An effective controllable capacity evaluation and reservation method for energy storage under generalized adequacy demand

Abstract

The present application relates to a kind of generalized adequacy demand under energy storage effective controllable capacity evaluation and reservation method, belong to novel power system operation control and planning technical field.The present application method includes: in each control cycle, based on the key factor of influencing energy storage effective controllable capacity generation grid system each node's communication risk reservation coefficient, low-carbon discharge reservation coefficient and low-carbon charging margin coefficient;Based on communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient and the access relationship of each node and energy storage, energy storage dynamic reservation domain is calculated;Based on energy storage dynamic reservation domain, the dischargeable power and chargeable power of energy storage in current control cycle are corrected and the corresponding energy storage control instruction is generated and sent.The present application method effectively improves the accuracy of energy storage effective controllable capacity evaluation, and can provide reliable energy storage adjustment boundary for generalized adequacy supply-demand matching and source-grid-load-storage collaborative planning in low-carbon transformation scenario.

Description

Technical Field

[0001] This invention relates to the field of new power system operation control and planning technology, and in particular to a method for assessing and reserving the effective and controllable capacity of energy storage under a generalized sufficiency requirement. Background Technology

[0002] With the high proportion of renewable energy integration and the increasing digitalization of distribution networks, energy storage systems have become a key flexible resource in new power systems, supporting safe operation, peak shaving and valley filling, promoting renewable energy consumption, reducing electricity purchases during high-carbon periods, and enhancing resilience. Existing energy storage planning and scheduling methods typically generate installed capacity and charge / discharge plans based on electricity prices, renewable energy output, load forecasting, energy storage state of charge, and power constraints, assuming that the energy storage installed capacity can be fully observed, reliably commanded, and timely dispatched when needed.

[0003] However, in the planning and control scenarios of digital distribution networks and source-grid-load-storage coordination, the actual dispatchable capacity of energy storage after planned installation depends not only on the rated capacity of the batteries, the power of the converter, and the current state of charge, but also on key factors affecting the effective controllable capacity of energy storage, such as communication links, control platforms, and reliable connections of terminal equipment. When these key factors change during grid operation, the energy storage may still have physical energy capacity, but its ability to be accurately observed, promptly instructed, coordinated activated, and reliably fed back will also change accordingly. If economic dispatch is still carried out according to the nominal physical capacity of the installed capacity plan, it may lead to insufficient controllability during critical periods such as faults, peak loads, low-carbon regulation, or fluctuations in renewable energy sources. Summary of the Invention

[0004] Based on the above analysis, this invention aims to disclose a method for assessing and reserving the effective controllable capacity of energy storage under generalized sufficiency demand. By transforming the key factors affecting the effective controllable capacity of energy storage into effective controllability signals during the operation of energy storage, and based on grid operation data and low-carbon operation data, it determines whether the current energy storage needs to retain discharge capacity or charging absorption space. This effectively improves the accuracy of the assessment of the effective controllable capacity of energy storage, enhances the operational resilience of energy storage, and effectively improves the controllability of energy storage scheduling. It provides a reliable energy storage adjustment boundary for the supply and demand matching of generalized sufficiency and the coordinated planning of source-grid-load-storage under the low-carbon transformation scenario.

[0005] This invention provides a method for assessing and reserving effective controllable energy storage capacity under a generalized sufficiency demand, comprising the following steps in each control cycle: Based on the key factors affecting the effective and controllable capacity of energy storage, the communication risk reservation coefficient, low-carbon discharge reservation coefficient, and low-carbon charging margin coefficient of each node in the power grid system are generated. Based on the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node, and the access relationship between each node and energy storage, the effective controllable capacity of energy storage, i.e., the dynamic reservation domain of energy storage, is calculated. Based on the dynamic reserved domain of energy storage, the discharge power and rechargeable power of energy storage in the current control cycle are corrected, and corresponding energy storage control commands are generated and sent.

[0006] Furthermore, the effective controllable capacity of energy storage, i.e., the dynamic reserve domain of energy storage, is calculated based on the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node, and the access relationship between each node and energy storage. Based on the rated energy of the energy storage, the communication risk reservation amount, low-carbon discharge reservation amount and low-carbon charging margin of the energy storage are determined by using the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node and the access relationship between each node and the energy storage. The dynamic energy ceiling is determined based on the difference between the rated energy capacity and the low-carbon charging margin. The dynamic energy lower limit is determined based on the sum of the physical minimum energy, communication risk reserve, and low-carbon discharge reserve. A dynamic energy storage reserve domain is constructed based on a dynamic energy upper limit and a dynamic energy lower limit; where, if the dynamic energy upper limit is less than the dynamic energy lower limit, the dynamic energy storage reserve domain is 0.

[0007] Furthermore, the step of correcting the discharge power and rechargeable power of the energy storage based on the dynamic reservation domain in the current control cycle includes: The discharge power of the energy storage is determined based on the rated discharge power of the energy storage, the actual stored energy, and the lower limit of the dynamic energy. The rechargeable power of the energy storage in the current control cycle is determined based on the rated charging power of the energy storage, the actual stored energy, and the dynamic energy upper limit.

[0008] Furthermore, the key factors affecting the effective and controllable capacity of energy storage include communication task type, carbon emission factor of electricity purchase, marginal carbon emission factor, peak load, demand for replacement of high-carbon units, power supply support risk indicators, new energy surplus, wind and solar curtailment risk, degree of back-transmission restriction, and low-carbon absorption demand indicators. The communication risk reservation coefficient, low-carbon discharge reservation coefficient, and low-carbon charging margin coefficient for each node of the power grid system, generated based on key factors affecting the effective and controllable capacity of energy storage, include: Based on the communication task type, generate the communication risk reservation coefficient for each node in the power grid system; Based on the carbon emission factor of electricity purchase or the marginal carbon emission factor, as well as peak load, demand for replacement of high-carbon units, and power supply support risk indicators, low-carbon discharge reserve coefficients are generated for each node. The low-carbon charging margin coefficient for each node is generated based on indicators such as renewable energy surplus, wind and solar curtailment risk, degree of restriction on reverse transmission, and demand for low-carbon absorption.

[0009] Furthermore, the communication type is an ultra-reliable low-latency communication task, an enhanced mobile broadband task, or a massive machine-type communication task. When the communication task is an ultra-reliable low-latency communication task, key communication quality indicators are calculated based on the satisfaction of key services; when the communication task is an enhanced mobile broadband task or a massive machine communication task, key communication quality indicators are calculated based on the success rate of control commands, telemetry integrity, link availability, and communication latency. A communication risk reservation coefficient is generated based on the key communication quality indicators and communication quality security thresholds.

[0010] Furthermore, a communication risk reservation coefficient is generated based on the key communication quality indicators and communication quality security thresholds using the following formula: ; in, Represents a node During the time period corresponding to the current control cycle Communication risk reservation coefficient; Represents a node During the time period corresponding to the current control cycle Key communication quality indicators; For communication quality and safety thresholds; This indicates the upper limit of the communication risk reservation coefficient; Indicates the communication degradation sensitivity coefficient; The calculation method for the key communication quality indicators is as follows: When the communication task is an ultra-reliable low-latency communication task, the calculation formula is: ;in, Represents nodes The set of associated key terminals or control objects Indicates a key terminal or control object During the time period corresponding to the current control cycle Satisfaction with key communication services; When the communication task is an enhanced mobile broadband task or a massive machine-type communication task, the calculation formula is: ;in, To normalize the success rate of control commands, To normalize the integrity of telemetry, To normalize link availability, To normalize communication delay; , , , These are the corresponding weights.

[0011] Furthermore, the calculation method for the low-carbon discharge reserve coefficient is as follows: ; in, For nodes During the time period corresponding to the current control cycle Low-carbon discharge reserve coefficient; The upper limit of the coefficient reserved for low-carbon discharge; The sensitivity coefficient is low-carbon discharge. To meet the low-carbon requirements of the discharge side; ; This refers to the normalized carbon emission factor for electricity purchases or the marginal carbon emission factor. To normalize peak load; To meet the demand for replacement of normalized high-carbon units; To provide risk indicators for normalized power supply support; , , , These are the corresponding weights.

[0012] Furthermore, the calculation method for the low-carbon charging margin coefficient is as follows: ; in, For nodes During the time period corresponding to the current control cycle Low-carbon charging margin coefficient; This represents the upper limit of the low-carbon charging margin coefficient. The sensitivity coefficient for low-carbon charging; To meet the low-carbon requirements of the charging side; ; To normalize the surplus of new energy sources; Risks of curtailment of wind and solar power due to normalization; To normalize the degree of restriction on back-transmission; As a low-carbon absorption demand indicator; , , , These are the corresponding weights.

[0013] Furthermore, the determination of the communication risk reservation, low-carbon discharge reservation, and low-carbon charging margin of the energy storage based on the rated energy capacity of the energy storage, using the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node, and the access relationship between each node and the energy storage, includes: ; ; ; in, Indicates energy storage at the node n During the time period corresponding to the current control cycle Communication risk reserve; Indicates energy storage at the node n During the time period corresponding to the current control cycle Low-carbon discharge reserve; Indicates energy storage at the node n During the time period corresponding to the current control cycle Low-carbon charging margin; Indicates energy storage Access Node The relationship is 1 for connected and 0 for not connected; For energy storage nodes n The equivalent rated energy capacity; , , They are nodes During the time period corresponding to the current control cycle Communication risk reservation coefficient, low-carbon discharge reservation coefficient, and low-carbon charging margin coefficient.

[0014] Furthermore, the method also includes setting a dynamic reservation domain conflict coordination mechanism, which includes: when the dynamic energy upper limit is less than the dynamic energy lower limit, reducing the lower priority reservation amount according to priority order, wherein, When the system is in a power supply support scenario, the priority order from high to low is: communication risk reserve, low-carbon discharge reserve, and low-carbon charging margin. When the system is in a scenario of mandatory consumption of new energy, the priority order from high to low is: communication risk reserve, low-carbon charging margin, and low-carbon discharge reserve.

[0015] The present invention can achieve at least one of the following beneficial effects: This invention relates to the field of novel power system operation control and planning technology, and can be applied to digital distribution networks, energy storage capacity planning, energy storage management, communication-power coupling control, low-carbon dispatch, and flexible resource reliable control technologies. Specifically, this invention is used in scenarios involving high-proportion renewable energy access, digital monitoring of distribution networks, energy storage participation in peak shaving and valley filling and renewable energy consumption, and potential fluctuations in the quality of critical communication services. Based on changes in key factors affecting the effective controllable capacity of energy storage and the direction of low-carbon regulation needs, it assesses the effective controllable capacity of energy storage, generates a dynamic energy reservation domain for energy storage, and provides a reliable energy storage regulation boundary for generalized sufficiency supply and demand matching and source-grid-load-storage coordinated planning in low-carbon transition scenarios.

[0016] By real-time correction of the upper and lower boundaries of energy storage state variables, chargeable and dischargeable power, and reserve release conditions based on the effective controllable capacity of energy storage, i.e., the dynamic energy reserve domain, and outputting executable control commands to the energy storage management system, energy storage converter controller, or source-grid-load-storage collaborative control platform, it has excellent engineering feasibility.

[0017] The method of this invention can feed back the effective adjustable capacity of energy storage, the amount of reserve locked, and the low-carbon absorption space to the upper-level source-grid-load-storage collaborative platform, providing a reliable boundary for diversified energy storage layout and flexible load scale analysis.

[0018] The method of this invention takes into account many key factors affecting energy storage, including: carbon emission factor of electricity purchase, marginal carbon emission factor, peak load, demand for replacement of high-carbon units, power supply support risk indicators, renewable energy surplus, wind and solar curtailment risk, degree of back-transmission restriction, low-carbon absorption demand indicators and communication task type. It can effectively assess the impact of each key factor on the effective controllable capacity, scientifically quantify the changes in the effective controllable capacity of energy storage, and enhance the operational resilience of energy storage.

[0019] This invention calculates a low-carbon discharge reserve coefficient and a low-carbon charging margin coefficient, providing a basis for subsequent calculations of low-carbon discharge reserve and low-carbon charging margin. This makes low-carbon factors a substantial quantitative technical input for energy storage control, avoiding keeping low-carbon goals at the level of abstract optimization objectives. By distinguishing whether the direction of low-carbon regulation demand requires preserving discharge capacity or preserving charging space, energy storage can retain discharge capacity during high-carbon periods and charging space during periods of abundant renewable energy.

[0020] By calculating the dynamic reserve domain for energy storage, including both a dynamic lower limit and a dynamic upper limit of State of Charge (SOC), the dynamic lower limit is used to lock in communication risk reserves and low-carbon discharge reserves, while the dynamic upper limit is used to reserve the charging margin required for renewable energy absorption. By defining the dynamic reserve domain, the effective operating range of energy storage is no longer a fixed range, but a controllable range that changes synchronously with key communication quality, carbon emission factors, renewable energy surplus, peak and valley load conditions, and grid operation risks. This solves the problems in existing technologies, such as the inconsistency between nominal and effective controllable capacity of energy storage, the inability to feed back communication degradation to the energy storage control boundary, the lack of differentiation in the direction of low-carbon regulation demand, and the over-release or over-conservatism caused by long-term fixed energy storage reserves.

[0021] This invention generates a communication risk reservation coefficient by calculating key communication quality indicators, providing a scientific basis for subsequent steps to calculate the communication risk reservation amount. This method does not treat communication quality merely as a service quality indicator or constraint on the communication side, nor does it simply view communication facilities as electrical loads. Instead, it interprets critical communication quality degradation as a technical signal of decreased observable, commanded, and reliably activated energy storage capacity, generating a communication risk reservation coefficient. This coefficient is used in subsequent steps to calculate the communication risk reservation energy, enabling energy storage to automatically tighten its discharge boundary when critical communication quality deteriorates. This solves the problem in existing technologies where fixed energy storage reserves can only be set based on grid safety margins, load forecasting errors, or uncertainties in new energy output, but the effective controllable energy capacity of energy storage due to communication controllability cannot be accurately identified, leading to energy storage control failure or insufficient response.

[0022] By collecting and updating data in each control cycle, communication quality is monitored in real time, avoiding the long-term fixation of communication risk reserves. This prevents excessive release of energy storage under communication degradation and also avoids retaining too much unusable capacity in energy storage after communication is restored. By setting a smooth update method for the communication risk reserve coefficient, this approach avoids frequent jumps in energy storage boundaries caused by short-term communication quality fluctuations, further ensuring the safe operation of the power grid system.

[0023] The dynamic reserved domain conflict coordination mechanism effectively solves the problems of lacking communication security, low-carbon security, and economic coordination mechanisms in existing technologies. It ensures that energy storage can still output an executable control boundary under extreme constraints.

[0024] By separating communication risk reserves and low-carbon reserves from ordinary arbitrage opportunities, energy storage is prevented from being over-released due to economic goals such as low-cost charging and high-cost discharging, resulting in good economic benefits.

[0025] By outputting executable control commands based on calculation results, theoretical calculations are applied to practical situations, demonstrating excellent engineering feasibility. By setting up a dynamic reserved energy storage domain and actually sending control commands to schedule energy storage, the problem of the lack of distinction between "installed flexible capacity" and "effectively controllable flexible capacity" in energy storage scheduling in existing technologies is solved. By setting up a tiered release mechanism, the system's emergency response mechanism is improved, further enhancing the engineering feasibility and protection level of the method of this invention.

[0026] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0027] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0028] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0029] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0030] Method Implementation Examples One embodiment of the present invention discloses a method for assessing and reserving effective and controllable energy storage capacity under a generalized sufficiency requirement.

[0031] It should be noted that the energy storage in this invention includes, but is not limited to, distribution network-side electrochemical energy storage, transformer area energy storage, user-side controllable energy storage, photovoltaic-energy storage integrated energy storage, electric vehicle aggregated energy storage, communication site collaborative energy storage, and aggregated energy storage in virtual power plants.

[0032] In a power grid system, the connection relationship between energy storage and nodes is either one-to-one or one-to-many. When energy storage is a single physical energy storage device, it is connected to a node one-to-one, and the energy storage capacity is the rated energy capacity of the corresponding node. When energy storage is aggregated energy storage, shared energy storage, or virtual power plant energy storage, it can be connected to multiple nodes. For each node, the capacity provided by the energy storage is the equivalent rated energy capacity allocated to that node. In this embodiment, the method includes steps S1 to S3 in each control cycle of the energy storage.

[0033] Step S1: Generate the communication risk reservation coefficient, low-carbon discharge reservation coefficient, and low-carbon charging margin coefficient for each node of the power grid system based on the key factors affecting the effective and controllable capacity of energy storage.

[0034] The key factors affecting the effective and controllable capacity of energy storage include communication task type, carbon emission factor of electricity purchase, marginal carbon emission factor, peak load, demand for replacement of high-carbon units, power supply support risk indicators, new energy surplus, wind and solar curtailment risk, degree of restriction on reverse transmission, and low-carbon absorption demand indicators.

[0035] Specifically, step S1 includes steps S11 to S13.

[0036] S11. Generate communication risk reservation coefficients for each node in the power grid system based on the communication task type.

[0037] In energy storage control of power grid systems, different communication service scenarios correspond to the control time scale, data type, and reliability requirements, including: ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC).

[0038] The communication type is ultra-reliable low-latency communication task, enhanced mobile broadband task, or massive machine-type communication task.

[0039] Each node in a power grid system refers to an electrical connection point in a distribution network that has electrical topological significance and can be monitored, controlled, or configured for resources, including but not limited to bus nodes, transformer substation nodes, feeder segment nodes, load access points, energy storage access points, or equivalent control areas.

[0040] The communication risk reservation coefficient is used to characterize the degree of reduction in the effective controllable capacity of energy storage due to the degradation of critical communication quality.

[0041] Step S11 includes steps S11-1 and S11-2.

[0042] S11-1 When the communication task is an ultra-reliable low-latency communication task, the key communication quality indicators of each node in the power grid system are calculated based on the satisfaction of key services; when the communication task is an enhanced mobile broadband task or a massive machine communication task, the key communication quality indicators of each node in the power grid system are calculated based on the control command success rate, telemetry integrity, link availability, and communication latency.

[0043] Specifically, when the communication task is an ultra-reliable low-latency communication task, the power grid system nodes The formula for calculating the key communication quality indicators is as follows: ;in, Represents nodes The set of associated key terminals or control objects Indicates a key terminal or control object During the time period corresponding to the current control cycle The key communication service satisfaction. In this invention, one control cycle corresponds to one time period, and the time interval between adjacent control cycles is expressed as... .

[0044] It should be noted that in the energy storage control of the power grid system, the terminal refers to the specific physical device that executes the command (such as the energy storage converter, the slave control unit of the battery management system, etc.), and the controlled object refers to the physical quantity or state that the terminal wants to adjust (such as power, voltage, frequency, battery state of charge, etc.).

[0045] When the communication task is an enhanced mobile broadband task or a massive machine-type communication task, the power grid system nodes The formula for calculating the key communication quality indicators is as follows: ;in, To normalize the success rate of control commands, To normalize the integrity of telemetry, To normalize link availability, To normalize communication delay; , , , These are the corresponding weights, all of which are non-negative, and .

[0046] in, , , , The specific values ​​of each weight are determined based on the differences in the sensitivity of communication service types to various communication indicators. The magnitude of the weight directly reflects the degree of dependence of that type of service on specific communication indicators. For example, for enhanced mobile broadband (eMBB), which is highly sensitive to transmission rate and latency, the weight coefficient for "communication latency" is given the largest in the calculation. For massive machine-type communication (mMTC), the core requirement is the online rate of a large number of terminals rather than the ultimate speed, so the weights for "link availability" and "telemetry integrity" are given the largest.

[0047] In step S11-1, the power grid system nodes The key communication service satisfaction, control command success rate, telemetry integrity, link availability, and communication latency are all key communication data, which are collected in the current control cycle.

[0048] In step S11-1, when the communication task is an ultra-reliable low-latency communication task, this type of high-risk control service is extremely vulnerable to latency and packet loss, which can easily lead to the failure of the entire service and trigger a physical power outage. Therefore, if the service type carried by the terminal is URLLC service, the power grid system automatically detects that the key communication quality index is calculated based on the satisfaction level of the key service. The communication risk is amplified to the minimum satisfaction limit, forcing energy storage to perform maximum physical reservation for safety. When the communication task is an enhanced mobile broadband task or a massive machine-type communication task, it has strong elasticity and tolerance to short-term communication jitter. The power grid system calculates the key communication quality index by fusing multiple key communication data through normalized weights, which can ensure that the reservation boundary of energy storage can be smoothly adjusted, maximizing the flexibility of energy storage in daily low-carbon consumption and conventional economic arbitrage.

[0049] Furthermore, the calculated key communication quality indicators The smaller the value, the more likely it is to be a node in the power grid system. The worse the critical communication quality, the lower the effective controllability of energy storage.

[0050] S11-2. Based on the key communication quality indicators and communication quality security thresholds of each node in the power grid system, generate the corresponding node's communication risk reservation coefficient.

[0051] The calculation formula is: ; in, Represents a node During the time period corresponding to the current control cycle Communication risk reservation coefficient; Represents a node During the time period corresponding to the current control cycle Key communication quality indicators; The communication quality and safety threshold can be determined based on the reliability baseline requirements for various services in the 5G standard, or it can be determined based on engineering experience, with a range of [0.4, 0.5] and a preferred value of 0.45. This indicates the upper limit of the communication risk reservation coefficient, with a preferred value range of 10% to 20%. The value represents the communication degradation sensitivity coefficient, ranging from [0.05, 0.2], with a preferred value of 0.15. It can be adaptively fine-tuned according to the actual risk preferences of the nodes.

[0052] This section explains the principle of step S11: During the operation of the power grid system, the nominal energy capacity of energy storage is not equal to the effective controllable energy capacity. When critical communication quality deteriorates, even if the energy storage still physically possesses energy, it cannot be assumed that all of its energy can be accurately, timely, and reliably deployed during a power grid emergency. Therefore, it is necessary to lock a portion of the energy storage energy out of ordinary economic dispatch as a physical adjustment margin under communication degradation.

[0053] Step S11 calculates the communication risk reservation coefficient for each node by measuring the key communication quality indicators of each node in the power grid system, providing a scientific basis for calculating the communication risk reservation in subsequent steps. Step S11 does not treat communication quality merely as a service quality indicator or constraint on the communication side, nor does it simply view communication facilities as power load. Instead, it interprets the degradation of key communication quality as a technical signal of decreased observable, commanded, and reliably activated energy storage capacity, generating a communication risk reservation coefficient. This coefficient is used in subsequent step S2 to calculate the communication risk reservation energy, enabling the energy storage to automatically tighten its discharge boundary when key communication quality deteriorates. This solves the problem in existing technologies where fixed energy storage reserves can only be set based on grid safety margins, load forecasting errors, or uncertainties in new energy output, but the effective controllable energy capacity of energy storage due to communication controllability cannot be accurately identified, leading to energy storage control failure or insufficient response.

[0054] Step S12: Generate the low-carbon discharge reserve coefficient for each node based on the carbon emission factor of electricity purchase or marginal carbon emission factor, as well as peak load, high-carbon unit replacement demand, and power supply support risk indicators.

[0055] The low-carbon discharge reserve coefficient of each node is used to increase the dynamic energy lower limit of the energy storage connected to that node, so that the energy storage can retain a certain discharge capacity during periods of high carbon electricity purchase, peak load, or strong power supply support demand.

[0056] The calculation method for the low-carbon discharge reservation coefficient of each node is as follows: ; in, For nodes During the time period corresponding to the current control cycle Low-carbon discharge reserve coefficient; The upper limit of the coefficient reserved for low-carbon discharge; The sensitivity coefficient is low-carbon discharge. For nodes The low-carbon requirements of the discharge side; ; For nodes Normalized carbon emission factor for electricity purchases or marginal carbon emission factor; For nodes Normalized peak load; For nodes The demand for replacement of normalized high-carbon units; For nodes Normalized power supply support risk indicators; , , , These are the corresponding weights. .

[0057] Among them, the low-carbon demand on the discharge side of the node is used to indicate that energy storage should retain dischargeable energy at the node so as to replace high-carbon power supply during high-carbon power purchase or peak load.

[0058] The weight values ​​can be subjectively determined based on the actual operating experience of the distribution network, that is, weights are assigned according to their importance; or they can be objectively determined based on the fluctuation of real-time collected data, and the system will automatically assign a higher weight to the indicator whose data fluctuation is the most drastic and abnormal; or the subjective and objective value methods can be combined to determine the weight values ​​of each weight by weighting subjectively and objectively.

[0059] In step S12, the normalized peak load is determined by dividing the real-time load of the current control cycle by the maximum load; the normalized high-carbon unit replacement demand is determined by time-of-use pricing; the higher the price, the greater the system's dependence on high-carbon units and the stronger the demand for low-carbon energy storage as a substitute; the power supply support risk index is obtained by mapping node voltage and line load rate. When the node voltage approaches the limit or the line load rate approaches the upper limit threshold, this risk index increases, triggering the energy storage discharge side to reserve power to support grid operation. All the above relevant data are collected within the current control cycle.

[0060] In step S12, the normalized electricity purchase carbon emission factor lacks real-time unit-level carbon emission monitoring data and can be determined through day-ahead or intraday long-cycle scheduling. The electricity purchase carbon emission factor represents the average carbon emission intensity of the system purchasing electricity from the grid within time period t, and is suitable for application scenarios with weak data foundation or coarse time scale. The marginal carbon emission factor can be calculated in real time by the dispatch center based on unit start-up and shutdown plans, load forecasts, and renewable energy output forecasts, or obtained through a carbon emission factor prediction model. In one implementation, the marginal carbon emission factor is used first. When the marginal carbon emission factor is unavailable, the normalized electricity purchase carbon emission factor is automatically used as a fallback. When both are unavailable, time-of-use pricing (normalized) can be used as a proxy signal for the carbon emission factor.

[0061] In step S12, the upper limit of the low-carbon discharge reservation coefficient. This is determined by the constraints of grid safety operation. Energy storage must prioritize reserving a minimum amount of electricity for grid safety services such as frequency regulation, voltage regulation, and emergency backup. After deducting this minimum amount, the maximum remaining capacity that can participate in low-carbon dispatch is the upper limit. Preferably, this upper limit is 20% to 30%, representing 20% ​​to 30% of the equivalent rated energy capacity of energy storage allocated to nodes.

[0062] In step S12, the low carbon discharge sensitivity coefficient This value is derived by comprehensively weighing the marginal benefits of low-carbon regulation against the marginal cost of energy storage batteries. The marginal benefit of low-carbon regulation refers to the weighted sum of the reductions in high-carbon electricity purchase costs and carbon emission costs for each additional unit of energy storage discharge reserve. The marginal cost of energy storage batteries refers to the increase in battery aging cost corresponding to each additional unit of deep charge-discharge cycle. In practical applications, if local emission reduction requirements are high or carbon subsidies exist, the sensitivity coefficient can be appropriately increased; if battery aging costs are a greater concern, it can be appropriately decreased. A preferred value is 0.1 to 0.5.

[0063] This section explains the principle of step S12: When the power grid system is in scenarios such as high-carbon power purchase, peak load, peak demand of fossil fuel units, low-carbon substitution, and power grid shortage, energy storage needs to retain discharge support capacity, which corresponds to low-carbon demand on the discharge side. Step S2 avoids simply equating the low-carbon goal with "more energy storage discharge," but instead uses a calculated low-carbon discharge reserve coefficient to provide a basis for calculating the low-carbon discharge reserve in step S2. This makes the low-carbon factor a substantial quantitative technical input for energy storage control, avoiding keeping the low-carbon goal at the level of abstract optimization objectives.

[0064] Step S13: Generate the low-carbon charging margin coefficient for each node based on indicators such as new energy surplus, wind and solar curtailment risk, degree of back-transmission restriction, and low-carbon absorption demand.

[0065] The low-carbon charging margin coefficient is used to reduce the dynamic upper limit of energy storage, allowing energy storage to retain a certain charging absorption capacity.

[0066] The method for calculating the low-carbon charging margin factor of a node is as follows: ; in, For nodes During the time period corresponding to the current control cycle Low-carbon charging margin coefficient; This represents the upper limit of the low-carbon charging margin coefficient. Low-carbon charging sensitivity coefficient; For nodes The low-carbon requirements of the charging side; ; For nodes Normalized new energy surplus; For nodes The risk of normalized wind and solar power curtailment; For nodes The degree of limitation on normalized backfeeding; For nodes Low-carbon absorption demand indicators; , , , These are the corresponding weights.

[0067] node Low-carbon demand on the charging side This is used to indicate that energy storage should reserve charging space so as to absorb low-carbon electricity when there is a surplus of new energy sources or when the risk of curtailment is high.

[0068] The method for determining the weights is the same as the method for determining the weights in step S12. .

[0069] In step S13, the renewable energy surplus can be determined based on the difference between the predicted renewable energy output and local load demand; the risk of wind and solar curtailment can be determined based on the renewable energy surplus, historical power curtailment, or predicted absorption gap; the degree of reverse power transmission restriction can be determined based on the ratio between the reverse power flow at the grid connection point or line and its allowable operating limit; the low-carbon absorption demand index can be obtained by weighting the renewable energy surplus, wind and solar curtailment risk, and the degree of reverse power transmission restriction. The above data can be collected or predicted by the power grid system during the current control cycle.

[0070] This section explains the principle of step S13: When the power grid system is in scenarios such as surplus renewable energy, risk of wind and solar curtailment, limited local reverse transmission, and low-carbon energy consumption window, energy storage needs to retain charging absorption space, which corresponds to low-carbon demand on the charging side. Step S13 avoids simply equating the low-carbon goal with "more charging of energy storage," but instead calculates the low-carbon charging margin coefficient, providing a basis for calculating the low-carbon charging margin in step S2, making low-carbon factors a substantial quantitative technical input for energy storage control.

[0071] Steps S12 and S13 work together to fully distinguish whether the direction of low-carbon regulation needs to retain discharge capacity or charging space, so that energy storage can retain discharge capacity during high-carbon periods and charging space during periods of abundant new energy.

[0072] It should be noted that steps S11, S12, and S13 are not limited in any particular order and can be performed simultaneously.

[0073] Step S2: Calculate the dynamic reserve domain for energy storage based on the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node, and the access relationship between each node and energy storage. Specifically, this includes steps S21 to S24.

[0074] S21. Based on the rated energy of the energy storage, the communication risk reservation amount, low-carbon discharge reservation amount and low-carbon charging margin of the energy storage are determined by using the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node and the access relationship between each node and the energy storage.

[0075] Specifically, the calculation formula is as follows: ; ; ; in, Indicates energy storage at the node n During the time period corresponding to the current control cycle Communication risk reserve; Indicates energy storage at the node n During the time period corresponding to the current control cycle Low-carbon discharge reserve; Indicates energy storage at the node n During the time period corresponding to the current control cycle Low-carbon charging margin; Indicates energy storage Access Node The relationship is 1 for connected and 0 for not connected; This represents the equivalent rated energy capacity of the energy storage at node n. When the energy storage is a single physical energy storage device, this capacity corresponds to the rated energy capacity of its actual connected node; when the energy storage is aggregated energy storage, shared energy storage, or virtual power plant energy storage, this capacity represents the capacity allocated to the node. n The equivalent rated energy capacity (in practical applications, the equivalent rated energy capacity of each node is an adjustable capacity).

[0076] Communication risk reserves are used to address the decline in energy storage controllability; low-carbon discharge reserves are used to replace power supply during high-carbon periods; and low-carbon charging margins are used to absorb low-carbon energy.

[0077] S22. The dynamic energy ceiling is determined based on the difference between the rated energy capacity and the low-carbon charging margin, expressed as: ; in, For energy storage nodes n The dynamic energy limit.

[0078] S23. The dynamic energy lower limit is determined based on the sum of the physical minimum energy, communication risk reserve, and low-carbon discharge reserve, and is expressed as: ; in, For energy storage nodes n The lower limit of dynamic energy.

[0079] S24. Construct a dynamic energy storage reserve domain based on the dynamic energy upper limit and the dynamic energy lower limit; wherein, if the dynamic energy upper limit is less than the dynamic energy lower limit, the energy storage dynamic reserve domain is 0.

[0080] Specifically, dynamic reserve domain for energy storage Represented as: ； in, Indicates energy storage During the period At the node n The actual stored energy.

[0081] Both the dynamic reservation limit and upper limit for energy storage vary with changes in communication quality and low-carbon requirements. When communication quality deteriorates, As demand for high-carbon electricity purchases increases, the dynamic energy lower limit rises; when the demand for high-carbon electricity purchases as a substitute increases, Increased energy density raises the dynamic energy lower limit; when there is a surplus of new energy sources or an increased risk of power curtailment. Increase the dynamic energy limit and decrease the dynamic energy limit in order to reserve charging space.

[0082] Furthermore, if the margins for communication risks, low-carbon discharge, and low-carbon charging are all large simultaneously, it may lead to... .

[0083] To ensure the executable output control boundary, this invention sets up a dynamic reserved domain conflict coordination mechanism, with conflict amount... Defined as: ;when That is, when the upper limit of dynamic energy is less than the lower limit of dynamic energy, the lower priority reserve is reduced according to priority order. When the system is in a power supply support scenario, the priority order from high to low is: communication risk reserve, low-carbon discharge reserve, and low-carbon charging margin. When the system is in a scenario of mandatory consumption of new energy, the priority order from high to low is: communication risk reserve, low-carbon charging margin, and low-carbon discharge reserve. In both of the above scenarios, ordinary economic arbitrage dispatch (the conventional economic dispatch behavior based on peak-valley electricity prices) is reduced first.

[0084] In one embodiment of the present invention, when the dynamic energy upper limit is less than the dynamic energy lower limit, in different scenarios, ordinary economic arbitrage scheduling is reduced first, and the communication risk reserve has the highest priority. The conflict between the low-carbon discharge reserve and the low-carbon charging margin is distributed according to the scenario weight, as follows: ; ; Among them, the weighting coefficient ; and This refers to the low-carbon discharge reserve and low-carbon charging margin, coordinated according to scenario weights. The dynamic upper and lower limits are recalculated after coordination until they are met. .

[0085] and The calculation is performed in real time using a mixed-integer linear programming model based on factors such as the current grid operating status, communication quality, and low-carbon demand priority. Specifically, when the discharge side of a node has a higher priority, such as during peak load or high-carbon periods, The value is relatively large; when the charging side of the node has higher priority, the risk of wind and solar power curtailment is extremely high. The value is relatively large.

[0086] In one embodiment of the present invention, the communication risk reserve can be set as a non-reducible amount: ; This indicates an amount that cannot be reduced.

[0087] In practical applications, during emergency power supply or black start support scenarios, some communication risk reserves can be temporarily released through authorized commands, but the reason for release, release duration, and recovery conditions need to be recorded.

[0088] Step S2 calculates the dynamic reserve domain for energy storage, including a dynamic lower limit and a dynamic upper limit of State of Charge (SOC). The dynamic lower limit is used to lock in communication risk reserves and low-carbon discharge reserves, while the dynamic upper limit is used to retain the charging margin required for renewable energy absorption. By determining the dynamic reserve domain for energy storage, the effective operating range of energy storage is no longer a fixed range, but a controllable range that changes synchronously with key communication quality, carbon emission factors, renewable energy surplus, load peak and valley conditions, and grid operation risks. This solves the problems in existing technologies, such as the inconsistency between nominal energy storage capacity and effective controllable capacity, the inability to feed back communication degradation to the energy storage control boundary, the lack of differentiation in the direction of low-carbon regulation demand, and the problem of over-release or over-conservatism caused by long-term fixed energy storage reserves.

[0089] The dynamic reserved domain conflict coordination mechanism effectively solves the problems of lacking communication security, low-carbon security, and economic coordination mechanisms in existing technologies. It ensures that energy storage can still output an executable control boundary under extreme constraints.

[0090] By separating communication risk reserves and low-carbon reserves from ordinary arbitrage opportunities, energy storage is prevented from being over-released due to economic goals such as low-cost charging and high-cost discharging, resulting in good economic benefits.

[0091] Step S3: Based on the dynamic reserved domain of energy storage, correct the discharge power and chargeable power of energy storage in the current control cycle and generate and send the corresponding energy storage control commands.

[0092] Specifically, the dischargeable power of the energy storage in the current control cycle is determined based on the rated discharge power of the energy storage, the actual stored energy, and the dynamic energy lower limit: ; The rechargeable power of the energy storage in the current control cycle is determined based on the rated charging power of the energy storage, the actual stored energy, and the dynamic energy upper limit. ; in, Indicates the discharge power. Indicates the rechargeable power. and These are the rated discharge power and rated charging power of energy storage, respectively. and These are discharge efficiency and charging efficiency, respectively. To control the cycle.

[0093] Furthermore, the energy storage charging and discharging power command must meet the following requirements: ; .

[0094] Furthermore, in the next control cycle, i.e. the next time period, the energy storage state is updated as follows: .

[0095] Furthermore, energy storage control commands are generated. These commands include: Energy storage charging power command ; Energy storage discharge power command ; Dynamic SOC lower limit , ; Dynamic SOC upper limit , ; Communication risk backup lock-in quantity ; Low-carbon discharge reserve ; Low-carbon charging margin ; The prohibited discharge range refers to the lower limit of dynamic energy and below; The low-carbon absorption charging range extends from low-carbon charging margin to dynamic energy upper limit. Reserve release conditions refer to the triggering rules that allow energy reserved in energy storage for safety or low-carbon purposes to be put back into scheduling use when specific conditions are met; for example, releasing communication risk reserves when communication quality is restored, releasing low-carbon discharge reserves when high-carbon periods end, etc. Abnormal alarm signals, such as communication quality abnormality alarms, are based on Sure.

[0096] Step S3, based on the calculation results of the preceding steps, utilizes the energy storage management system or converter controller to output executable control commands, thus translating theoretical calculations into practical applications and demonstrating excellent engineering feasibility. By setting a dynamic reserved domain for energy storage and actually sending control commands to schedule energy storage, the problem of the lack of distinction between "installed flexible capacity" and "effectively controllable flexible capacity" in energy storage scheduling in existing technologies is solved.

[0097] Furthermore, in one embodiment, a tiered release mechanism can be triggered when events such as emergency power supply, equipment overload, fault isolation, critical load protection, or mandatory low-carbon consumption occur.

[0098] Define the emergency release flag of the node as Set when any of the following conditions are met. Allowing the release of some reserved energy: Risks include node voltage exceeding limits, feeder overload, main transformer overload, loss of supply to critical loads, severe power curtailment, authorization from higher-level dispatching, confirmation of communication recovery, and autonomous strategies of local controllers.

[0099] Minimum energy boundary after release It can be represented as: ; in, and These are the reserved release ratios for communication risks and low-carbon discharge, with a value range of [missing information]. After the release is complete, the system returns the energy storage to the dynamic reserved domain according to the recovery strategy.

[0100] In this embodiment, by setting a tiered release mechanism, the emergency response mechanism of the system is improved, further enhancing the engineering feasibility and protection level of the method of the present invention.

[0101] It should be noted that steps S1 to S3 of the method of this invention are the execution process of each control cycle. In practical applications, each control cycle involves re-collecting multi-source data from the power grid system and updating the dynamic reserved domain for energy storage. The control cycle can be at the second, minute, fifteen-minute, or day-ahead / intra-day scheduling level. For local energy storage controllers, shorter cycles can be used; for provincial source-grid-load-storage platforms or distribution automation master stations, longer cycles can be used and boundary constraints can be issued. The method of this invention can adapt to multi-timescale control.

[0102] By collecting and updating data in each control cycle, communication quality is monitored in real time, avoiding the long-term fixed reservation of communication risks. This prevents excessive release of energy storage under communication degradation and also avoids the long-term retention of too much unusable capacity of energy storage after communication is restored.

[0103] Furthermore, the control commands sent in each control cycle are fed back in real time. If control commands fail for several consecutive cycles, the communication risk reservation coefficient is increased; if communication quality stabilizes and recovers, the communication risk reservation coefficient is gradually decreased. A smooth update method for the communication risk reservation coefficient is as follows: ; in, To reserve a coefficient for smoothed communication risks, This is the smoothing coefficient, and its value range is... .

[0104] In this embodiment, by setting a smooth update method for the communication risk reservation coefficient, it is possible to avoid frequent changes in the energy storage boundary caused by short-term communication quality fluctuations, thereby further ensuring the safe operation of the power grid system.

[0105] System Implementation Examples Another specific embodiment of the present invention discloses an effective and controllable capacity assessment and reservation system for energy storage under generalized sufficiency demand, including a multi-source data acquisition module, a communication risk reservation generation module, a low-carbon adjustment direction identification module, an energy storage dynamic reservation domain generation module, a correction module, and an instruction module.

[0106] Multi-source data acquisition module, used to acquire multi-source data from the power grid system; The communication risk reservation generation module is used to generate communication risk reservation coefficients for each node in the power grid system based on the communication task type. The low-carbon adjustment direction identification module is used to generate the low-carbon charging margin coefficient of each node based on the indicators of new energy surplus, wind and solar curtailment risk, reverse transmission restriction degree, and low-carbon absorption demand. It also generates the low-carbon discharge reserve coefficient of each node based on the carbon emission factor of electricity purchase or marginal carbon emission factor, as well as peak load, high-carbon unit replacement demand, and power supply support risk indicators. The energy storage dynamic reservation domain generation module is used to calculate the effective controllable capacity of energy storage, i.e., the energy storage dynamic reservation domain, based on the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node and the access relationship between each node and energy storage. The correction module is used to correct the discharge power and rechargeable power of the energy storage in the current control cycle based on the dynamic reservation domain of the energy storage. The instruction module is used to generate and send corresponding energy storage control instructions based on the dischargeable and rechargeable power of the energy storage in the current control cycle.

[0107] Furthermore, it may also include a boundary conflict coordination module and a closed-loop feedback module. Among these, The boundary conflict coordination module is used to implement a dynamic reserved domain conflict coordination mechanism; The closed-loop feedback module is used to receive real-time feedback results of control commands.

[0108] Compared to existing technologies, the beneficial effects of the energy storage effective and controllable capacity assessment and reservation system under the generalized adequacy demand provided in this embodiment are basically the same as those provided in the method embodiment, and will not be elaborated here.

[0109] It should be noted that the above embodiments are based on the same inventive concept, and any parts not described repeatedly can be referenced from each other.

[0110] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing and reserving effective and controllable energy storage capacity under a generalized abundance demand, characterized in that, Each control cycle includes the following steps: Based on the key factors affecting the effective and controllable capacity of energy storage, the communication risk reservation coefficient, low-carbon discharge reservation coefficient, and low-carbon charging margin coefficient of each node in the power grid system are generated. Based on the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node, and the access relationship between each node and energy storage, the effective controllable capacity of energy storage, i.e., the dynamic reservation domain of energy storage, is calculated. Based on the dynamic reserved domain of energy storage, the discharge power and rechargeable power of energy storage in the current control cycle are corrected, and corresponding energy storage control commands are generated and sent.

2. The energy storage dynamic reservation control method according to claim 1, characterized in that, The effective controllable capacity of energy storage, i.e., the dynamic reserve domain of energy storage, is calculated based on the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node, and the access relationship between each node and energy storage. This includes: Based on the rated energy of the energy storage, the communication risk reservation amount, low-carbon discharge reservation amount and low-carbon charging margin of the energy storage are determined by using the communication risk reservation coefficient, low-carbon discharge reservation coefficient, low-carbon charging margin coefficient of each node and the access relationship between each node and the energy storage. The dynamic energy ceiling is determined based on the difference between the rated energy capacity and the low-carbon charging margin. The dynamic energy lower limit is determined based on the sum of the physical minimum energy, communication risk reserve, and low-carbon discharge reserve. A dynamic energy storage reserve domain is constructed based on a dynamic energy upper limit and a dynamic energy lower limit; where, if the dynamic energy upper limit is less than the dynamic energy lower limit, the dynamic energy storage reserve domain is 0.

3. The energy storage dynamic reservation control method according to claim 2, characterized in that, The method for correcting the discharge and rechargeable power of energy storage in the current control cycle based on the dynamic reserved domain of energy storage includes: The discharge power of the energy storage is determined based on the rated discharge power of the energy storage, the actual stored energy, and the lower limit of the dynamic energy. The rechargeable power of the energy storage in the current control cycle is determined based on the rated charging power of the energy storage, the actual stored energy, and the dynamic energy upper limit.

4. The energy storage dynamic reservation control method according to claim 3, characterized in that, The key factors affecting the effective and controllable capacity of energy storage include communication task type, carbon emission factor of electricity purchase, marginal carbon emission factor, peak load, demand for replacement of high-carbon units, power supply support risk indicators, new energy surplus, wind and solar curtailment risk, degree of restriction on reverse transmission, and low-carbon absorption demand indicators. The communication risk reservation coefficient, low-carbon discharge reservation coefficient, and low-carbon charging margin coefficient for each node of the power grid system, generated based on key factors affecting the effective and controllable capacity of energy storage, include: Based on the communication task type, generate the communication risk reservation coefficient for each node in the power grid system; Based on the carbon emission factor of electricity purchase or the marginal carbon emission factor, as well as peak load, demand for replacement of high-carbon units, and power supply support risk indicators, low-carbon discharge reserve coefficients are generated for each node. The low-carbon charging margin coefficient for each node is generated based on indicators such as renewable energy surplus, wind and solar curtailment risk, degree of restriction on reverse transmission, and demand for low-carbon absorption.

5. The energy storage dynamic reservation control method according to claim 4, characterized in that, The communication type is ultra-reliable low-latency communication task, enhanced mobile broadband task, or massive machine communication task. When the communication task is an ultra-reliable low-latency communication task, key communication quality indicators are calculated based on the satisfaction of key services; when the communication task is an enhanced mobile broadband task or a massive machine communication task, key communication quality indicators are calculated based on the success rate of control commands, telemetry integrity, link availability, and communication latency. A communication risk reservation coefficient is generated based on the key communication quality indicators and communication quality security thresholds.

6. The energy storage dynamic reservation control method according to claim 4, characterized in that, The communication risk reservation coefficient is generated based on the key communication quality indicators and communication quality security thresholds using the following formula: ； in, Represents a node During the time period corresponding to the current control cycle Communication risk reservation coefficient; Represents a node During the time period corresponding to the current control cycle Key communication quality indicators; For communication quality and safety thresholds; This indicates the upper limit of the communication risk reservation coefficient; Indicates the communication degradation sensitivity coefficient; The calculation method for the key communication quality indicators is as follows: When the communication task is an ultra-reliable low-latency communication task, the calculation formula is: ;in, Represents nodes The set of associated key terminals or control objects Indicates a key terminal or control object During the time period corresponding to the current control cycle Satisfaction with key communication services; When the communication task is an enhanced mobile broadband task or a massive machine-type communication task, the calculation formula is: ;in, To normalize the success rate of control commands, To normalize the integrity of telemetry, To normalize link availability, To normalize communication delay; , , , These are the corresponding weights.

7. The energy storage dynamic reservation control method according to claim 4, characterized in that, The calculation method for the low-carbon discharge reserve coefficient is as follows: ； in, For nodes During the time period corresponding to the current control cycle Low-carbon discharge reserve coefficient; The upper limit of the coefficient reserved for low-carbon discharge; The sensitivity coefficient is low-carbon discharge. To meet the low-carbon requirements of the discharge side; ; This refers to the normalized carbon emission factor for electricity purchases or the marginal carbon emission factor. To normalize peak load; To meet the demand for replacement of normalized high-carbon units; To provide risk indicators for normalized power supply support; , , , These are the corresponding weights.

8. The energy storage dynamic reservation control method according to claim 4, characterized in that, The calculation method for the low-carbon charging margin coefficient is as follows: ； in, For nodes During the time period corresponding to the current control cycle Low-carbon charging margin coefficient; This represents the upper limit of the low-carbon charging margin coefficient. The sensitivity coefficient for low-carbon charging; To meet the low-carbon requirements of the charging side; ; To normalize the surplus of new energy sources; Risks of curtailment of wind and solar power due to normalization; To normalize the degree of restriction on back-transmission; As a low-carbon absorption demand indicator; , , , These are the corresponding weights.

9. The energy storage dynamic reservation control method according to claim 4, characterized in that, The determination of the communication risk reserve, low-carbon discharge reserve, and low-carbon charging margin of the energy storage based on the rated energy capacity of the energy storage, using the communication risk reserve coefficient, low-carbon discharge reserve coefficient, low-carbon charging margin coefficient of each node, and the access relationship between each node and the energy storage, includes: ； ； ； in, Indicates energy storage at the node n During the time period corresponding to the current control cycle Communication risk reserve; Indicates energy storage at the node n During the time period corresponding to the current control cycle Low-carbon discharge reserve; Indicates energy storage at the node n During the time period corresponding to the current control cycle Low-carbon charging margin; Indicates energy storage Access Node The relationship is 1 for connected and 0 for not connected; For energy storage nodes n The equivalent rated energy capacity; , , They are nodes During the time period corresponding to the current control cycle Communication risk reservation coefficient, low-carbon discharge reservation coefficient, and low-carbon charging margin coefficient.

10. The energy storage dynamic reservation control method according to claim 4, characterized in that, The method further includes setting a dynamic reservation domain conflict coordination mechanism, which includes: when the dynamic energy upper limit is less than the dynamic energy lower limit, reducing the lower priority reservation amount according to priority order, wherein... When the system is in a power supply support scenario, the priority order from high to low is: communication risk reserve, low-carbon discharge reserve, and low-carbon charging margin. When the system is in a scenario of mandatory consumption of new energy, the priority order from high to low is: communication risk reserve, low-carbon charging margin, and low-carbon discharge reserve.

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