A method for collaborative optimization of frequency modulation and peak regulation of thermal power with consideration of dynamic balance of energy storage cluster

Abstract

The application discloses a kind of energy storage cluster auxiliary thermal power frequency modulation peak shaving collaborative optimization method considering dynamic balance, belong to power system operation and control technical field;Including: constructing double-layer collaborative optimization control model;Including: energy storage collaborative thermal power frequency modulation peak shaving power distribution layer introduces frequency modulation demand in peak shaving process, designs power distribution and frequency modulation distribution strategy to up peak, down peak stage, by constructing fire-storage frequency modulation peak shaving power optimization distribution model, complete fire-storage frequency modulation peak shaving power distribution;Multiple types of energy storage power station power distribution layer is constructed by using dynamic balance coefficient multiple types of energy storage power station power optimization distribution model;The objective function of multiple types of energy storage power station power optimization distribution model is obtained energy storage power station power distribution value and SOC state;Based on energy storage power station power distribution value and SOC state, new energy power system is optimized and dispatched.The application realizes the balanced control of multiple types of energy storage power station calling degree and SOC.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and control technology, and specifically relates to a collaborative optimization method for energy storage clusters to assist thermal power frequency regulation and peak shaving considering dynamic equilibrium. Background Technology

[0002] In the desert and Gobi regions, the proportion of thermal power in kilowatt-level new energy bases is low, and their limited regulation capacity is insufficient to independently meet the dual demands of system frequency regulation and peak shaving. Electrochemical energy storage, with its rapid and flexible regulation characteristics, has become an ideal auxiliary regulation resource. With the unified dispatch of hundreds of energy storage power stations in the Jiangsu and Shandong power grids, it marks the practical application of large-scale energy storage clusters in grid regulation. Currently, new energy bases are forming various types of energy storage clusters, necessitating research into corresponding collaborative control methods to fully leverage the synergistic effect of energy storage and thermal power, thereby enhancing the overall system regulation capability.

[0003] Currently, research on the coordinated frequency regulation and peak shaving of energy storage and thermal power is insufficient. Existing work mainly focuses on single scenarios where energy storage participates in frequency regulation or peak shaving, such as configuration optimization, joint peak shaving of thermal power and energy storage, or market-coordinated dispatch, and lacks research on the coordinated response of the two to the joint needs of frequency regulation and peak shaving.

[0004] Currently, there is a lack of effective coordination and control methods for the coordinated frequency regulation and peak shaving of multi-energy storage clusters and thermal power units. Existing research has explored this issue from multiple perspectives, such as: constructing cross-regional robust scheduling models based on the uncertainty of new energy sources and system frequency regulation requirements; constructing joint energy storage scheduling models by simplifying the frequency response process; or designing multi-resource coordinated scheduling methods based on frequency constraints and peak shaving requirements. Some studies have also attempted to use neural networks to establish a mapping relationship between frequency and power to formulate scheduling strategies, or to optimize the ancillary service model of independent energy storage from the perspective of market mechanism design to improve its economic efficiency. However, these studies mostly focus on the overall system regulation capability and rarely consider the inherent differences in frequency regulation and peak shaving capabilities of different thermal power units, nor have they explored in depth the coordinated operation and control methods of multiple types of energy storage within energy storage clusters. This remains a key issue that requires further research. Summary of the Invention

[0005] This invention aims to address the challenges of frequency regulation and peak shaving in high-proportion renewable energy power systems in desert and Gobi regions by proposing a two-layer collaborative optimization control strategy. The upper layer constructs a power optimization allocation model considering the comprehensive regulation capabilities of thermal power units, achieving optimal coordination between thermal power plants and energy storage clusters in frequency regulation and peak shaving tasks. The lower layer establishes a power allocation model for multiple types of energy storage power stations, incorporating dynamic equilibrium coefficients to ensure balanced distribution of regulation commands within the energy storage clusters. Ultimately, this strategy aims to enhance the regulation potential of thermal power plants, promote efficient renewable energy consumption, and significantly improve the economic efficiency and operational balance of energy storage power stations, while ensuring system frequency security and power supply reliability.

[0006] To achieve the above objectives, the present invention provides the following solution: a collaborative optimization method for energy storage clusters assisting thermal power plant frequency regulation and peak shaving considering dynamic equilibrium, the optimization method comprising: A two-layer collaborative optimization control model is constructed, which includes: a power allocation layer for energy storage-coordinated thermal power frequency regulation and peak shaving, and a power allocation layer for multiple types of energy storage power stations. The energy storage-coordinated thermal power frequency regulation and peak shaving power allocation layer introduces frequency regulation requirements during the peak shaving process, designs power allocation and frequency regulation allocation strategies for the upward and downward peak shaving stages, and completes the allocation of thermal power frequency regulation and peak shaving power by constructing a thermal-storage frequency regulation and peak shaving power optimization allocation model. The multi-type energy storage power station power allocation layer constructs a multi-type energy storage power station power optimization allocation model by using a dynamic equalization coefficient; the power allocation value and SOC state of the energy storage power station are obtained through the objective function of the multi-type energy storage power station power optimization allocation model; The new energy power system is optimized and scheduled based on the power allocation value of the energy storage power station and the SOC status.

[0007] More preferably, the power allocation and frequency modulation allocation strategy includes: The peak shaving and frequency regulation reserve capacity of the thermal power units are met. The thermal power units allocate power according to the adjustment factor and the energy storage does not operate. When thermal power units cannot meet the peak-shaving demand but meet the frequency regulation demand, the peak-shaving discharge of energy storage power stations supplements the peak-shaving demand. The peak-shaving discharge power of energy storage power stations includes: ; In the formula, Indicates peak-shaving discharge power; For thermal power units t Constantly increase peak demand; Indicates the first i The maximum uphill climbing rate of the thermal power unit; This indicates the maximum output of the thermal power unit; express t- Thermal power output at 1 moment; Indicates frequency up-modulation for standby; When thermal power units meet peak-shaving demand but cannot meet frequency regulation demand, energy storage power stations supplement the frequency regulation demand with discharge; the frequency regulation discharge power of the energy storage power station includes: ; In the formula, Indicates frequency-modulated discharge power; Indicates the first i The maximum uphill climbing rate of the thermal power unit per unit time; express t time Time period; When the peak-shaving and frequency regulation capabilities of thermal power units are insufficient, energy storage power stations serve as a supplementary discharge source for peak-shaving and frequency regulation. The frequency regulation and peak-shaving discharge power of energy storage power stations includes: ; In the formula, For thermal power units t Constantly increase peak demand; This indicates that the thermal power unit is on standby for frequency regulation.

[0008] More preferably, the objective function of the thermal power-storage frequency regulation and peak shaving power optimization allocation model includes: ; In the formula, For system operating costs; It is the sum of the operating cost, start-up and shutdown cost, and standby cost of the thermal power unit; Total operating cost of energy storage; This represents the total cost of power abandonment and load loss in the system.

[0009] More preferably, the constraints of the thermal power-storage frequency regulation and peak shaving power optimization allocation model include: system power balance constraints, thermal power unit constraints, energy storage power station constraints, wind / solar curtailment and load shedding constraints, and system frequency deviation constraints; The system frequency deviation constraint includes: ; In the formula, Indicates the first i Taiwan thermal power units t time The rate of ascent per unit time period; For the first i The primary frequency regulation capability of the thermal power unit; For the first i Taiwan thermal power units t The reference power at that moment, For the first i Adjustment factor of thermal power unit in Taiwan; Representation system t time Frequency deviation over time period; α Indicates the unit rate of change of load; Indicates the first j Energy storage power station t time The unit adjustable power of the uphill support during the time period; J This indicates the total number of energy storage power stations.

[0010] More preferably, the adjustment factor includes: ; In the formula, These are the weighting parameters of primary frequency regulation capability, ramp rate, and rated power capability in the frequency regulation command, respectively, and their sum is 1. N g Indicates the number of thermal power units; This represents the maximum unit climbing rate. Indicates the first i The maximum output of the thermal power unit.

[0011] More preferably, the objective function of the multi-type energy storage power station power optimization allocation model includes: ; In the formula, For the first j Taiwan Energy Storage Power Station t The charging power at any given time; For the first j Taiwan Energy Storage Power Station t Discharge power at any given moment; T Indicates the total time; , These are the numbers after being corrected by dynamic equilibrium coefficients. j Taiwan Energy Storage Power Station t The cost of charging and discharging at any time.

[0012] More preferably, the constraints of the multi-type energy storage power station power optimization allocation model include: multi-type energy storage command balance constraints, energy storage power station state of charge deviation constraints, and energy storage power station first-end consistency constraints. The multi-type energy storage command balance constraints include: ; In the formula: For multiple types of energy storage t Total frequency modulation peak power at all times; For the first j Energy storage power station t Power at any moment; The state-of-charge deviation constraints of the energy storage power station include: ; In the formula, for t Average state of charge of various types of energy storage power stations at different times. The maximum allowable deviation from the state of charge; S j,t Indicates the first j Taiwan Energy Storage Power Station t The state of charge at any given moment; Consistency constraints for energy storage power stations include: ; In the formula: For the first j The state of charge of an energy storage power station at one moment; For the first j One energy storage power station T The state of charge at any given moment.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By establishing a safety model that takes into account frequency dynamic constraints, the balanced distribution and operational safety of thermal power and energy storage regulation capacity at different times in the desert scenario where the penetration rate of new energy is higher than 70% are effectively guaranteed.

[0014] (2) By introducing a power allocation model for energy storage-coordinated thermal power frequency regulation and peak regulation that takes into account the regulation factor, the regulation capability of thermal power units is significantly improved while maintaining the economic efficiency of system operation.

[0015] (3) By designing an internal energy storage allocation strategy based on dynamic equilibrium coefficient, the equalization control of the call level and SOC of multiple types of energy storage power stations was realized, thereby extending the overall service life of the energy storage system while completing the system frequency regulation and peak regulation tasks. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of an energy storage-coordinated thermal power unit frequency regulation and peak shaving system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the control strategy framework of an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the power output and reserve status of thermal power units under Strategy 1 according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the power output and reserve status of thermal power units under strategy 2 in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the situation of renewable energy curtailment in an embodiment of the present invention; Figure 6 The diagrams illustrate the changes in SOC of the energy storage power station under strategies 1 and 2 according to embodiments of the present invention; wherein, (a) is a diagram illustrating the changes in SOC of the energy storage power station under strategy 1, and (b) is a diagram illustrating the changes in SOC of the energy storage power station under strategy 2. Figure 7This is a schematic diagram comparing the degree of energy storage power station utilization under strategy 1 and strategy 2 in the embodiments of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Example 1: This embodiment provides a collaborative optimization method for energy storage clusters to assist thermal power frequency regulation and peak shaving, considering dynamic equilibrium, to solve the following problems in the Shagohuang New Energy Base: First, how to coordinate thermal power units and multiple types of energy storage power stations so that they can participate in system frequency regulation and peak shaving as collaborative resources, achieving optimal economic efficiency while ensuring control effect; Second, how to allocate power within multiple types of energy storage power stations to simultaneously ensure their economic efficiency and the balanced regulation capability among power stations.

[0021] The system structure of the Shagohuang New Energy Base is as follows: Figure 1 As shown, the power plants include photovoltaic, wind power, thermal power and various types of energy storage power stations, with a new energy penetration rate of up to 70%, which is significantly higher than the conventional level. Figure 1 middle, for t Real-time wind power grid-connected capacity; for t Real-time photovoltaic grid-connected power; for t Constant load demand; for t Thermal power output at all times; for t time Time-of-use system frequency regulation backup requirements, including t time Time period system frequency adjustment backup and t time Time-based system frequency adjustment backup ; For thermal power units t Constantly increase peak demand; For thermal power units t Peak shaving demand is constantly being reduced; for j Energy storage power stationt Momentary power, including t Peak charging power at any time Frequency modulation charging power Peak-shaving discharge power and frequency modulation discharge power The relationship between the five is as follows: (1) (2) In the formula, This is a discharge indicator for energy storage power stations; a value of 1 indicates... t The energy storage power station is in peak-shaving discharge and frequency-regulating discharge at any time (the energy storage power station performs peak-shaving and frequency regulation according to system needs); when it is set to 0, it is in a static state. The charging symbol for the energy storage power station is represented by 1. t The instantaneous energy storage power station performs peak-shaving charging and frequency-regulating charging; a value of 0 represents the static state. In this embodiment... Charging is "negative", discharging is "positive".

[0022] During the peak-shaving process at the Shagohuang New Energy Base, the combined output of thermal power units, grid-connected new energy power, and the power of various types of energy storage power stations meet the load center's demand, namely: (3) In the formula, J Indicates the total number of energy storage power stations; N g This indicates the number of thermal power units.

[0023] During normal operation of the power system, the system frequency is generally maintained at 50Hz. This embodiment considers frequency regulation needs during peak shaving. To alleviate the frequency regulation pressure on the grid-connected system of the Shagohuang energy base, an energy storage power station is introduced to actively participate in frequency regulation backup, and thermal power units and energy storage are used together as frequency regulation resources to participate in system regulation.

[0024] When a disturbance occurs in the system, considering system inertia, primary frequency response characteristics of the load, primary and secondary frequency regulation response characteristics of thermal power units, and frequency regulation response characteristics of energy storage, the relationship between frequency deviation and unbalanced power is as follows: (4) In the formula: M The equivalent inertia of the system; This represents the total secondary frequency regulation response power of the thermal power unit; Indicates the first i The start / stop status of the generator set is 1 when it is on and 0 when it is off. For the first i The primary frequency regulation capability of the thermal power unit; This refers to the system frequency deviation. For energy storage response secondary frequency regulation capacity; This refers to the load disturbance power. D This is the load frequency regulation power coefficient.

[0025] Formula (4) describes the dynamic coupling relationship between the frequency regulation demand and frequency deviation of the system during the joint frequency regulation process of energy storage power station and thermal power unit.

[0026] The following formula can be derived from formula (4): (5) In the formula: For the system t time Frequency deviation value for the time period; express t time The difference between the unit ramp rate of thermal power units and the rate at which energy storage power stations delay net load change; express t time The ability of a time-based system to delay frequency changes.

[0027] This embodiment addresses the challenge of frequency security and peak shaving issues in the Shagohuang New Energy Base, where relying solely on reserved frequency regulation reserves for thermal power units is insufficient. It proposes a two-layer collaborative optimization control model for frequency regulation and peak shaving of thermal power units in conjunction with energy storage. This model includes a power allocation layer for frequency regulation and peak shaving of thermal power units based on adjustment factors and a power allocation layer for multi-type energy storage power stations considering dynamic equilibrium coefficients. The control strategy framework diagram is shown below. Figure 2 As shown.

[0028] Among them, the energy storage-coordinated thermal power frequency regulation and peak shaving power allocation layer introduces an adjustment factor that is affected by the primary frequency regulation capability, ramp rate and rated power of thermal power in the frequency regulation layer. Together with the peak shaving layer that takes into account economic efficiency, it forms a thermal-storage frequency regulation and peak shaving power optimization allocation model, realizing the allocation of thermal-storage frequency regulation and peak shaving power.

[0029] Specifically, frequency regulation requirements are introduced during peak shaving, and power allocation and frequency regulation reserve allocation strategies are designed for the peak shaving and down-peak stages respectively. This fully leverages the synergistic effect of energy storage power plants and thermal power units to achieve coordinated response in system frequency regulation and peak shaving. The control strategy for thermal-storage coordinated frequency regulation and peak shaving can be divided into the following operating states.

[0030] (1) When the peak-shaving and frequency regulation reserve capacity of the thermal power unit is met (see equation (6)), the thermal power unit allocates power according to the adjustment factor to achieve a balanced distribution of peak-shaving and frequency regulation needs, ensuring that the system frequency is within a safe range. At this time, energy storage does not need to operate.

[0031] (6) In the formula, Indicates the firsti The maximum uphill climbing rate of the thermal power unit; , They represent the first i The maximum and minimum output of the thermal power unit; express t Thermal power output at -1 hour; , These represent standby for up-frequency modulation and standby for down-frequency modulation, respectively. Indicates thermal power unit t Constantly increase peak demand; , They represent the first i The maximum uphill and downhill speeds per unit time of the thermal power unit; express t time Time period.

[0032] When the remaining capacity of the energy storage power station is insufficient, the thermal power unit actively charges the energy storage power station to restore its state of charge. The peak-shaving charging power of the energy storage power station is: (7) In the formula, Indicates the first i The maximum downhill / climbing rate of the thermal power unit; This indicates that the thermal power unit is under frequency regulation for standby.

[0033] (2) When the thermal power unit does not meet the peak regulation demand but meets the frequency regulation demand (see equation (8)), the peak regulation discharge of the energy storage power station will supplement the peak regulation demand. The peak regulation discharge power of the energy storage power station is shown in equation (9).

[0034] (8) (9) In the formula, This indicates that the thermal power unit is on standby for frequency regulation.

[0035] (3) When the thermal power unit can meet the peak regulation demand but cannot meet the frequency regulation demand (see equation (10)), the energy storage power station will discharge to supplement the frequency regulation demand. The frequency regulation discharge power of the energy storage power station is shown in equation (11).

[0036] (10) (11) (5) When the peak-shaving and frequency regulation capabilities of the thermal power unit are not met (see equation (12)), the energy storage power station is used as a supplementary discharge source for peak-shaving and frequency regulation. The discharge power of the energy storage power station for frequency regulation and peak regulation is shown in equation (13).

[0037] (12) (13) To achieve frequency regulation and peak shaving power allocation between energy storage and thermal power units, this invention constructs an optimized allocation model for thermal-storage frequency regulation and peak shaving power, completing the allocation of power between the two. The key allocation principle is: minimizing the operating cost of the Shagohuang New Energy Base as the objective, with the regulation capability and economy of the thermal power units as secondary objectives. The better the regulation capability and economy of the thermal power unit, the larger its regulation factor and the greater its proportion of frequency regulation power; conversely, the lower its participation in frequency regulation power, the smaller its proportion of frequency regulation power.

[0038] Among them, the cost of thermal power units includes peak shaving cost and frequency regulation reserve cost, as shown in equation (14).

[0039] (14) In the formula, It is the sum of the operating cost, start-up and shutdown cost, and standby cost of the thermal power unit; , , For the first i Cost coefficient of output power of thermal power units in Taiwan; For thermal power units i of t Constant output power; For the first i Start-up and shutdown costs of thermal power units in Taiwan; This is the standby cost coefficient for thermal power units. This represents the standby cost coefficient for thermal power units. For the first i Taiwan thermal power units t Always have backup FM power available; For the first i Taiwan thermal power units t Standby FM power at any time; T Indicates the total time (taken as 96); Represents an integer variable (taking values ​​of 0 and 1).

[0040] The cost of energy storage for frequency regulation and peak shaving is: (15) In the formula, Total operating cost of energy storage; For the first j Discharge cost coefficient of an energy storage power station; For the first j The charging cost coefficient of an energy storage power station; For the first j Energy storage power station t Peak charging power at any given time; For the firstj Energy storage power station t time Frequency-modulated charging power during a given time period.

[0041] The system also includes the cost of curtailing renewable energy and the cost of load shedding, calculated as follows: (16) In the formula, The total cost of power curtailment and load loss in the system; This refers to the penalty coefficient for wind and solar power curtailment. For the system wind farm t Wind curtailment power at any given time; For the system photovoltaic electric field t The power of light discarded at any given moment; This is the penalty coefficient for loss of load. For the system t Power loss at any given moment.

[0042] Therefore, the objective function of the thermal power-storage frequency regulation and peak-shaving power allocation model is: (17) In the formula: This refers to the system operating cost.

[0043] The optimal allocation model for frequency regulation and peak shaving power between thermal power and energy storage satisfies the following constraints: (1) System power balance constraints.

[0044] (18) (2) Constraints of thermal power units.

[0045] Upper and lower limits of thermal power unit output constraints: (19) In the formula, Indicates the first i Taiwanese unit t The start / stop status at any given moment.

[0046] Thermal power unit start-up and shutdown constraints: (20) In the formula, For the first i Minimum continuous operating time of the unit; For the first i Minimum downtime for the unit.

[0047] Thermal power unit ramping constraints: ;(twenty one) In the formula, For the firsti The maximum uphill climbing rate of the thermal power unit.

[0048] To avoid over-regulation of economically efficient thermal power units in the system, the strategy of this invention incorporates a participation factor to control the frequency regulation degree of the units. The frequency regulation power is allocated through the participation factor. The determination of the participation factor is closely related to the rated power, primary frequency regulation coefficient, and climbing ability of the thermal power unit. The stronger its comprehensive capability, the larger the participation factor, and vice versa.

[0049] The adjustment factors for thermal power units are as follows: ;(twenty two) In the formula, These are the weighting parameters of primary frequency regulation capability, ramp rate, and rated power capability in the frequency regulation command, respectively, and their sum is 1. For the first i The primary frequency regulation capability of the thermal power unit; This represents the maximum unit climbing rate. Indicates the first i The maximum output of the thermal power unit.

[0050] For example, the participation factor of a certain thermal power plant is: .(twenty three) (3) Constraints of energy storage power stations.

[0051] Consistent constraint on total charge and discharge volume during the dispatch cycle of energy storage power stations: ;(twenty four) In the formula, For the first j The capacity of an energy storage power station at the last moment; For the first j The initial capacity of an energy storage power station.

[0052] (4) Curtailment of wind / solar power and load shedding constraints.

[0053] (25) In the formula, For wind power t The theory of maximum output at a given moment; For photovoltaic t The maximum output of the time theory.

[0054] (5) System frequency deviation constraint.

[0055] When a disturbance occurs in the system, the frequency change at different times within different time periods satisfies the following constraints: (26) In the formula, This represents the maximum permissible frequency deviation of the system.

[0056] Insufficient power supply to the system will cause a decrease in frequency, resulting in the following system frequency deviation: (27) In the formula, for t time The difference between the adjustment rate of thermal power units per unit time and the rate of increase in the rate of change of net load slowed down by energy storage power stations.

[0057] When the system experiences a disturbance and demand increases, among which The following conditions must be met: (28) In the formula, Indicates the first i Taiwan thermal power units t time The rate of ascent per unit time period; Indicates the first j Energy storage power station t time The unit adjustable power of the uphill support during the time period; α This indicates the rate of change of load per unit.

[0058] From equation (28), it can be derived that the climbing ability of thermal power units and the climbing frequency regulation power of energy storage support satisfy the following inequality constraints: (29) In the formula, For the first i Taiwan thermal power units t The reference power at that moment, For the first i Adjustment factor of thermal power unit in Taiwan; Representation system t time Frequency deviation over time period: Indicates the first i Taiwan thermal power units t time The rate of ascent per unit time period; For the first i The primary frequency regulation capability of the thermal power unit; α Indicates the unit rate of change of load; Indicates the first j Energy storage power station t time The unit adjustable power of the uphill support during the time period; J This indicates the total number of energy storage power stations.

[0059] Depend on Figure 2 It can be seen that the upper layer is the "thermal energy storage frequency regulation and peak shaving power allocation layer based on adjustment factors". Combining frequency regulation and peak shaving requirements, the overall frequency regulation and peak shaving command of the energy storage system, i.e. the total output power of energy storage, is obtained through the optimization model. At the same time, the peak shaving command of the thermal power unit is generated. Then, this total output result of energy storage is input into the lower layer. Based on factors such as the state of charge and the degree of utilization of energy storage, the internal fine allocation of power is completed among the various types of energy storage power stations. Finally, the specific power value and SOC status of each energy storage power station are output.

[0060] The power allocation layer for multi-type energy storage power stations constructs a power optimization allocation model for multi-type energy storage power stations using dynamic balancing coefficients. The power allocation value and SOC state of the energy storage power stations are obtained through the objective function of the power optimization allocation model. Based on the power allocation value and SOC state of the energy storage power stations, specific control instructions for each energy storage power station are provided for scheduling execution, and the safe response of the system frequency regulation and peak regulation is supported. At the same time, the operating efficiency can be improved by balancing the energy storage state, which is the key basis for realizing the closed loop of optimized scheduling of new energy power systems.

[0061] Specifically, during the joint frequency regulation and peak shaving process of multiple types of energy storage power stations, the regulation capability of each power station is jointly affected by its rated capacity, state of charge (SOC), and operating costs. If only economic efficiency is considered when controlling the frequency regulation and peak shaving of each power station, it will lead to the overuse of high-economic-efficiency power stations, seriously affecting the utilization rate of multiple types of energy storage power stations and the SOC balance problem. Therefore, this layer introduces a dynamic balance coefficient to construct a power optimization allocation model for multiple types of energy storage power stations that takes into account both economic efficiency and balance.

[0062] To improve the balance of multiple energy storage power stations, this invention considers a dynamic balance coefficient based on the degree of state of charge (SOC) deviation. This coefficient is calculated based on the deviation between the SOC of each energy storage power station and the average SOC of the multiple types of energy storage power stations. By quantifying the deviation between the SOC of each energy storage power station at the previous moment and the average SOC at the previous moment, a dynamic balance coefficient is generated and incorporated into the cost coefficient to guide power allocation and correct the cost model of the multiple types of energy storage power stations. When the SOC of an energy storage power station at the previous moment is higher than the average, its charging behavior is suppressed and its discharging behavior is stimulated at the next moment; conversely, its charging is stimulated and its discharging is suppressed. The dynamic balance coefficient and the corrected operating cost model of the energy storage power station are as follows: (30) In the formula, , The first j Taiwan Energy Storage Power Station t The dynamic balance coefficient of charging and discharging at any given moment; , The firstj Taiwan Energy Storage Power Station t The coefficient of charge-discharge imbalance at any given moment; For the first j Taiwan Energy Storage Power Station t State of charge at time -1; In the system t The average state of charge of all energy storage power stations at time -1.

[0063] The cost after adjustment by the dynamic equilibrium coefficient is as follows: (31) In the formula: , These are the numbers after being corrected by dynamic equilibrium coefficients. j Taiwan Energy Storage Power Station t The cost of charging and discharging at any given moment; This indicates the operating cost of an energy storage power station.

[0064] In summary, the objective function of the power optimization allocation model for multiple types of energy storage power stations is: (32) In the formula, For the first j Taiwan Energy Storage Power Station t The charging power at any given time; For the first j Taiwan Energy Storage Power Station t Discharge power at any given moment.

[0065] The constraints of the power optimization allocation model for multiple types of energy storage power stations are as follows: (1) Balance constraints of multi-type energy storage commands.

[0066] (33) In the formula: For multiple types of energy storage t Total frequency modulation peak power at all times; For the first j Energy storage power station t Power at any given moment.

[0067] (2) Constraints on the state of charge deviation of energy storage power stations.

[0068] Energy storage power stations are affected not only by economic factors but also by their rated capacity. If only the energy storage power station after dynamic equilibrium coefficient correction is considered, its capacity will be too small when faced with excessively high regulation commands, exacerbating the system's state of charge deviation. Therefore, this embodiment introduces a constraint on the energy storage state of charge deviation: (34) In the formula, fort Average state of charge of various types of energy storage power stations at different times. The maximum allowable deviation from the state of charge; S j,t Indicates the first j Taiwan Energy Storage Power Station t The state of charge at any given moment;

[0069] (3) Consistency constraint between the beginning and end of the energy storage power station.

[0070] (35) In the formula: For the first j The state of charge of an energy storage power station at one moment; For the first j One energy storage power station T The state of charge at any given moment.

[0071] Example 2: To make the objectives, technical solutions, and advantages of this invention clearer, the technical solution of this invention will be clearly and completely described below in conjunction with an embodiment of this invention. The embodiments described herein are merely some embodiments of this invention, and not all embodiments.

[0072] To verify the effectiveness of the strategy of this invention, this embodiment is based on the analysis of a 5-node structure of the Shagohuang New Energy Base. The time resolution is 15 minutes, and the time scale is 1 day. The Shagohuang Energy Base includes 7 thermal power units, 4 wind farms, 7 photovoltaic power plants, and 3 energy storage power stations.

[0073] To verify the effectiveness of the proposed strategy in frequency regulation and peak shaving, this embodiment sets two strategies: Strategy 1, without considering the system frequency response process, sets the system's reserve to 10% of the predicted load and 1% of the wind and solar power output for each time period. The system's reserve is set at 6% of the predicted load and 1% of the wind and solar power output for each time period. );in, P L , P W , P PV These represent the load demand power, wind power output, and solar power output, respectively. Strategy 2 considers the system frequency response process, with energy storage and thermal power jointly participating as frequency regulation resources in the system reserve, where the system disturbance settings are consistent with Strategy 1.

[0074] The system operating costs are shown in Table 1. Compared with Strategy 1, Strategy 2 increases the operating cost of thermal power units by 3.62%, reduces standby costs by 15.68%, reduces system curtailment costs by 4.61%, and increases energy storage operating costs by 18.44%. The total cost is reduced by 19.78%. This strategy effectively improves the renewable energy absorption capacity and power supply reliability while ensuring no load loss.

[0075] Table 1 according to Figure 3 , Figure 4 As shown in Table 1, both strategies require thermal power units to be kept running to meet the system's spinning reserve requirements. While Strategy 1 can meet the reserve requirements, its focus solely on economic efficiency leads to a significant decrease in the regulation capacity of units G1 and G2 during certain periods, and units G4-G7 lack downward adjustment space due to prolonged periods of minimum output. Strategy 2, by introducing a regulation factor, balances unit economics and regulation capacity, ensuring that units G1-G7 retain upper reserve capacity for most of the time.

[0076] In terms of cost, the operating cost of thermal power units under Strategy 2 is higher than that under Strategy 1, but the reserve cost is lower. The increase in operating cost is mainly due to the close correlation between reserve allocation and regulation factor in this strategy, and the provision of upper reserve support for the system by energy storage, which releases the regulation potential of thermal power units, resulting in a total output increase of 1377.65 MW compared to Strategy 1. In addition, the strategy allows energy storage to participate in frequency regulation and withstand a certain frequency offset during peak shaving, which is also one of its characteristics.

[0077] Table 2 shows the situation of renewable energy curtailment at different times. Figure 5 According to the data in Table 2, during the period from December to 15th, Strategy 1 resulted in 71.75 MW of curtailed power, while Strategy 2 resulted in no curtailment. During the period from December to 64th, Strategy 1 resulted in 7281.00 MW of curtailed power, while Strategy 2 resulted in 7013.79 MW, a reduction of 267.21 MW. The results indicate that Strategy 2 effectively reduces curtailment during all key periods, thus improving the overall capacity for renewable energy absorption. This strategy, through energy storage participation in frequency regulation and peak shaving, reduces renewable energy curtailment by 4.61% compared to Strategy 1.

[0078] Table 2 To verify the effectiveness of the power allocation layer for multiple types of energy storage power stations, this embodiment compares the energy storage utilization and SOC balance under Strategy 1 and Strategy 2. In Strategy 1, energy storage power station 1 has the highest charging and discharging frequency, while power station 2 has the lowest, especially during the 72-96 period, when the more economical power station 1 is frequently utilized. In contrast, the strategy of this invention achieves a balanced distribution of charging and discharging frequencies among the energy storage power stations, avoiding utilization bias caused by economic differences, thus confirming the effectiveness of the proposed power allocation layer in promoting balanced energy storage utilization.

[0079] from Figure 6 A comparison of the SOC change curves shows that, without considering balancing, the lower-cost energy storage power station 1 was frequently used after the evening peak, even reaching the lower limit of SOC and being unable to discharge; however, after introducing the balancing coefficient, the discharge of each power station was more balanced during the evening peak, and none of them reached the minimum discharge depth. The data in Table 2 show that the strategy of this invention improved the SOC balancing degree of energy storage by 35.71% compared with strategy 1.

[0080] like Figure 7 The results show significant differences in the utilization rate of energy storage power stations under different strategies. Strategy 1 overutilizes the economically viable energy storage power station 1, while underutilizing the remaining power stations. The strategy of this invention significantly improves the utilization balance by introducing a dynamic equilibrium coefficient to dynamically adjust the operating costs of the power stations: the utilization rate of power station 1 is relatively reduced, while the utilization rates of power stations 2 and 3 are correspondingly increased. Table 2 data shows that the energy storage utilization balance under this strategy is 54.55% higher than that under Strategy 1.

[0081] In summary, introducing a dynamic balancing coefficient into the power distribution layer can achieve balanced distribution of regulating power among various types of energy storage power stations while maintaining SOC balance, effectively avoiding the overuse of high-economic-efficiency power stations.

[0082] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A collaborative optimization method for frequency regulation and peak shaving of thermal power plants assisted by energy storage clusters, considering dynamic equilibrium, characterized in that, The optimization method includes: A two-layer collaborative optimization control model is constructed, which includes: a power allocation layer for energy storage-coordinated thermal power frequency regulation and peak shaving, and a power allocation layer for multiple types of energy storage power stations. The energy storage-coordinated thermal power frequency regulation and peak shaving power allocation layer introduces frequency regulation requirements during the peak shaving process, designs power allocation and frequency regulation allocation strategies for the upward and downward peak shaving stages, and completes the allocation of thermal power frequency regulation and peak shaving power by constructing a thermal-storage frequency regulation and peak shaving power optimization allocation model. The multi-type energy storage power station power allocation layer constructs a multi-type energy storage power station power optimization allocation model by using a dynamic equalization coefficient; the power allocation value and SOC state of the energy storage power station are obtained through the objective function of the multi-type energy storage power station power optimization allocation model; The new energy power system is optimized and scheduled based on the power allocation value of the energy storage power station and the SOC status. The power allocation and frequency modulation allocation strategies include: The peak shaving and frequency regulation reserve capacity of the thermal power units are met. The thermal power units allocate power according to the adjustment factor and the energy storage does not operate. When thermal power units cannot meet the peak-shaving demand but meet the frequency regulation demand, the peak-shaving discharge of energy storage power stations supplements the peak-shaving demand. The peak-shaving discharge power of energy storage power stations includes: ； In the formula, Indicates peak-shaving discharge power; For thermal power units t Constantly increase peak demand; Indicates the first i The maximum uphill climbing rate of the thermal power unit; Indicates the first i The maximum output of the thermal power unit; express t- Thermal power output at 1 moment; Indicates frequency up-modulation for standby; When thermal power units meet peak-shaving demand but cannot meet frequency regulation demand, energy storage power stations supplement the frequency regulation demand with discharge; the frequency regulation discharge power of the energy storage power station includes: ； In the formula, Indicates frequency-modulated discharge power; Indicates the first i The maximum uphill climbing rate of the thermal power unit per unit time; express t time Time period; When the peak-shaving and frequency regulation capabilities of thermal power units are insufficient, energy storage power stations serve as a supplementary discharge source for peak-shaving and frequency regulation. The frequency regulation and peak-shaving discharge power of energy storage power stations includes: ； In the formula, For thermal power units t Constantly increase peak demand; This indicates that the thermal power unit is on standby for frequency regulation.

2. The method for coordinated optimization of energy storage clusters assisting thermal power frequency regulation and peak shaving considering dynamic equilibrium as described in claim 1, characterized in that, The objective function of the thermal power-storage frequency regulation and peak shaving power optimization allocation model includes: ； In the formula, For system operating costs; It is the sum of the operating cost, start-up and shutdown cost, and standby cost of the thermal power unit; Total operating cost of energy storage; This represents the total cost of power abandonment and load loss in the system.

3. The method for coordinated optimization of energy storage clusters assisting thermal power frequency regulation and peak shaving considering dynamic equilibrium as described in claim 1, characterized in that, The constraints of the thermal power-storage frequency regulation and peak shaving power optimization allocation model include: system power balance constraints, thermal power unit constraints, energy storage power station constraints, wind / solar curtailment and load shedding constraints, and system frequency deviation constraints. The system frequency deviation constraint includes: ； In the formula, Indicates the first i Taiwan thermal power units t time The unit uphill rate over a given time period; For the first i The primary frequency regulation capability of the thermal power unit; For the first i Taiwan thermal power units t The reference power at that moment, For the first i Adjustment factor of thermal power unit in Taiwan; system representation t time Frequency deviation over time period; α Indicates the unit rate of change of load; Indicates the first j Energy storage power station t time The unit adjustable power of the uphill support during the time period; J This indicates the total number of energy storage power stations.

4. The method for coordinated optimization of energy storage clusters assisting thermal power frequency regulation and peak shaving considering dynamic equilibrium as described in claim 3, characterized in that, The regulating factors include: ； In the formula, These are the weighting parameters of primary frequency regulation capability, ramp rate, and rated power capability in the frequency regulation command, respectively, and their sum is 1. N g Indicates the number of thermal power units; This represents the maximum unit climbing rate. Indicates the first i The maximum output of the thermal power unit.

5. The method for coordinated optimization of energy storage clusters assisting thermal power frequency regulation and peak shaving considering dynamic equilibrium as described in claim 4, characterized in that, The objective function of the power optimization allocation model for the multi-type energy storage power station includes: ； In the formula, For the first j Taiwan Energy Storage Power Station t The charging power at any given time; For the first j Taiwan Energy Storage Power Station t Discharge power at any given moment; T Indicates the total time; , These are the numbers after being corrected by dynamic equilibrium coefficients. j Taiwan Energy Storage Power Station t The cost of charging and discharging at any time.

6. The method for coordinated optimization of energy storage clusters assisting thermal power frequency regulation and peak shaving considering dynamic equilibrium as described in claim 5, characterized in that, The constraints of the power optimization allocation model for the multi-type energy storage power station include: multi-type energy storage command balance constraint, energy storage power station state of charge deviation constraint, and energy storage power station first-end consistency constraint. The multi-type energy storage command balance constraints include: ； In the formula: For multiple types of energy storage t Total frequency modulation peak power at all times; For the first j Energy storage power station t Power at any moment; The state-of-charge deviation constraints of the energy storage power station include: ； In the formula, for t Average state of charge of various types of energy storage power stations at different times. The maximum allowable deviation from the state of charge; S j,t Indicates the first j Taiwan Energy Storage Power Station t The state of charge at any given moment; Consistency constraints for energy storage power stations include: ； In the formula: For the first j The state of charge of an energy storage power station at one moment; For the first j One energy storage power station T The state of charge at any given moment.

Images