Day-ahead scheduling method and system for power grid side energy storage to participate in dual auxiliary service

Abstract

The invention provides a day-ahead scheduling method and system for power grid side energy storage to participate in dual auxiliary services, and the method comprises the steps: constructing a power system basic model and a multivariate resource input parameter set, and building a system dynamic frequency security constraint model containing multi-stage indexes; constructing a dynamic frequency security constraint system of the multi-stage indexes; constructing a mutual exclusion cooperation and energy sustainable constraint model of energy storage participating in double auxiliary services; constructing a multi-target security constraint unit combination model containing a peak shaving smooth target; and a multi-objective optimization model considering both the operation economy and the net load smoothing characteristic of the system is constructed. The maximum frequency change rate of the system is controlled to be 0.381 Hz / s, the lowest frequency point is maintained to be 49.800 Hz, the net load bandwidth and the total climbing amount are reduced by 46.4% and 80.0% respectively, the new energy utilization rate reaches 95.00%, 67.7% of AGC standby capacity of the whole system is contributed by energy storage, and the safety and economical efficiency of the system are improved.

Description

A method and system for day-ahead dispatching of grid-side energy storage for dual ancillary services Technical Field

[0001] This invention relates to the field of power system operation and maintenance and energy storage dispatch. Background Technology

[0002] With the deepening implementation of the "dual-carbon" strategy, the penetration rate of new energy sources, represented by wind power and photovoltaics, in the power system continues to rise. The gradual replacement of conventional thermal power units has led to a significant reduction in the system's online rotating inertia, weakening its anti-disturbance capability and posing a severe challenge to grid frequency security. Electrochemical energy storage, with its rapid power response and bidirectional regulation characteristics, has become a key resource for maintaining the frequency stability of the new power system.

[0003] However, existing Safety Constrained Unit Combination (SCUC) models have significant limitations when dealing with high proportions of renewable energy integration. These limitations primarily manifest in the fact that traditional models focus on power balance and line flow constraints, failing to explicitly incorporate key safety indicators such as the rate of change of frequency (RoCoF), the lowest frequency point (Nadir), and quasi-steady-state frequency deviation. This results in the generated dispatch plans implicitly containing frequency instability risks during actual operation. Furthermore, existing methods often optimize peak-shaving demand in the electricity market separately from frequency regulation demand in the ancillary services market. This fails to fully leverage the synergistic benefits of energy storage resources across multiple services and is difficult to adapt to complex market exclusion rules in specific regional power grids, such as the Liaoning power grid's prohibition on spot electricity declarations during AGC frequency regulation periods. This leads to compliance challenges in the actual implementation of dispatch results.

[0004] Therefore, there is an urgent need to develop a day-ahead optimization scheduling method for energy storage that can take into account the system's frequency security boundaries, balance the dual benefits of peak shaving and frequency regulation, and strictly adapt to specific market trading rules. Summary of the Invention

[0005] To overcome compliance issues in existing models, this invention provides a day-ahead scheduling method and system for grid-side energy storage to participate in dual ancillary services.

[0006] The technical solution adopted by the present invention to achieve the above objectives is as follows:

[0007] A day-ahead scheduling method for grid-side energy storage to participate in dual ancillary services includes:

[0008] Construct a basic power system model and a set of multi-resource input parameters: collect and input network topology parameters, node load forecast data and tie-line planning curves within the target power grid area, and set the time scale of day-ahead scheduling as a numerical sequence [0:0.25:24] (unit: h).

[0009] Using the above parameter set, a thermal power unit operation model is established. The rated capacity, minimum technical output, ramp rate, start-up and shutdown cost, no-load cost, electricity price, and inertial time constant and frequency regulation performance parameters used for frequency response calculation are entered as limiting parameters of the power system basic model. The time scale of day-ahead dispatch is set as a numerical sequence as the input of the parameters.

[0010] An electrochemical energy storage operation model was constructed using the aforementioned numerical sequence. Its rated charge / discharge power, rated capacity, charge / discharge efficiency, upper and lower limits of state of charge (SOC), and initial and final SOC requirements were set. The response time parameters for energy storage participation in primary and secondary frequency regulation, as well as capacity application ratio limits, were also entered into the electrochemical energy storage operation model. Day-ahead power forecast curves from wind farms and photovoltaic power plants were imported, and wind and solar utilization rate constraints were set.

[0011] Set the line power flow transmission limit and the critical section safety and stability limit, and initialize the system frequency safety threshold parameters, including the system rated frequency, the maximum frequency change rate limit, the allowable deviation of the lowest frequency point, and the quasi-steady-state frequency deviation limit, to provide boundary conditions for the subsequent construction of an optimization model with embedded frequency safety constraints.

[0012] A dynamic frequency security constraint model for the system, which includes multi-stage indicators, is established using the aforementioned system frequency security threshold parameters: In order to address the problem of reduced system inertia and weakened anti-disturbance capability caused by the high proportion of new energy access, a dynamic frequency security constraint system with multi-stage indicators is constructed in the day-ahead optimization scheduling model.

[0013] For the inertial response phase of the system during the instant of maximum active power disturbance, a maximum rate of change of frequency (RoCoF) constraint is constructed. Based on the rotor motion equations, this constraint limits the initial rate of change of the system frequency to be directly proportional to the disturbance power deficit and inversely proportional to the total online inertia of the system, ensuring that it does not exceed the critical threshold for safe system operation. Its mathematical expression is as follows:

[0014] ;

[0015] In the formula, Represents the rated reference frequency of the power system; This represents the maximum active power disturbance deficit under a pre-defined severe fault condition in the system. This is the maximum allowable rate of change of frequency for the system. The sum of the inertial constants of all online synchronous generator units and the virtual inertial constants of energy storage devices in the current system period is calculated using the following formula:

[0016] ;

[0017] In the formula, , , Generator sets The inertial constant, rated capacity, and start / stop status (0-1 variables); , Energy storage The virtual inertial constant and rated power.

[0018] For the quasi-steady-state phase following the completion of primary frequency regulation, a quasi-steady-state frequency deviation (QSS) constraint is constructed. This constraint aims to ensure that the system frequency eventually stabilizes within the allowable safe deviation range by balancing the disturbance power deficit with the total primary frequency regulation reserve capacity provided by online generators and energy storage stations, and by considering the frequency damping characteristics of the load. The constraint formula is defined as follows:

[0019] ;

[0020] In the formula, This represents the total primary frequency regulation reserve capacity provided by all online thermal power units; Represents the total reserve capacity provided by the energy storage power station cluster via Fast Frequency Response (FFR); The load frequency damping coefficient; This represents the total load level of the system during the current time period. This is the maximum permissible quasi-steady-state frequency deviation limit.

[0021] The minimum frequency point (Nadir) constraint is constructed based on a first-order equivalent frequency response model of the power system. During the response time after a disturbance, both the primary frequency regulation power of the synchronous generator and the fast frequency response power of the energy storage station increase linearly with time. Since the load damping effect has limited effect in the initial transient phase of a sharp frequency drop, it is neglected for calculation simplification. Based on this, a nonlinear constraint inequality describing the coupling relationship between the system's total inertia, thermal power frequency regulation reserve capacity, energy storage frequency regulation reserve capacity, and the maximum transient frequency deviation is derived by integrating the rotor motion equations.

[0022] ;

[0023] In the formula: The system reference frequency; The maximum active power deficit for a pre-set severe fault; The total online inertia constant of the system; , The total frequency regulation reserve capacity provided for online thermal power units and energy storage power stations, respectively; The load frequency damping coefficient; This represents the total system load. This is the maximum quasi-steady-state frequency deviation limit; , These are the complete response times for energy storage and thermal power units to complete frequency regulation response, respectively. This is the maximum allowable transient frequency deviation limit of the system (the threshold for the lowest frequency point).

[0024] A mutually exclusive and synergistic model for energy storage participating in dual ancillary services and an energy sustainability constraint model was constructed: Based on the specific operating rules of the electricity market that "energy storage power stations are not allowed to apply for spot electricity market during the frequency regulation (AGC) period", a strict mutual exclusion mechanism of "peak shaving-frequency regulation" mode was constructed in the optimization model.

[0025] In the mutual exclusion and energy sustainability constraint model, binary state variables are defined to characterize the real-time operation mode of the energy storage power station. By introducing the big M method or direct logic constraints, the spot charging and discharging power of energy storage in the AGC frequency regulation mode is forced to be zero, while the AGC reserve capacity in the power peak shaving mode is zero, thereby realizing the physical isolation and time decoupling of the two businesses at the mathematical level.

[0026] The formula for the mutual exclusion constraint of the modes used is expressed as follows:

[0027] ;

[0028] ;

[0029] In the formula, and Energy storage power stations exist The charging and discharging power during the period of participation in the spot electricity market; This refers to the rated power of the energy storage power station; This is the energy storage operation mode identifier bit, when A value of 0 indicates that the system is in AGC frequency modulation mode, while a value of 0 indicates that the system is in peak shaving mode. AGC frequency regulation backup capacity provided for energy storage; This is the maximum frequency regulation capacity that can be declared for energy storage.

[0030] Based on the mutual exclusion of modes, a power capacity coupling constraint is further constructed under multiple services. Considering that energy storage may simultaneously undertake baseline power dispatch (peak shaving), AGC backup, and primary frequency regulation (PFR / EFR) backup tasks, its total output must be strictly limited within the rated capacity range. Therefore, a joint capacity constraint is established, requiring that the sum of the baseline power, AGC backup capacity, and primary frequency regulation backup capacity of energy storage in any time period does not exceed the rated power. This ensures that energy storage always maintains sufficient power margin when dealing with sudden frequency disturbances, avoiding equipment overload caused by the superposition of multiple commands.

[0031] The power coupling constraint formula used when constructing power capacity coupling constraints is as follows:

[0032] ;

[0033] ;

[0034] In the formula, The primary frequency regulation (or fast frequency response) reserve capacity reserved for energy storage power stations ensures that the energy storage still has sufficient power space to respond to AGC commands and primary frequency regulation commands while performing baseline discharge tasks.

[0035] Finally, considering the limited capacity of energy storage, a sustainability constraint on the state of charge (SOC) is established, taking into account ancillary service requirements. Unlike conventional generator sets, energy storage needs to reserve energy margins to meet continuous operation requirements when providing AGC services. This invention introduces a time parameter to quantify the continuous demand of AGC commands and correct the safety boundary of SOC. The constraint requires that the real-time state of charge of the energy storage must reserve the energy required for continuous charging or discharging for a certain period of time under the current AGC capacity. This ensures that after winning the bid for AGC services, the energy storage has the energy basis to continuously respond to dispatch commands within a specified time, preventing frequency regulation service interruptions due to depletion or full charging.

[0036] Its energy sustainability constraint formula is as follows:

[0037] ;

[0038] In the formula, For energy storage power stations Real-time energy storage over a given period of time; and These are the minimum and maximum energy limits allowed for energy storage, respectively. The continuous response time for AGC commands ( ); and These represent the charging efficiency and discharging efficiency of energy storage, respectively.

[0039] Construct a multi-objective safety-constrained unit combination model that includes peak shaving and smoothing objectives: Based on the established frequency safety boundary and energy storage operation rules, a multi-objective optimization model that takes into account both system operation economy and net load smoothing characteristics is further constructed.

[0040] This model takes minimizing the total system operating cost as its core objective and organically combines traditional economic dispatch with deep peak-shaving demand by introducing a penalty term. The objective function consists of four sub-functions: first, the operating cost of thermal power units, covering coal costs, start-up costs, and shutdown costs; second, the ancillary service procurement cost, including frequency regulation capacity compensation fees and mileage compensation fees provided by thermal power units and energy storage power stations; third, the penalty cost for renewable energy curtailment, to promote the maximum utilization of wind and solar resources; and finally, to cope with the drastic fluctuations in net load caused by a high proportion of renewable energy, a peak-shaving smoothing objective based on the shape of the net load curve is introduced, that is, by minimizing the sum of the peak-valley bandwidth of the net load curve and the ramp rate of adjacent time periods, the source-grid-load-storage resources are guided to actively participate in peak shaving and valley filling, thereby alleviating the grid regulation pressure.

[0041] The mathematical expression for the system's overall objective function is as follows:

[0042] ;

[0043] In the formula, This represents the number of time periods in the scheduling cycle. A collection of thermal power units; For the unit exist Efforts during a specific time period; , , These are the unit's electricity price, startup cost, and shutdown cost, respectively. , , This serves as an indicator of the unit's start-up and shutdown status. For the procurement cost of auxiliary services for the entire system; The cost of penalizing the abandonment of wind and solar power; and These are the system net load curves, which are the equivalent loads after subtracting the output of new energy sources and the net discharge of energy storage from the total load, and the maximum and minimum values ​​of these curves over the entire scheduling cycle. This is a slack variable representing the absolute value of the rate of change of the net load curve over adjacent time periods. ; and These are the bandwidth weighting coefficient and ramp weighting coefficient for net load smoothing control, respectively.

[0044] A day-ahead dispatch system in which grid-side energy storage participates in dual ancillary services includes:

[0045] The basic modeling unit is used to construct the basic model of the power system and the set of multi-source input parameters, and to establish a system dynamic frequency security constraint model that includes multi-stage indicators.

[0046] The safety constraint modeling unit is used to construct a dynamic frequency safety constraint system for multi-stage indicators.

[0047] The sustainable constraint modeling unit, connected to the safe energy constraint construction unit, is used to construct a mutually exclusive and synergistic energy sustainability constraint model for energy storage participating in dual auxiliary services.

[0048] The combined model building unit, connected to the sustainable constraint modeling unit, is used to build a multi-objective safety constraint combined unit model that includes peak shaving and smoothing objectives;

[0049] The multi-objective optimization model building unit, connected to the combined model building unit, is used to construct a multi-objective optimization model that takes into account both the economic efficiency of system operation and the smoothing characteristics of net load.

[0050] The power supply unit is connected to the basic modeling unit, the safety constraint modeling unit, the combined model construction unit, and the multi-objective optimization model construction unit, respectively, and provides a stable power supply to each of them.

[0051] A computer-readable storage medium for a day-ahead dispatching method based on grid-side energy storage participating in dual ancillary services includes computer instructions that can be recognized by a computer and are used to execute a program of the method.

[0052] The beneficial effects of this invention are as follows: In general, it effectively solves the risk of system frequency instability and multi-service scheduling conflicts under the high proportion of new energy access.

[0053] Regarding frequency security and rule adaptation, the method explicitly introduces dynamic frequency security constraints covering the entire process of inertial response, primary frequency regulation, and secondary frequency regulation into the optimization model. It also utilizes piecewise linearization techniques to handle nonlinear frequency minimum point constraints, effectively mitigating the risk of frequency exceeding limits while ensuring model solvability. Furthermore, based on the rule that the frequency regulation market and the spot market operate independently, a mutual exclusion constraint model for "AGC-peak shaving" periods is established. This precisely addresses the compliance issue of energy storage being prohibited from declaring applications in the spot electricity market during frequency regulation periods, ensuring the feasibility of scheduling plans.

[0054] In terms of multi-energy storage synergy and peak shaving, differentiated configuration guides power-type energy storage to prioritize participation in AGC frequency regulation, while energy-type energy storage undertakes peak shaving and valley filling tasks, maximizing the utilization of energy storage advantages. The innovatively introduced net load smoothing penalty term significantly alleviates the unit's regulation pressure by minimizing net load bandwidth and ramp rate. Implementation results show that the system's maximum frequency change rate is controlled at 0.381Hz / s, the lowest frequency point is maintained at 49.800Hz, net load bandwidth and total ramp rate are reduced by 46.4% and 80.0% respectively, the renewable energy utilization rate reaches 95.00%, and energy storage contributes 67.7% of the system's AGC reserve capacity, significantly improving the system's safety and economy. Attached Figure Description

[0055] Figure 1 is a flowchart of the algorithm according to an embodiment of the present invention.

[0056] Figure 2 is a system principle block diagram of an embodiment of the present invention.

[0057] Figure 3 is a diagram showing the power balance and net load evolution of the power system according to an embodiment of the present invention.

[0058] Figure 4 is a diagram showing the start-stop combination state of the thermal power unit and the power flow distribution of the power line according to an embodiment of the present invention.

[0059] Figure 5 is a timing diagram of the optimized system frequency security key indicators according to an embodiment of the present invention. Detailed Implementation

[0060] The present invention will be further explained and described below with reference to the accompanying drawings and embodiments.

[0061] This embodiment takes a typical high-proportion renewable energy local power grid as an example, and conducts day-ahead optimization scheduling for a source-grid-load-storage system that includes thermal power units, wind farms, photovoltaic power plants, and different types of electrochemical energy storage (power-type lithium iron phosphate batteries LFP and energy-type vanadium redox flow batteries VRFB). The effectiveness of the proposed method is verified by introducing specific operating parameters and weighting coefficients. The entire implementation process follows a logical closed loop of "basic model construction—frequency security constraint embedding—energy storage mutual exclusion collaborative modeling—construction of conventional physical constraints—construction of multi-objective functions—solution and analysis".

[0062] Step 1: Building the basic model and initializing parameters

[0063] Step 1-1: Based on the actual operating rules, set the daytime scheduling cycle as follows: Time period, step size per time period It takes 15 minutes (i.e., 0.25 hours).

[0064] Steps 1-2: Collect and input power grid topology parameters, node load forecast curves, and wind and solar power forecast output data; initialize thermal power unit parameters and set the unit's inertial time constant. And enter the rated capacity, minimum technical output (e.g., 50% of rated capacity), ramp rate, start-up and shutdown costs, and no-load costs;

[0065] Steps 1-3: Construct differentiated energy storage models, defining LFP energy storage as a power-type regulation resource and VRFB energy storage as an energy-type regulation resource; input the charging and discharging efficiencies of various energy storage types (e.g., ... ), and the allowable range of state of charge (e.g., SOC 10%-90%).

[0066] Steps 1-4: Initialize system frequency safety threshold parameters: Set the system rated reference frequency. Maximum rate of change of frequency limit Maximum permissible deviation at the lowest frequency point and quasi-steady-state frequency deviation limit Load frequency damping coefficient Set to 0.015.

[0067] Step 2: Establish a system dynamic frequency security constraint model that includes multi-stage indicators.

[0068] In this step, the system transforms the frequency safety index into specific linear inequality constraints and loads them into the optimization model. Step 2-1: For the inertial response stage, the system calculates the total inertia for each time period and applies constraints according to the rotor motion equation to ensure that the rate of frequency change does not exceed the limit when the maximum power deficit occurs. The calculation formula is as follows:

[0069] ;

[0070] In the formula, The system reference frequency; The preset active power deficit for severe faults (set to 0.8MW in this embodiment); This represents the total inertia constant of the system during the current time period; This is the maximum rate of change limit for frequency.

[0071] Step 2-2: For the primary frequency regulation response process, the system uses a piecewise linearization technique based on a first-order frequency response model to handle the nonlinear relationship at the frequency minimum point (Nadir). The system mandates that online generating units and energy storage reserve sufficient frequency regulation capacity to satisfy the following inequality, thereby ensuring that transient frequency drops do not reach the low-frequency load shedding threshold:

[0072] ;

[0073] In the formula, , These are frequency regulation reserve capacities provided for energy storage and thermal power, respectively. , The complete response times are for energy storage (set to 1.0s) and thermal power (set to 10.0s), respectively. This represents the allowable deviation at the lowest frequency point.

[0074] Steps 2-3: For the quasi-steady-state stage, the system's constrained steady-state frequency deviation is within a safe range:

[0075] ;

[0076] In the formula, The load frequency damping coefficient; This represents the total system load. This is the limit for quasi-steady-state frequency deviation.

[0077] Step 3: Mutual Exclusion of Energy Storage Services and Energy Management Constraints:

[0078] Step 3-1: To adapt to market rules, this step establishes an operational logic lock for energy storage and introduces binary state variables. When its value is 1, the energy storage is locked in AGC mode, forcing the spot power to be 0; when its value is 0, it is locked in peak shaving mode, forcing the AGC capacity to be 0; the system executes the following mutual exclusion constraints:

[0079] ;

[0080] ;

[0081] In the formula, , For spot charging and discharging power; Rated power; For AGC capacity; This is the maximum number of applications that can be submitted.

[0082] Step 3-2: Perform system power coupling verification to ensure that the baseline power of energy storage and the sum of various reserve capacities do not exceed the limit.

[0083] ;

[0084] In the formula, This is for primary frequency regulation reserve capacity;

[0085] Step 3-3: The system also corrects the upper and lower limits of the state of charge (SOC), and forcibly reserves space to meet the continuous response of AGC commands (set to). Energy margin required (minutes):

[0086] ;

[0087] In the formula, Real-time energy status; Energy limits; This refers to the charge / discharge efficiency.

[0088] Step 4: Basic Constraints of Physical and Network Security

[0089] In addition to the specific constraints mentioned above, the system needs to add a set of basic constraints to ensure the physical operation of the power grid in order to form a complete feasible domain.

[0090] Step 4-1: Apply real-time power balance constraints across the entire network to ensure real-time matching of supply and demand:

[0091] ;

[0092] In the formula, These are collections of various power supplies; It provides power to various types of power sources; For energy storage charging and discharging power; For load;

[0093] Step 4-2: Apply operating constraints to thermal power units This includes upper and lower limits of output (including reserve capacity), climbing speed, and minimum start-stop time:

[0094] ,

[0095] ,

[0096] ;

[0097] In the formula, Indicating start / stop status; indicating technical output limit; For upward adjustment and reserve; This is the climbing limit; The runtime and minimum runtime;

[0098] Step 4-3: Apply system standby constraints. In this embodiment, the system is configured to increase standby requirements. 5% of net load:

[0099] ;

[0100] In the formula, For energy storage AGC capacity; Net load;

[0101] Step 4-4: Apply network security constraints and verify line power flow based on the PTDF matrix:

[0102] ;

[0103] In the formula, For line transmission limits; Power transfer distribution factor; Inject power into the node.

[0104] Step 5: Construct and solve the multi-objective function.

[0105] The system is constructed with the core objective function of minimizing operating costs. The objective function includes thermal power operating costs, ancillary service costs, curtailment penalties, and net load smoothing penalties. It also introduces a penalty term to guide peak shaving.

[0106] ;

[0107] In the formula, Quotations for thermal power energy and start-up / shutdown; For ancillary service costs; Cost of power curtailment; This represents the extreme value of net load. For slack variables during hill climbing; Net load bandwidth weight; This represents the climbing weight.

[0108] Finally, input the above model into the CPLEX solver and set the relative duality gap to . After the solution is completed, the system outputs the start-up and shutdown plan and output plan of thermal power plants, as well as the AGC / peak-shaving mode switching sequence of energy storage, forming the final day-ahead dispatch scheme.

[0109] Analysis of the results of the example:

[0110] This embodiment uses the above-mentioned optimized scheduling method to solve the problem based on actual operating data of a certain region. By comparing the system operating indicators before and after energy storage configuration, the significant effects of the present invention in ensuring frequency security, improving the absorption of new energy sources, and optimizing peak-shaving performance are verified.

[0111] (1) Regarding the effects of new energy consumption and peak shaving, the optimization results show that the scheduling strategy proposed in this invention effectively promotes the consumption of new energy. During periods of high wind and solar power generation, energy storage power stations (LFP and VRFB) automatically switch to charging (peak shaving) mode to absorb excess power, resulting in a daily utilization rate of 95.00% for both wind and solar power, significantly reducing the phenomenon of wind and solar curtailment. By introducing a net load smoothing objective function, the system net load curve is significantly optimized, with the peak-to-valley difference decreasing from 3.0 MW to 1.6 MW, a reduction of 46.4%, and the total net load ramping amount decreasing from 13.4 MW to 2.7 MW, a reduction of 80.0%. The adjustment process of net energy storage power injection during the low load period charging and the high load period discharging effectively smooths out net load fluctuations.

[0112] (2) Regarding frequency safety indicators, the dynamic frequency safety constraint model constructed in this invention plays a crucial role in actual scheduling. Simulation results show that when the system faces a preset maximum power disturbance, all frequency safety indicators are controlled within the safety threshold range. The maximum rate of change of system frequency (RoCoF) is 0.381 Hz / s, which is lower than the set safety limit of 0.50 Hz / s, indicating that the system has sufficient online rotational inertia to resist the rapid frequency change in the early stage of the disturbance; the lowest system frequency (Nadir) is controlled at 49.800 Hz, accurately meeting the set safety threshold of 49.80 Hz, effectively preventing the malfunction of the low-frequency load shedding device. This proves that the proposed model can accurately quantify and reserve sufficient frequency safety margin in scheduling.

[0113] (3) Regarding the participation of energy storage in ancillary services, the energy storage power station demonstrated a flexible mode switching capability in response to the mutual exclusion rule of "peak shaving-frequency regulation". Statistical results show that the energy storage power station participated in AGC frequency regulation services for a total of 80 time periods throughout the day, accounting for 41.7% of the total number of time periods. During these time periods, energy storage contributed 67.7% of the AGC standby capacity of the entire system.

[0114] This strictly mutually exclusive operating mode, which prioritizes peak shaving and supplements frequency regulation, not only meets the grid's rigid demand for peak shaving resources but also fully utilizes the rapid response characteristics of energy storage to provide high-quality frequency regulation services, thereby maximizing the value of energy storage assets in multiple electricity markets.

[0115] (4) In terms of unit combination and power flow distribution, the optimized thermal power unit combination shows that the unit start-up and shutdown status and output plan meet the load demand while strictly adhering to the minimum start-up and shutdown time and ramping constraints.

[0116] The power flow distribution of the lines shows that the power flow of all branches in the network is operating within the safety limits, and no limit exceedances have occurred, verifying the effectiveness of network security constraints.

[0117] The results show that the maximum frequency change rate of the system was controlled at 0.381 Hz / s, the lowest frequency point was maintained at 49.800 Hz, the net load bandwidth and the total ramp amount were reduced by 46.4% and 80.0% respectively, the utilization rate of new energy reached 95.00%, and energy storage contributed 67.7% of the AGC backup capacity of the entire system.

[0118] In summary, the day-ahead optimization scheduling method proposed in this invention can not only significantly improve the system's renewable energy absorption capacity and peak-shaving performance, but also ensure the frequency security of the power grid under disturbances while meeting strict market rules, thus having significant engineering application value.

[0119] The results show that the maximum frequency change rate of the system was controlled at 0.381 Hz / s, the lowest frequency point was maintained at 49.800 Hz, the net load bandwidth and the total ramp amount were reduced by 46.4% and 80.0% respectively, the utilization rate of new energy reached 95.00%, and energy storage contributed 67.7% of the AGC backup capacity of the entire system.

[0120] This invention has been described through embodiments. Those skilled in the art will understand that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of this invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of this invention.

Claims

1. A day-ahead dispatching method for grid-side energy storage participating in dual ancillary services, characterized in that: include: A basic power system model and a set of multi-resource input parameters are constructed to establish a dynamic frequency security constraint model for the system, which includes multi-stage indicators. Using the parameter set of the above dynamic frequency security model, a dynamic frequency security constraint system with multi-stage indicators is constructed, and the maximum frequency change rate constraint is constructed. Using the maximum frequency change rate constraint, a mutual exclusion and synergy constraint model for energy storage participating in dual ancillary services and energy sustainability constraint model is constructed. Using the output results of the mutual exclusion and synergy constraint model and energy sustainability constraint model, a multi-objective security constraint unit combination model including peak shaving and smoothing objectives is constructed.

2. The method according to claim 1, characterized in that: When constructing the basic model of the power system and the set of input parameters for multiple resources, network topology parameters, node load forecast data, and tie-line planning curves within the target power grid area are collected and entered as limiting parameters of the basic model of the power system, and the time scale of day-ahead scheduling is set as a numerical sequence as the input of the parameters.

3. The method according to claim 2, characterized in that: When constructing the basic model of the power system and the set of input parameters for multiple resources, a thermal power unit operation model is established. In this model, the rated capacity, minimum technical output, ramp rate, start-up and shutdown costs, no-load costs, electricity price quotation, and inertial time constant and frequency regulation performance parameters used for frequency response calculation are entered.

4. The method according to claim 1, characterized in that: When constructing the basic model of the power system and the set of input parameters for multiple resources, an electrochemical energy storage operation model is constructed, and its rated charging and discharging power, rated capacity, charging and discharging efficiency, upper and lower limits of state of charge, initial and final state of charge requirements are set. The electrochemical energy storage operation model is also entered, including the response time parameters of energy storage participating in primary and secondary frequency regulation and the capacity application ratio limit.

5. The method according to claim 2, characterized in that: When constructing the basic model of the power system and the set of input parameters for multiple resources, the day-ahead power prediction curves of wind farms and photovoltaic power plants are imported, and wind and solar utilization rate constraints are set.

6. The method according to claim 1, characterized in that: When constructing a dynamic frequency safety constraint system with multi-stage indicators, a maximum frequency change rate constraint is constructed. This constraint is based on the rotor motion equation and limits the initial rate of change of the system frequency to be proportional to the disturbance power deficit and inversely proportional to the total online inertia of the system. Its mathematical expression is as follows: In the formula, Represents the rated reference frequency of the power system; This represents the maximum active power disturbance deficit under a pre-defined severe fault condition in the system. This is the maximum allowable rate of change of frequency for the system. The sum of the inertial constants of all online synchronous generator units and the virtual inertial constants of energy storage devices in the current system period is calculated using the following formula: In the formula, 、 、 Generator sets The inertial constant, rated capacity, and start / stop status are set as variables 0-1; 、 Energy storage The virtual inertia constant and rated power; for the quasi-steady-state stage after the primary frequency regulation operation, a quasi-steady-state frequency deviation constraint is constructed. By balancing the disturbance power deficit with the total primary frequency regulation reserve capacity provided by online generator units and energy storage power stations, and combined with the frequency damping characteristics of the load, its constraint formula is defined as: In the formula, This represents the total primary frequency regulation reserve capacity provided by all online thermal power units; Represents the total reserve capacity provided by the energy storage power station cluster via Fast Frequency Response (FFR); The load frequency damping coefficient; This represents the total load level of the system during the current time period. The maximum permissible quasi-steady-state frequency deviation limit is set; a minimum frequency constraint is constructed, the establishment process of which is based on the first-order equivalent frequency response model of the power system; by integrating the rotor motion equation, a nonlinear constraint inequality describing the coupling relationship between the system's total inertia, thermal power frequency regulation reserve capacity, energy storage frequency regulation reserve capacity, and the maximum transient frequency deviation is obtained: The variables in the above formula are defined as follows: The system reference frequency; The maximum active power deficit for a pre-set severe fault; The total online inertia constant of the system; 、 The total frequency regulation reserve capacity provided for online thermal power units and energy storage power stations, respectively; The load frequency damping coefficient; This represents the total system load. This is the maximum quasi-steady-state frequency deviation limit; 、 These are the complete response times for energy storage and thermal power units to complete frequency regulation response, respectively. This is the maximum allowable transient frequency deviation limit of the system, i.e., the threshold value for the lowest frequency point.

7. The method according to claim 1, characterized in that: When constructing the mutually exclusive and energy-sustainable constraint model, a binary state variable is defined to characterize the real-time operation mode of the energy storage power station. The Big M method or direct logic constraint is introduced to force the spot charging and discharging power of the energy storage to be zero in the AGC frequency regulation mode, and the AGC reserve capacity to be zero in the power peak shaving mode. This mathematically achieves physical isolation and time-period decoupling between the two operations. The formula for the mutual exclusion constraint of these modes is expressed as follows: ； In the formula, and Energy storage power stations exist The charging and discharging power during the period of participation in the spot electricity market; This refers to the rated power of the energy storage power station; This is the energy storage operation mode identifier bit, when A value of 0 indicates that the system is in AGC frequency modulation mode, while a value of 0 indicates that the system is in peak shaving mode. AGC frequency regulation backup capacity provided for energy storage; The maximum frequency regulation capacity allowed for energy storage applications is set at the upper limit. Based on mode mutual exclusion, power capacity coupling constraints under multiple services are further constructed. A joint capacity constraint is established, requiring that the sum of the baseline power, AGC reserve capacity, and primary frequency regulation reserve capacity of energy storage in any given time period does not exceed the rated power. The power coupling constraint formula is as follows: ； In the formula, The primary frequency regulation (or fast frequency response) reserve capacity reserved for energy storage power stations ensures that the energy storage still has sufficient power space to respond to AGC commands and primary frequency regulation commands while performing baseline discharge tasks; To address the limited capacity of energy storage, a sustainability constraint on the State of Energy (SOC) considering ancillary service demands is established. A time parameter is introduced to quantify the continuous demand for AGC (Automatic Guided Service) commands, thus revising the SOC safety boundary. The energy sustainability constraint formula is as follows: In the formula, For energy storage power stations Real-time energy storage over a given period of time; and These are the minimum and maximum energy limits allowed for energy storage, respectively. The continuous response time for AGC commands ( ）； and These represent the charging efficiency and discharging efficiency of energy storage, respectively.

8. The method according to claim 1, characterized in that: When constructing the basic power system model and multi-resource input parameter set, network topology parameters, node load forecast data, and tie-line planning curves within the target power grid area are collected and entered, and the time scale of day-ahead scheduling is set as a numerical sequence. A thermal power unit operation model is established, and system parameters, inertial time constants, and frequency regulation performance parameters are entered. An electrochemical energy storage operation model is constructed, and its rated charging and discharging power, rated capacity, charging and discharging efficiency, upper and lower limits of state of charge, and initial and final state of charge requirements are set. The response time parameters and capacity application ratio limits for energy storage participation in primary and secondary frequency regulation are entered. Day-ahead power forecast curves of wind farms and photovoltaic power plants are imported, and wind and solar utilization rate constraints are set. Line power flow transmission limits and key section safety and stability limits are set, and system frequency safety threshold parameters are initialized, including the system rated frequency, maximum frequency change rate limit, allowable deviation of the lowest frequency point, and quasi-steady-state frequency deviation limit, providing boundary conditions for the subsequent construction of an optimization model embedded with frequency safety constraints.

9. A day-ahead dispatching system in which grid-side energy storage participates in dual ancillary services, characterized in that: include: The basic modeling unit is used to construct the basic model of the power system and the set of multi-source input parameters, and to establish a system dynamic frequency security constraint model that includes multi-stage indicators. The system comprises the following components: a safety constraint modeling unit, used to construct a dynamic frequency safety constraint system with multi-stage indicators; a sustainability constraint modeling unit, connected to the safety energy constraint construction unit, used to construct a mutually exclusive and synergistic energy sustainability constraint model for energy storage participation in dual auxiliary services; a combined model construction unit, connected to the sustainability constraint modeling unit, used to construct a multi-objective safety constraint unit combined model including peak shaving and smoothing objectives; a multi-objective optimization modeling unit, connected to the combined model construction unit, used to construct a multi-objective optimization model that balances system operating economy and net load smoothing characteristics; and a power supply unit, connected to the basic modeling unit, safety constraint modeling unit, combined model construction unit, and multi-objective optimization model construction unit respectively, providing a stable power supply to these units.

10. A computer-readable storage medium for a day-ahead dispatching method based on grid-side energy storage participating in dual ancillary services, comprising identifiable computer instructions, characterized in that, The computer instructions are used to execute the program of the method described in claims 1-8.

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