A source-load-storage multi-time scale regulation method and system considering multi-element energy storage energy margin

Abstract

The application discloses a source-load-storage multi-time scale regulation method and system considering multi-element energy storage energy margin, and belongs to the technical field of power system operation control. The method collects source-load-storage measured data, generates a day-ahead benchmark plan at a hourly level, generates an intra-day correction plan by rolling at a minute level, and generates a real-time to-be-regulated power instruction by the difference between an actual net load and a planned net load at a second level. After the instruction frequency is decomposed into low-frequency and high-frequency components, the direction-related energy margin of the low-frequency side and the high-frequency side is calculated according to the current charging and discharging direction, the frequency band mutual power is generated according to the mutual aid coefficient for the limited side with smaller margin, and the power is transferred between the two frequency bands according to a unified rule to keep the total amount unchanged. Then, the high-frequency instruction is secondarily distributed with the minimum total operation loss of the power type storage group as the target. Finally, the instructions are converted into control signals and issued. The application can avoid that a single storage touches the operation boundary in advance, and reduce the power tracking deviation and cumulative operation loss.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, specifically to a source-load-storage multi-timescale regulation method and system that takes into account the energy margin of multiple energy storage systems. Background Technology

[0002] With the rapid growth of wind and solar power installed capacity, their proportion in the power system's power structure is continuously increasing. Compared with traditional power sources, wind and solar power output is significantly affected by meteorological conditions, exhibiting strong randomness, volatility, and uncertainty. The superposition of new energy output changes and load fluctuations causes the system's net load to exhibit fluctuation characteristics at multiple time scales, including hourly, minute, and second-level, increasing the difficulty of regulating system power balance, safe operation, and new energy absorption. Under conditions of high proportion of new energy integration, the power system needs to coordinate power sources, loads, and energy storage resources during the day-ahead and intraday phases to ensure energy balance and improve the level of new energy absorption, while also needing to quickly track source and load power changes, correct plan execution deviations, and smooth instantaneous power fluctuations during the real-time control phase. Therefore, single-time-scale scheduling or control methods are insufficient to simultaneously meet the multiple demands of economical system operation, continuous energy support, and rapid power regulation.

[0003] Energy storage, with its fast response, flexible regulation, and bidirectional charging and discharging capabilities, has become an important means of mitigating renewable energy fluctuations and improving system net load characteristics. However, different types of energy storage vary significantly in terms of power capacity, energy capacity, response speed, continuous regulation time, and charge / discharge efficiency: capacity-type energy storage such as pumped hydro storage and compressed air storage has large energy capacity and long continuous operating time, making it suitable for energy transfer and power support over longer timescales; energy-type energy storage such as lithium batteries combines a certain response speed with continuous regulation capability, making it suitable for power regulation on a minute-to-hour scale; and power-type energy storage such as flywheel energy storage and supercapacitor energy storage has fast response speed and high power density, making it suitable for suppressing instantaneous power fluctuations. Therefore, it is necessary to coordinate and regulate capacity-type, energy-type, and power-type energy storage, and to coordinate power sources, loads, and various types of energy storage resources to achieve effective connection between different scheduling and control stages.

[0004] Existing methods for coordinated regulation of energy sources, loads, and storage, as well as power allocation for various types of energy storage, mainly suffer from the following problems:

[0005] Firstly, most source-load-storage coordinated control methods focus on day-ahead optimization scheduling or intraday rolling correction, primarily formulating operation plans based on wind power, photovoltaic, and load forecast results. Although some methods incorporate real-time control mechanisms, the real-time time resolution is generally low, making them insufficiently adaptable to the demands of rapid load fluctuations and high-precision dynamic control.

[0006] Secondly, existing energy storage power allocation methods mainly include fixed-ratio allocation, ratio allocation based on rated capacity or rated power, and power allocation based on preset frequency band decomposition, with relatively fixed power allocation relationships. However, the actual regulation capability of energy storage depends not only on rated power and rated capacity, but also on its current energy state, charging and discharging direction, available energy, power margin, and operating boundaries. When the energy state of energy storage changes, fixed allocation parameters cannot reflect its actual available regulation capability in a timely manner. This may cause energy storage close to the upper or lower limit of the energy state to still bear a large charging or discharging power, resulting in some energy storage reaching the operating boundary prematurely. Subsequently, this energy storage loses its regulation capability in the same direction, and the system power tracking deviation increases significantly.

[0007] Third, some methods alleviate the above problems by adjusting the filter time constant according to the energy storage state of charge. However, adjusting the filter time constant changes the frequency decomposition itself, which will simultaneously change the spectral characteristics of high and low frequency components, thus disrupting the frequency band division that originally matched the response characteristics of various energy storage types. Moreover, the adjustment of the filter time constant does not distinguish between charging and discharging directions, while the available margin of energy storage in the charging and discharging directions is asymmetrical—energy storage with an energy state close to the upper limit is no longer suitable for further charging, but its discharge capacity is still sufficient, and vice versa. Therefore, adjusting only based on a single state of charge without distinguishing between charging and discharging directions can easily produce unnecessary allocation corrections in directions where the energy storage actually has sufficient adjustment capacity.

[0008] Fourth, both flywheel energy storage and supercapacitor energy storage can withstand high-frequency rapid power disturbances. However, existing methods usually allocate high-frequency power commands according to rated power, rated capacity, or a fixed ratio, or use a certain type of power-type energy storage as the main regulation resource. They do not fully consider the operating losses and real-time available regulation capabilities of each energy storage unit under different power commands, which may result in an unreasonable allocation of high-frequency power tasks between the two, increase the total operating losses of the power-type energy storage group, and affect the economic efficiency of operation.

[0009] Therefore, in scenarios with high proportions of new energy access, enhanced fluctuations on the source and load sides, and coordinated operation of multiple types of energy storage, there is an urgent need for a multi-timescale regulation method for source, load, and storage. This method would enable different types of energy storage to reasonably undertake regulation tasks at the day-ahead, intraday, and real-time scales according to their own response characteristics and operating status, and to dynamically correct and optimize the allocation of power commands based on the energy storage status, power margin, and loss during real-time control. Summary of the Invention

[0010] The technical problem to be solved by this invention is that the existing source-load-storage coordinated regulation method has low time resolution at the real-time control layer, and the power distribution relationship between different types of energy storage is relatively fixed and does not distinguish between charging and discharging directions, which leads to the energy storage reaching the energy state boundary in advance and the power tracking deviation increasing; at the same time, the high-frequency power is allocated in a fixed proportion within the power-type energy storage group without taking into account the operating loss characteristics, resulting in high cumulative operating loss.

[0011] To address the aforementioned technical problems, this invention provides a multi-timescale regulation method for source-load-storage systems that considers the energy margin of multiple energy storage systems. This method is executed by a source-load-storage coordinated regulation system, which is communicatively connected to the source-side measurement devices and load-side measurement devices of the regional power system, as well as the converter of the multiple energy storage system. The multiple energy storage system includes capacity-type energy storage, energy-type energy storage, and a power-type energy storage group composed of flywheel energy storage and supercapacitor energy storage. The method includes:

[0012] S1. Collect measured operational data from the source side, load side, conventional regulation resources, grid side, and energy storage side, and preprocess them into a standardized operational dataset;

[0013] S2. Solve the day-ahead operating baseline plan by using hourly time scale and minimizing the total system operating cost. This plan includes thermal power unit start-up and shutdown and output, wind and solar power consumption, external power purchase, adjustable load response and capacity-type energy storage charging and discharging plan.

[0014] S3. Using minute-level rolling cycles, and under the condition that the start-up and shutdown status of thermal power units remains unchanged, the daily correction plan is obtained by rolling the solution based on the updated source-load prediction data.

[0015] S4. Using seconds as the real-time control cycle, the difference between the actual net load and the planned net load is taken as the real-time power command to be adjusted. ;

[0016] S5, will Decomposed into low-frequency power components With high-frequency power components The sum of the two equals , and respectively serve as the initial power commands for energy-type energy storage and power-type energy storage groups;

[0017] S6. Determine the direction-dependent energy margin according to the current charging / discharging direction—the charging direction is the difference between the upper limit of the energy state and the current energy state, and the discharging direction is the difference between the current energy state and the upper limit of the energy state—thereby obtaining the low-frequency side margin. The high-frequency side margin is obtained by weighting flywheel energy storage and supercapacitor energy storage according to their rated capacity. The smaller of the two is determined as the restricted side, and the frequency band mutual assistance power is generated according to the mutual assistance coefficient that monotonically increases as the margin of the restricted side decreases. When the low-frequency side is limited, the mutual support coefficient is taken as... When the high-frequency side is limited, the mutual assistance coefficient is taken as the product. Find the opposite of the product; then follow the unified rule. and Corrections completed;

[0018] S7, in Under the constraints of their respective power and energy state boundaries, the goal is to minimize the total operating loss of the power-type energy storage group, and the power commands of flywheel energy storage and supercapacitor energy storage are obtained. The flywheel energy storage loss is segmented according to the charging and discharging conditions and increases with the increase of its absolute power value. The supercapacitor energy storage loss includes components that increase with the increase of its absolute power value and with the decrease of its energy state in the previous cycle.

[0019] S8. After verifying the executability of each power command and limiting and redistributing the deficit, the command is converted into a control signal and sent to the corresponding energy storage converter.

[0020] S9. Collect the measured output power after execution, update the energy status and remaining available charging and discharging power of each energy storage unit, and use it as the input for the next real-time control cycle steps S6 to S8.

[0021] The present invention also provides a source-load-storage coordinated control system for implementing the above method, as well as a computer-readable storage medium.

[0022] The core of the above technical solution lies in the following three points:

[0023] First, a three-tiered control architecture is constructed, consisting of "daytime hourly level, intraday minute level, and real-time second level." Capacity-type energy storage, energy-type energy storage, and power-type energy storage are configured according to their respective response characteristics at corresponding time scales, so that the control results at the three time scales are connected through the difference between the planned net load and the actual net load.

[0024] Second, a direction-dependent energy margin and a frequency band mutual assistance mechanism based on this margin are proposed at the real-time control layer. Unlike existing methods that adjust the filter time constant according to the state of charge, this invention does not change the frequency decomposition process itself, but instead superimposes a power transfer amount on the frequency decomposition result. This transfer amount is generated based on a comparison of the available energy margins of the low-frequency side and the high-frequency side in the current charging / discharging direction: transfer is triggered only when one side has insufficient margin in the direction it is about to operate in, and no correction is made when the margin is sufficient on that side in that direction. Because the correction adopts a unified rule… and Regardless of whether the restricted side is the low-frequency side or the high-frequency side, the total power before and after the correction is always equal to the real-time power command to be adjusted, thereby mitigating single-side over-limit while strictly maintaining system power balance.

[0025] Third, within the power-type energy storage unit, the modified high-frequency power command is secondary-allocated based on the actual loss characteristics of flywheel energy storage and supercapacitor energy storage. The loss of flywheel energy storage is characterized by a standby loss term related to rotational speed (i.e., energy state) as a piecewise linear function of its absolute power value. The loss of supercapacitor energy storage is characterized by a linear term coupled with the energy state as a quadratic power term. The resulting univariate convex optimization problem has a closed-form solution and can be directly calculated within a second-level control cycle without iterative solving.

[0026] The beneficial effects of this invention are:

[0027] (1) The real-time control layer adopts a second-level time resolution and forms a closed loop with the intraday minute-level and day-ahead hour-level scheduling, so that the system can have rapid power tracking capability while ensuring the consumption of new energy and energy balance.

[0028] (2) The generation of frequency band mutual assistance power is based on the direction-dependent energy margin, which can distinguish the asymmetric available capacity of energy storage in the charging and discharging directions. Power transfer is triggered only in the actually limited direction, avoiding unnecessary corrections caused by adjusting based on a single state of charge. At the same time, this mechanism does not change the frequency decomposition link itself, and the matching relationship between each frequency band and the response characteristics of various types of energy storage is maintained. Simulation verification shows that after adopting frequency band mutual assistance, energy storage no longer stays at the upper limit of the energy state for a long time, and the power deviation that was originally significantly increased due to the saturation of a single energy storage is effectively suppressed.

[0029] (3) Revise and adopt unified rules and satisfy This avoids potential deviations in total power or errors in the correction direction that might occur when using different correction signs for the restricted and unrestricted sides.

[0030] (4) A feasibility backoff mechanism is set after mutual assistance. When the high-frequency power command after transfer exceeds the total available power of the power-type energy storage group, the excess part will be returned to the low-frequency side to avoid the mutual assistance mechanism itself from inducing new over-limit.

[0031] (5) The power-type energy storage group is allocated in a secondary manner based on minimizing the total operating loss, and the allocation result has a closed-form solution. The computation is small and can be completed within a second period. Simulation shows that this allocation method can significantly reduce the cumulative operating loss of the power-type energy storage group compared with the fixed ratio allocation method, and the reduction increases with the increase of operating time.

[0032] (6) The method of the present invention is applied to actual energy storage converters. It forms a physical closed loop by collecting measured operating data, generating and sending control signals, and collecting execution results to update the status. It belongs to the control method for the operation process of power system. Attached Figure Description

[0033] Figure 1This is a schematic diagram of the multi-timescale control framework of the present invention;

[0034] Figure 2 This is a flowchart of the source-load-storage multi-timescale control method of the present invention;

[0035] Figure 3 This is a structural block diagram of the source-load-storage coordinated control system of the present invention;

[0036] Figure 4 This is a schematic diagram of the frequency band mutual assistance principle of the present invention, wherein (a) is the frequency band mutual assistance logic, and (b) is the curve of the mutual assistance coefficient as a function of the normalized direction-related energy margin.

[0037] Figure 5 The figures shown are the day-ahead and intraday scheduling power balance and capacity-type energy storage energy state change curves in the embodiment, where (a) is the day-ahead hourly scheduling result and (b) is the intraday 15-minute rolling scheduling result;

[0038] Figure 6 The above are comparison curves before and after frequency band mutual assistance in the embodiment, where (a) is the high and low frequency power command, (b) is the energy state of each energy storage unit without frequency band mutual assistance, (c) is the power deviation, and (d) is the energy state of each energy storage unit with frequency band mutual assistance.

[0039] Figure 7 The cumulative operating loss curves of the power-type energy storage group are shown in the examples, with and without high-frequency loss optimization. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0041] 1. Overall Architecture

[0042] This embodiment provides a multi-timescale regulation method for source-load-storage that considers the energy margin of multiple energy storage systems. It is designed for regional power systems with a high proportion of renewable energy integration. The method coordinates and regulates source-side, load-side, and multiple energy storage resources across three timescales: day-ahead dispatch, intraday rolling optimization, and real-time power control. The regulation framework is as follows: Figure 1 As shown, the method flow is as follows: Figure 2 As shown.

[0043] The method is executed by a source-load-storage coordinated control system. This system can be installed in a power dispatch center server, an energy storage management system, an industrial control computer, or an edge control device capable of data acquisition, optimization calculation, and power command issuance. Its structure is as follows: Figure 3As shown, the system includes a processor, memory, and communication interface, and is functionally divided into a data acquisition and preprocessing module 101, a day-ahead scheduling module 102, an intraday rolling optimization module 103, a real-time deviation calculation module 104, a frequency decomposition module 105, a frequency band mutual assistance module 106, a high-frequency loss optimization module 107, a power command issuance module 108, and a status feedback module 109. The communication interface is connected to the source-side measurement device, the load-side measurement device of the regional power system, and the converter of the multi-element energy storage system, respectively. Measured data is sent up through the communication interface, and control signals are sent down through the communication interface.

[0044] The regional power system includes new energy power generation units such as wind power and photovoltaic power, regulating resources such as thermal power units, externally purchased power and adjustable loads, and a multi-element energy storage system composed of pumped hydro storage, compressed air storage, lithium battery storage, flywheel storage, and supercapacitor storage. Among them, pumped hydro storage and compressed air storage, as capacity-type energy storage, participate in energy balance and new energy consumption on day-ahead and intraday time scales; lithium battery storage, as energy-type energy storage, handles low-frequency power fluctuations in real-time control; flywheel storage and supercapacitor storage constitute a power-type energy storage group, handling high-frequency rapid power disturbances in real-time control.

[0045] In this embodiment, it is stipulated that power is taken as a positive value when energy storage discharges to the grid, and as a negative value when energy storage charges from the grid. All operating data from the source side, load side, and energy storage side are time-aligned according to their corresponding control time scales, and use a unified power unit, energy unit, and sign convention. It should be noted that energy state... Defined as the ratio of the currently available energy of an energy storage unit to its rated capacity, its meaning is consistent with the state of charge for electrochemical energy storage, proportional to the square of the rotor speed for flywheel energy storage, and proportional to the square of the terminal voltage for supercapacitor energy storage.

[0046] 2. Step S1: Collect and preprocess system operation data

[0047] This step is performed by the data acquisition and preprocessing module 101, with the input being the original operational dataset of the regional power system. .

[0048] The original running dataset This includes source-side operation data, load-side operation data, conventional regulation resource operation data, grid-side operation data, and energy storage-side operation data. The data includes the following: Source-side operation data: day-ahead wind and solar power forecasts, intraday updated wind and solar power forecasts, and real-time wind and solar power output; Load-side operation data: day-ahead load forecasts, intraday updated load forecasts, real-time load power, and the maximum upward and downward adjustment capabilities and response compensation costs of adjustable loads; Conventional regulation resource operation data: rated capacity, minimum output, maximum output, start-up and shutdown status, start-up and shutdown costs, ramp rate, and fuel costs of thermal power units; Grid-side operation data: regional power system power balance requirements, tie-line transmission power limits, external power purchase channel capacity and power purchase price, and other boundary parameters related to system safety operation; Energy storage-side operation data: rated power, rated capacity, initial state of energy, upper limit of state of energy, lower limit of state of energy, maximum charging power, maximum discharging power, charging efficiency, discharging efficiency, self-discharge coefficient, ramp rate, and operating loss parameters of pumped hydro storage, compressed air storage, lithium battery storage, flywheel storage, and supercapacitor storage.

[0049] Preprocessing includes: time alignment of data according to each control time scale; standardization of power to MW and energy to MWh; removal of data exceeding the range, abrupt changes, or missing data, and completion using either previous value preservation or linear interpolation; standardization of the signs of power and energy according to the aforementioned conventions; and validation of the preprocessed data. The output is a standardized operational dataset. The data is then transmitted to the day-ahead scheduling module 102, the intraday rolling optimization module 103, the real-time deviation calculation module 104, the frequency band mutual assistance module 106, and the high-frequency loss optimization module 107 according to the data requirements of each module.

[0050] Step S2: Construct the day-ahead scheduling model and generate the day-ahead running baseline plan.

[0051] This step is executed by the day-ahead scheduling module 102, and the input is... The data includes day-ahead power generation and load forecasts, thermal power unit operating parameters, external power purchase parameters, adjustable load parameters, and capacity-type energy storage operating parameters.

[0052] The day-ahead scheduling module 102 establishes a day-ahead scheduling cycle on an hourly time scale, dividing a scheduling day into 24 consecutive scheduling periods. It then calculates the day-ahead net load forecast based on the predicted wind power, predicted photovoltaic power, and predicted load power for each period. Finally, it solves for the minimum total system operating cost using the objective function:

[0053]

[0054] In the formula, This represents the total system operating cost during the day-ahead scheduling phase. Operating costs of thermal power units include fuel costs, start-up and shutdown costs, and other costs related to the operating status of the thermal power units. The cost of adjusting load response compensation is determined based on the adjusting load's response power, response time, and compensation price. The funds are allocated to cover the operating costs of new energy sources and the penalties for curtailing wind and solar power, in order to limit unnecessary curtailment of new energy power. External electricity purchase cost; The operating costs for pumped hydro storage and compressed air storage include charging and discharging energy loss costs, operation and maintenance costs, and other quantifiable operating expenses.

[0055] It should be noted that the cost terms for new energy operation and wind / solar curtailment penalties in the objective function used in this step are uniformly denoted as... .

[0056] The day-ahead dispatch module 102 solves the following constraints: power balance, upper and lower limits of thermal power unit output and ramp-up constraints, minimum start-up and shutdown time constraints of thermal power units, external power purchase channel capacity constraints, adjustable load up-and down-adjustment capability constraints, capacity-type energy storage charging and discharging power constraints, energy state upper and lower limits constraints, and energy state consistency constraints at the beginning and end of the dispatch cycle. This determines the start-up and shutdown and output plans of thermal power units, wind power consumption plans, photovoltaic power consumption plans, external power purchase plans, adjustable load response plans, and charging and discharging plans for pumped storage and compressed air storage, thus forming the day-ahead operating baseline plan. The data is then transmitted to the intraday rolling optimization module 103. The above model is a mixed-integer linear programming model, which can be solved using a combination of the branch and bound method and the simplex method.

[0057] Step S3: Construct an intraday rolling optimization model and generate an intraday correction plan.

[0058] This step is performed by the intraday rolling optimization module 103, and the input is... The daily updated forecasts for wind power, solar power, and load, as well as the current operating status of thermal power units, externally purchased electricity, adjustable loads, and capacity-type energy storage.

[0059] The intraday rolling optimization module 103 operates on a 15-minute rolling cycle. Based on updated forecast information, it constructs a 4-hour rolling optimization window at the current moment and optimizes the source, load, and storage resources within the window. Each optimization only executes the scheduling results corresponding to the current rolling period. When the next rolling cycle arrives, the solution is recalculated based on the latest forecast data and system operating status, thus forming a continuously updated intraday rolling correction process.

[0060] Intraday rolling optimization module 103 For reference, a rolling optimization model is constructed within a day, keeping the start-up and shutdown status of thermal power units unchanged. This model, satisfying constraints such as system power balance, thermal power unit output and ramp-up, external power purchase capacity, available wind and solar power, adjustable load response capability, and charging / discharging power and energy state of capacity-type energy storage, aims to minimize the total system operating cost within the rolling window. It performs rolling corrections on thermal power unit output, wind and solar power absorption capacity, external power purchase capacity, adjustable load response power, and charging / discharging power of pumped hydro storage and compressed air storage, thus obtaining the intraday correction plan. Since the start-up and shutdown states of thermal power units are fixed, this model is a linear programming model.

[0061] Used to compensate for day-ahead source load forecast deviations and update the system's short-term operation plan, the planned net load corresponding to the current rolling period is transmitted to the real-time deviation calculation module 104.

[0062] 5. Step S4: Calculate the real-time power command to be adjusted.

[0063] This step is performed by the real-time deviation calculation module 104, and the input is... In addition, real-time power outputs from wind power, solar power, and loads are measured. This embodiment controls the sampling period in real time. Take 1 second.

[0064] The real-time deviation calculation module 104 calculates the first deviation based on the real-time output power of wind power, the real-time output power of photovoltaic power, and the real-time load power. Actual net load for each real-time control cycle:

[0065]

[0066] according to Calculate the planned net load based on the current real-time control cycle's corresponding load plan, wind power plan, and photovoltaic plan:

[0067]

[0068] The difference between the actual net load and the planned net load is used as the real-time power command to be adjusted.

[0069]

[0070] In the formula, , , The first Real-time load power, wind power output power and photovoltaic output power for each real-time control cycle; , , These are the planned load power, planned wind power absorption power, and planned photovoltaic absorption power for the same period, respectively. and These are the actual net load and the planned net load, respectively. This is a real-time power control command. Since the planned net load is given by the intraday revised plan at the minute level, while the actual net load is collected at the second level, the difference between the two includes the rapid fluctuations at the second level that the intraday plan fails to cover.

[0071] when When the actual net load is higher than the planned net load, the real-time energy storage system needs to increase the discharge power or decrease the charging power. According to the positive and negative sign convention in this embodiment, this instruction indicates the discharge direction. When the actual net load is lower than the planned net load, the real-time energy storage system needs to increase charging power or decrease discharging power, and this instruction indicates the charging direction.

[0072] To avoid frequent charging and discharging of energy storage caused by measurement noise, communication errors, or extremely small power fluctuations, Perform dead zone detection and rate of change limiting: when Less than the dead zone threshold season In this embodiment Take 0.5% of the total rated power of the real-time adjustable energy storage; when At that time, Limiting ,in The maximum allowable rate of change. After processing. Transmitted to frequency decomposition module 105.

[0073] Step S5: Perform frequency decomposition on the real-time power command to be adjusted.

[0074] This step is performed by the frequency decomposition module 105, and the input is... Low-frequency power component of the previous real-time control cycle Real-time control of sampling period and frequency decomposition parameters.

[0075] The frequency decomposition module 105 decomposes the real-time power command to be adjusted into a relatively gently changing low-frequency power component and a rapidly changing high-frequency power component, based on the response speed and continuous adjustment capability of different types of energy storage. In this embodiment, a first-order low-pass filter is used. The low-frequency power component is:

[0076]

[0077] The high-frequency power components are:

[0078]

[0079] The filter coefficients are:

[0080]

[0081] In the formula, To control the sampling period in real time, The filtering time constant is These are the filter coefficients. The low-frequency power component of the previous real-time control cycle is taken at the initial time. From the above two equations, it can be seen that... .

[0082] Filtering time constant It shall be determined according to the following principles: On the one hand, The response time constant should be significantly greater than that of the power storage group to ensure that the power change included in the high-frequency side is within the frequency band that flywheel energy storage and supercapacitor energy storage can track. In this embodiment, we take... It should be no less than 10 times the response time constant of the power-type energy storage unit; on the other hand... The duration of regulation should be significantly shorter than that allowed for energy storage, so that the power supplied by the low-frequency side does not deplete the available energy of the lithium battery storage during a single fluctuation. In this embodiment, we take... The response time constant of the power storage group is approximately 2 seconds, and the allowable continuous adjustment time of the lithium battery energy storage is approximately 1 hour. Therefore... The feasible range is 20s to 720s, and this embodiment takes... s, corresponding to Increase More rapidly changing power is allocated to the high-frequency side, reducing The power variation range borne by the low-frequency side increases accordingly.

[0083] Frequency decomposition module 105 will As the initial power command for lithium battery energy storage, As the initial power command for the power-type energy storage unit, both are transmitted to the frequency band mutual assistance module 106.

[0084] It should be noted that the frequency decomposition in this step is not limited to first-order low-pass filtering. Moving average filtering, empirical mode decomposition, or wavelet decomposition can also be used, as long as the sum of the low-frequency components and the high-frequency components is equal to the real-time power command to be adjusted.

[0085] 7. Step S6: Generate band mutual power based on directional correlation energy margin

[0086] This step is performed by the frequency band mutual assistance module 106, and the input is... , The current state of energy for lithium battery energy storage, flywheel energy storage, and supercapacitor energy storage, as well as the available charge / discharge power of each energy storage unit. The principle of this step is as follows: Figure 4 As shown.

[0087] 7.1 Overall Energy State on the High-Frequency Side

[0088] Since the high-frequency side is jointly handled by two units, flywheel energy storage and supercapacitor energy storage, the energy states of the two units must first be weighted according to their rated capacities and aggregated into an equivalent quantity to obtain the comprehensive energy state of the high-frequency side:

[0089]

[0090] In the formula, The overall energy state on the high-frequency side; and Flywheel energy storage and supercapacitor energy storage were respectively used in the first two months of the year. Energy status in each real-time control cycle; and These are the rated capacities of flywheel energy storage and supercapacitor energy storage, respectively. A weighted average based on rated capacity, rather than an arithmetic average, is used because the total available energy on the high-frequency side is determined by the sum of the absolute energies of the two, with larger capacity units contributing more to the overall available energy.

[0091] 7.2 Direction-dependent energy margin

[0092] The available capacity of energy storage in the charging and discharging directions is asymmetrical: energy storage near its upper limit of energy state is no longer suitable for charging, but its discharging capacity is still sufficient; conversely, energy storage near its lower limit of energy state is no longer suitable for discharging, but its charging capacity is still sufficient. Therefore, this invention does not measure the degree of limitation of energy storage by a single energy state quantity, but defines the direction-dependent energy margin according to the charging and discharging direction to be performed.

[0093] The charging and discharging direction on the low-frequency side is from The sign is determined, and the charging and discharging direction on the high-frequency side is determined by... The sign is determined. When one side is in the charging direction, its direction-dependent energy margin is the difference between the upper limit of the energy state and the current energy state:

[0094]

[0095]

[0096] When one side is in the discharge direction, its direction-dependent energy margin is the difference between the current energy state and the upper limit of the energy state:

[0097]

[0098]

[0099] In the formula, For lithium battery energy storage in the first Energy status in each real-time control cycle; and These are the upper and lower limits of the energy state, respectively, which are set to 0.8 and 0.2 in this embodiment. and These represent the available adjustment margins for the low-frequency and high-frequency sides, respectively, during the charging direction. and These represent the available adjustment margins for the low-frequency and high-frequency sides under the discharge direction, respectively. Let the direction-dependent energy margins for the low-frequency and high-frequency sides, selected according to their respective directions, be denoted as follows: and .

[0100] 7.3 Mutual Aid Coefficient

[0101] Compare and The side with the smaller value is identified as the restricted side. The direction-dependent energy margin of the restricted side is denoted as... Its normalized value is:

[0102]

[0103] Mutual assistance coefficient Determine the segments according to the following formula:

[0104]

[0105] In the formula, The mutual assistance start-up margin threshold ranges from 0.15 to 0.35. The mutual aid acceleration margin threshold is set to a value ranging from 0.05 to 0.20. This is the mutual assistance coefficient at the inflection point of the segmentation, with a value ranging from 0.2 to 0.4; and This function is in , and The values ​​are continuous and take values ​​within the range of . And follow Decrease and monotonically increase, its shape is like... Figure 4 (b) shows the reason for using two segments instead of a single segment: when the margin is moderate, a small amount of power is transferred at a gentler slope to avoid frequent and large adjustments to the allocation ratio; when the margin is close to being exhausted, a rapid transfer is made at a steeper slope to ensure that the restricted side exits regulation in that direction before reaching the boundary.

[0106] This embodiment takes , , , Taking the low-frequency side as an example, which is in the charging direction and is the restricted side, at this time... Substituting into the above equation, we obtain the equivalent piecewise expression:

[0107]

[0108] That is, when the energy state of a lithium battery is below 0.6, mutual assistance is not triggered. When it exceeds 0.6, the mutual assistance coefficient increases with the energy state. When it reaches the upper limit of 0.8, all low-frequency charging power is transferred to the high-frequency side. The discharge direction and the situation where the high-frequency side is restricted are determined symmetrically according to the same normalization formula, which will not be elaborated here.

[0109] 7.4 Frequency Band Interoperability Power and Unified Correction Rules

[0110] Frequency band mutual assistance power is generated by the following formula:

[0111]

[0112] based on The low-frequency power components and high-frequency power components are corrected according to a unified correction rule:

[0113]

[0114]

[0115] In the formula, and These are the revised low-frequency power command and the revised high-frequency power command, respectively.

[0116] It should be noted that the above correction rules apply to both cases where the restricted side is a low-frequency side and the restricted side is a high-frequency side; there is no need to set correction rules with opposite signs for each case. Its correctness can be verified by the following four operating conditions:

[0117] (1) The low-frequency side is restricted and is in the charging direction ( ): ,but Its absolute value decreases; The charging direction is increased, meaning that the charging task on the low-frequency side is transferred to the high-frequency side.

[0118] (2) The low-frequency side is restricted and is in the discharge direction ( ): ,but Its absolute value decreases; Increase in the direction of discharge.

[0119] (3) The high-frequency side is restricted and is in the charging direction ( ): ,but Its absolute value decreases; The charging direction is increased, meaning that the charging task on the high-frequency side is transferred to the low-frequency side.

[0120] (4) The high-frequency side is restricted and is in the discharge direction ( ): ,but Its absolute value decreases; Increase in the direction of discharge.

[0121] It is evident that under all four operating conditions, the absolute value of the power command on the restricted side is compressed according to the mutual assistance coefficient, while the transferred power is borne by the other side, ensuring the correct correction direction remains constant. Furthermore, adding the two corrective actions together yields:

[0122]

[0123] That is, mutual adjustment strictly maintains the total power unchanged and will not introduce new system power imbalance.

[0124] Compared with adjusting the filter time constant according to the energy storage state of charge, this step does not change the frequency decomposition link in step S5. The spectrum division of high and low frequency components remains unchanged, and the matching relationship between each frequency band and various energy storage response characteristics is not destroyed. The mutual assistance power is the transfer amount superimposed on the decomposition result. Its triggering condition, magnitude and direction are determined by the direction-related energy margin, so it only plays a role in the direction where energy storage is actually limited.

[0125] 7.5 Retreat on the Feasibility of Mutual Assistance

[0126] To avoid the mutual aid mechanism itself inducing new limits to be exceeded, after obtaining Then, a feasibility rollback is performed. First, the total available power of the power-type energy storage unit under the current charge / discharge direction is calculated:

[0127]

[0128] The above formula is the expression for the discharge direction; for the charging direction, the expression within the parentheses is... Replace with and will Replace with In the formula, For the first The maximum charging power or maximum discharging power of each power-type energy storage unit in the current direction; Its rated capacity; and These are the discharge efficiency and the charging efficiency, respectively. This formula takes the smaller of the power boundary and the energy-feasible power determined by the remaining energy and one control cycle, thus simultaneously considering both power constraints and energy constraints.

[0129] when At that time, the high-frequency power command will be limited:

[0130]

[0131] The excess portion will be returned to the low-frequency side:

[0132]

[0133] After rollback The available power of lithium battery energy storage is further limited, and the remaining unmet power is fed back to the intraday rolling optimization module 103 as unmet power, which will compensate for it in the next rolling cycle by adjusting the output of thermal power units, external power purchase, or adjustable load response.

[0134] Transmitted to power command issuing module 108; Transmitted to high-frequency loss optimization module 107; The relevant status judgment results are recorded in the running database for subsequent performance evaluation and parameter correction.

[0135] Step S7: Optimize the allocation of high-frequency power commands based on losses.

[0136] This step is performed by the high-frequency loss optimization module 107, and the input is... The current energy state, available charge / discharge power, charge / discharge efficiency, and operating loss parameters of flywheel energy storage and supercapacitor energy storage.

[0137] 8.1 Loss Model

[0138] The high-frequency loss optimization module 107 uses flywheel energy storage power and supercapacitor energy storage power As decision variables, for Perform secondary allocation while satisfying high-frequency power balance constraints:

[0139]

[0140] The optimization objective is to minimize the total operating loss of the power-type energy storage unit within the current real-time control cycle.

[0141]

[0142] The flywheel energy storage operating loss function is:

[0143]

[0144] The energy storage loss function of a supercapacitor is:

[0145]

[0146] In the formula, For the first Total operating loss of power-type energy storage units within a real-time control cycle; and These are the operating losses of flywheel energy storage and supercapacitor energy storage, respectively. and These are the power-related loss coefficients of flywheel energy storage under discharge and charging conditions, respectively, and are calibrated by the conversion efficiency of the converter and motor under the two conditions. and These are the standby loss coefficients of flywheel energy storage under discharge and charging conditions, respectively. This is the second-order loss coefficient for supercapacitor energy storage; The energy state coupling loss coefficient for supercapacitor energy storage; and These represent the energy states of flywheel energy storage and supercapacitor energy storage respectively in the previous real-time control cycle. All the above coefficients are positive values, where... It can take the value zero.

[0147] The physical basis of the above loss function is as follows. For flywheel energy storage, the power-related term characterizes the conversion loss between the motor and the converter, and its magnitude is proportional to the absolute value of power transmission. Therefore, it adopts... Instead To ensure that the loss remains non-negative during charging; since the motor's efficiency differs between motoring and generating modes, the charging and discharging modes are divided into two segments with different coefficients. , The standby loss term characterizes the wind resistance loss and bearing friction loss within the vacuum chamber; both increase with increasing rotor speed. The energy state of the flywheel's stored energy is proportional to the square of the rotor speed. The approximate representation of this is a linear function, also divided into two segments according to operating conditions. For supercapacitor energy storage, the quadratic term... Characterizing Joule loss on the equivalent series resistance; factor in the first term This reflects the characteristic that the terminal voltage of a supercapacitor decreases as the state of energy decreases—when transmitting the same power, the lower the terminal voltage, the greater the current, and the additional losses on the equivalent series resistance increase accordingly. Therefore, this term increases as the state of energy decreases.

[0148] It should be noted that, and Given the known state variables from the previous period, in the [number]th period... In the optimization of each real-time control cycle, it is a constant, therefore and It does not affect the position of the optimal solution in this cycle, but retaining this term can make... It reflects the true total loss of the power-type energy storage unit, which facilitates the cumulative loss statistics and operational performance evaluation in step S9.

[0149] 8.2 Constraints

[0150] The optimization problem must satisfy the power boundary constraints and energy state boundary constraints of both flywheel energy storage and supercapacitor energy storage. For the first... One power type energy storage unit ( Its feasible power range is:

[0151]

[0152]

[0153] In the formula, and These are the maximum charging power and maximum discharging power of the unit, respectively. and These are the lower and upper bounds of its feasible power range, respectively.

[0154] 8.3 Closed-form solution

[0155] High-frequency power balance constraint Substituting the objective function, the original problem can be transformed into a problem concerning a single variable. The univariate optimization problem. and , Under normal operating conditions of the same number, due to The objective function is about A strictly convex function whose unconstrained minimum point is determined by first-order conditions.

[0156] Under discharge conditions ( For example, the objective function can be written as:

[0157]

[0158] make ,have to:

[0159]

[0160] Charging conditions ( The derivation follows the same method. Representing both operating conditions uniformly, the unconstrained minimum point is:

[0161]

[0162] Among them when Time to take ,when Time to take .

[0163] After considering the constraints, the lower and upper bounds of the feasible interval are:

[0164]

[0165]

[0166] In the formula, and This represents the boundary of the feasible power range for supercapacitor energy storage calculated using the formula in Section 8.2; the latter term is derived from the feasible range of flywheel energy storage through power balance constraints. Due to the properties of univariate convex functions, the constrained optimal solution is the projection of the unconstrained minimum point onto the feasible range:

[0167]

[0168]

[0169] The above solution process involves several arithmetic and comparison operations, without the need for iterative optimization. It can be reliably completed within a real-time control cycle on the order of 1 second, which is of substantial significance to the feasibility of the real-time control layer.

[0170] Depend on The expression reveals the working mechanism of the secondary allocation in this invention: the power carried by the supercapacitor energy storage is determined by the marginal loss coefficient of the flywheel energy storage. Marginal losses of supercapacitor energy storage The difference determines this. When the energy state of a supercapacitor is low... Increase When the power level decreases, more high-frequency power is transferred to flywheel energy storage; conversely, when the supercapacitor's energy state is higher, the power it handles increases. This allocation relationship is automatically adjusted according to the real-time operating status of each unit, rather than being fixed according to rated parameters.

[0171] The output is , and The first two are transmitted to the power command issuing module 108.

[0172] Step S8: Verify and issue the final power command to each energy storage unit.

[0173] This step is executed by the power command issuing module 108, and the input is... , , And the energy status of each energy storage unit.

[0174] Before issuing power commands to each energy storage unit, the power command issuing module 108 verifies their executability, determining whether the power command exceeds the maximum charging and discharging power of the energy storage unit, and whether its energy state exceeds the set upper and lower limits after running one real-time control cycle according to the command. When the power command of an energy storage unit exceeds the executable range, the command is limited, and the resulting power deficit is redistributed according to the remaining power margin of the other energy storage units. During redistribution, the power deficit is distributed in descending order of remaining power margin until the power deficit is fully covered or all energy storage units' margins are exhausted. If the remaining energy storage units still cannot cover the entire power deficit, the unmet power amount is fed back to the intraday rolling optimization module 103.

[0175] After completing the executability verification, the power command issuing module 108 converts the power command into a corresponding control signal according to the communication protocol and control interface of each energy storage device, and issues it to the converter of the corresponding energy storage unit via the communication interface: issuing to the lithium battery energy storage converter. Send to flywheel energy storage converter Send to supercapacitor energy storage converter Each energy storage unit's converter receives a control signal and then performs corresponding charging or discharging adjustments.

[0176] Step S9: Feedback on actual energy storage output power and updated energy state.

[0177] This step is executed by the status feedback module 109, and the inputs are the measured output power and equipment operating status after the power command is executed by the lithium battery energy storage, flywheel energy storage and supercapacitor energy storage.

[0178] The status feedback module 109 collects data from each energy storage unit at the [missing information - likely a date or time]. The measured charge and discharge power within each real-time control cycle is compared with the corresponding power command to obtain the power tracking deviation. Then, the power tracking deviation is updated according to the following formula. Energy state of each energy storage unit:

[0179]

[0180] In the formula, For the first The energy storage unit in the first Energy status in each real-time control cycle; The measured output power is represented by discharge as positive and charging as negative. Its rated capacity; and These are its discharge efficiency and charging efficiency, respectively; It is the self-discharge coefficient within a single real-time control cycle; To control the sampling period in real time. Since additional compensation for discharge losses needs to be drawn from stored energy during discharge, the value is divided by... During charging, only a portion of the electricity enters the energy storage, so it is multiplied by... ; When this term is negative, it cancels out the preceding negative sign, causing the energy state to rise.

[0181] Simultaneously, the remaining available charging and discharging power of each energy storage unit is updated, and the updated energy status and equipment operating status are verified; when the energy storage energy status is close to the operating boundary, the power tracking deviation exceeds the set threshold, or the equipment malfunctions, a corresponding status flag is generated.

[0182] The output is the first Energy storage state feedback dataset for each real-time control cycle This dataset includes the measured output power of each energy storage unit, the updated state of energy, the remaining available charge / discharge power, power tracking error, operating boundary conditions, and equipment availability. This dataset is transmitted to the data acquisition and preprocessing module 101 and used as the first... The inputs of step S6 (frequency band mutual assistance), step S7 (high frequency loss optimization), and step S8 (power command verification) in a real-time control cycle form a real-time rolling closed-loop control process.

[0183] 11 Simulation Verification

[0184] 11.1 Simulation System and Parameter Settings

[0185] To verify the effectiveness of the method described in this invention, this embodiment selects a regional power grid in Northwest China with a high proportion of new energy access as the research object. An equivalent regional power grid model is established based on its grid structure and source / storage resource configuration. Simulations are then used to verify the day-ahead scheduling, intraday rolling optimization, real-time frequency band mutual assistance, and high-frequency loss optimization processes. The capacity configurations of various power sources and energy storage resources are shown in Table 1, where the energy storage configuration is the equivalent aggregated parameter of similar energy storage power stations.

[0186] Table 1. Regional Power System Source and Storage Configuration Parameters

[0187]

[0188] In the simulation, the sampling period is controlled in real time. Set the filter time constant to 1 second. The time frame is 60 seconds. The upper and lower limits of the energy state are set to 0.8 and 0.2, respectively. The initial energy state of each energy storage unit is set to 0.5. The mutual adjustment parameter is set to... , , The flywheel energy storage loss coefficient is taken as... , , The energy storage loss coefficient of supercapacitors is taken as follows: , The above coefficients are obtained by fitting the efficiency curves provided by the manufacturer to the corresponding operating points.

[0189] Simulation results of scheduling before and during November 2nd.

[0190] The simulation results of the intraday scheduling are as follows: Figure 5 As shown, where Figure 5 (a) shows the hourly scheduling results for the day before. Figure 5 (b) shows the rolling scheduling results for 15 minutes within the day.

[0191] Depend on Figure 5 It is evident that during periods of high photovoltaic output and surplus renewable energy, pumped hydro storage and compressed air storage absorb surplus renewable energy through charging, resulting in a corresponding increase in their state of energy (SGE). Conversely, during periods of declining photovoltaic output or high load, both types of energy storage transition to a discharge state to compensate for system power deficits, leading to a corresponding decrease in their SGE. Intraday rolling optimization can continuously correct the output of thermal power units, wind and solar power absorption, external power purchases, adjustable load response power, and the charging and discharging power of capacity-type energy storage based on updated wind power, photovoltaic, and load forecast information, thereby improving the system's adaptability to source-load forecast deviations and net load fluctuations. The participation of capacity-type energy storage in day-ahead and intraday dispatch can reduce renewable energy curtailment and system operating costs. The coordinated operation of pumped hydro storage and compressed air storage can leverage their complementary roles in terms of operating efficiency, capacity scale, and continuous regulation capabilities.

[0192] 11.3 Real-time frequency band mutual assistance simulation results

[0193] To analyze the impact of frequency band mutual assistance mechanisms on real-time control performance, two comparative scenarios were set up, one without frequency band mutual assistance and one without, based on high-frequency loss optimization in both cases. The simulation results are as follows: Figure 6 As shown.

[0194] Figure 6 (a) High and low frequency power commands before and after frequency band mutual assistance are given. When the energy state of each energy storage unit is far from the operating boundary, the power commands before and after mutual assistance are basically the same; when the energy state of the lithium battery energy storage is close to the operating limit, the frequency band mutual assistance module 106 corrects the high and low frequency power commands and transfers some low frequency charging power to flywheel energy storage and supercapacitor energy storage.

[0195] contrast Figure 6 (b) and Figure 6(d) It can be seen that: without frequency band mutual assistance, the energy state of the lithium battery reaches the upper limit of 0.8 in about 43 minutes. After that, its charging regulation capability is limited and the energy state remains at the upper limit for a long time. After adopting frequency band mutual assistance, the energy state changes of each energy storage unit are more coordinated. Before the energy state of the lithium battery approaches the upper limit, it transfers part of the charging task to the high frequency side through mutual assistance, and there is no situation where it stays at the upper limit for a long time.

[0196] Depend on Figure 6 (c) It can be seen that the power deviation of the frequency band mutual assistance scheme increases significantly during the period from about 43 min to 52 min. This is because the lithium battery energy storage has reached the upper limit of the energy state and can no longer undertake the charging task. The low frequency charging power during this period cannot be fully executed. However, the power deviation during this period is effectively suppressed after the frequency band mutual assistance is adopted, and its magnitude is basically the same as that of the other periods.

[0197] The above results show that the frequency band mutual assistance method of the present invention can utilize the remaining adjustment margin of energy storage units in different frequency bands in the current charging and discharging direction to alleviate the adjustment limitation problem when a single energy storage unit approaches the energy state boundary, and improve the power tracking capability and continuous adjustment capability of the multi-energy storage system.

[0198] 11.4 Simulation Results of High-Frequency Loss Optimization

[0199] Based on the principle of frequency band mutual assistance, two high-frequency power allocation schemes are set up: fixed ratio allocation and loss-optimized allocation. Both schemes use the same frequency band mutual assistance method and real-time power data to be adjusted. Simulation results are as follows: Figure 7 As shown.

[0200] Depend on Figure 7 It is evident that the cumulative losses of the two schemes are relatively small in the initial stage of operation. As flywheel energy storage and supercapacitor energy storage continue to participate in high-frequency power regulation, the cumulative losses of the loss optimization scheme gradually fall below those of the fixed-ratio allocation scheme, and the gap between the two gradually increases with operating time. This is because the loss optimization allocation always adjusts the allocation ratio according to the real-time energy state and marginal losses of the two types of power storage, while the fixed-ratio allocation cannot reflect changes in operating status, and its deviation from the optimal allocation gradually accumulates during operation. The results show that the high-frequency loss optimization method described in this invention can reduce the cumulative operating losses of power storage while meeting the requirements of high-frequency power regulation.

[0201] 12. Technical Effects Description

[0202] Through steps S1 to S9 above, this embodiment forms a closed-loop control link consisting of data acquisition and preprocessing, day-ahead scheduling, intraday rolling optimization, real-time deviation calculation, frequency decomposition, frequency band mutual assistance, high-frequency loss optimization, power command issuance, and status feedback. The data acquisition and preprocessing module 101 provides source-load forecast data and energy storage operating status for day-ahead, intraday, and real-time control; the day-ahead scheduling module 102 determines the baseline operating plan for various resources; the intraday rolling optimization module 103 corrects the day-ahead plan based on the updated forecast information; the real-time deviation calculation module 104 and the frequency decomposition module 105 generate high- and low-frequency power commands; the frequency band mutual assistance module 106 corrects the power allocation based on the directional energy margin of each energy storage unit; the high-frequency loss optimization module 107 completes power optimization between flywheel energy storage and supercapacitor energy storage; the power command issuance module 108 issues control signals to each energy storage converter; and the status feedback module 109 retransmits the measured operating results to the data acquisition and preprocessing module 101, forming the input for the next real-time control cycle.

[0203] In terms of control effectiveness, day-ahead scheduling and intraday rolling optimization can coordinate the operation of thermal power units, wind power, photovoltaic power, external power purchase, adjustable loads, and capacity-type energy storage, realizing the cross-time transfer of new energy power and reducing the impact of source-load prediction deviations on system power balance. In the real-time control phase, lithium battery energy storage handles low-frequency power fluctuations, while flywheel energy storage and supercapacitor energy storage handle high-frequency power fluctuations. When energy storage in a certain frequency band approaches the energy state boundary in its current charge-discharge direction, the frequency band mutual assistance module 106 transfers part of the power task to another frequency band with remaining adjustment margin in that direction, preventing a single energy storage from prematurely reaching the operating boundary. At the same time, the high-frequency loss optimization module 107 optimizes power allocation based on the loss characteristics of flywheel energy storage and supercapacitor energy storage, reducing the operating losses of power-type energy storage.

[0204] It should be noted that the method described in this invention is not a simple numerical calculation process, but a control method for the operation of a regional power system. Its input is measured operating data collected by source-side measuring devices, load-side measuring devices, and energy storage converters. Its output is control signals sent to each energy storage converter via a communication interface. Each energy storage unit performs actual charging or discharging actions accordingly, thereby producing technical effects such as ensuring that the energy storage state does not exceed the limit, reducing system power tracking deviation, and reducing the cumulative operating loss of power-type energy storage. The steps form a physical closed loop through the measured data and control signals.

[0205] 13 Other Implementation Methods

[0206] Those skilled in the art should understand that the specific parameter values ​​in the above embodiments are merely examples. For instance, the intraday rolling cycle can be 5 min, 10 min, or 15 min; the real-time control sampling cycle can be 0.1 s to 5 s; energy storage can use flow batteries in addition to lithium battery energy storage; capacity storage can use molten salt thermal energy storage in addition to pumped hydro storage and compressed air energy storage; power storage groups can include other energy storage forms with fast response speed and high power density in addition to flywheel energy storage and supercapacitor energy storage. In this case, the objective function in step S7 is expanded into a multivariate optimization problem according to the loss characteristics of the participating units, and can be solved using a convex optimization solver.

[0207] The functional modules of the aforementioned source-load-storage coordinated control system can be implemented by the same processor, or they can be distributed across multiple physical devices and work collaboratively through a communication network. The above methods can be stored in the form of computer programs on a computer-readable storage medium, including read-only memory, random access memory, magnetic disks, or optical disks.

[0208] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A source-load-storage multi-timescale regulation method considering the energy margin of multiple energy storage systems, characterized in that, It is executed by the source-load-storage coordinated control system, which is communicatively connected to the source-side measurement device, the load-side measurement device of the regional power system, and the converter of the multi-energy storage system. The multi-element energy storage system includes capacity-type energy storage, energy-type energy storage, and a power-type energy storage group composed of flywheel energy storage and supercapacitor energy storage; the method includes: S1. Collect measured operational data from the source side, load side, conventional regulation resources, grid side, and energy storage side, and preprocess them into a standardized operational dataset; S2. Solve the day-ahead operating baseline plan by using hourly time scale and minimizing the total system operating cost. This plan includes thermal power unit start-up and shutdown and output, wind and solar power consumption, external power purchase, adjustable load response and capacity-type energy storage charging and discharging plan. S3. Using minute-level rolling cycles, and under the condition that the start-up and shutdown status of thermal power units remains unchanged, the daily correction plan is obtained by rolling the solution based on the updated source-load prediction data. S4. Using seconds as the real-time control cycle, the difference between the actual net load and the planned net load is taken as the real-time power command to be adjusted. ; S5, will Decomposed into low-frequency power components With high-frequency power components The sum of the two equals , and respectively serve as the initial power commands for energy-type energy storage and power-type energy storage groups; S6. Determine the direction-dependent energy margin according to the current charging / discharging direction—the charging direction is the difference between the upper limit of the energy state and the current energy state, and the discharging direction is the difference between the current energy state and the upper limit of the energy state—thereby obtaining the low-frequency side margin. The high-frequency side margin is obtained by weighting flywheel energy storage and supercapacitor energy storage according to their rated capacity. The smaller of the two is determined as the restricted side, and the frequency band mutual assistance power is generated according to the mutual assistance coefficient that monotonically increases as the margin of the restricted side decreases. When the low-frequency side is limited, the mutual support coefficient is taken as... When the high-frequency side is limited, the mutual assistance coefficient is taken as the product. Find the opposite of the product; then follow the unified rule. and Corrections completed; S7, in Under the constraints of their respective power and energy state boundaries, the goal is to minimize the total operating loss of the power-type energy storage group, and the power commands of flywheel energy storage and supercapacitor energy storage are obtained. The flywheel energy storage loss is segmented according to the charging and discharging conditions and increases with the increase of its absolute power value. The supercapacitor energy storage loss includes components that increase with the increase of its absolute power value and with the decrease of its energy state in the previous cycle. S8. After verifying the executability of each power command and limiting and redistributing the deficit, the command is converted into a control signal and sent to the corresponding energy storage converter. S9. Collect the measured output power after execution, update the energy status and remaining available charging and discharging power of each energy storage unit, and use it as the input for the next real-time control cycle steps S6 to S8.

2. The method according to claim 1, characterized in that, The method specifically includes: S1. Collect measured operational data from the source side, load side, conventional regulation resources, grid side, and energy storage side of the power system in the region. After time alignment, unit unification, abnormal data removal, and validity verification, a standardized operational dataset is obtained. S2. Using hourly time scales, based on the day-ahead source-load forecast data in the standardized operation dataset, and with the goal of minimizing the total system operating cost, the day-ahead operation baseline plan is obtained under the constraints of power balance, thermal power unit operation, external power purchase, adjustable load, and capacity-type energy storage operation. The day-ahead operation baseline plan includes thermal power unit start-up and shutdown and output plans, wind power and photovoltaic consumption plans, external power purchase plans, adjustable load response plans, and capacity-type energy storage charging and discharging plans. S3. Using minutes as the rolling cycle, while keeping the start-up and shutdown status of thermal power units determined by the daily operating baseline plan unchanged, the daily revised plan is obtained by solving the problem within the rolling optimization window based on the updated source load forecast data with the objective of minimizing the total system operating cost within the window, and each rolling cycle only executes the result corresponding to the current rolling period. S4. Calculate the first control cycle based on the measured power of wind power, photovoltaic power, and load, using a real-time control period of seconds. The actual net load for each real-time control cycle is used to calculate the planned net load for the same real-time control cycle based on the intraday correction plan. The difference between the actual net load and the planned net load is taken as the real-time power command to be adjusted. ; S5, will Decomposed into low-frequency power components and high-frequency power components And satisfy ;by As the initial power command for the energy storage, As the initial power command for the power-type energy storage unit; S6. Determine the direction-related energy margin according to the charging and discharging direction of the current real-time control cycle, wherein in the charging direction, the difference between the upper limit of the energy state and the current energy state is taken, and in the discharging direction, the difference between the current energy state and the lower limit of the energy state is taken; accordingly, calculate the low-frequency side direction-related energy margin corresponding to the energy-type energy storage. And the high-frequency side directional energy margin corresponding to the high-frequency side comprehensive energy state obtained by weighting the energy states of the flywheel energy storage and the supercapacitor energy storage according to their rated capacities. ;Compare and The side with the smaller value is identified as the restricted side, and the frequency band mutual assistance power is generated according to the mutual assistance coefficient. The mutual assistance coefficient monotonically increases as the direction-dependent energy margin of the restricted side decreases, and its value range is [value range missing]. When the low-frequency side is the restricted side, Take the mutual assistance coefficient and The product; when the high-frequency side is the restricted side, Take the mutual assistance coefficient and The opposite of the product; when and When all are not less than the set margin threshold, Then, follow the unified revision rules. ； Obtain the corrected low-frequency power command and the revised high-frequency power command And satisfy ; S7, using flywheel energy storage power and supercapacitor energy storage power As decision variables, under high-frequency power balance constraints Under the respective charge / discharge power boundary constraints and energy state boundary constraints of flywheel energy storage and supercapacitor energy storage, the flywheel energy storage power command is obtained by minimizing the total operating loss of the power-type energy storage group within the current real-time control cycle. and supercapacitor energy storage power command The total operating loss is the sum of the flywheel energy storage operating loss and the supercapacitor energy storage operating loss. The flywheel energy storage operating loss is characterized by segments according to charging and discharging conditions and increases with the absolute value of the flywheel energy storage power. The supercapacitor energy storage operating loss includes a component that increases with the absolute value of the supercapacitor energy storage power and increases with the decrease of the energy state of the supercapacitor energy storage in the previous real-time control cycle. S8, respectively for , and An executability check is performed to determine whether each power command exceeds the charging and discharging power boundary of the corresponding energy storage unit, and whether the energy state of the corresponding energy storage unit exceeds the energy state boundary after running one real-time control cycle according to the power command. When any power command exceeds the executable range, the power command is limited, and the resulting power deficit is redistributed according to the remaining power margin of the other energy storage units. After the check is completed, each power command is converted into a control signal and sent to the converter of the corresponding energy storage unit through the communication interface to control each energy storage unit to perform charging or discharging. S9. Collect the measured output power of each energy storage unit after execution, and update the energy state and remaining available charge / discharge power of each energy storage unit accordingly. The updated results will then be used as the basis for further analysis. The inputs for steps S6 to S8 in a real-time control cycle.

3. The method according to claim 2, characterized in that, In step S5, a first-order low-pass filter is used to achieve the decomposition: ； ； ； In the formula, To control the sampling period in real time, The filtering time constant is These are the filter coefficients. The low-frequency power component of the previous real-time control cycle; and The value shall be no less than 10 times the response time constant of the power-type energy storage group and no more than one-fifth of the allowable continuous adjustment time of the energy-type energy storage.

4. The method according to claim 2, characterized in that, In step S6, the high-frequency side integrated energy state is calculated using the following formula: ； The charging and discharging direction on the low-frequency side is from The sign is determined, and the charging and discharging direction on the high-frequency side is determined by... The sign is determined; when When the low-frequency side is the charging direction, its direction-dependent energy margin is: ;when The low-frequency side is the discharge direction, and its direction-dependent energy margin is: The same reason applies to the high-frequency side. The sign is determined ;in: ； ； In the formula, , and These are flywheel energy storage, supercapacitor energy storage, and lithium battery energy storage as an energy storage type, respectively, in the [missing information]. Energy status in each real-time control cycle and These are the rated capacities of flywheel energy storage and supercapacitor energy storage, respectively. and These are the upper and lower limits of the energy state, respectively.

5. The method according to claim 4, characterized in that, The mutual assistance coefficient mentioned in step S6 is determined according to the normalized direction-related energy margin on the restricted side: ； ； In the formula, For the direction-dependent energy margin on the restricted side, Its normalized value, The mutual assistance coefficient, The mutual assistance start-up margin threshold is set at 0.15 to 0.

35. The mutual aid acceleration margin threshold is set to a value between 0.05 and 0.

20. This is the mutual assistance coefficient at the inflection point of the segmentation, and its value ranges from 0.2 to 0.

4. .

6. The method according to claim 2, characterized in that, Step S6 after obtaining This is followed by a mutual feasibility backoff: the smaller of the power boundaries of the flywheel energy storage and the supercapacitor energy storage and the energy-feasible power determined by their energy state and real-time control sampling period is taken, and the sum of the two is taken as the total available power of the power-type energy storage group under the current charge and discharge direction. ;when At that time, Limiting and order To back down the excess portion to the low-frequency side; for the backed-down portion The available power of the energy storage is further limited, and the remaining unaccepted power is fed back to the daily rolling optimization process as unmet power.

7. The method according to claim 2, characterized in that, The objective function for the total operating loss in step S7 is: ； ； ； In the formula, For the first The total operating loss of the power-type energy storage unit within a real-time control cycle. and These are the operating losses for flywheel energy storage and supercapacitor energy storage, respectively. and These are the power-related loss coefficients of flywheel energy storage under discharge and charging conditions, respectively. and These are the standby loss coefficients, representing wind resistance loss and bearing loss, respectively, for flywheel energy storage under discharge and charging conditions. The coefficients of the quadratic term representing the Joule loss of the equivalent series resistance in supercapacitor energy storage are given. This is the additional loss coefficient for supercapacitor energy storage, representing the increase in current caused by the decrease in terminal voltage as the energy state decreases. and These represent the energy states of flywheel energy storage and supercapacitor energy storage respectively in the previous real-time control cycle, and , , , , , .

8. The method according to claim 7, characterized in that, In step S7, Substituting into the objective function, we obtain about The unconstrained minimum point of a univariate convex function is: ； Among them when Time to take ,when Time to take ;Will Projected onto the feasible region jointly defined by the power boundary and energy state boundary of flywheel energy storage and supercapacitor energy storage. The supercapacitor energy storage power command is obtained: ； And command the flywheel energy storage power .

9. The method according to claim 2, characterized in that, Step S4 also includes... Perform dead zone handling and rate of change limiting: when When less than the dead zone threshold ,when When the amplitude exceeds the product of the maximum rate of change and the real-time control sampling period, the amplitude is limited according to that product; in step S9, the value is updated according to the following formula. Energy state of each energy storage unit: ； In the formula, For the first The energy storage unit in the first Energy status in each real-time control cycle This represents the measured output power, with discharge being positive and charging being negative. For its rated capacity, and These are its discharge efficiency and charging efficiency, respectively. Its single-cycle self-discharge coefficient, To control the sampling period in real time.

10. A source-load-storage coordinated control system, characterized in that, The system includes a processor, a memory, and a communication interface. The communication interface is connected to the source-side measurement device, the load-side measurement device, and the converter of the multi-energy storage system in the regional power system, respectively, and is used to receive measured operating data and send control signals to the converter of each energy storage unit. The memory stores a computer program, which, when executed by the processor, implements the method as described in any one of claims 1 to 9. The computer program includes a data acquisition and preprocessing module, a day-ahead scheduling module, an intraday rolling optimization module, a real-time deviation calculation module, a frequency decomposition module, a frequency band mutual assistance module, a high-frequency loss optimization module, a power command issuance module, and a status feedback module, respectively used to execute steps S1 to S9.

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