An inter-province power mutual assistance optimization clearing method based on energy storage dynamic adjustment margin

Abstract

The application provides an inter-province power mutual aid optimization clearing method based on energy storage dynamic adjustment margin, comprising: dividing two parallel time scales of long-period coarse granularity scale and short-period fine granularity scale according to load characteristics of a receiving end power grid; constructing an adjustment quality matching degree coefficient based on the long-period coarse granularity scale and the short-period fine granularity scale; correcting the utility of the declared cost of the energy storage resource through the adjustment quality matching degree coefficient, and constructing a multi-time scale nested heterogeneous energy storage joint clearing optimization model with the minimum comprehensive power purchase cost as an objective function; solving the optimization model, and outputting the energy storage winning output plan under different time scales. The application reduces the adjustment deviation and loss cost while improving the configuration accuracy, and accurately realizes the deep coupling of the heterogeneous energy storage adjustment capacity and the multi-resolution demand characteristics of the receiving end.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology, and in particular to an optimized clearing method for inter-provincial power mutual assistance based on dynamic adjustment margin of energy storage. Background Technology

[0002] Inter-provincial power sharing is a crucial means to optimize the allocation of energy resources across a wide geographical area and support the cross-regional consumption of a high proportion of new energy sources. With the continuous increase in the penetration rate of fluctuating renewable energy, the demand of receiving-end grids for cross-regional flexibility resources has shifted from simply ensuring power leveling to providing multi-dimensional support capabilities, including frequency support and peak / frequency regulation. Energy storage systems, with their excellent bidirectional regulation characteristics and rapid response capabilities, have become a core component of inter-provincial flexible regulation resources.

[0003] Currently, inter-provincial energy trading is evolving from simple electricity exchange to multi-granularity capacity exchange that considers different regulation response rates. Because sending-end energy storage resources exhibit significant heterogeneity in response speed, duration, and energy ratio, and receiving-end load gaps also exhibit a complex characteristic of long-cycle energy gaps and short-cycle dynamic fluctuations, establishing a precise match between trading granularity and resource regulation capacity is key to improving exchange efficiency.

[0004] The heterogeneity of energy storage regulation characteristics and the uniformity of the time resolution of existing trading mechanisms mainly affect inter-provincial mutual assistance clearing in the following two aspects: (1) Mismatch between transaction time resolution and physical response capability of resources: The current mechanism usually adopts a fixed time resolution, which cannot distinguish between hourly energy balance demand and minute-level power regulation demand at the reporting end. Due to the lack of finer time steps, the high ramp-up response advantage of short-term energy storage is averaged out in coarse-grained time periods, while long-term energy storage is difficult to meet the standards when dealing with short-term power surges due to mechanical performance or response delay.

[0005] (2) Lack of a coordinated mechanism for the joint clearing of heterogeneous resources: Different types of energy storage have fundamental differences in terms of state switching costs and operational inertia. Existing clearing models fail to take into account the coordinated requirements of stable operation of long-term resources and high-frequency power replenishment of short-term resources, resulting in the inability of long-term and short-term resources to form a regulatory synergy.

[0006] Currently, the two main inter-provincial power trading and resource allocation schemes are as follows: (1) Electricity clearing method based on a unified time scale: The receiving province issues time-of-use trading targets based on load forecasts. Its technical logic is to match electricity based on a unified time step. The limitation of this scheme is that the declaration resolution is not adjustable, and the receiving power grid cannot simultaneously express the demand for large electricity over long periods and fine power over short periods. Since the mechanism cannot distinguish between electricity and power demand, the sending-end energy storage can only participate in clearing in a homogeneous form, resulting in the inability to fully utilize the characteristics of both long-term and short-term resources.

[0007] (2) Resource prioritization method based on spatial priority: Some regional rules use a fixed order of "first within the province, then between provinces" to determine the priority of energy storage calls, and then perform simple sorting based on price difference within the same level. The limitation of this method is that the configuration logic is too rigid, and the priority is divided only from the spatial administrative location, rather than based on the matching degree between the physical characteristics of resources and demand for global optimization, which often results in the clearing result not being economically optimal.

[0008] The above-mentioned existing technical solutions have the following shortcomings: (1) For the pre-clearing curve-based method, when dealing with regulation demands with different time scale characteristics, it is unable to identify the time resolution differences of load gap signals at the reporting end, resulting in poor targeting of resource allocation. In particular, for short-term demand with drastic power fluctuations, the current mechanism has too coarse a resolution, which forces long-term resources with smooth regulation response to undertake rapid regulation tasks at a fine granular level, resulting in significant regulation deviation and response lag, and there is a large error between the clearing result and the actual operating demand of the system.

[0009] (2) For priority-based resource sorting methods, the rigid calling order (e.g., intra-provincial first, inter-provincial second) ignores the adaptability of resource physical response performance to the multi-scale demand of the receiving end. This mandatory rule disrupts the continuity of the dynamic adjustment margin of energy storage resources and may "cut off" the optimal output scheme of the original problem, resulting in waste of inter-provincial channel resources or physical overruns during the planning and execution phase. In addition, for multi-period mutual assistance trading problems, introducing static priority constraints on large-scale heterogeneous resources will significantly increase the difficulty of solving the problem. Summary of the Invention

[0010] In view of this, the present invention provides an optimized clearing method for inter-provincial power mutual assistance based on dynamic adjustment margin of energy storage, so as to at least solve the above-mentioned problems.

[0011] This invention provides an optimized clearing method for inter-provincial power exchange based on dynamic adjustment margin of energy storage, comprising: dividing the time scale into two parallel time scales, a long-cycle coarse-grained scale and a short-cycle fine-grained scale, according to the load characteristics of the receiving-end power grid; constructing an adjustment quality matching coefficient based on the long-cycle coarse-grained scale and the short-cycle fine-grained scale, wherein the adjustment quality matching coefficient is used to measure the degree of fit between the physical response characteristics of energy storage resources and the trading demand of different granularities; performing utility correction on the application cost of energy storage resources through the adjustment quality matching coefficient, and constructing a multi-time-scale nested heterogeneous energy storage joint clearing optimization model with the objective function of minimizing the comprehensive power purchase cost, wherein the optimization model includes long-term energy storage state continuity locking constraints, short-term energy storage high-frequency response ramping constraints, and inter-provincial tie line dynamic transmission capacity constraints; solving the optimization model to output the energy storage winning bid output plan under different time scales.

[0012] Optionally, the total mutual assistance power demand of the receiving-end power grid satisfies:

[0013] in, D total ( t )for t The total mutual power demand of the receiving-end power grid at all times, the long-term coarse-grained scale defines the total trading period as... T The receiving end submits a power balance demand block D long ( T ); short-period fine-grained scale in each long period T Nested with several short-cycle time slots t The receiving end declares flexible power demand block D short ( t ).

[0014] Optionally, the adjustment quality matching coefficient includes a response rate matching coefficient. Energy sustainability matching coefficient ,in: For short-cycle, fine-grained markets, construct response rate matching coefficients. :

[0015] in, t resp,i For energy storage i Response time t For short-cycle time slot length, R i,t For energy storage i exist tRated power ramp rate for the time period R req,t To meet the fine-grained requirements of the receiving end t The rate of change of power over a period of time α This is the sensitivity coefficient; For long-cycle, coarse-grained markets, construct an energy sustainability matching coefficient. :

[0016] in, β To reserve a safety margin for energy, E rem,k,T For energy storage k exist T Remaining available energy at the start of the period P bid,k,T For energy storage k exist T The willingness and effort to contribute during the designated time period. T The length of the long-cycle time slot.

[0017] Optionally, the objective function is expressed as:

[0018] in, F The total transaction clearing cost of the power system. M This represents the total number of long-cycle periods included within the total trading period. N This represents the total number of short-cycle periods included within the total trading period. Ω l For long-term energy storage clusters that participate in mutual assistance at the sending end, Ω s For short-term energy storage clusters that participate in mutual assistance at the sending end, C l,k,T / C s,i,T For long-term energy storage k Short-term energy storage i In long-cycle markets t The unit electricity cost reported at all times C l,k,t / C s,i,t For long-term energy storage k Short-term energy storage i In short-cycle markets t The unit electricity cost reported at all times P l,k,T / P s,i,T For long-term energy storage k Short-term energy storage i In long-cycle markets t The output variable at any given time,P l,k,t / P s,i,t For long-term energy storage k Short-term energy storage i In short-cycle markets t The variable of the winning bid output at any given time.

[0019] Optionally, the long-term energy storage state continuity locking constraint is expressed as:

[0020] in, For long-term energy storage k In short period moments t The charging and discharging 0-1 state variables, For long-term energy storage k During long-term periods T Given the charge / discharge 0-1 state variables, For long-term energy storage k The maximum number of state transitions allowed throughout the entire transaction cycle.

[0021] Optionally, the short-time energy storage high-frequency response ramp constraint is expressed as:

[0022]

[0023] in, For short-term energy storage i exist t Total output at all times For short-term energy storage i Maximum power ramp rate limit For short-term energy storage i exist t State of charge at time t, This represents the minimum state of charge range allowed for short-term energy storage. This represents the maximum allowable state of charge range for short-term energy storage. The charging efficiency coefficient for short-term energy storage. The discharge efficiency coefficient for short-term energy storage. P c,i,t For short-term energy storage t The actual charging power variable at any given time. P d,i,t For short-term energy storage t The actual discharge power variable at any given time. E i This refers to the rated capacity for short-term energy storage.

[0024] Optionally, the dynamic transmission capacity constraint of the inter-provincial link line is expressed as:

[0025] in, For inter-provincial communication lines t Maximum available transmission capacity at any given time A correction factor is used to account for network losses during inter-provincial transmission.

[0026] In another aspect, this invention provides an inter-provincial power mutual assistance optimization clearing system based on dynamic adjustment margin of energy storage, comprising: a time scale division module, used to divide the power grid at the receiving end into two parallel time scales: a long-cycle coarse-grained scale and a short-cycle fine-grained scale; an adjustment quality matching degree coefficient construction module, used to construct an adjustment quality matching degree coefficient based on the long-cycle coarse-grained scale and the short-cycle fine-grained scale, wherein the adjustment quality matching degree coefficient is used to measure the degree of fit between the physical response characteristics of energy storage resources and the trading demand at different granularities; a model construction module, used to perform utility correction on the application cost of energy storage resources through the adjustment quality matching degree coefficient, and construct a multi-time-scale nested heterogeneous energy storage joint clearing optimization model with the objective function of minimizing the comprehensive power purchase cost, wherein the optimization model includes long-term energy storage state continuity locking constraints, short-term energy storage high-frequency response ramping constraints, and inter-provincial tie line dynamic transmission capacity constraints; and a solution module, used to solve the optimization model and output the energy storage winning bid output plan under different time scales.

[0027] In another aspect, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of an inter-provincial power mutual assistance optimization clearing method based on dynamic energy storage adjustment margin as described in any of the preceding claims. In another aspect, the present invention provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of an inter-provincial power mutual assistance optimization clearing method based on dynamic energy storage adjustment margin as described in any of the preceding claims.

[0028] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention achieves efficient resource clearing by adopting a dual-timescale hierarchical demand declaration and a heterogeneous energy storage classification and matching strategy. The logical complexity of the model is significantly reduced compared to the traditional single-resolution clearing model. By limiting the number of charge and discharge state flips of long-term energy storage, unnecessary integer variable fluctuations in the joint clearing model are greatly reduced, resulting in a smaller computational scale and a faster solution speed.

[0029] (2) For inter-provincial power exchange transactions, a strategy of joint clearing of long-cycle and short-cycle energy demand blocks is adopted. This invention fully utilizes the demand characteristics of the receiving-end load under the dimensions of energy support and instantaneous fluctuations, as well as the different response characteristics of long- and short-term energy storage resources. By establishing a physical mapping relationship based on the regulation quality matching coefficient and considering the dynamic transmission capacity constraints of inter-provincial interconnection lines, the accuracy of the model in the resource allocation process is improved, effectively avoiding response lag or waste of regulation resources due to regulation capacity mismatch. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The accompanying drawings are only for illustrating preferred embodiments and are not intended to limit the present invention. In the accompanying drawings: Figure 1 This is a flowchart of the steps of the method of the present invention.

[0031] Figure 2 To and Figure 1 The overall flowchart of the method of the present invention is shown below. Detailed Implementation

[0032] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0033] The main technical problem addressed by this invention is that, in inter-provincial power reconciliation scenarios, the current trading mechanism suffers from a single time resolution and a lack of differentiation among different energy storage physical characteristics, leading to misallocation of reconciliation resources, low efficiency of long-term and short-term energy storage coordination, and difficulty in highlighting the flexibility value. In actual power operation, the power deficit of the receiving-end grid simultaneously includes hourly or longer-term trending power shortages and minute-level power fluctuations. This requires the reconciliation mechanism to have the ability to distinguish between energy balance and power regulation characteristics during the application and clearing stages.

[0034] Traditional inter-provincial energy transfer technologies typically employ a uniform time granularity for application and clearing, treating energy storage resources as homogeneous power supply entities and lacking a mechanism to decouple demand characteristics over time. Because it's impossible to distinguish between long-term baseline power demand and short-term fine-grained power demand during the application phase, existing models struggle to spontaneously guide heterogeneous energy storage into suitable trading tiers, leading to functional mismatches in physical characteristics: long-term resources with slower response times may be forced to cope with short-term power fluctuations, resulting in significant response lag and exacerbated energy losses; while energy-constrained short-term resources may be allocated to provide continuous power support, causing their regulation potential to be prematurely depleted. Furthermore, existing mechanisms lack an evaluation logic based on the matching degree of regulation quality, failing to calculate utility based on the degree of fit between energy storage response speed and demand fluctuation frequency. This makes it difficult for clearing results to reflect the core advantages of short-term energy storage in coping with fine-grained fluctuations at the mechanism level.

[0035] The method of this invention introduces a dual-timescale reporting mechanism on the demand side, consisting of long-cycle energy demand blocks and short-cycle power demand blocks, and implements a classified participation mode based on the response speed and duration of energy storage. The model limits the number of charge / discharge direction reversals of long-term energy storage by detecting state continuity variables, ensuring its operational stability in long-cycle trading. Simultaneously, it incentivizes the flexibility of short-term energy storage in short-cycle trading by detecting indicators such as ramp rate, and scientifically calculates the regulatory utility of different resources using a joint clearing logic based on regulation matching degree weighting. This enables precise mapping between regulation quality and multi-granularity demand modes while respecting the physical characteristics of energy storage, thereby improving the accuracy of power system resource allocation, optimizing operational efficiency, and enhancing mutual economic benefits.

[0036] See Figure 1 , Figure 2 The present invention provides an optimized clearing method for inter-provincial power mutual assistance based on dynamic adjustment margin of energy storage, which mainly includes: Step S1: Based on the load characteristics of the receiving-end power grid, divide the time scale into two parallel scales: long-cycle coarse-grained scale and short-cycle fine-grained scale. Step S2: Based on the long-cycle coarse-grained scale and the short-cycle fine-grained scale, construct the adjustment quality matching coefficient. The adjustment quality matching coefficient is used to measure the degree of fit between the physical response characteristics of energy storage resources and the trading needs of different granularities. Step S3: The utility correction of the application cost of energy storage resources is performed by adjusting the quality matching degree coefficient, and a multi-time-scale nested heterogeneous energy storage joint clearing optimization model is constructed with the goal of minimizing the comprehensive power purchase cost. The optimization model includes long-term energy storage state continuity locking constraints, short-term energy storage high-frequency response ramping constraints, and inter-provincial tie line dynamic transmission capacity constraints. Step S4: Solve the optimization model and output the energy storage power output plan under different time scales.

[0037] Optionally, the total mutual assistance power demand of the receiving-end power grid satisfies:

[0038] in, D total ( t )for t The total mutual power demand of the receiving-end power grid at all times, the long-term coarse-grained scale defines the total trading period as... T The receiving end submits a power balance demand block D long ( T ); short-period fine-grained scale in each long period T Nested with several short-cycle time slots t The receiving end declares flexible power demand block D short ( t ).

[0039] Optionally, the adjustment quality matching coefficient includes a response rate matching coefficient. Energy sustainability matching coefficient ,in: For short-cycle, fine-grained markets, construct response rate matching coefficients. :

[0040] in, t resp,i For energy storage i Response time t For short-cycle time slot length, R i,t For energy storage i exist t Rated power ramp rate for the time period R req,t To meet the fine-grained requirements of the receiving end t The rate of change of power over a period of time α This is the sensitivity coefficient; For long-cycle, coarse-grained markets, construct an energy sustainability matching coefficient. :

[0041] in, β To reserve a safety margin for energy, E rem,k,T For energy storage k exist TRemaining available energy at the start of the period P bid,k,T For energy storage k exist T The willingness and effort to contribute during the designated time period. T The length of the long-cycle time slot.

[0042] Optionally, the objective function is expressed as:

[0043] in, F The total transaction clearing cost of the power system. M This represents the total number of long-cycle periods included within the total trading period. N This represents the total number of short-cycle periods included within the total trading period. Ω l For long-term energy storage clusters that participate in mutual assistance at the sending end, Ω s For short-term energy storage clusters that participate in mutual assistance at the sending end, C l,k,T / C s,i,T For long-term energy storage k Short-term energy storage i In long-cycle markets t The unit electricity cost reported at all times C l,k,t / C s,i,t For long-term energy storage k Short-term energy storage i In short-cycle markets t The unit electricity cost reported at all times P l,k,T / P s,i,T For long-term energy storage k Short-term energy storage i In long-cycle markets t The output variable at any given time, P l,k,t / P s,i,t For long-term energy storage k Short-term energy storage i In short-cycle markets t The variable of the winning bid output at any given time.

[0044] Optionally, the long-term energy storage state continuity locking constraint is expressed as:

[0045] in, For long-term energy storage k In short period moments tThe charging and discharging 0-1 state variables, For long-term energy storage k During long-term periods T Given the charge / discharge 0-1 state variables, For long-term energy storage k The maximum number of state transitions allowed throughout the entire transaction cycle.

[0046] Optionally, the short-time energy storage high-frequency response ramp constraint is expressed as:

[0047]

[0048] in, For short-term energy storage i exist t Total output at all times For short-term energy storage i Maximum power ramp rate limit For short-term energy storage i exist t State of charge at time t, This represents the minimum state of charge range allowed for short-term energy storage. This represents the maximum allowable state of charge range for short-term energy storage. The charging efficiency coefficient for short-term energy storage. The discharge efficiency coefficient for short-term energy storage. P c,i,t For short-term energy storage t The actual charging power variable at any given time. P d,i,t For short-term energy storage t The actual discharge power variable at any given time. E i This refers to the rated capacity for short-term energy storage.

[0049] Optionally, the dynamic transmission capacity constraint of the inter-provincial link line is expressed as:

[0050] in, For inter-provincial communication lines t Maximum available transmission capacity at any given time A correction factor is used to account for network losses during inter-provincial transmission.

[0051] Specifically, the solution of the present invention is further described with reference to the following examples: The purpose of this invention is to provide an optimized clearing method for inter-provincial power mutual assistance based on dynamic energy storage adjustment margin. This scheme achieves demand characteristic stripping based on a dual-timescale reporting mechanism and constructs a joint clearing model for hierarchical adaptation of long- and short-term energy storage. Through more precise resource performance matching and utility conversion processing, it improves configuration accuracy while reducing adjustment deviations and loss costs, accurately achieving deep coupling between heterogeneous energy storage adjustment capabilities and multi-resolution demand characteristics at the receiving end.

[0052] The core technical principle of this invention lies in the accurate mapping between the physical characteristics of heterogeneous energy storage and the multi-level regulation needs of the power grid by constructing a time-granularity differentiated reporting mechanism and a multi-time-scale nested heterogeneous energy storage joint clearing optimization model based on regulation quality.

[0053] The specific plan is described below: (1) Time slot partitioning and demand declaration based on dual scales The system first divides the time scale into two parallel scales based on the load characteristics of the receiving-end power grid: a long-cycle, coarse-grained scale, defining the total transaction period as... T The receiving end submits a power balance demand block D long ( T ); short-period fine-grained scale in each long period T Nested with several short-cycle time slots t The receiving end declares flexible power demand block D short ( t Therefore, the total demand at the receiving end satisfies relation (1), where D total ( t )for t Total mutual power demand of the receiving-end power grid at any given time: (1) (2) Quantitative construction of energy storage regulation quality matching coefficient This invention constructs an adjustable quality matching coefficient. It is used to measure the degree to which the physical response characteristics of energy storage resources match the trading demands at different granularities.

[0054] For short-cycle, fine-grained markets, construct response rate matching coefficients. For equation (2), where, t resp,i For energy storage i Response time; t The length of the short-cycle time slot; R i,t For energy storage i exist tRated power ramp rate for the specified time period; R req,t To meet the fine-grained requirements of the receiving end t The rate of change of power over a given period; α This is a sensitivity coefficient. This coefficient ensures that the adjustment utility is significantly reduced when slow-responding resources participate in short-cycle transactions.

[0055] (2) For long-cycle, coarse-grained markets, construct an energy sustainability matching coefficient. Equation (3) is given, where, β A safety margin should be reserved for energy consumption; E rem,k,T For energy storage k exist T The remaining available energy at the start of the time period; P bid,k,T For energy storage k exist T The willingness and effort to contribute during the designated time period; T This refers to the length of the long-cycle time slot. This coefficient ensures that the utility of energy-limited resources is limited when participating in long-cycle support.

[0056] (3) (3) Joint clearing objective function based on matching degree weighting An optimization model is constructed with the objective of minimizing the overall cost of electricity purchase, using coefficients... γ The utility adjustment is applied to the application costs of different resources, as shown in equation (4): (4) in, F The total transaction clearing cost of the system; M , N The total number of long-cycle periods / the total number of short-cycle periods included in the total trading period; Ω l , Ω s A collection of long-term / short-term energy storage systems for sending-end participants to help each other; C l,k,T / C s,i,T For long-term energy storage k Short-term energy storage i In long-cycle markets t The unit electricity cost that is reported at all times; C l,k,t / C s,i,t For long-term energy storage k Short-term energy storage i In short-cycle markets tThe unit electricity cost that is reported at all times; P l,k,T / P s,i,T For long-term energy storage k Short-term energy storage i In long-cycle markets t The variable of the winning bid output at any given time; P l,k,t / P s,i,t For long-term energy storage k Short-term energy storage i In short-cycle markets t The variable of the winning bid output at any given time.

[0057] (4) Core physical coordination constraint system To ensure that the physical characteristics of heterogeneous energy storage are fully utilized, the model establishes the following key constraints: 1) Long-term energy storage state continuity locking constraint: For long-term resources participating in long-cycle markets, the model limits the number of state transitions to Equation (5): (5) in, For long-term energy storage k In short period moments t The charging and discharging 0-1 state variables, For long-term energy storage k During long-term periods T Given the charge / discharge 0-1 state variables, For long-term energy storage k The maximum number of state transitions allowed throughout the entire transaction cycle.

[0058] 2) The ramp-up constraint for short-term energy storage high-frequency response is given by equations (6) and (7): (6) (7) in, For short-term energy storage i exist t Total output at all times For short-term energy storage i Maximum power ramp rate limit For short-term energy storage i exist t State of charge at time t, This represents the minimum state of charge range allowed for short-term energy storage. This represents the maximum allowable state of charge range for short-term energy storage. The charging efficiency coefficient for short-term energy storage. The discharge efficiency coefficient for short-term energy storage. P c,i,t For short-term energy storage t The actual charging power variable at any given time. P d,i,t For short-term energy storage t The actual discharge power variable at any given time. E i This refers to the rated capacity for short-term energy storage.

[0059] 3) The dynamic transmission capacity constraint of inter-provincial connection lines is given by equation (8): (8) in, For inter-provincial communication lines t Maximum available transmission capacity at any given time A correction factor is used to account for network losses during cross-provincial transmission.

[0060] 4) Logic for generating hierarchical plans By solving the aforementioned nested multi-timescale heterogeneous energy storage joint clearing optimization model, the power system automatically achieves optimal resource allocation: long-term energy storage receives hourly stable plans consistent with its physical inertia, serving as the energy base at the receiving end; short-term energy storage receives minute-level fine-tuning plans, rapidly filling gaps to resolve power deviations caused by long-term resource lockouts. This mechanism-guided approach maximizes the advantages of both long-term and short-term energy storage in inter-provincial mutual assistance.

[0061] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention achieves efficient resource clearing by adopting a dual-timescale hierarchical demand declaration and a heterogeneous energy storage classification and matching strategy. The logical complexity of the model is significantly reduced compared to the traditional single-resolution clearing model. By limiting the number of charge and discharge state flips of long-term energy storage, unnecessary integer variable fluctuations in the joint clearing model are greatly reduced, resulting in a smaller computational scale and a faster solution speed.

[0062] (2) For inter-provincial power exchange transactions, a strategy of joint clearing of long-cycle and short-cycle energy demand blocks is adopted. This invention fully utilizes the demand characteristics of the receiving-end load under the dimensions of energy support and instantaneous fluctuations, as well as the different response characteristics of long- and short-term energy storage resources. By establishing a physical mapping relationship based on the regulation quality matching coefficient and considering the dynamic transmission capacity constraints of inter-provincial interconnection lines, the accuracy of the model in the resource allocation process is improved, effectively avoiding response lag or waste of regulation resources due to regulation capacity mismatch.

[0063] Another aspect of the present invention provides an inter-provincial power mutual assistance optimization and clearing system based on dynamic adjustment margin of energy storage, comprising: The time scale division module is used to divide the power grid at the receiving end into two parallel time scales: a long-cycle coarse-grained scale and a short-cycle fine-grained scale, based on the load characteristics of the power grid at the receiving end. The adjustment quality matching coefficient construction module is used to construct the adjustment quality matching coefficient based on the long-cycle coarse-grained scale and the short-cycle fine-grained scale. The adjustment quality matching coefficient is used to measure the degree of fit between the physical response characteristics of energy storage resources and the trading needs of different granularities. The model building module is used to perform utility correction on the application cost of energy storage resources by adjusting the quality matching degree coefficient, and to build a multi-time-scale nested heterogeneous energy storage joint clearing optimization model with the objective function of minimizing the comprehensive power purchase cost. The optimization model includes long-term energy storage state continuity locking constraints, short-term energy storage high-frequency response ramping constraints, and inter-provincial tie line dynamic transmission capacity constraints. The solution module is used to solve the optimization model and output the energy storage winning bid output plan at different time scales.

[0064] It should be understood that the system in this embodiment is used to implement the corresponding methods in the foregoing multiple method embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0065] In another aspect of the present invention, the electronic device includes: a processor, a memory, and a communication bus and a communication interface.

[0066] in: The processor, memory, and communication interface communicate with each other via a communication bus.

[0067] A communication interface is used to communicate with other electronic devices or servers.

[0068] The processor is used to execute programs, specifically, it can execute any of the steps of the inter-provincial power mutual assistance optimization and clearing method based on the dynamic adjustment margin of energy storage in the above embodiments.

[0069] Specifically, the program may include program code, which includes computer operation instructions.

[0070] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0071] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0072] Specifically, the program can be used to cause the processor to execute the steps of any of the inter-provincial power mutual assistance optimization clearing methods based on energy storage dynamic adjustment margin described in the embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units executed in any of the above-mentioned inter-provincial power mutual assistance optimization clearing methods based on energy storage dynamic adjustment margin, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described devices and modules can be referred to the corresponding process descriptions in the foregoing method embodiments.

[0073] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods of various embodiments of this application.

[0074] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0075] Specific embodiments of the present invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result.

[0076] It should be noted that all directional indications (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components in a certain order (as shown in the figure). If the specific order changes, the directional indication will also change accordingly.

[0077] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.

[0078] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0079] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.

[0080] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing and clearing inter-provincial power mutual assistance based on dynamic adjustment margin of energy storage, characterized in that, include: Based on the load characteristics of the receiving-end power grid, two parallel time scales are divided: long-cycle coarse-grained scale and short-cycle fine-grained scale. Based on the long-cycle coarse-grained scale and the short-cycle fine-grained scale, an adjustment quality matching coefficient is constructed. The adjustment quality matching coefficient is used to measure the degree of fit between the physical response characteristics of energy storage resources and the trading needs of different granularities. The utility correction of the application cost of energy storage resources is carried out by adjusting the quality matching degree coefficient. A multi-time-scale nested heterogeneous energy storage joint clearing optimization model is constructed with the goal of minimizing the comprehensive power purchase cost. The optimization model includes long-term energy storage state continuity locking constraint, short-term energy storage high-frequency response ramping constraint and inter-provincial tie line dynamic transmission capacity constraint. Solve the optimization model to output the energy storage power output plan at different time scales.

2. The method according to claim 1, characterized in that, The total mutual assistance power demand of the receiving-end power grid satisfies: in, D total ( t )for t The total mutual power demand of the receiving-end power grid at all times, the long-term coarse-grained scale defines the total trading period as... T The receiving end submits a power balance demand block D long ( T ); short-period fine-grained scale in each long period T Nested with several short-cycle time slots t The receiving end declares flexible power demand block D short ( t ).

3. The method according to claim 1, characterized in that, The adjustment quality matching coefficient includes the response rate matching coefficient. Energy sustainability matching coefficient ,in: For short-cycle, fine-grained markets, construct response rate matching coefficients. : in, t resp,i For energy storage i Response time t For short-cycle time slot length, R i,t For energy storage i exist t Rated power ramp rate for the time period R req,t To meet the fine-grained requirements of the receiving end t The rate of change of power over a period of time α This is the sensitivity coefficient; For long-cycle, coarse-grained markets, construct an energy sustainability matching coefficient. : in, β To reserve a safety margin for energy, E rem,k,T For energy storage k exist T Remaining available energy at the start of the period P bid,k,T For energy storage k exist T The willingness and effort to contribute during the designated time period. T The length of the long-cycle time slot.

4. The method according to claim 1, characterized in that, The objective function is expressed as: in, F The total transaction clearing cost of the power system. M This represents the total number of long-cycle periods included within the total trading period. N This represents the total number of short-cycle periods included within the total trading period. Ω l For long-term energy storage clusters that participate in mutual assistance at the sending end, Ω s For short-term energy storage clusters that participate in mutual assistance at the sending end, C l,k,T / C s,i,T For long-term energy storage k Short-term energy storage i In long-cycle markets t The unit electricity cost reported at all times C l,k,t / C s,i,t For long-term energy storage k Short-term energy storage i In short-cycle markets t The unit electricity cost reported at all times P l,k,T / P s,i,T For long-term energy storage k Short-term energy storage i In long-cycle markets t The output variable at any given time, P l,k,t / P s,i,t For long-term energy storage k Short-term energy storage i In short-cycle markets t The variable of the winning bid output at any given time.

5. The method according to claim 1, characterized in that, The long-term energy storage state continuity locking constraint is expressed as: in, For long-term energy storage k In short period moments t The charging and discharging 0-1 state variables, For long-term energy storage k During long-term periods T Given the charge / discharge 0-1 state variables, For long-term energy storage k The maximum number of state transitions allowed throughout the entire transaction cycle.

6. The method according to claim 1, characterized in that, The short-time energy storage high-frequency response ramping constraint is expressed as follows: in, For short-term energy storage i exist t Total output at all times For short-term energy storage i Maximum power ramp rate limit For short-term energy storage i exist t State of charge at time t, This represents the minimum state of charge range allowed for short-term energy storage. This represents the maximum allowable state of charge range for short-term energy storage. The charging efficiency coefficient for short-term energy storage. The discharge efficiency coefficient for short-term energy storage. P c,i,t For short-term energy storage t The actual charging power variable at any given time. P d,i,t For short-term energy storage t The actual discharge power variable at any given time. E i This refers to the rated capacity for short-term energy storage.

7. The method according to claim 1, characterized in that, The dynamic transmission capacity constraint of the inter-provincial link is expressed as follows: in, For inter-provincial communication lines t Maximum available transmission capacity at any given time A correction factor is used to account for network losses during inter-provincial transmission.

8. A provincial power exchange optimization and clearing system based on dynamic energy storage adjustment margin, characterized in that, include: The time scale division module is used to divide the power grid at the receiving end into two parallel time scales: a long-cycle coarse-grained scale and a short-cycle fine-grained scale, based on the load characteristics of the power grid at the receiving end. The adjustment quality matching coefficient construction module is used to construct the adjustment quality matching coefficient based on the long-cycle coarse-grained scale and the short-cycle fine-grained scale. The adjustment quality matching coefficient is used to measure the degree of fit between the physical response characteristics of energy storage resources and the trading needs of different granularities. The model building module is used to perform utility correction on the application cost of energy storage resources by adjusting the quality matching degree coefficient, and to build a multi-time-scale nested heterogeneous energy storage joint clearing optimization model with the objective function of minimizing the comprehensive power purchase cost. The optimization model includes long-term energy storage state continuity locking constraints, short-term energy storage high-frequency response ramping constraints, and inter-provincial tie line dynamic transmission capacity constraints. The solution module is used to solve the optimization model and output the energy storage winning bid output plan at different time scales.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the method as described in any one of claims 1-7.

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