A method, system, medium and product for coordinated control of a fire storage combined system

Abstract

The application discloses a kind of fire storage combined system's synergic control method, system, medium and product, belong to fire storage collaborative control field, the method is: obtaining future electricity price, thermal power plan output, frequency modulation market bid result, and current power generation instruction, thermal power output, real-time electricity price and energy storage state of charge;Determine energy storage operating mode and power partition parameter containing frequency modulation and arbitrage power upper limit based on bid result, match collaborative strategy to obtain charge-discharge strategy;Calculate total power deviation and filter decomposition fast fluctuation component;In combination with real-time data, partition parameter, charge-discharge strategy and fluctuation component, respectively generate energy storage frequency modulation and peak shaving arbitrage instruction, superimposed as total power instruction and calculate thermal power output target, respectively regulate energy storage and thermal power.By implementing the present application, the problem of multiple adjustment target conflicts and low source and storage collaborative efficiency caused by lack of forward-looking control strategy and lack of resource allocation mechanism in the fire storage combined system under the multi-market environment can be solved.

Description

Technical Field

[0001] This invention belongs to the field of fire-storage integrated control, and relates to a method, system, medium and product for integrated fire-storage system. Background Technology

[0002] With the deepening of power market reforms, thermal power plants have transformed into competitive entities that must simultaneously participate in both the energy spot market and the frequency regulation ancillary service market. Configuring electrochemical energy storage systems to form a combined thermal power and energy storage operation mode has become an important means of enhancing the power plant's regulation capabilities. This mode requires the control system to be able to handle complex market signals and coordinate thermal power units and energy storage systems with different response characteristics to jointly execute grid commands in order to cope with changing load demands and market environments.

[0003] Existing technologies for handling combined thermal and energy storage operations often employ fixed power allocation ratios or simple priority rules, lacking a mechanism for dynamically adjusting control strategies based on market conditions. This leads to rigid decision-making when facing multiple regulation objectives, making it difficult to accommodate the operational needs of different market segments. Furthermore, existing solutions often treat thermal power and energy storage as homogeneous regulation resources for superimposed control, ignoring the fundamental differences between the two in response speed and time scale. This forces thermal power units to frequently respond to rapidly fluctuating commands, reducing the stability of unit operation and limiting the full utilization of the rapid regulation advantages of energy storage systems. Consequently, the efficiency of source-storage synergy is low, and the optimal balance of overall system performance cannot be achieved. Summary of the Invention

[0004] This application provides a collaborative control method, system, medium, and product for a combined fire and energy storage system, which can solve the problems of conflicting regulation objectives and low source-storage coordination efficiency caused by the lack of forward-looking control strategies and resource allocation mechanisms in multi-market environments.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a coordinated control method for a combined fire and energy storage system, comprising: It obtains electricity price forecast data for the period in the future dispatch cycle, planned output data of thermal power units, and bidding results of the automatic generation control frequency regulation market, as well as the current generation instructions of the power grid, current output data of thermal power units, real-time electricity price data, and real-time state of charge data of energy storage systems. Based on the bidding results, the operating mode and power capacity zoning parameters of the energy storage system are determined. Then, based on the operating mode, power capacity zoning parameters, electricity price forecast data, and planned output data, a preset coordination strategy is matched to obtain the charging and discharging strategy of the energy storage system. The power capacity zoning parameters include the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power. Based on the current power generation command and current output data, calculate the real-time total power deviation data and perform frequency domain filtering decomposition to obtain the fast fluctuation power component with a frequency higher than the preset cutoff frequency. Based on the real-time electricity price data, real-time state of charge data, power capacity zoning parameters, charging and discharging strategies, and rapid fluctuation power components, calculate the energy storage frequency regulation power command data and the energy storage peak shaving arbitrage power command data, and superimpose them to obtain the total energy storage power command data. Based on the real-time total power deviation data and the total energy storage power command data, the output target data of the thermal power unit is calculated; Based on the total energy storage power command data and output target data, the energy storage system and thermal power unit are regulated and operated respectively.

[0006] Compared to existing technologies, the embodiments of this application have the following beneficial effects: By acquiring comprehensive sensing data covering market forward-looking information and real-time system status, complete data support is provided for multi-objective collaborative decision-making; based on the bidding results, power capacity partitioning parameters including the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power are determined, and charging and discharging strategies are obtained by matching preset collaborative strategies according to the operating mode and multi-dimensional data. This resource isolation mechanism based on rigid partitioning solves the problem of power resource competition under multiple concurrent tasks from the physical constraint level, ensuring that different control objectives do not interfere with each other and execute independently in resource allocation, overcoming the problem of neglecting one aspect due to fixed allocation in existing technologies; by performing frequency domain filtering decomposition on the real-time total power deviation to obtain the fast fluctuating power component, the time scale difference between the millisecond-level response of the energy storage system and the minute-level inertia of the thermal power unit is utilized to achieve decoupling of power commands in the frequency domain, solving the problem of control lag and equipment loss caused by the mismatch of source and storage response characteristics. This solution overcomes the low collaborative efficiency caused by homogeneous control in existing technologies. It calculates and superimposes energy storage frequency regulation and peak shaving arbitrage commands based on rapid fluctuation components and zoning parameters, enabling energy storage to maintain rapid regulation capabilities while also considering energy time-shifting functions. This achieves functional integration and maximizes the efficiency of a single device across multiple control dimensions. Finally, based on the total deviation and the energy storage command computer group output target, it separately regulates and controls the system, forming a deep collaborative closed loop of "energy storage supplementing high frequencies and generating units tracking low frequencies." This not only ensures zero steady-state error tracking of the total output of the combined system to the grid commands but also significantly improves the overall stability, economy, and equipment lifespan of the system by smoothing the generating unit regulation trajectory. Through the organic combination of "scenario strategy matching - rigid capacity zoning - frequency domain command decoupling - source-storage residual compensation," this solution systematically solves the technical problems of conflicting regulation targets and low source-storage collaborative efficiency in thermal power-storage combined systems under complex market environments caused by a lack of forward-looking control strategies and resource allocation mechanisms.

[0007] In some embodiments of the first aspect of this application, determining the operating mode and power capacity zoning parameters of the energy storage system based on the winning bid results includes: If the bidding result is a successful bid, the operating mode will be set to frequency regulation priority mode, and the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power will both be equal to half of the rated power of the energy storage system. If the bidding result is "unsuccessful", then the operating mode will be set to pure arbitrage mode, the upper limit of the arbitrage-specific power will be set to the rated power of the energy storage system, and the upper limit of the frequency regulation-specific power will be set to zero.

[0008] Compared with existing technologies, the above embodiments have the following beneficial effects: By setting the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power to half of the rated power of the energy storage system in the winning bid or default state, a clear resource 50 / 50 allocation mechanism is established, ensuring the fixed capacity reserve required for frequency regulation services and preventing performance risks caused by excessive occupation of frequency regulation resources by arbitrage behavior; while in the unsuccessful bid state, setting all rated power as the upper limit of arbitrage dedicated power and setting the upper limit of frequency regulation to zero realizes the complete tilt of resources towards a single high-yield target, eliminates unnecessary capacity reservation restrictions, thereby maximizing the power utilization rate and price difference capture capability of the energy storage system in the pure arbitrage mode, and demonstrating the effect of dynamic resource optimization configuration brought about by the operation mode switching.

[0009] In some embodiments of the first aspect of this application, the step of matching a preset coordination strategy with the energy storage system based on the operating mode, power capacity zoning parameters, electricity price forecast data, and planned output data to obtain a charging and discharging strategy for the energy storage system includes: The future scheduling cycle is divided into several consecutive time segments, and the electricity price forecast data for each time segment is compared with the predefined collaborative strategy to determine the market scenario type of each time segment; wherein, the market scenario type includes deep valley period, peak peak period, ramp transition period and flat fluctuation period; Based on the aforementioned operating mode, power capacity zoning parameters, and planned output data, a charging / discharging strategy and state of charge threshold matching the aforementioned market scenario type are generated, wherein: If the market scenario type is a deep valley period, a charging priority strategy is generated, and a first charge state lower limit threshold and a first charge state upper limit threshold are set. If the market scenario type is a peak period, a discharge priority strategy is generated, and a second charge state lower limit threshold and a second charge state upper limit threshold are set. If the market scenario type is a flat fluctuation period or a ramp-up transition period, a strategy to maintain the balance of the state of charge is generated, and a third state of charge lower limit threshold and a third state of charge upper limit threshold are set. Wherein, the first charge state lower limit threshold is greater than the second charge state lower limit threshold, and the first charge state upper limit threshold is greater than the second charge state upper limit threshold; the third charge state lower limit threshold is less than the second charge state lower limit threshold, and the third charge state upper limit threshold is greater than the first charge state upper limit threshold.

[0010] Compared to existing technologies, the above embodiments have the following beneficial effects: By dividing the future cycle into time segments and comparing the strategy matrix to determine the type of each market scenario, the control strategy and market electricity price characteristics are accurately mapped, enabling the system to adapt to different market environments; a higher first state of charge threshold is set for valley periods and charging priority instructions are generated to guide energy storage to fully store energy in low-price areas, reducing charging costs; a lower second state of charge threshold is set for peak periods and discharging priority instructions are generated to encourage energy storage to release energy in high-price areas, improving discharging revenue; a broad third state of charge threshold is set for flat or ramp periods and maintaining balance instructions are generated to maintain the adjustment margin of energy storage to cope with sudden fluctuations; by limiting the relationship between the first threshold being greater than the second threshold, the third lower limit being less than the second lower limit, and the third upper limit being greater than the first upper limit, a differentiated state of charge management range is constructed, which not only ensures the extreme charging and discharging depth during peak-valley arbitrage but also ensures the bidirectional adjustment flexibility during transition periods, thereby improving the overall economic benefits and operational safety throughout the entire cycle.

[0011] In some embodiments of the first aspect of this application, the step of calculating energy storage frequency regulation power command data and energy storage peak shaving arbitrage power command data based on the real-time electricity price data, real-time state of charge data, power capacity zoning parameters, charging and discharging strategies, and fast fluctuation power components includes: If the operating mode is frequency regulation priority mode, then the rapid fluctuation power component data is restricted to the upper limit of the frequency regulation dedicated power to obtain the energy storage frequency regulation power command data, and the rated power of the energy storage system is subtracted from the energy storage frequency regulation power command data to obtain the remaining power quota; the smaller value between the arbitrage dedicated power upper limit and the remaining power quota is taken as the current available arbitrage power upper limit; the expected arbitrage power value is calculated according to the charging and discharging strategy, real-time electricity price data and real-time state of charge data, and the expected arbitrage power value is restricted to the upper limit of the available arbitrage power to obtain the energy storage peak shaving arbitrage power command data; If the operating mode is pure arbitrage mode, the energy storage frequency regulation power command data is set to zero, and the expected arbitrage power value is calculated based on the charging and discharging strategy, real-time electricity price data, and real-time state of charge data; the expected arbitrage power value is limited to the rated power range of the energy storage system to obtain the energy storage peak shaving arbitrage power command data.

[0012] Compared to existing technologies, the above embodiments have the following beneficial effects: In frequency regulation priority mode, frequency regulation commands are obtained by limiting the rapid fluctuation components within the frequency regulation dedicated power limit, ensuring the immediacy and compliance of frequency regulation response; by calculating the rated power minus the absolute value of the frequency regulation command to obtain the remaining power quota, and taking the smaller value between the arbitrage dedicated power limit and the remaining quota as the available arbitrage power limit, this dynamic coupling constraint mechanism mathematically strictly guarantees that arbitrage power will not erode the physical capacity boundary required for frequency regulation, achieving maximum benefit under safety constraints; by limiting the expected arbitrage power within the available limit, frequency regulation capacity loss due to excessive arbitrage is prevented; while in pure arbitrage mode, by setting the frequency regulation command to zero and limiting the expected arbitrage power within the rated power, the constraint of frequency regulation capacity is removed, allowing energy storage to participate in market arbitrage at full power, thereby achieving differentiated control objectives of "safety priority" and "profit priority" in different operating modes.

[0013] In some embodiments of the first aspect of this application, the superposition to obtain the total energy storage power command data includes: The initial superimposed power value is obtained by adding the energy storage frequency regulation power command data and the energy storage peak shaving arbitrage power command data. If the absolute value of the initial superimposed power exceeds the rated power of the energy storage system, then the energy storage frequency regulation power command data is kept unchanged, and the energy storage peak shaving arbitrage power command data is adjusted so that the adjusted initial superimposed power is equal to the rated power of the energy storage system, and is used as the total energy storage power command data; otherwise, the initial superimposed power value is directly used as the total energy storage power command data.

[0014] Compared with existing technologies, the above embodiments have the following beneficial effects: by adding the frequency modulation and arbitrage commands to obtain the initial superposition value and determining whether its absolute value exceeds the rated power, a final hardware physical limit verification step is introduced; if it exceeds the limit, the frequency modulation command remains unchanged and the arbitrage command is adjusted. This "frequency modulation priority" truncation logic ensures the absolute fulfillment of the grid auxiliary service obligations under extreme operating conditions, avoiding equipment shutdown or assessment penalties due to exceeding the total power limit; if it does not exceed the limit, the initial value is directly adopted, preserving the normal collaborative control results, thereby achieving smooth output of control commands and improved system stability while ensuring the safe operation boundary of the equipment.

[0015] In some embodiments of the first aspect of this application, calculating the output target data of the thermal power unit based on the real-time total power deviation data and the total energy storage power command data includes: The actual output data of the thermal power unit at the current moment is added to the real-time total power deviation data to obtain the theoretical total demand power value. The total energy storage power command data is then subtracted from the theoretical total demand power value to obtain the output target data of the thermal power unit.

[0016] Compared with existing technologies, the above embodiments have the following beneficial effects: by adding the actual output data of the thermal power unit at the current moment with the real-time total power deviation data to obtain the theoretical total demand power value, the total system output level required by the grid command is accurately reconstructed; by subtracting the total power command data of energy storage from it, the output target of the thermal power unit is obtained. This calculation logic essentially separates the power already undertaken by energy storage from the total demand, so that the thermal power unit only needs to compensate for the remaining power gap. This "residual compensation" control strategy makes full use of the rapid adjustment capability of energy storage to smooth fluctuations, making the output command of the thermal power unit smoother and the rate of change lower, effectively reducing the thermal stress loss and fuel waste of the unit, while ensuring that the total output of the joint system tracks the automatic generation control command of the grid without steady-state error.

[0017] Secondly, the present invention also provides a collaborative control system for a combined thermal power and energy storage system, comprising: a data acquisition module, a matching module, a decomposition module, an energy storage calculation module, a thermal power meter module, and a control module; The data acquisition module is used to acquire electricity price forecast data, planned output data of thermal power units, and winning bid results of the automatic generation control frequency regulation market for the period in the future scheduling cycle, as well as the current power generation instructions of the power grid, the current output data of thermal power units, real-time electricity price data, and real-time state of charge data of the energy storage system. The matching module is used to determine the operating mode and power capacity zoning parameters of the energy storage system based on the bidding results, and to match the preset coordination strategy according to the operating mode, power capacity zoning parameters, electricity price forecast data and planned output data to obtain the charging and discharging strategy of the energy storage system; wherein, the power capacity zoning parameters include the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power. The decomposition module is used to calculate real-time total power deviation data and perform frequency domain filtering decomposition based on the current power generation command and current output data to obtain fast fluctuation power components with frequencies higher than the preset cutoff frequency. The energy storage calculation module is used to calculate energy storage frequency regulation power command data and energy storage peak shaving arbitrage power command data based on the real-time electricity price data, real-time state of charge data, power capacity zoning parameters, charging and discharging strategies and rapid fluctuation power components, and then superimpose them to obtain total energy storage power command data. The thermal power meter module is used to calculate the output target data of the thermal power unit based on the real-time total power deviation data and the total energy storage power command data; The control module is used to control the operation of the energy storage system and the thermal power unit based on the total energy storage power command data and the output target data.

[0018] Compared with existing technologies, the above embodiments of this application have the following beneficial effects: By acquiring full-dimensional perception data covering market forward-looking information and real-time system status, complete data support is provided for multi-objective collaborative decision-making; based on the bidding results, power capacity partitioning parameters including the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power are determined, and charging and discharging strategies are obtained according to the operating mode and multi-dimensional data matching preset collaborative strategies. This resource isolation mechanism based on rigid partitioning solves the problem of power resource competition under multiple concurrent tasks from the physical constraint level, ensuring that different control objectives do not interfere with each other and execute independently in resource allocation, overcoming the problem of neglecting one aspect due to fixed allocation in existing technologies; by performing frequency domain filtering decomposition on the real-time total power deviation to obtain the fast fluctuating power component, the time scale difference between the millisecond-level response of the energy storage system and the minute-level inertia of the thermal power unit is utilized to achieve decoupling of power commands in the frequency domain, solving the problem of control lag and equipment loss caused by the mismatch of source and storage response characteristics. This solution overcomes the low collaborative efficiency caused by homogeneous control in existing technologies. It calculates and superimposes energy storage frequency regulation and peak shaving arbitrage commands based on rapid fluctuation components and zoning parameters, enabling energy storage to maintain rapid regulation capabilities while also considering energy time-shifting functions. This achieves functional integration and maximizes the efficiency of a single device across multiple control dimensions. Finally, it adjusts the output target of the computer group based on the total deviation and energy storage command, forming a deep collaborative closed loop of "energy storage supplementing high frequency and generating units tracking low frequency." This not only ensures zero steady-state error tracking of the total output of the combined system to the grid command but also significantly improves the overall stability, economy, and equipment lifespan of the system by smoothing the generating unit's regulation trajectory. Through the organic combination of "scenario strategy matching - rigid capacity zoning - frequency domain command decoupling - source-storage residual compensation," this solution systematically solves the technical problems of conflicting regulation targets and low source-storage collaborative efficiency in thermal power-storage combined systems caused by a lack of forward-looking control strategies and resource allocation mechanisms in complex market environments.

[0019] In some embodiments of the second aspect of this application, the matching module includes: a mode conversion unit; The mode conversion unit is configured to, if the bidding result is a successful bid, set the operating mode to frequency regulation priority mode and set the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power to be equal to half of the rated power of the energy storage system; if the bidding result is a failed bid, set the operating mode to pure arbitrage mode, set the upper limit of arbitrage dedicated power to the rated power of the energy storage system, and set the upper limit of frequency regulation dedicated power to zero.

[0020] Compared with existing technologies, the above embodiments have the following beneficial effects: By setting the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power to half of the rated power of the energy storage system in the winning bid or default state, a clear resource 50 / 50 allocation mechanism is established, ensuring the fixed capacity reserve required for frequency regulation services and preventing performance risks caused by excessive occupation of frequency regulation resources by arbitrage behavior; while in the unsuccessful bid state, setting all rated power as the upper limit of arbitrage dedicated power and setting the upper limit of frequency regulation to zero realizes the complete tilt of resources towards a single high-yield target, eliminates unnecessary capacity reservation restrictions, thereby maximizing the power utilization rate and price difference capture capability of the energy storage system in the pure arbitrage mode, and demonstrating the effect of dynamic resource optimization configuration brought about by the operation mode switching.

[0021] Thirdly, the present invention also provides a computer program product, including a computer program or instructions, which, when executed, implement the collaborative control method of any of the fire-storage combined systems of the present invention.

[0022] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the collaborative control method of any of the fire-storage combined systems of the present invention. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a collaborative control method for a combined fire and energy storage system provided in some embodiments of the present invention.

[0024] Figure 2 This is a three-level collaborative control architecture and data flow diagram of a combined fire and energy storage system provided in some embodiments of the present invention.

[0025] Figure 3 This is a schematic diagram of energy storage power and energy rigid partitioning in a frequency modulation priority mode provided in some embodiments of the present invention.

[0026] Figure 4 This is a mid-level real-time coordination control logic diagram provided in some embodiments of the present invention.

[0027] Figure 5 This is a schematic diagram of the structure of a collaborative control system for a combined fire and energy storage system provided in some embodiments of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely 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 without creative effort are within the scope of protection of the present invention.

[0029] Example 1: Please refer to Figure 1 To address the problems of conflicting regulatory objectives and low source-storage coordination efficiency in combined fire-storage systems under multi-market environments due to the lack of forward-looking control strategies and resource allocation mechanisms, an embodiment of the present invention provides a coordinated control method for combined fire-storage systems, comprising steps S1 to S6: Step S1: Obtain electricity price forecast data, planned output data of thermal power units, and bidding results of the automatic generation control frequency regulation market for the period in the future scheduling cycle, as well as the current generation instructions of the power grid, current output data of thermal power units, real-time electricity price data, and real-time state of charge data of energy storage systems. Step S2: Based on the bidding results, determine the operating mode and power capacity zoning parameters of the energy storage system, and match the preset coordination strategy according to the operating mode, power capacity zoning parameters, electricity price forecast data and planned output data to obtain the charging and discharging strategy of the energy storage system; wherein, the power capacity zoning parameters include the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power. Furthermore, step S2 can be implemented through the following preferred embodiments, including steps S21-S23: S21: If the bidding result is a successful bid, the operating mode is set to frequency regulation priority mode, and the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power are both set to half of the rated power of the energy storage system; if the bidding result is a failed bid, the operating mode is set to pure arbitrage mode, and the upper limit of arbitrage dedicated power is set to the rated power of the energy storage system, while the upper limit of frequency regulation dedicated power is set to zero.

[0030] In this preferred embodiment, by setting both the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power to half of the rated power of the energy storage system in the winning bid or default state, a clear resource 50 / 50 allocation mechanism is established, ensuring the fixed capacity reserve required for frequency regulation services and preventing performance risks caused by excessive occupation of frequency regulation resources by arbitrage behavior; while in the unsuccessful bid state, all rated power is set as the upper limit of arbitrage dedicated power and the upper limit of frequency regulation is set to zero, realizing the complete tilt of resources towards a single high-yield target, eliminating unnecessary capacity reservation restrictions, thereby maximizing the power utilization rate and price difference capture capability of the energy storage system in the pure arbitrage mode, reflecting the effect of dynamic resource optimization configuration brought about by the operation mode switching.

[0031] S22: Divide the future scheduling cycle into several consecutive time segments, and compare the electricity price forecast data of each time segment with the predefined collaborative strategy to determine the market scenario type of each time segment; wherein, the market scenario type includes deep valley period, peak peak period, ramp transition period and flat fluctuation period; S23: Based on the aforementioned operating mode, power capacity partitioning parameters, and planned output data, generate a charging / discharging strategy and state-of-charge threshold that match the aforementioned market scenario type, wherein: If the market scenario is a low-end period, a charging priority strategy is generated, and a first state of charge lower limit threshold and a first state of charge upper limit threshold are set; if the market scenario is a peak period, a discharging priority strategy is generated, and a second state of charge lower limit threshold and a second state of charge upper limit threshold are set; if the market scenario is a flat or fluctuating period or a ramp-up transition period, a state of charge balance maintenance strategy is generated, and a third state of charge lower limit threshold and a third state of charge upper limit threshold are set. Wherein, the first charge state lower limit threshold is greater than the second charge state lower limit threshold, and the first charge state upper limit threshold is greater than the second charge state upper limit threshold; the third charge state lower limit threshold is less than the second charge state lower limit threshold, and the third charge state upper limit threshold is greater than the first charge state upper limit threshold.

[0032] In this preferred embodiment, the core principle of the above-mentioned differentiated threshold setting is to use the state of charge (SOC) of the energy storage system as an energy buffer, and to adapt to the economic and safety requirements of different market scenarios by dynamically adjusting its operating range.

[0033] Specifically, during off-peak hours, when electricity prices are extremely low or even negative, the system's primary objective is to store energy at low cost. Therefore, a higher first state of charge threshold (e.g., 70%-90%) is set to guide energy storage to charge as much as possible, maintaining the SOC within this high range. This ensures sufficient "energy" for profitable discharge during peak hours. Simultaneously, the higher lower threshold avoids the risk of being unable to respond to sudden frequency regulation commands due to overcharging. During peak hours, when electricity prices are extremely high, the system's objective shifts to releasing energy at a high value. Therefore, a lower second state of charge threshold (e.g., 10%-30%) is set. This means that when the SOC exceeds the upper limit of this range, the system will implement a strong "discharge priority" strategy, prompting energy storage to rapidly release electricity to lower the SOC until it falls into the low range. This maximizes electricity sales revenue during high-price periods and reduces overall generation costs. This "high charging, low discharging" threshold misalignment design essentially constructs an energy transfer mechanism based on price differences. In addition, during flat or ramp-up periods, when electricity price fluctuations are unclear or the main task is to track the plan, the system sets a wide third state of charge threshold (e.g., 20%-80%, with a wider coverage) to keep the energy storage SOC in a broad middle range. This ensures that it has sufficient adjustment margin to cope with two-way AGC commands and capture small two-way price differences, and avoids losing flexibility due to the SOC being in an extreme position.

[0034] Step S3: Based on the current power generation command and current output data, calculate the real-time total power deviation data and perform frequency domain filtering decomposition to obtain the fast fluctuation power component with a frequency higher than the preset cutoff frequency. Due to the inertia of large thermal equipment such as boilers and turbines, thermal power units exhibit a lag of minutes in their output regulation, making it difficult to keep up with grid frequency fluctuations or AGC commands that occur at the second or even millisecond level. In contrast, electrochemical energy storage systems have a millisecond-level response speed, enabling them to instantly handle power fluctuations. Directly attributing all total power deviations to thermal power units would lead to frequent and significant adjustments, increasing equipment wear and fuel consumption. Conversely, attributing all deviations to energy storage would be limited by storage capacity, making it impossible to maintain power output for extended periods.

[0035] Therefore, this scheme uses digital filters (such as first-order low-pass filters) to perform spectral separation on the real-time total power deviation: fast fluctuation components with frequencies higher than the preset cutoff frequency (e.g., 0.05Hz, corresponding to a 20-second period) are identified as "high-frequency noise" or "fast disturbances." These instructions are allocated to the energy storage system, which utilizes its rapid response advantage for immediate suppression, satisfying the grid's requirements for regulation speed while avoiding ineffective actions by thermal power units. Meanwhile, low-frequency, slowly varying components with frequencies lower than the cutoff frequency represent the long-term trend of load changes. These instructions are implicit in the subsequent target calculations for thermal power units, which then smoothly track the load. This time-scale-based task division fully leverages the physical strengths of both types of equipment, achieving a synergistic effect of "energy storage addressing the symptoms (fast fluctuations) and thermal power addressing the root cause (long-term trends)," significantly improving the overall regulation quality and operational economy of the combined system.

[0036] Step S4: Based on the real-time electricity price data, real-time state of charge data, power capacity zoning parameters, charging and discharging strategies, and rapid fluctuation power components, calculate the energy storage frequency regulation power command data and the energy storage peak shaving arbitrage power command data, and superimpose them to obtain the total energy storage power command data; Furthermore, step S4 can be implemented through the following preferred embodiments, including steps S41-S44: S41: If the operating mode is frequency regulation priority mode, then the rapid fluctuation power component data is restricted to the upper limit of the frequency regulation dedicated power to obtain the energy storage frequency regulation power command data, and the rated power of the energy storage system is subtracted from the energy storage frequency regulation power command data to obtain the remaining power quota; the smaller value between the arbitrage dedicated power upper limit and the remaining power quota is taken as the current available arbitrage power upper limit; the expected arbitrage power value is calculated according to the charging and discharging strategy, real-time electricity price data and real-time state of charge data, and the expected arbitrage power value is restricted to the upper limit of the available arbitrage power to obtain the energy storage peak shaving arbitrage power command data; S42: If the operating mode is pure arbitrage mode, the energy storage frequency regulation power command data is set to zero, and the expected arbitrage power value is calculated based on the charging and discharging strategy, real-time electricity price data and real-time state of charge data; the expected arbitrage power value is limited within the rated power range of the energy storage system to obtain the energy storage peak shaving arbitrage power command data.

[0037] In frequency regulation priority mode, the system first ensures the absolute priority of frequency regulation tasks, directly mapping rapid fluctuation components to frequency regulation commands and strictly limiting them within the reserved frequency regulation-specific power limit. Subsequently, instead of simply using all remaining power for arbitrage, the system introduces a "remaining power quota," which dynamically calculates the truly available arbitrage space at the current moment after deducting the currently used frequency regulation power from the total quota. The smaller value between the "arbitrage-specific power limit" and the "remaining power quota" is taken as the final constraint, a double-insurance mechanism: the former prevents arbitrage power from exceeding the pre-planned economic boundary, and the latter prevents arbitrage power from crowding out the necessary frequency regulation physical capacity at the current moment. This dynamic constraint ensures that at any given moment, the total output of the energy storage system will not affect its ability to fulfill frequency regulation obligations due to arbitrage activities. In pure arbitrage mode, due to the absence of frequency regulation tasks, the system removes all constraints on frequency regulation capacity, directly limiting the expected arbitrage power within the physical rated power range of the energy storage system, thereby maximizing the revenue capture capability. This differentiated computing strategy, which uses different modes, allows the system to find the optimal balance between "ensuring security" and "seeking profit".

[0038] S43: Add the energy storage frequency regulation power command data and the energy storage peak shaving arbitrage power command data to obtain the initial superimposed power value; S44: Determine whether the absolute value of the initial superimposed power value exceeds the rated power of the energy storage system. If so, keep the energy storage frequency regulation power command data unchanged, adjust the energy storage peak shaving arbitrage power command data so that the adjusted initial superimposed power value is equal to the rated power of the energy storage system, and use it as the total energy storage power command data; otherwise, directly use the initial superimposed power value as the total energy storage power command data.

[0039] In this preferred embodiment, although frequency regulation and arbitrage power have been limited separately in the preceding steps, since they are calculated independently and then superimposed, and there may be phase overlap or instantaneous peak superposition during dynamic processes, the initial superimposed power value may briefly exceed the physical tolerance limit (rated power) of the energy storage converter (PCS) or battery pack. If an over-limit command is issued directly, it may cause the equipment overcurrent protection to trip, hardware damage, or grid assessment penalties. Therefore, this solution introduces a final cutoff logic based on the "frequency regulation priority" principle: when the total power exceeds the limit, the system unconditionally retains the frequency regulation power command because frequency regulation involves grid frequency stability and has the highest priority; at the same time, the arbitrage power command is compressed through mathematical adjustments (such as peak shaving) so that the sum of the two is strictly equal to the rated power. This approach not only physically protects the equipment safety but also establishes the operating principle of "ancillary services take precedence over market transactions" in business logic, ensuring the robustness and compliance of the system under extreme operating conditions.

[0040] Step S5: Calculate the output target data of the thermal power unit based on the real-time total power deviation data and the total energy storage power command data; Furthermore, step S5 can be implemented through the following preferred embodiment, including step S51: S51: Add the actual output data of the thermal power unit at the current moment and the real-time total power deviation data to obtain the theoretical total demand power value, and subtract the total energy storage power command data from the theoretical total demand power value to obtain the output target data of the thermal power unit.

[0041] In this preferred embodiment, the theoretical total demand power value is obtained by adding the actual output data of the thermal power unit at the current moment to the real-time total power deviation data, thus accurately reconstructing the total system output level required by the grid command. The output target of the thermal power unit is obtained by subtracting the total power command data of the energy storage from it. This calculation logic essentially separates the power already undertaken by the energy storage from the total demand, so that the thermal power unit only needs to compensate for the remaining power gap. This "residual compensation" control strategy makes full use of the rapid adjustment capability of the energy storage to smooth out fluctuations, making the output command of the thermal power unit smoother and the rate of change lower, effectively reducing the thermal stress loss and fuel waste of the unit, while ensuring that the total output of the joint system tracks the automatic generation control command of the grid without steady-state error.

[0042] Step S6: Based on the total energy storage power command data and the output target data, regulate and operate the energy storage system and the thermal power unit respectively.

[0043] In specific implementation, refer to Figure 2 The diagram illustrates a three-level collaborative control architecture and data flow diagram for a combined fire and energy storage system. This application can adopt a three-level control architecture of "upper-level optimized scheduling + middle-level real-time coordination + lower-level rapid execution": I. Upper-level optimized scheduling: (1) Obtain the future spot market electricity price forecast curve, thermal power unit planned output curve, and AGC frequency regulation market bidding results. Divide the future dispatch cycle into consecutive 15-minute time periods. Based on the forecast electricity price characteristics of each time period, match it with the built-in collaborative strategy (as shown in Table 1 below) to determine the market scenario to which each time period belongs, including: valley period, peak / peak period, ramp / transition period, flat / fluctuation period.

[0044] Table 1 Collaborative Strategies (2) Referring to Table 2 below, determine the global operation mode based on the AGC bidding results: The default setting for the current or future period is "Frequency Priority Mode" for AGC frequency modulation bidding. In this mode, if... Figure 3The diagram shown illustrates a rigid partitioning of energy storage power and energy in a frequency regulation priority mode, where the rated power P_ess_max and rated energy E_ess_max of the energy storage system are rigidly partitioned. Power partitioning: P_fr_max = P_arb_max = 0.5×P_ess_max. Where P_fr_max is the upper limit of the frequency modulation dedicated power pool, and P_arb_max is the upper limit of the peak shaving arbitrage dedicated power pool.

[0045] Energy allocation: Reserve E_fr = P_fr_max × 1 hour of continuous regulation power for frequency modulation; The remaining power E_arb = E_ess_max - E_fr is used for peak arbitrage.

[0046] When configuring 4 hours of energy storage, E_arb = 7 × P_fr_max × 1 hour.

[0047] When AGC frequency modulation fails to win the bid, it is set to "pure arbitrage mode". The partition is removed, and all resources are used for arbitrage: P_arb_max = P_ess_max, E_arb = E_ess_max.

[0048] Table 2 Mode Switching (3) Combine the matching scenario strategy and the unit's planned output to generate a dynamic SOC management strategy: If a peak-hour strategy is applied and the unit output is below the threshold (e.g., 50% of rated capacity), then a high SOC boundary is adopted (e.g., high > 90%, optimal 70-90%, low < 70%), with the control objective being "charging priority".

[0049] If a peak-hour strategy is applied and the unit output is higher than the threshold (e.g., 80% of rated capacity), then a low SOC boundary is adopted (e.g., high > 30%, optimal 10-30%, low < 10%), and the control objective is "discharge priority".

[0050] If a strategy is used to match flat or climbing periods, a broad SOC boundary (e.g., 10%-90%) is adopted, with the control objective being "maintaining regulatory capacity".

[0051] The allocation logic for SOC dynamic management is shown in Table 3 below: Table 3 SOC Allocation Logic Table Specifically, when generating scheduling instructions in step (3), the determination of the SOC target interval follows the following dynamic strategy: Strategy inputs and decision factors: Main decision factor: matched market scenario. Based on the matching result of step (1), determine the macro strategy orientation to which the current time period belongs (such as "deep valley charging", "peak discharging", etc.).

[0052] Auxiliary decision factor: (planned / actual) output level of thermal power generating units. It is used for fine-tuning under the macro scenario to realize refined source-storage coordination. Two key thresholds are defined: P_G_low_th: unit low output threshold, for example, 50% of the rated output.

[0053] P_G_high_th: unit high output threshold, for example, 80% of the rated output.

[0054] Dynamic SOC interval definition: Three SOC functional zones are defined for each time period, with the boundaries being: [SOC_min, SOC_opt_low, SOC_opt_high, SOC_max].

[0055] Wherein, in the high SOC zone (SOC>SOC_opt_high): discharging is prioritized, and charging is strictly restricted or prohibited, aiming to release energy and avoid overcharging.

[0056] In the optimal frequency regulation zone (SOC_opt_low ≤ SOC ≤ SOC_opt_high): the energy storage is in the optimal working state, and can perform bidirectional power response freely and quickly to undertake frequency regulation tasks or execute low-power arbitrage.

[0057] In the low SOC zone (SOC<SOC_opt_low): charging is prioritized, and discharging is strictly restricted or prohibited, aiming to absorb energy, avoid over-discharging, and prepare for subsequent discharging or frequency regulation.

[0058] Scenario-based dynamic management strategy: Adopt differentiated boundary settings and charge-discharge priorities according to the matched scenario: Scenario A: matched with "deep valley time period" or similar strategies (target: minimum cost charging); SOC boundaries: set relatively high boundaries to encourage the SOC to move to a high level.

[0059] For example: SOC_opt_low = 70%, SOC_opt_high = 90%; SOC_max = 95% (hard safety upper limit), SOC_min = 20% (hard safety lower limit).

[0060] Linked with the unit: If the actual or planned output of the unit P_G<P_G_low_th, it is determined that the system has surplus power generation capacity or is in a low load interval. At this time, the charging power is given the highest priority, and the energy storage shall absorb low-cost electric energy as much as possible to quickly raise the SOC to the upper part of the optimal zone.

[0061] Control output: Generate a "charging priority" instruction. At the real-time coordination layer, the maximum available power is allocated for charging as much as possible unless it is occupied by the frequency regulation instruction.

[0062] Scenario B: Matching "peak / sharp peak period" or similar strategies (objective: highest value discharge): SOC boundary: Set a relatively low boundary to encourage the SOC to move to a low level.

[0063] For example: SOC_opt_low = 10%, SOC_opt_high = 30%; SOC_max = 95%, SOC_min = 5%.

[0064] Linked with the unit: If the actual or planned output of the unit P_G>P_G_high_th, it is determined that the system is in a high load operation state with high marginal power generation cost. At this time, the discharging power is given the highest priority, and the energy storage shall release electric energy as much as possible to replace the power generation of high-cost units, so as to quickly reduce the SOC to the lower part of the optimal zone.

[0065] Control output: Generate a "discharging priority" instruction. At the real-time coordination layer, on the premise of meeting the frequency regulation demand, the maximum available power is allocated for discharging as much as possible.

[0066] Scenario C: Matching "flat / fluctuation period" or "ramping / transition period" strategies (objective: maintain regulation capability or track the plan): SOC boundary: Set a wide boundary to maintain sufficient regulation margin.

[0067] For example: SOC_opt_low = 20%, SOC_opt_high = 80%; SOC_max = 90%, SOC_min = 10%. This wide interval aims to keep the SOC of the energy storage in the middle range, ensuring that it has sufficient capacity to cope with bidirectional AGC instructions and capture bidirectional small price differentials.

[0068] Linked with the unit: The unit operates according to the economic plan or the ramping curve. The main goal of the energy storage is to keep its own SOC within the interval [SOC_opt_low, SOC_opt_high], so as to prepare for the frequency regulation task or sudden price difference opportunity that may come at any time. The charging and discharging behavior is relatively moderate, which mainly serves the self-balance of SOC.

[0069] Control output: Generates commands for "maintain SOC" or "frequency modulation priority". The charge and discharge power is mainly used to compensate for the deviation of SOC from the target median (e.g., 50%), and the power change is gradual.

[0070] II. Real-time coordination at the middle layer: instruction decomposition and power aggregation: This layer receives policies and real-time data from the upper layer, performs rapid decision-making and command coordination, and its logical flow is as follows: Figure 4 The diagram shown is a mid-level real-time coordination and control logic diagram, including: (1) Data reception: Receive the scheduling plan output from the upper layer, as well as the real-time AGC command P_AGC, the real-time node price π, the actual output of the unit P_G, and the real-time SOC and power of the energy storage.

[0071] (2) AGC instruction decomposition: Calculate the total power deviation ΔP_total = P_AGC - P_G. Use a digital filter (such as a first-order high-speed filter) to decompose ΔP_total into: ① High-frequency fluctuation component ΔP_high: represents rapid random fluctuations on the order of seconds to minutes, which are planned to be handled by the energy storage system.

[0072] ②Low-frequency slowly varying component ΔP_low: Represents slow trend changes at the minute level or above, which are planned to be tracked by thermal power units.

[0073] (3) Power coordination based on operating mode and scenario: ① In "Frequency Priority Mode": Frequency modulation power allocation: P_fr_cmd = f_limit(ΔP_high, P_fr_max). This instruction has the highest real-time priority.

[0074] Arbitrage power calculation: Based on the current matching scenario strategy (such as "peak discharge"), real-time electricity price, and energy storage SOC status, calculate the expected arbitrage power P_arb_desired.

[0075] Rigid constraints and superposition: To ensure frequency modulation capability, arbitrage power must comply with capacity coupling constraints: |P_arb_cmd| ≤ min(P_arb_max, P_ess_max - |P_fr_cmd|).

[0076] The total energy storage command is then obtained by superimposing the commands: P_ess_cmd = P_fr_cmd + P_arb_cmd.

[0077] ② Under the "pure arbitrage model": P_fr_cmd = 0.

[0078] P_arb_cmd is generated entirely based on the arbitrage strategy of the current scenario and is only constrained by P_ess_max and SOC limits.

[0079] P_ess_cmd = P_arb_cmd.

[0080] (4) Unit target calculation: The new output target value of the computer unit: P_G_target = P_G + ΔP_total - P_ess_cmd. This design ensures that the total output of the combined system strictly tracks the AGC command, and that the unit primarily responds to low-frequency, smooth variation components.

[0081] III. Rapid execution at the lower level, device control and closed-loop feedback: (1) Instruction execution: The energy storage control system (PCS / EMS) receives P_ess_cmd and controls the battery pack to achieve precise charging / discharging of the target power within milliseconds to seconds.

[0082] The thermal power unit control system (DCS / CCS) receives P_G_target and smoothly adjusts the boiler, turbine and other equipment within minutes to bring the unit output close to the target value.

[0083] (2) Closed-loop feedback: The actual output of the unit, the real-time SOC of energy storage and power and other status information after execution are fed back to the middle coordination layer in real time to form a closed-loop control to deal with model errors and unknown disturbances.

[0084] Furthermore, taking a power plant with one 660MW high-efficiency supercritical unit and a supporting 30MW / 120MWh lithium iron phosphate battery energy storage system as an example, the proposed solution is implemented as follows: I. System Initialization and Strategy Matrix Loading: In the plant-level energy management system (EMS), collaborative strategies are pre-configured. This matrix serves as the core knowledge base for the upper-level optimization scheduling module.

[0085] II. Application Scenario Examples: Scenario A: During a deep valley period, AGC frequency modulation wins the bid; Time: 3:00 AM. The predicted spot market electricity price is -20 yuan / MWh (deep negative electricity price), and the AGC frequency regulation market has already won the bid.

[0086] Upper-level optimized scheduling (executed at 02:45): Scenario matching: "Peak period" in the electricity price feature matching strategy matrix.

[0087] Mode Decision: Since AGC won the bid, the mode is set to "frequency priority mode".

[0088] Capacity partitioning: P_fr_max = P_arb_max = 15 MW; E_fr = 15 MWh; E_arb = 105 MWh.

[0089] Strategy Generation: Based on the "peak season" strategy, formulate a scheduling plan: 1) Units: Keep the high-efficiency 660MW units operating at the minimum technical output of 380MW as the base load.

[0090] 2) Energy storage: The SOC target range is set at a high level (70%-90%), and it is planned to absorb low-priced electricity from the grid to charge the battery with the maximum available power (considering frequency regulation reserves).

[0091] Mid-level real-time coordination (time k, 03:00:00): Real-time data: P_AGC = 385 MW, P_G = 380 MW (output of 660MW unit), SOC = 40%, real-time electricity price = -25 yuan / MWh.

[0092] Command decomposition: ΔP_total = 5 MW. Assume that after decomposition, ΔP_high = 4.2 MW, ΔP_low = 0.8MW.

[0093] Power coordination: P_fr_cmd = 4.2 MW (frequency modulation response).

[0094] According to the "deep valley charging" strategy, the desired charging power P_arb_desired = -15 MW. Application constraint: Available arbitrage power = min(15, 30-4.2) = 15 MW. Therefore, P_arb_cmd = -15 MW.

[0095] P_ess_cmd = 4.2 - 15 = -10.8 MW (net charge).

[0096] Unit target: P_G_target = 380 + 5 - (-10.8) = 395.8 MW. A signal is sent to the 660MW unit, requesting it to slowly increase its output by 15.8MW.

[0097] Underlying Execution and Results: The energy storage system responded within 1 second, actually charging at a power output of 10.8MW. The 660MW unit smoothly increased its output to approximately 395.8MW within minutes. The total output of the combined system tracked the AGC command. In this process, the energy storage system not only completed the frequency regulation task but also achieved extremely low-cost charging, while the unit output changes smoothly.

[0098] Scenario B: During the evening rush hour, AGC frequency tuning fails to achieve its target. Time: 19:30. The predicted spot market electricity price is 1200 yuan / MWh, and the AGC frequency regulation project failed to win the bid.

[0099] Upper-level optimized scheduling: Scene matching: Matches the "peak / peak hours" strategy.

[0100] Model decision: Set as "pure arbitrage model".

[0101] Strategy generation, plan: 1) Units: Both units are upgraded to operate near the upper limit of the economic output range (e.g., 660MW unit to 550MW).

[0102] 2) Energy storage: The target range of SOC is set to the low level (10%-30%), and full power discharge is planned.

[0103] Real-time coordination at the middle management level: Real-time data: P_AGC = 550 MW (daily plan), P_G = 550 MW, SOC = 65%, real-time electricity price = 1180 yuan / MWh.

[0104] Power coordination: P_fr_cmd = 0.

[0105] According to the "peak discharge" strategy, P_arb_desired = +30 MW. In pure arbitrage mode, P_arb_cmd = +30 MW is directly taken.

[0106] P_ess_cmd = 30 MW.

[0107] Unit target: P_G_target = 550 + 0 - 30 = 500 MW. This means that the output of one unit needs to be reduced.

[0108] Underlying Execution and Synergistic Effects: The energy storage system discharges at full power (30MW). Based on the strategy matrix principle of "prioritizing the appropriate reduction of output for units with high coal consumption and high costs," the control system selects to reduce the unit output by approximately 30MW. Ultimately, the total plant output remains 500 + 30 = 530MW, but 30MW of power generation has been transferred from the generating units to the zero-fuel-cost energy storage, significantly reducing the total power generation cost and increasing overall profit during this period.

[0109] In summary, compared with the prior art, the above embodiments of this application have the following beneficial effects: By acquiring full-dimensional perception data covering market forward-looking information and real-time system status, complete data support is provided for multi-objective collaborative decision-making; based on the bidding results, power capacity partitioning parameters including the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power are determined, and charging and discharging strategies are obtained according to the operating mode and multi-dimensional data matching preset collaborative strategies. This resource isolation mechanism based on rigid partitioning solves the problem of power resource competition under multiple concurrent tasks from the physical constraint level, ensuring that different control objectives do not interfere with each other and execute independently in resource allocation, overcoming the problem of neglecting one aspect due to fixed allocation in the prior art; by performing frequency domain filtering decomposition on the real-time total power deviation to obtain the fast fluctuating power component, the time scale difference between the millisecond-level response of the energy storage system and the minute-level inertia of the thermal power unit is utilized to achieve decoupling of power commands in the frequency domain, solving the control lag and equipment loss problem caused by the mismatch of source and storage response characteristics. This solution addresses the problem of low collaborative efficiency caused by homogeneous control in existing technologies. It calculates and superimposes energy storage frequency regulation and peak shaving arbitrage commands based on rapid fluctuation components and zoning parameters, enabling energy storage to maintain rapid regulation capabilities while also incorporating energy time-shifting functions. This achieves functional integration and maximizes the efficiency of a single device across multiple control dimensions. Finally, it adjusts the unit output target based on the total deviation and energy storage command, forming a deep collaborative closed loop of "energy storage supplementing high frequencies and generating units tracking low frequencies." This not only ensures zero steady-state error tracking of the total output of the combined system to the grid commands but also significantly improves the overall stability, economy, and equipment lifespan of the system by smoothing the generating unit's regulation trajectory. Through the organic combination of "scenario strategy matching, rigid capacity zoning, frequency domain command decoupling, and source-storage residual compensation," this solution systematically solves the technical problems of conflicting regulation targets and low source-storage collaborative efficiency in thermal power-storage combined systems under complex market environments due to the lack of forward-looking control strategies and resource allocation mechanisms.

[0110] Example 2: Please refer to Figure 5 Based on the same inventive concept, the present invention discloses a collaborative control system for a combined thermal power and energy storage system, comprising: a data acquisition module M1, a matching module M2, a decomposition module M3, an energy storage calculation module M4, a thermal power meter module M5, and a control module M6. The data acquisition module M1 is used to acquire electricity price forecast data, planned output data of thermal power units, and winning bid results of the automatic generation control frequency regulation market for the period in which the future scheduling cycle is located, as well as the current power generation instructions of the power grid, the current output data of thermal power units, real-time electricity price data, and real-time state of charge data of the energy storage system. The matching module M2 is used to determine the operating mode and power capacity zoning parameters of the energy storage system based on the bidding results, and to match the preset coordination strategy according to the operating mode, power capacity zoning parameters, electricity price forecast data and planned output data to obtain the charging and discharging strategy of the energy storage system; wherein, the power capacity zoning parameters include the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power. Furthermore, the matching module M2 includes: a mode conversion unit; The mode conversion unit is configured to, if the bidding result is a successful bid, set the operating mode to frequency regulation priority mode and set the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power to be equal to half of the rated power of the energy storage system; if the bidding result is a failed bid, set the operating mode to pure arbitrage mode, set the upper limit of arbitrage dedicated power to the rated power of the energy storage system, and set the upper limit of frequency regulation dedicated power to zero.

[0111] In this preferred embodiment, by setting both the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power to half of the rated power of the energy storage system in the winning bid or default state, a clear resource 50 / 50 allocation mechanism is established, ensuring the fixed capacity reserve required for frequency regulation services and preventing performance risks caused by excessive occupation of frequency regulation resources by arbitrage behavior; while in the unsuccessful bid state, all rated power is set as the upper limit of arbitrage dedicated power and the upper limit of frequency regulation is set to zero, realizing the complete tilt of resources towards a single high-yield target, eliminating unnecessary capacity reservation restrictions, thereby maximizing the power utilization rate and price difference capture capability of the energy storage system in the pure arbitrage mode, reflecting the effect of dynamic resource optimization configuration brought about by the operation mode switching.

[0112] Furthermore, the matching module M2 also includes: a comparison unit and an instruction generation unit; The comparison unit is used to divide the future scheduling cycle into several consecutive time segments, and compare the electricity price forecast data of each time segment with the predefined collaborative strategy to determine the market scenario type of each time segment; wherein, the market scenario type includes deep valley period, peak peak period, ramp transition period and flat fluctuation period; The instruction generation unit is used to generate a charging / discharging strategy and a state of charge threshold that match the market scenario type based on the operating mode, power capacity partitioning parameters, and planned output data, wherein: If the market scenario is a low-end period, a charging priority strategy is generated, and a first state of charge lower limit threshold and a first state of charge upper limit threshold are set; if the market scenario is a peak period, a discharging priority strategy is generated, and a second state of charge lower limit threshold and a second state of charge upper limit threshold are set; if the market scenario is a flat or fluctuating period or a ramp-up transition period, a state of charge balance maintenance strategy is generated, and a third state of charge lower limit threshold and a third state of charge upper limit threshold are set. Wherein, the first charge state lower limit threshold is greater than the second charge state lower limit threshold, and the first charge state upper limit threshold is greater than the second charge state upper limit threshold; the third charge state lower limit threshold is less than the second charge state lower limit threshold, and the third charge state upper limit threshold is greater than the first charge state upper limit threshold.

[0113] In this preferred embodiment, by dividing the future cycle into time segments and comparing the strategy matrix to determine the type of each market scenario, a precise mapping between the control strategy and market electricity price characteristics is achieved, enabling the system to adapt to different market environments. A higher first state of charge threshold is set for valley periods, and a charging priority command is generated to guide energy storage to fully store energy in low-price areas, reducing charging costs. A lower second state of charge threshold is set for peak periods, and a discharging priority command is generated to encourage energy storage to release energy in high-price areas, increasing discharging revenue. A broad third state of charge threshold is set for flat or ramp-up periods, and a balance-maintaining command is generated to maintain the energy storage's adjustment margin to cope with sudden fluctuations. By limiting the relationship between the first threshold being greater than the second threshold, the third lower limit being less than the second lower limit, and the third upper limit being greater than the first upper limit, a differentiated state of charge management range is constructed. This ensures both the maximum charging and discharging depth during peak-valley arbitrage and the bidirectional adjustment flexibility during transition periods, thereby improving the overall economic benefits and operational safety throughout the entire cycle.

[0114] The decomposition module M3 is used to calculate the real-time total power deviation data based on the current power generation command and the current power output data, and to perform frequency domain filtering decomposition to obtain the fast fluctuation power component with a frequency higher than the preset cutoff frequency. The energy storage calculation module M4 is used to calculate energy storage frequency regulation power command data and energy storage peak shaving arbitrage power command data based on the real-time electricity price data, real-time state of charge data, power capacity zoning parameters, charging and discharging strategies and rapid fluctuation power components, and then superimpose them to obtain total energy storage power command data. Furthermore, the energy storage calculation module M4 includes: a frequency regulation calculation unit and an arbitrage calculation unit; The frequency regulation calculation unit is configured to, if the operating mode is frequency regulation priority mode, limit the rapid fluctuation power component data to the frequency regulation dedicated power limit to obtain the energy storage frequency regulation power command data, and subtract the energy storage frequency regulation power command data from the rated power of the energy storage system to obtain the remaining power quota; take the smaller value between the arbitrage dedicated power limit and the remaining power quota as the current available arbitrage power limit; calculate the expected arbitrage power value based on the charging and discharging strategy, real-time electricity price data, and real-time state of charge data, and limit the expected arbitrage power value to the available arbitrage power limit to obtain the energy storage peak shaving arbitrage power command data; The arbitrage calculation unit is configured to, if the operating mode is pure arbitrage mode, set the energy storage frequency regulation power command data to zero, and calculate the expected arbitrage power value based on the charging and discharging strategy, real-time electricity price data, and real-time state of charge data; and limit the expected arbitrage power value within the rated power range of the energy storage system to obtain the energy storage peak shaving arbitrage power command data.

[0115] In this preferred embodiment, in the frequency regulation priority mode, the frequency regulation command is obtained by limiting the rapid fluctuation component within the frequency regulation dedicated power limit, ensuring the immediacy and compliance of the frequency regulation response. The remaining power quota is obtained by calculating the rated power minus the absolute value of the frequency regulation command, and the smaller value between the arbitrage dedicated power limit and the remaining quota is taken as the available arbitrage power limit. This dynamic coupling constraint mechanism mathematically ensures that the arbitrage power will not erode the physical capacity boundary required for frequency regulation, achieving maximum benefit under safety constraints. By limiting the expected arbitrage power within the available limit, the frequency regulation capacity loss caused by excessive arbitrage is prevented. In the pure arbitrage mode, by setting the frequency regulation command to zero and limiting the expected arbitrage power within the rated power, the constraint of frequency regulation capacity is removed, allowing energy storage to participate in market arbitrage at full power. Thus, the differentiated control objectives of "safety priority" and "profit priority" are achieved in different operating modes.

[0116] Furthermore, the energy storage calculation module M4 also includes: an initial calculation unit and an adjustment unit; The initial calculation unit is used to add the energy storage frequency regulation power command data and the energy storage peak shaving arbitrage power command data to obtain the initial superimposed power value. The adjustment unit is used to determine whether the absolute value of the initial superimposed power value exceeds the rated power of the energy storage system. If so, the energy storage frequency regulation power command data is kept unchanged, and the energy storage peak shaving arbitrage power command data is adjusted so that the adjusted initial superimposed power value is equal to the rated power of the energy storage system, and is used as the total energy storage power command data; otherwise, the initial superimposed power value is directly used as the total energy storage power command data.

[0117] In this preferred embodiment, an initial superposition value is obtained by adding the frequency modulation and arbitrage commands, and it is determined whether the absolute value exceeds the rated power. This introduces a final hardware physical limit verification step. If it exceeds the limit, the frequency modulation command remains unchanged and the arbitrage command is adjusted. This "frequency modulation priority" truncation logic ensures the absolute fulfillment of the grid auxiliary service obligations under extreme operating conditions, avoiding equipment shutdown or assessment penalties due to exceeding the total power limit. If it does not exceed the limit, the initial value is directly adopted, preserving the normal collaborative control results. Thus, under the premise of ensuring the safe operation boundary of the equipment, the smooth output of control commands and the improvement of system stability are achieved.

[0118] The thermal power meter module M5 is used to calculate the output target data of the thermal power unit based on the real-time total power deviation data and the total energy storage power command data; Furthermore, the thermal power meter module M5 includes: a thermal power processing and calculation unit; The thermal power processing and calculation unit is used to add the actual output data of the thermal power unit at the current moment and the real-time total power deviation data to obtain the theoretical total demand power value, and subtract the energy storage total power command data from the theoretical total demand power value to obtain the output target data of the thermal power unit.

[0119] In this preferred embodiment, the theoretical total demand power value is obtained by adding the actual output data of the thermal power unit at the current moment to the real-time total power deviation data, thus accurately reconstructing the total system output level required by the grid command. The output target of the thermal power unit is obtained by subtracting the total power command data of the energy storage from it. This calculation logic essentially separates the power already undertaken by the energy storage from the total demand, so that the thermal power unit only needs to compensate for the remaining power gap. This "residual compensation" control strategy makes full use of the rapid adjustment capability of the energy storage to smooth out fluctuations, making the output command of the thermal power unit smoother and the rate of change lower, effectively reducing the thermal stress loss and fuel waste of the unit, while ensuring that the total output of the joint system tracks the automatic generation control command of the grid without steady-state error.

[0120] The control module M6 is used to control the operation of the energy storage system and the thermal power unit based on the total energy storage power command data and the output target data.

[0121] In summary, compared with existing technologies, the embodiments of this application have the following beneficial effects: By acquiring comprehensive sensing data covering market forward-looking information and real-time system status, complete data support is provided for multi-objective collaborative decision-making; based on the bidding results, power capacity partitioning parameters including the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power are determined, and charging and discharging strategies are obtained according to the operating mode and multi-dimensional data matching preset collaborative strategies. This resource isolation mechanism based on rigid partitioning solves the problem of power resource competition under multiple concurrent tasks from the physical constraint level, ensuring that different control objectives do not interfere with each other and execute independently in resource allocation, overcoming the problem of neglecting one aspect due to fixed allocation in existing technologies; by performing frequency domain filtering decomposition on the real-time total power deviation to obtain the fast fluctuating power component, the time scale difference between the millisecond-level response of the energy storage system and the minute-level inertia of the thermal power unit is utilized to achieve decoupling of power commands in the frequency domain, solving the problem of control lag and equipment loss caused by the mismatch of source and storage response characteristics. This solution overcomes the low collaborative efficiency caused by homogeneous control in existing technologies. It calculates and superimposes energy storage frequency regulation and peak shaving arbitrage commands based on rapid fluctuation components and zoning parameters, enabling energy storage to maintain rapid regulation capabilities while also considering energy time-shifting functions. This achieves functional integration and maximizes the efficiency of a single device across multiple control dimensions. Finally, it adjusts the output target of the computer group based on the total deviation and energy storage command, forming a deep collaborative closed loop of "energy storage supplementing high frequency and generating units tracking low frequency." This not only ensures zero steady-state error tracking of the total output of the combined system to the grid command but also significantly improves the overall stability, economy, and equipment lifespan of the system by smoothing the generating unit's regulation trajectory. Through the organic combination of "scenario strategy matching - rigid capacity zoning - frequency domain command decoupling - source-storage residual compensation," this solution systematically solves the technical problems of conflicting regulation targets and low source-storage collaborative efficiency in thermal power-storage combined systems caused by a lack of forward-looking control strategies and resource allocation mechanisms in complex market environments.

[0122] Example 3: This invention also provides a computer program product, including a computer program or instructions, capable of running on a computing device or stored in any available medium. When the computer program product is run on at least one computing device, it causes the at least one computing device to execute the coordinated control method of any of the fire-storage combined systems of this invention.

[0123] Example 4: This invention also provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is executed on the collaborative control system of the combined fire and energy storage system, the collaborative control system of the combined fire and energy storage system performs a collaborative control method for a combined fire and energy storage system according to any of the above method embodiments.

[0124] The specific working processes of each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. The division of modules is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple modules may be combined or integrated into another system.

[0125] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A collaborative control method for a combined fire and energy storage system, characterized in that, include: It obtains electricity price forecast data for the period in the future dispatch cycle, planned output data of thermal power units, and bidding results of the automatic generation control frequency regulation market, as well as the current generation instructions of the power grid, current output data of thermal power units, real-time electricity price data, and real-time state of charge data of energy storage systems. Based on the bidding results, the operating mode and power capacity zoning parameters of the energy storage system are determined. Then, based on the operating mode, power capacity zoning parameters, electricity price forecast data, and planned output data, a preset coordination strategy is matched to obtain the charging and discharging strategy of the energy storage system. The power capacity zoning parameters include the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power. Based on the current power generation command and current output data, calculate the real-time total power deviation data and perform frequency domain filtering decomposition to obtain the fast fluctuation power component with a frequency higher than the preset cutoff frequency. Based on the real-time electricity price data, real-time state of charge data, power capacity zoning parameters, charging and discharging strategies, and rapid fluctuation power components, calculate the energy storage frequency regulation power command data and the energy storage peak shaving arbitrage power command data, and superimpose them to obtain the total energy storage power command data. Based on the real-time total power deviation data and the total energy storage power command data, the output target data of the thermal power unit is calculated; Based on the total energy storage power command data and output target data, the energy storage system and thermal power unit are regulated and operated respectively.

2. The collaborative control method for a combined fire and energy storage system as described in claim 1, characterized in that, Based on the bidding results, the determination of the operating mode and power capacity zoning parameters of the energy storage system includes: If the bidding result is a successful bid, the operating mode will be set to frequency regulation priority mode, and the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power will both be equal to half of the rated power of the energy storage system. If the bidding result is "unsuccessful", then the operating mode will be set to pure arbitrage mode, the upper limit of the arbitrage-specific power will be set to the rated power of the energy storage system, and the upper limit of the frequency regulation-specific power will be set to zero.

3. The collaborative control method for a combined fire and energy storage system as described in claim 2, characterized in that, The step of matching a preset coordination strategy with the operating mode, power capacity zoning parameters, electricity price forecast data, and planned output data to obtain the charging and discharging strategy of the energy storage system includes: The future scheduling cycle is divided into several consecutive time segments, and the electricity price forecast data for each time segment is compared with the predefined collaborative strategy to determine the market scenario type of each time segment; wherein, the market scenario type includes deep valley period, peak peak period, ramp transition period and flat fluctuation period; Based on the aforementioned operating mode, power capacity zoning parameters, and planned output data, a charging / discharging strategy and state of charge threshold matching the aforementioned market scenario type are generated, wherein: If the market scenario type is a deep valley period, a charging priority strategy is generated, and a first charge state lower limit threshold and a first charge state upper limit threshold are set. If the market scenario type is a peak period, a discharge priority strategy is generated, and a second charge state lower limit threshold and a second charge state upper limit threshold are set. If the market scenario type is a flat fluctuation period or a ramp-up transition period, a strategy to maintain the balance of the state of charge is generated, and a third state of charge lower limit threshold and a third state of charge upper limit threshold are set. Wherein, the first charge state lower limit threshold is greater than the second charge state lower limit threshold, and the first charge state upper limit threshold is greater than the second charge state upper limit threshold; the third charge state lower limit threshold is less than the second charge state lower limit threshold, and the third charge state upper limit threshold is greater than the first charge state upper limit threshold.

4. The collaborative control method for a combined fire and energy storage system as described in claim 3, characterized in that, The calculation of energy storage frequency regulation power command data and energy storage peak shaving arbitrage power command data based on the real-time electricity price data, real-time state of charge data, power capacity zoning parameters, charging and discharging strategies, and rapid fluctuation power components includes: If the operating mode is frequency regulation priority mode, then the rapid fluctuation power component data is restricted to the upper limit of the frequency regulation dedicated power to obtain the energy storage frequency regulation power command data, and the rated power of the energy storage system is subtracted from the energy storage frequency regulation power command data to obtain the remaining power quota; the smaller value between the arbitrage dedicated power upper limit and the remaining power quota is taken as the current available arbitrage power upper limit; the expected arbitrage power value is calculated according to the charging and discharging strategy, real-time electricity price data and real-time state of charge data, and the expected arbitrage power value is restricted to the upper limit of the available arbitrage power to obtain the energy storage peak shaving arbitrage power command data; If the operating mode is pure arbitrage mode, the energy storage frequency regulation power command data is set to zero, and the expected arbitrage power value is calculated based on the charging and discharging strategy, real-time electricity price data, and real-time state of charge data; the expected arbitrage power value is limited to the rated power range of the energy storage system to obtain the energy storage peak shaving arbitrage power command data.

5. The collaborative control method for a combined fire and energy storage system as described in claim 4, characterized in that, The superposition of the total energy storage power command data includes: The initial superimposed power value is obtained by adding the energy storage frequency regulation power command data and the energy storage peak shaving arbitrage power command data. If the absolute value of the initial superimposed power exceeds the rated power of the energy storage system, then the energy storage frequency regulation power command data is kept unchanged, and the energy storage peak shaving arbitrage power command data is adjusted so that the adjusted initial superimposed power is equal to the rated power of the energy storage system, and is used as the total energy storage power command data; otherwise, the initial superimposed power value is directly used as the total energy storage power command data.

6. The collaborative control method for a combined fire and energy storage system as described in claim 1, characterized in that, The step of calculating the output target data of the thermal power unit based on the real-time total power deviation data and the total energy storage power command data includes: The actual output data of the thermal power unit at the current moment is added to the real-time total power deviation data to obtain the theoretical total demand power value. The total energy storage power command data is then subtracted from the theoretical total demand power value to obtain the output target data of the thermal power unit.

7. A collaborative control system for a combined fire and energy storage system, characterized in that, include: Data acquisition module, matching module, decomposition module, energy storage calculation module, thermal power meter module, and control module; The data acquisition module is used to acquire electricity price forecast data, planned output data of thermal power units, and winning bid results of the automatic generation control frequency regulation market for the period in the future scheduling cycle, as well as the current power generation instructions of the power grid, the current output data of thermal power units, real-time electricity price data, and real-time state of charge data of the energy storage system. The matching module is used to determine the operating mode and power capacity zoning parameters of the energy storage system based on the bidding results, and to match the preset coordination strategy according to the operating mode, power capacity zoning parameters, electricity price forecast data and planned output data to obtain the charging and discharging strategy of the energy storage system; wherein, the power capacity zoning parameters include the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power. The decomposition module is used to calculate real-time total power deviation data and perform frequency domain filtering decomposition based on the current power generation command and current output data to obtain fast fluctuation power components with frequencies higher than the preset cutoff frequency. The energy storage calculation module is used to calculate energy storage frequency regulation power command data and energy storage peak shaving arbitrage power command data based on the real-time electricity price data, real-time state of charge data, power capacity zoning parameters, charging and discharging strategies and rapid fluctuation power components, and then superimpose them to obtain total energy storage power command data. The thermal power meter module is used to calculate the output target data of the thermal power unit based on the real-time total power deviation data and the total energy storage power command data; The control module is used to control the operation of the energy storage system and the thermal power unit based on the total energy storage power command data and the output target data.

8. The collaborative control system for a combined fire and energy storage system as described in claim 7, characterized in that, The matching module includes: a mode conversion unit; The mode conversion unit is configured to, if the bidding result is a successful bid, set the operating mode to frequency regulation priority mode and set the upper limit of frequency regulation dedicated power and the upper limit of arbitrage dedicated power to be equal to half of the rated power of the energy storage system; if the bidding result is a failed bid, set the operating mode to pure arbitrage mode, set the upper limit of arbitrage dedicated power to the rated power of the energy storage system, and set the upper limit of frequency regulation dedicated power to zero.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement a collaborative control method for a combined fire and energy storage system as described in any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a collaborative control method for a combined fire and energy storage system as described in any one of claims 1-6.

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