aggregate and schedule both. Furthermore, in optimization scheduling models, energy storage systems often only consider charge and discharge constraints and state of charge constraints, rarely discussing lifetime and cost models, thus neglecting the cost of accessing energy storage resources.

[0003] Meanwhile, there are still some shortcomings in the research on the source-grid-load-storage optimal scheduling model. For example, in addition to the uncertainty of new energy output, the uncertainty of demand-side response will also affect the accuracy of day-ahead scheduling plans; and the research on the "source-grid-load-storage" scheduling model is limited to the day-ahead scheduling of the power grid, without considering the power system's ability to cope with abnormal situations and emergencies, which is not conducive to improving the stability and security of the system.

[0004] The proposed multi-timescale source-grid-load-storage (PGS) scheduling method, which considers the participation of hybrid energy storage systems composed of vanadium redox flow batteries and lithium-ion batteries, employs a stochastic programming multi-timescale scheduling model to achieve day-ahead and day-intraday scheduling plans. By leveraging the energy storage characteristics of lithium-ion and PPS, and comprehensively considering the time-scale characteristics of conventional generating units, wind turbines, energy storage power stations, and demand response loads, a multi-timescale PPS scheduling model is established with the goal of minimizing operating costs. This model not only establishes a hybrid energy storage system model that considers the inherent characteristics of PPS and lithium-ion batteries, but also establishes a day-ahead and day-intraday PPS optimization scheduling model based on different demand-side response types. This provides a new approach to day-ahead and day-intraday optimization scheduling methods for novel power systems that consider both energy storage and demand-side response. Summary of the Invention

[0005] The purpose of this invention is to extend the research on the source-grid-load-storage optimization scheduling model to the scope of day-ahead and intraday coordinated scheduling, and to flexibly combine vanadium redox flow batteries and lithium-ion batteries, considering their respective characteristics, to establish a hybrid energy storage system model, and to provide a multi-timescale source-grid-load-storage day-ahead and intraday coordinated scheduling method that takes into account the participation of a hybrid energy storage system composed of vanadium redox flow batteries and lithium-ion batteries.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A multi-timescale source-grid-load-storage day-ahead intraday coordination and scheduling method involving a hybrid energy storage system composed of vanadium redox flow batteries and lithium-ion batteries includes the following steps:

[0008] In the daytime dispatch, the start-up and shutdown plans of conventional generating units, time-of-use pricing, and Class A IDR load dispatch plans are determined. These plans are used to optimize the economics of system operation and improve the overall utilization of renewable energy, and to provide basic inputs for intraday dispatch.

[0009] The established start-up and shutdown plans for conventional units, PDR response quantities, and Class A IDR load response quantities are used as input quantities in intraday scheduling to determine the output plans for each unit, lithium-ion energy storage charging and discharging quantities, vanadium redox flow storage charging and discharging quantities, Class B IDR load call plans, and the final spinning reserve plans. These plans are used to further optimize system operation and ensure the stability and reliability of the system.

[0010] By coordinating multiple time scales of energy sources, grids, loads, and storage, and combining forecast information obtained from day-ahead and intraday scheduling models (including forecasts of new energy output and loads) with time-scale-related characteristics (such as equipment start-up and shutdown characteristics and energy storage system charging and discharging characteristics), a scheduling plan is formulated.

[0011] According to the established dispatch plan, the operating status of the power system is monitored in real time, including the output of each unit, the charging and discharging status of the energy storage system, and the actual demand of the load.

[0012] Based on real-time monitoring data, adjust the output of each generating unit, the charging and discharging plan of the energy storage system, and the dispatch plan of the demand response load to ensure the stable operation and economic dispatch of the power system.

[0013] When necessary, the allocation of spinning reserve capacity will be dynamically adjusted based on the actual operation of the system and forecast information to cope with possible abnormal situations and emergencies, and to ensure the safety and reliability of the power system.

[0014] The day-ahead scheduling includes establishing an optimization model for the operation of a hybrid energy storage system, the specific mathematical expression of which is as follows:

[0015] f1 = min(f2 + f3)

[0016]

[0017] In the formula, f1 is the objective function, representing the maximization of energy storage cycle life and economic benefits; f2 is a sub-objective function representing energy storage cycle life; f3 is a sub-objective function representing economic benefits; P1 is the charge / discharge power of the lithium-ion energy storage battery; P2 is the charge / discharge power of the vanadium redox flow battery; S SOC,1η1 represents the state of charge of the lithium-ion energy storage battery; η2 represents the efficiency of the lithium-ion energy storage battery; Δt represents the time step; C bat This refers to the rated capacity of the lithium-ion battery.

[0018] The constraints of the hybrid energy storage model are:

[0019]

[0020] In the formula, P1 cha P1 dis , These are the charge / discharge power limits of lithium-ion batteries and vanadium redox flow batteries, respectively; S SOC,1,min S SOC,1,max S SOC,2,min S SOC,2,max These are the upper and lower limits of the state of charge for lithium-ion batteries and vanadium redox flow batteries, respectively.

[0021] The day-ahead scheduling is achieved by establishing a day-ahead scheduling model with the goal of minimizing the total system operating cost. The total operating cost objective function F1 of this model includes at least one of the following: the operating cost of conventional units, the operating cost of renewable energy units, the charging, discharging and maintenance costs of hybrid energy storage systems, and the penalty cost incurred by wind or solar curtailment.

[0022] The specific expression is as follows:

[0023]

[0024] In the formula: F1 is the objective function of the day-ahead scheduling optimization model; C G,t C erss,t C DG,t C load,t Cost functions for conventional generating units, hybrid energy storage power stations, new energy generating units, and user loads, respectively; N s P represents the number of scenes; s Let N be the probability coefficient for the s-th scenario; G The number of conventional generating units; P Di,t,s Let a be the power generation of conventional unit i at time t in scenario s; i b i c i These are the power generation cost coefficients for conventional unit i; S i U is the start-up and shutdown cost coefficient for conventional unit i; Gi,t This represents the start / stop status of conventional unit i at time t, where 1 indicates start and 0 indicates stop; N erss P represents the number of energy storage power stations. erssi,t,s For the output power of energy storage power station i at time t in scenario s; C(P erssi,t,s Let W(P) be the cost function of energy storage power station i.erssi,t,s Let N be the maintenance cost function of energy storage power station i; DG For the number of new energy generating units; P DGi,t,s For the output of the new energy unit i at time t in scenario s; C(P DGi,t,s Let U be the cost function of the new energy unit i at time t in scenario s; DGi,t,s For the start-stop state of new energy unit i, k DG,c This represents the cost coefficient for wind curtailment penalties. To contribute to the prediction of new energy at time t under scenario s; k IDRA,s k IDRB,s Cost coefficients for IDRs of type A and type B, respectively; Δ|P IDRA,t, | is the force exerted by class A IDR at time t; Δ|P IDRB,t |Contribute to the B-type IDR in the s scenario at time t.

[0025] The current scheduling model's constraints include constraints on each device itself and system transmission line capacity constraints, as detailed below:

[0026] The following power balance constraints must be met:

[0027]

[0028] In the formula: P load,t Let ΔP be the day-ahead forecast of the load at time t; PDR,t Let ΔP be the amount of PDR load called at time t. IDRA,t Let ΔP be the number of calls made by class A IDR load at time t; IDRB,t,s This represents the number of calls to the B-type IDR load in scenario s at time t.

[0029] And satisfy the following constraints:

[0030] Output constraints of conventional generating units:

[0031]

[0032] In the formula, u i,t This represents the start / stop status of a conventional unit i at time t, where 1 indicates operation and 0 indicates shutdown. and These are the upper and lower limits of the output of conventional unit i, respectively;

[0033] Unit ramping constraints:

[0034]

[0035] In the formula R i The ramp rate is for conventional unit i.

[0036] Constraints on the output of distributed renewable energy sources:

[0037]

[0038] The maximum output of new energy power generation should be less than the predicted value at that moment.

[0039] Energy storage power station operation constraints.

[0040] Energy storage battery charge / discharge power constraints and charge constraints:

[0041]

[0042] In the formula: and These are the rated charging power and rated discharging power of the energy storage power station i, respectively; S SOCi State of charge (S) of the energy storage power station; upper and lower limits; S SOCimax S SOCimin These represent the upper and lower limits of the i-state of charge of the energy storage power station.

[0043] Transmission power constraints of transmission lines:

[0044]

[0045] In the formula: B represents the maximum transmission capacity of the transmission line between nodes i and j. ij θ is the susceptance between nodes i and j; i,t,s ,θ j,t,s Let i and j be the phase angles at time t in scene s.

[0046] Adjusting constraints for each scenario:

[0047]

[0048] In the formula: P Gi,t,bs and P erssi,t,bs ψ represents the baseline output values ​​for conventional unit i and energy storage power station i, respectively; i and ψ erss These refer to the flexible adjustment capabilities of conventional unit i and energy storage power station i, respectively.

[0049] Constraints on various DR resources:

[0050]

[0051] In the formula: and These are the upper and lower limits of the PDR load call volume, respectively; and These represent the maximum call volume for IDR loads of types A and B, respectively.

[0052] The intraday scheduling is achieved by establishing and solving an intraday optimal scheduling model. This model uses the regular unit start-up and shutdown plans, PDR response quantities, and Class A IDR load call status determined by the day-ahead scheduling as fixed boundary conditions. The specific mathematical expression is as follows:

[0053]

[0054] In the formula: F2 is the intraday scheduling objective function; ΔT is the duration of one intraday scheduling cycle; t0 is the initial time of the current scheduling period; For system spin-off backup cost; k R,G This is the rotating reserve cost coefficient for conventional generating units; These represent the positive / negative spinning reserve capacity of conventional unit i, respectively.

[0055] The intraday optimized scheduling model includes confidence constraints on the system's positive and negative spinning reserve capacity, and the mathematical expression is as follows:

[0056]

[0057] In the formula: Pr{} is the confidence expression; α and β are the confidence levels for satisfying positive and negative spin-off reserve capacity, respectively, with a value of 0.95.

[0058] The final output of the method is a specific start-up, shutdown, charging / discharging, and dispatch plan that covers multiple time scales and is accurate to each conventional unit, hybrid energy storage unit, and various demand response loads, based on the solution results of the day-ahead and intraday optimization scheduling model.

[0059] A multi-timescale source-grid-load-storage coordination day-ahead intraday scheduling system includes:

[0060] The processing module is used to determine the start-up and shutdown plans of conventional generating units, time-of-use pricing, and Class A IDR load dispatch plans in day-ahead scheduling;

[0061] The input module is used to input the determined conventional unit start-up and shutdown plans, PDR response quantities, and Class A IDR load response quantities into the intraday scheduling.

[0062] The determination module is used to determine the output plan of each unit, the charging and discharging capacity of lithium-ion energy storage, the charging and discharging capacity of vanadium redox flow storage, the call-up plan of Class B IDR load, and the final spinning reserve plan.

[0063] The coordination module is used to develop a more accurate scheduling plan by coordinating the source, grid, load, and storage across multiple time scales, combining forecast information obtained from day-ahead and intraday scheduling models with time-scale-related characteristics. This plan includes determining the system's use of spinning reserve capacity.

[0064] The monitoring module is used to monitor the operating status of the power system in real time according to the established scheduling plan, including the output of each unit, the charging and discharging status of the energy storage system, and the actual load demand.

[0065] The adjustment module is used to adjust the output of each unit, the charging and discharging plan of the energy storage system, and the dispatch plan of the demand response load based on real-time monitoring data, so as to ensure the stable operation and economic dispatch of the power system.

[0066] The dynamic adjustment module is used to dynamically adjust the allocation of spinning reserve capacity when necessary, based on the actual operation of the system and forecast information, in order to cope with possible abnormal situations and emergencies and ensure the safety and reliability of the power system.

[0067] The day-ahead scheduling handled by the processing module includes establishing an optimization model for the operation of a hybrid energy storage system. The specific mathematical expression of this model is as follows:

[0068] f1 = min(f2 + f3)

[0069]

[0070] In the formula, f1 is the objective function, representing the maximization of energy storage cycle life and economic benefits; f2 is a sub-objective function representing energy storage cycle life; f3 is a sub-objective function representing economic benefits; P1 is the charge / discharge power of the lithium-ion energy storage battery; P2 is the charge / discharge power of the vanadium redox flow battery; S SOC,1 η1 represents the state of charge of the lithium-ion energy storage battery; η2 represents the efficiency of the lithium-ion energy storage battery; Δt represents the time step; C bat This refers to the rated capacity of the lithium-ion battery.

[0071] The constraints of the hybrid energy storage model are:

[0072]

[0073] In the formula, P1 cha P1 dis , These are the charge / discharge power limits of lithium-ion batteries and vanadium redox flow batteries, respectively; S SOC,1,min S SOC,1,max S SOC,2,min S SOC,2,max These are the upper and lower limits of the state of charge for lithium-ion batteries and vanadium redox flow batteries, respectively.

[0074] The day-ahead scheduling of the processing module is achieved by establishing a day-ahead scheduling model with the goal of minimizing the total system operating cost. The total operating cost objective function F1 of this model includes at least one of the following: the operating cost of conventional units, the operating cost of renewable energy units, the charging, discharging and maintenance costs of hybrid energy storage systems, and the penalty cost incurred by wind or solar curtailment.

[0075] The specific expression is as follows:

[0076]

[0077] In the formula: F1 is the objective function of the day-ahead scheduling optimization model; C G,t C erss,t C DG,t C load,t Cost functions for conventional generating units, hybrid energy storage power stations, new energy generating units, and user loads, respectively; N s P represents the number of scenes; s Let N be the probability coefficient for the s-th scenario; G The number of conventional generating units; P Di,t,s Let a be the power generation of conventional unit i at time t in scenario s; i b i c i These are the power generation cost coefficients for conventional unit i; S i U is the start-up and shutdown cost coefficient for conventional unit i; Gi,t This represents the start / stop status of conventional unit i at time t, where 1 indicates start and 0 indicates stop; N erss P represents the number of energy storage power stations. erssi,t,s For the output power of energy storage power station i at time t in scenario s; C(P erssi,t,s Let W(P) be the cost function of energy storage power station i. erssi,t,s Let N be the maintenance cost function of energy storage power station i; DG For the number of new energy generating units; P DGi,t,s For the output of the new energy unit i at time t in scenario s; C(P DGi,t,s Let U be the cost function of the new energy unit i at time t in scenario s; DGi,t,s For the start-stop state of new energy unit i, k DG,c This represents the cost coefficient for wind curtailment penalties. To contribute to the prediction of new energy at time t under scenario s; k IDRA,s k IDRB,s Cost coefficients for IDRs of type A and type B, respectively; Δ|P IDRA,t, | is the force exerted by class A IDR at time t; Δ|P IDRB,t |Contribute to the B-type IDR in the s scenario at time t.

[0078] A computer-readable storage medium storing computer program instructions that, when executed by a processor, cause the processor to perform any of the preceding scheduling methods.

[0079] An electronic device includes one or more processors and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, configure the device to perform any of the preceding scheduling methods.

[0080] Compared with the prior art, the present invention has the following advantages:

[0081] This invention presents a multi-timescale source-grid-load-storage coordinated day-ahead and intraday scheduling method based on a hybrid energy storage system composed of vanadium redox flow batteries and lithium-ion batteries. By comprehensively considering the characteristics of both vanadium redox flow batteries and lithium-ion batteries, and aiming to maximize energy storage cycle life and economic benefits, an optimization model is established for the hybrid energy storage system's participation in optimized scheduling. Furthermore, by comprehensively considering different types of demand-side response, the multi-timescale characteristics of source, grid, load, and storage resources, and the requirements of day-ahead and intraday scheduling, a multi-timescale day-ahead and intraday scheduling method suitable for new power systems is obtained. Numerical examples verify the effectiveness of the proposed multi-timescale source-grid-load-storage coordinated day-ahead and intraday scheduling method, which can be applied to multi-timescale day-ahead and intraday scheduling scenarios involving energy storage and demand-side response. As an exploration of multi-timescale day-ahead and intraday scheduling methods under the background of new power systems, this invention can be further used in the future to conduct more refined modeling of hybrid energy storage systems and day-ahead and intraday optimized scheduling models using the theoretical methods proposed in this invention. Attached Figure Description

[0082] Figure 1 This is a flowchart illustrating the invention process of this invention.

[0083] Figure 2 This is a schematic diagram of the multi-timescale scheduling framework of the present invention.

[0084] Figure 3 To improve the topology of the IEEE-30 node system.

[0085] Figure 4 This is a diagram illustrating the resource allocation status of Fengzhengfeng.

[0086] Figure 5 A schematic diagram illustrating the resource allocation for wind-induced peak flow.

[0087] Figure 6 This is a schematic diagram illustrating the scheduling of various DR resources at Fengzhengfeng.

[0088] Figure 7A schematic diagram illustrating the scheduling of various DR resources during wind-induced peak conditions.

[0089] Figure 8 This is a schematic diagram of the working status of the hybrid energy storage system at different times during the wind-driven peak.

[0090] Figure 9 This is a schematic diagram showing the working status of a hybrid energy storage system during different time periods of wind peak reversal. Detailed Implementation

[0091] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0092] Example

[0093] like Figure 1 As shown, this invention provides a multi-timescale source-grid-load-storage coordination day-ahead intraday scheduling method involving a hybrid energy storage system composed of vanadium redox flow batteries and lithium-ion batteries, comprising the following steps:

[0094] S1 determines the start-up and shutdown plans for conventional generating units, time-of-use pricing, and Class A IDR load dispatch plans during day-ahead dispatch.

[0095] S2 uses the routine unit start-up and shutdown plans, PDR response quantities, and Class A IDR load response quantities determined in the daytime dispatch as input quantities for intraday dispatch to determine the output plans of each unit, lithium-ion energy storage charging and discharging quantities, vanadium redox flow storage charging and discharging quantities, Class B IDR load call plans, and the final spinning reserve plans.

[0096] S3 coordinates multiple time scales of source, grid, load, and storage, combining forecast information and time-scale-related characteristics to formulate more accurate scheduling plans and determine the system's use of spinning reserve capacity.

[0097] Step S1 first establishes a hybrid energy storage system model consisting of vanadium redox flow batteries and lithium-ion batteries, and then substitutes this model into the day-ahead optimization scheduling model.

[0098] Step S11: Establish a hybrid energy storage system model.

[0099] When optimizing the operation of a hybrid energy storage system, to maximize the long cycle life of the vanadium redox flow battery, it should bear a greater load, while the lithium-ion battery should be kept in a state of shallow charge and discharge for extended periods. Therefore, the objective function of the established optimization model is to maximize both energy storage cycle life and economic benefits, as expressed mathematically below:

[0100] f1 = min(f2 + f3)

[0101]

[0102] In the formula, represents the charging and discharging power of the lithium-ion energy storage battery, and represents the charging and discharging power of the vanadium redox flow battery. During the scheduling process, to ensure that the lithium-ion energy storage battery operates under shallow charging and discharging conditions as much as possible during the operation of the hybrid energy storage system, the SOC of the energy storage battery needs to be controlled to fluctuate around 50%. Meanwhile, the vanadium redox flow battery can leverage its advantages of longer lifespan and high-power charging and discharging capabilities. The objective function for minimizing charging and discharging losses aims to control the overall energy loss of the hybrid energy storage system to minimize overall energy loss, maximize overall charging and discharging efficiency, and achieve maximum economic benefits during operation.

[0103] The constraints on hybrid energy storage systems participating in optimized scheduling, based on an analysis of their inherent characteristics, primarily involve limitations on the upper and lower limits of state of charge, charging and discharging power limits, and power direction alignment. Therefore, the constraints of the hybrid energy storage model in this invention are as follows:

[0104]

[0105] In the formula, P1 cha P1 dis , These are the charge / discharge power limits of lithium-ion batteries and vanadium redox flow batteries, respectively; S SOC,1,min S SOC,1,max S SOC,2,min S SOC,2,max These are the upper and lower limits of the state of charge for lithium-ion batteries and vanadium redox flow batteries, respectively.

[0106] Step S12: Establish a day-ahead optimization scheduling model.

[0107] To optimize system operating economy and improve the overall utilization rate of renewable energy, the objective function of the day-ahead scheduling model is to minimize the total system operating cost. This cost, considering current demands on new energy and energy storage output, also includes the penalty cost of curtailed wind power and the overall energy loss cost of the hybrid energy storage system. The specific expression is as follows:

[0108]

[0109] In the formula: F1 is the objective function of the day-ahead scheduling optimization model; C G,t C erss,t C DG,t C load,t The cost functions are respectively for conventional generating units, energy storage power stations (including vanadium redox flow storage and lithium-ion energy storage), new energy generating units, and user load; N s P represents the number of scenes; s Let N be the probability coefficient for the s-th scenario; G The number of conventional generating units; P Di,t,s Let a be the power generation of conventional unit i at time t in scenario s; ib i c i These are the power generation cost coefficients for conventional unit i; S i U is the start-up and shutdown cost coefficient for conventional unit i; Gi,t This represents the start / stop status of conventional unit i at time t, where 1 indicates start and 0 indicates stop; N erss P represents the number of energy storage power stations. erssi,t,s For the output power of energy storage power station i at time t in scenario s; C(P erssi,t,s Let W(P) be the cost function of energy storage power station i. erssi,t,s Let N be the maintenance cost function of energy storage power station i; DG For the number of new energy generating units; P DGi,t,s For the output of the new energy unit i at time t in scenario s; C(P DGi,t,s Let U be the cost function of the new energy unit i at time t in scenario s; DGi,t,s For the start-stop state of new energy unit i, k DG,c This represents the cost coefficient for wind curtailment penalties. To contribute to the prediction of new energy at time t under scenario s; k IDRA,s k IDRB,s Cost coefficients for IDRs of type A and type B, respectively; Δ|P IDRA,t, | is the force exerted by class A IDR at time t; Δ|P IDRB,t |Contribute to the B-type IDR in the s scenario at time t.

[0110] Constraints include individual device constraints and system transmission line capacity constraints, etc., and the specific constraint conditions are as follows:

[0111] Power balance constraints.

[0112]

[0113] In the formula: P load,t Let ΔP be the day-ahead forecast of the load at time t; PDR,t Let ΔP be the amount of PDR load called at time t. IDRA,t Let ΔP be the number of calls made by class A IDR load at time t; IDRB,t,s This represents the number of calls made by Class B IDR load in scenario s at time t.

[0114] Operating constraints of conventional generating units.

[0115] Unit output constraints.

[0116]

[0117] In the formula and These are the upper and lower limits of the output of conventional unit i, respectively.

[0118] Unit ramp-up constraints.

[0119]

[0120] In the formula R i The ramp rate of conventional unit i

[0121] Constraints on the output of distributed renewable energy sources.

[0122]

[0123] The maximum output of new energy power generation should be less than the predicted value at that moment.

[0124] Energy storage power station operation constraints.

[0125] Energy storage battery charging and discharging power constraints and charge constraints.

[0126]

[0127] In the formula: and These are the rated charging power and rated discharging power of the energy storage power station i, respectively; S SOCi State of charge (S) of the energy storage power station; upper and lower limits; S SOCimax S SOCimin These represent the upper and lower limits of the i-state of charge of the energy storage power station.

[0128] Power constraints of transmission lines.

[0129]

[0130] In the formula: B represents the maximum transmission capacity of the transmission line between nodes i and j. ij θ is the susceptance between nodes i and j; i,t,s ,θ j,t,s Let i and j be the phase angles at time t in scene s.

[0131] Adjustment of constraints for each scenario.

[0132]

[0133] In the formula: P Gi,t,bs and P erssi,t,bs ψ represents the baseline output values ​​for conventional unit i and energy storage power station i, respectively; i and ψ erss These refer to the flexible adjustment capabilities of conventional unit i and energy storage power station i, respectively.

[0134] Constraints of various DR resources.

[0135]

[0136] In the formula: and These are the upper and lower limits of the PDR load call volume, respectively; and These represent the maximum call volume for IDR loads of types A and B, respectively.

[0137] Step S13 solves the day-ahead rolling optimization model to obtain the start-up and shutdown plans of conventional units, the response quantities of PDR at each time point, and the load call status of Class A IDR. These are then substituted into the next day-ahead scheduling model as determinants.

[0138] In step S2, an intraday optimal scheduling model is established and solved.

[0139] Step S21: Establish an intraday optimization model:

[0140] The objective function remains the same as that used during intraday scheduling, aiming to minimize system operating costs. It is solved by combining forecast data for wind and solar loads over a 15-minute timescale within the next hour, the pre-determined start-up and shutdown schedules of conventional units in the daytime scheduling, the response quantities of PDR at various times, and the load allocation of Class A IDR. The specific mathematical expression is as follows:

[0141]

[0142] In the formula: F2 is the intraday scheduling objective function; ΔT is the duration of one intraday scheduling cycle; t0 is the initial time of the current scheduling period; For system spin-off backup cost; k R,G This is the rotating reserve cost coefficient for conventional generating units; These represent the positive / negative spinning reserve capacity of conventional unit i, respectively. The current dispatch schedule for conventional unit start-up and shutdown is as follows: U Gi,t The response volume of PDR at various times and the load call status of Class A IDR will no longer be optimized accordingly.

[0143] Intraday scheduling determines the start-up and shutdown status of conventional generating units, the amount of PDR (Power Deposit Rate) and Class A IDR (Increase in Demand Rate). The system constraints, individual generating unit constraints, energy storage station constraints, and load-side demand response constraints in intraday scheduling are similar to those in day-ahead scheduling. Therefore, only its unique spinning reserve constraints are presented separately, and their mathematical expressions are as follows:

[0144]

[0145] In the formula: Pr{} is the confidence expression; α and β are the confidence levels for satisfying positive and negative spin-off reserve capacity, respectively, with a value of 0.95.

[0146] Step S22 Solve the intraday optimization model: Solve the model to determine the start-up and shutdown plans of each unit, the charging and discharging amount of vanadium redox flow storage, the charging and discharging amount of lithium-ion storage, and the load dispatch amount of Class B IDR.

[0147] Step S3, based on the start-up and shutdown plans of each unit, the charging and discharging capacity of vanadium redox flow storage, the charging and discharging capacity of lithium-ion storage, and the load call amount of Class B IDR obtained from the above-mentioned day-ahead and intraday optimization model, combined with forecast information and time-scale-related characteristics, formulates a more accurate scheduling plan and determines the system's spinning reserve capacity.

[0148] This embodiment employs a multi-timescale day-ahead intraday scheduling process, as follows: Figure 2 As shown, to verify the effectiveness of the proposed day-ahead intraday scheduling method, this paper adopts... Figure 3 The improved IEEE 30-node power grid shown is used as a simulation example for verification. Based on this example, optimized scheduling is performed under conditions of no hybrid energy storage system, participation only in day-ahead scheduling, and the proposed method, respectively, to verify the effectiveness of the theoretical method of this invention.

[0149] According to the above method, Figure 4 This is a report on the resource allocation status of Fengzhengfeng. Figure 5 The status of resource allocation for wind counter-peak. Figure 6 This is a report on the scheduling of various DR resources at Fengzhengfeng. Figure 7 The scheduling status of various DR resources for wind-induced peak-shaving. Figure 8 This shows the operating status of the hybrid energy storage system at different times during the peak period. Figure 9 The table shows the operating status of the hybrid energy storage system during different time periods of wind curtailment. Table 1 compares the costs and system wind curtailment rates under the three conditions.

[0150] Table 1

[0151]

[0152]

[0153] This invention also relates to a multi-timescale source-grid-load-storage coordination day-ahead intraday scheduling system, comprising:

[0154] The processing module is used to determine the start-up and shutdown plans of conventional generating units, time-of-use pricing, and Class A IDR load dispatch plans in day-ahead scheduling;

[0155] The input module is used to input the determined conventional unit start-up and shutdown plans, PDR response quantities, and Class A IDR load response quantities into the intraday scheduling.

[0156] The determination module is used to determine the output plan of each unit, the charging and discharging capacity of lithium-ion energy storage, the charging and discharging capacity of vanadium redox flow storage, the call-up plan of Class B IDR load, and the final spinning reserve plan.

[0157] The coordination module is used to develop a more accurate scheduling plan by coordinating the source, grid, load, and storage across multiple time scales, combining forecast information obtained from day-ahead and intraday scheduling models with time-scale-related characteristics. This plan includes determining the system's use of spinning reserve capacity.

[0158] The monitoring module is used to monitor the operating status of the power system in real time according to the established scheduling plan, including the output of each unit, the charging and discharging status of the energy storage system, and the actual load demand.

[0159] The adjustment module is used to adjust the output of each unit, the charging and discharging plan of the energy storage system, and the dispatch plan of the demand response load based on real-time monitoring data, so as to ensure the stable operation and economic dispatch of the power system.

[0160] The dynamic adjustment module is used to dynamically adjust the allocation of spinning reserve capacity when necessary, based on the actual operation of the system and forecast information, in order to cope with possible abnormal situations and emergencies and ensure the safety and reliability of the power system.

[0161] The day-ahead scheduling handled by the processing module includes establishing an optimization model for the operation of a hybrid energy storage system. The specific mathematical expression of this model is as follows:

[0162] f1 = min(f2 + f3)

[0163]

[0164] In the formula, f1 is the objective function, representing the maximization of energy storage cycle life and economic benefits; f2 is a sub-objective function representing energy storage cycle life; f3 is a sub-objective function representing economic benefits; P1 is the charge / discharge power of the lithium-ion energy storage battery; P2 is the charge / discharge power of the vanadium redox flow battery; S SOC,1 η1 represents the state of charge of the lithium-ion energy storage battery; η2 represents the efficiency of the lithium-ion energy storage battery; Δt represents the time step; C bat This refers to the rated capacity of the lithium-ion battery.

[0165] The constraints of the hybrid energy storage model are:

[0166]

[0167] In the formula, P1 cha P1 dis , These are the charge / discharge power limits of lithium-ion batteries and vanadium redox flow batteries, respectively; S SOC,1,min S SOC,1,max S SOC,2,min S SOC,2,max These are the upper and lower limits of the state of charge for lithium-ion batteries and vanadium redox flow batteries, respectively.

[0168] The day-ahead scheduling of the processing module is achieved by establishing a day-ahead scheduling model with the goal of minimizing the total system operating cost. The total operating cost objective function F1 of this model includes at least one of the following: the operating cost of conventional units, the operating cost of renewable energy units, the charging, discharging and maintenance costs of hybrid energy storage systems, and the penalty cost incurred by wind or solar curtailment.

[0169] The specific expression is as follows:

[0170]

[0171] In the formula: F1 is the objective function of the day-ahead scheduling optimization model; C G,t C erss,t C DG,t C load,t Cost functions for conventional generating units, hybrid energy storage power stations, new energy generating units, and user loads, respectively; N s P represents the number of scenes; s Let N be the probability coefficient for the s-th scenario; G The number of conventional generating units; P Di,t,s Let a be the power generation of conventional unit i at time t in scenario s; i b i c i These are the power generation cost coefficients for conventional unit i; S i U is the start-up and shutdown cost coefficient for conventional unit i; Gi,t This represents the start / stop status of conventional unit i at time t, where 1 indicates start and 0 indicates stop; N erss P represents the number of energy storage power stations. erssi,t,s For the output power of energy storage power station i at time t in scenario s; C(P erssi,t,s Let W(P) be the cost function of energy storage power station i. erssi,t,s Let N be the maintenance cost function of energy storage power station i; DG For the number of new energy generating units; P DGi,t,s For the output of the new energy unit i at time t in scenario s; C(P DGi,t,s Let U be the cost function of the new energy unit i at time t in scenario s; DGi,t,s For the start-stop state of new energy unit i, k DG,c This represents the cost coefficient for wind curtailment penalties. To contribute to the prediction of new energy at time t under scenario s; k IDRA,s kIDRB,s Cost coefficients for IDRs of type A and type B, respectively; Δ|P IDRA,t, | is the force exerted by class A IDR at time t; Δ|P IDRB,t |Contribute to the B-type IDR in the s scenario at time t.

[0172] The present invention also relates to a computer-readable storage medium storing computer program instructions that, when executed by a processor, cause the processor to perform any of the scheduling methods described above.

[0173] The present invention also relates to an electronic device comprising one or more processors and a non-transitory computer-readable medium on which instructions are stored, which, when executed by the one or more processors, cause the device to be configured to perform any of the scheduling methods described above.

Claims

1. A multi-timescale source-grid-load-storage scheduling method for hybrid energy storage containing vanadium-lithium batteries, characterized in that, Includes the following steps: In the daytime dispatch, the start-up and shutdown plans of conventional generating units, time-of-use pricing, and Class A IDR load dispatch plans are determined. These plans are used to optimize the economics of system operation and improve the overall utilization of renewable energy, and to provide basic inputs for intraday dispatch. The established start-up and shutdown plans for conventional units, PDR response quantities, and Class A IDR load response quantities are used as input quantities in intraday scheduling to determine the output plans for each unit, lithium-ion energy storage charging and discharging quantities, vanadium redox flow storage charging and discharging quantities, Class B IDR load call plans, and the final spinning reserve plans. These plans are used to further optimize system operation and ensure the stability and reliability of the system. By coordinating multiple time scales of energy sources, grids, loads, and storage, and combining forecast information obtained from day-ahead and intraday scheduling models (including forecasts of new energy output and loads) with time-scale-related characteristics (such as equipment start-up and shutdown characteristics and energy storage system charging and discharging characteristics), a scheduling plan is formulated. According to the established dispatch plan, the operating status of the power system is monitored in real time, including the output of each unit, the charging and discharging status of the energy storage system, and the actual demand of the load. Based on real-time monitoring data, adjust the output of each generating unit, the charging and discharging plan of the energy storage system, and the dispatch plan of the demand response load to ensure the stable operation and economic dispatch of the power system. When necessary, the allocation of spinning reserve capacity will be dynamically adjusted based on the actual operation of the system and forecast information to cope with possible abnormal situations and emergencies, and to ensure the safety and reliability of the power system.

2. The multi-timescale source-grid-load-storage scheduling method for vanadium-lithium hybrid energy storage according to claim 1, characterized in that, The day-ahead scheduling includes establishing an optimization model for the operation of a hybrid energy storage system, the specific mathematical expression of which is as follows: f1 = min(f2 + f3) In the formula, f1 is the objective function, representing the maximization of energy storage cycle life and economic benefits; f2 is a sub-objective function representing energy storage cycle life; f3 is a sub-objective function representing economic benefits; P1 is the charge / discharge power of the lithium-ion energy storage battery; P2 is the charge / discharge power of the vanadium redox flow battery; S SOC,1 η1 represents the state of charge of the lithium-ion energy storage battery; η1 represents the efficiency of the lithium-ion energy storage battery. η2 is the efficiency of the all-vanadium redox flow battery; Δt is the time step; C bat This refers to the rated capacity of the lithium-ion battery. The constraints of the hybrid energy storage model are: In the formula, P1 cha P1 dis P2 cha P2 dis These are the charge / discharge power limits of lithium-ion batteries and vanadium redox flow batteries, respectively; S SOC,1,min S SOC,1,max S SOC,2,min S SOC,2,max These are the upper and lower limits of the state of charge for lithium-ion batteries and vanadium redox flow batteries, respectively.

3. The scheduling method according to claim 1 or 2, characterized in that, The day-ahead scheduling is achieved by establishing a day-ahead scheduling model with the goal of minimizing the total system operating cost. The total operating cost objective function F1 of this model includes at least one of the following: the operating cost of conventional units, the operating cost of renewable energy units, the charging, discharging and maintenance costs of hybrid energy storage systems, and the penalty cost incurred by wind or solar curtailment. The specific expression is as follows: In the formula: F1 is the objective function of the day-ahead scheduling optimization model; C G,t C erss,t C DG,t C load,t Cost functions for conventional generating units, hybrid energy storage power stations, new energy generating units, and user loads, respectively; N s P represents the number of scenes; s Let N be the probability coefficient for the s-th scenario; G The number of conventional generating units; P Di,t,s Let a be the power generation of conventional unit i at time t in scenario s; i b i c i These are the power generation cost coefficients for conventional unit i; S i Let be the start-up and shutdown cost coefficient for conventional unit i; U Gi,t This represents the start / stop status of conventional unit i at time t, where 1 indicates start and 0 indicates stop; N erss P represents the number of energy storage power stations. erssi,t,s For the output power of energy storage power station i at time t in scenario s; C(P erssi,t,s Let W(P) be the cost function of energy storage power station i. erssi,t,s Let N be the maintenance cost function of energy storage power station i; DG For the number of new energy generating units; P DGi,t,s For the output of the new energy unit i at time t in scenario s; C(P DGi,t,s Let U be the cost function of the new energy unit i at time t in scenario s; DGi,t,s For the start-stop state of new energy unit i, k DG,c This represents the cost coefficient for wind curtailment penalties. To contribute to the prediction of new energy at time t under scenario s; k IDRA,s k IDRB,s Cost coefficients for IDRs of type A and type B, respectively; Δ|P IDRA,t, | is the force exerted by class A IDR at time t; Δ|P IDRB,t |Contribute to the B-type IDR in the s scenario at time t.

4. The scheduling method according to claim 3, characterized in that, The current scheduling model's constraints include constraints on each device itself and system transmission line capacity constraints, as detailed below: The following power balance constraints must be met: ， In the formula: This represents the day-ahead forecast of the load at time t; The PDR load at time t; Let A be the number of calls made by the IDR load at time t. This represents the number of calls to the B-type IDR load in scenario s at time t. And satisfy the following constraints: Output constraints of conventional generating units: ， In the formula, For conventional units exist The start / stop status at any given time, 1 indicates running, 0 indicates stopped; and These are the upper and lower limits of the output of conventional unit i, respectively; Unit ramping constraints: ， In the formula The ramp rate for conventional unit i; Constraints on the output of distributed renewable energy sources: ， The maximum output of new energy power generation should be less than the predicted value at that moment; Energy storage power station operation constraints Energy storage battery charge / discharge power constraints and charge constraints: ， In the formula: and These are the rated charging power and rated discharging power of the energy storage power station. State of charge (SOC) of the energy storage power station; upper and lower limits; , These are the upper and lower limits of the i-state of charge of the energy storage power station. Transmission power constraints of transmission lines: ， In the formula: This represents the maximum transmission capacity of the transmission line between nodes i and j. The susceptance between nodes i and j; Let i and j be the phase angles of the scene at time t. Adjusting constraints for each scenario: ， In the formula: and These are the baseline scenario output values ​​for conventional unit i and energy storage power station i, respectively; and These refer to the flexible adjustment capabilities of conventional unit i and energy storage power station i, respectively. Constraints on various DR resources: ， ， ， In the formula: and These are the upper and lower limits of the PDR load call volume, respectively; and These represent the maximum call volume for IDR loads of types A and B, respectively.

5. The scheduling method according to claim 1, characterized in that, The intraday scheduling is achieved by establishing and solving an intraday optimal scheduling model. This model uses the regular unit start-up and shutdown plans, PDR response quantities, and Class A IDR load call status determined by the day-ahead scheduling as fixed boundary conditions. The specific mathematical expression is as follows: In the formula: F2 is the intraday scheduling objective function; ΔT is the duration of one intraday scheduling cycle; t0 is the initial time of the current scheduling period; For system spin-off backup cost; k R,G This is the rotating reserve cost coefficient for conventional generating units; These represent the positive / negative spinning reserve capacity of conventional unit i, respectively.

6. The scheduling method according to claim 5, characterized in that, The intraday optimized scheduling model includes confidence constraints on the system's positive and negative spinning reserve capacity, and the mathematical expression is as follows: In the formula: Pr{} is the confidence expression; α and β are the confidence levels for satisfying positive and negative spin-off reserve capacity, respectively, with a value of 0.

95.

7. The scheduling method according to any one of claims 1 to 6, characterized in that, The final output of the method is a specific start-up, shutdown, charging / discharging, and dispatch plan that covers multiple time scales and is accurate to each conventional unit, hybrid energy storage unit, and various demand response loads, based on the solution results of the day-ahead and intraday optimization scheduling model.

8. A multi-timescale source-grid-load-storage coordination day-ahead intraday scheduling system, characterized in that, include: The processing module is used to determine the start-up and shutdown plans of conventional generating units, time-of-use pricing, and Class A IDR load dispatch plans in day-ahead scheduling; The input module is used to input the determined conventional unit start-up and shutdown plans, PDR response quantities, and Class A IDR load response quantities into the intraday scheduling. The determination module is used to determine the output plan of each unit, the charging and discharging capacity of lithium-ion energy storage, the charging and discharging capacity of vanadium redox flow storage, the call-up plan of Class B IDR load, and the final spinning reserve plan. The coordination module is used to develop a more accurate scheduling plan by coordinating the source, grid, load, and storage across multiple time scales, combining forecast information obtained from day-ahead and intraday scheduling models with time-scale-related characteristics. This plan includes determining the system's use of spinning reserve capacity. The monitoring module is used to monitor the operating status of the power system in real time according to the established scheduling plan, including the output of each unit, the charging and discharging status of the energy storage system, and the actual load demand. The adjustment module is used to adjust the output of each unit, the charging and discharging plan of the energy storage system, and the dispatch plan of the demand response load based on real-time monitoring data, so as to ensure the stable operation and economic dispatch of the power system. The dynamic adjustment module is used to dynamically adjust the allocation of spinning reserve capacity when necessary, based on the actual operation of the system and forecast information, in order to cope with possible abnormal situations and emergencies and ensure the safety and reliability of the power system.

9. A multi-timescale source-grid-load-storage coordination day-ahead intraday scheduling system according to claim 8, characterized in that, The day-ahead scheduling handled by the processing module includes establishing an optimization model for the operation of a hybrid energy storage system. The specific mathematical expression of this model is as follows: f1 = min(f2 + f3) In the formula, f1 is the objective function, representing the maximization of energy storage cycle life and economic benefits; f2 is a sub-objective function representing energy storage cycle life; f3 is a sub-objective function representing economic benefits; P1 is the charge / discharge power of the lithium-ion energy storage battery; P2 is the charge / discharge power of the vanadium redox flow battery; S SOC,1 η1 represents the state of charge of the lithium-ion energy storage battery; η1 represents the efficiency of the lithium-ion energy storage battery. η2 is the efficiency of the all-vanadium redox flow battery; Δt is the time step; C bat This refers to the rated capacity of the lithium-ion battery. The constraints of the hybrid energy storage model are: In the formula, P1 cha P1 dis P2 cha P2 dis These are the charge / discharge power limits of lithium-ion batteries and vanadium redox flow batteries, respectively; S SOC,1,min S SOC,1,max S SOC,2,min S SOC,2,max These are the upper and lower limits of the state of charge for lithium-ion batteries and vanadium redox flow batteries, respectively.

10. A multi-timescale source-grid-load-storage coordination day-ahead intraday scheduling system according to claim 8, characterized in that, The day-ahead scheduling of the processing module is achieved by establishing a day-ahead scheduling model with the goal of minimizing the total system operating cost. The total operating cost objective function F1 of this model includes at least one of the following: the operating cost of conventional units, the operating cost of renewable energy units, the charging, discharging and maintenance costs of hybrid energy storage systems, and the penalty cost incurred by wind or solar curtailment. The specific expression is as follows: In the formula: F1 is the objective function of the day-ahead scheduling optimization model; C G,t C erss,t C DG,t C load,t Cost functions for conventional generating units, hybrid energy storage power stations, new energy generating units, and user loads, respectively; N s P represents the number of scenes; s Let N be the probability coefficient for the s-th scenario; G The number of conventional generating units; P Di,t,s Let a be the power generation of conventional unit i at time t in scenario s; i b i c i These are the power generation cost coefficients for conventional unit i; S i Let be the start-up and shutdown cost coefficient for conventional unit i; U Gi,t This represents the start / stop status of conventional unit i at time t, where 1 indicates start and 0 indicates stop; N erss P represents the number of energy storage power stations. erssi,t,s For the output power of energy storage power station i at time t in scenario s; C(P erssi,t,s Let W(P) be the cost function of energy storage power station i. erssi,t,s Let N be the maintenance cost function of energy storage power station i; DG For the number of new energy generating units; P DGi,t,s For the output of the new energy unit i at time t in scenario s; C(P DGi,t,s Let U be the cost function of the new energy unit i at time t in scenario s; DGi,t,s For the start-stop state of new energy unit i, k DG,c This represents the cost coefficient for wind curtailment penalties. To contribute to the prediction of new energy at time t under scenario s; k IDRA,s k IDRB,s Cost coefficients for IDRs of type A and type B, respectively; Δ|P IDRA,t, | is the force exerted by class A IDR at time t; Δ|P IDRB,t |Contribute to the B-type IDR in the s scenario at time t.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, cause the processor to perform the scheduling method according to any one of claims 1 to 7.

12. An electronic device, characterized in that, The device includes one or more processors and a non-transitory computer-readable medium on which instructions are stored, which, when executed by the one or more processors, cause the device to be configured to perform the scheduling method as described in any one of claims 1 to 7.

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