Power regulation and control method for wind and light storage micro-grid participating in frequency response service market

Abstract

According to the power regulation and control method for the wind and light storage micro-grid participating in the frequency response service market, the capacity attenuation cost of an energy storage system is fully considered, and efficient power regulation and control of the wind and light storage micro-grid are achieved by constructing a two-level optimization structure. The upper layer is a scheduling layer, takes an hour-level time scale as an optimization target, carries out electricity price arbitrage based on 24-hour electricity price difference, and maximizes market income; and the lower layer is a real-time regulation and control layer, focuses on minute-level optimization, and meets the frequency regulation requirement of the main power grid by utilizing wind power, photovoltaic adjustable capacity and the quick response capability of an energy storage system. And optimization algorithms such as particle swarm optimization (PSO) are adopted for solving the problem, so that the high efficiency and feasibility of an optimization scheme are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid energy management, and in particular to a power regulation method for a wind-solar-storage micro-grid participating in a frequency response service market. BACKGROUND

[0002] The wind-solar-storage micro-grid has become a key carrier for new energy grid connection due to its flexibility and efficiency. However, the large-scale access of new energy will reduce the system inertia and pose a challenge to frequency stability. Traditional frequency modulation methods are difficult to cope with output fluctuations, insufficient energy storage response, and DC lockout accidents. For example, frequency drop events have occurred several times in the East China Power Grid. Existing micro-grid regulation research focuses on energy management, lacks optimization for the frequency response service market, and makes it difficult to balance regulation efficiency and economic benefits.

[0003] Current wind-solar-storage micro-grid regulation strategies rarely consider the frequency response service market, and there are deficiencies in the optimization of rapid response based on power droop characteristics. Droop control adjusts energy storage power through frequency deviation, which can achieve a response in seconds. However, existing technologies mainly focus on stability analysis and have not been effectively combined with multi-time scale optimization strategies, making it difficult for energy storage power distribution to meet the demand for frequency response and achieve price arbitrage. Therefore, there is an urgent need to develop an optimization method based on droop characteristics to achieve the coordinated optimization of rapid frequency response and market benefits through hourly power reservation and minute-level dynamic regulation.

[0004] The existing technology has the following defects: Insufficient real-time power regulation: existing frequency modulation research mainly focuses on real-time control strategies, such as network-type control technologies such as virtual synchronous machines, and there are many achievements. However, there is less research on real-time power regulation for wind-solar-storage micro-grids, and there is a lack of efficient optimization examples, making it difficult to meet the demand for fast and accurate regulation in the frequency response service market.

[0005] Lack of market benefit quantification: existing technologies do not establish a benefit quantification model for wind-solar-storage micro-grids in the frequency modulation auxiliary service market, and national standards lack specific quantitative guidance. New energy stations, as the main source of frequency fluctuations, have not fully utilized their power regulation potential, limiting market participation efficiency and economic benefits.

[0006] Insufficient multi-time scale optimization: existing methods lack multi-time scale optimization capabilities in wind-solar-storage micro-grid power regulation, and conventional algorithms cannot coordinate the demand for real-time response, minute-level regulation, and hourly market transactions, resulting in limited comprehensive efficiency and long-term operational stability of frequency response services. SUMMARY

[0007] The present application aims to solve the problems in the prior art and proposes a power regulation method for a wind-solar-storage micro-grid participating in a frequency response service market.

[0008] The application is realized by the following technical scheme, the application provides a power regulation method for a wind-solar-storage micro-grid participating in a frequency response service market, and the method specifically comprises the following steps: Step S1, constructing a dynamic mathematical model of wind power, solar power and energy storage, considering the capacity attenuation characteristics; Step S2, generating a typical scenario set of wind-solar load through clustering; Step S3, optimizing the hourly scheduling based on electricity price arbitrage; Step S4, determining the frequency response demand in combination with real-time frequency data and frequency service mode; Step S5, constructing a minute-level regulation model, and integrating safety constraints and attenuation costs; Step S6, solving the optimization model to obtain a regulation scheme by using a particle swarm optimization algorithm.

[0009] Further, in step S1, (1) when the wind speed is , the output power of the wind turbine unit is approximately represented by a piecewise function as follows: (1) In the formula, is the rated output power of the wind turbine unit; is the cut-in wind speed; is the cut-out wind speed; is the rated wind speed; wherein the rated output power of the wind turbine unit is derived from the air density , the wind energy utilization coefficient , the tip speed ratio , the pitch angle , the number of wind turbines and the angular velocity of the wind turbine rotation in combination with the wind turbine dynamics model as follows: (2) wherein the tip speed ratio and the wind energy utilization coefficient are represented as: (3) (4) (2) the output power of the photovoltaic array is obtained from the output power under standard atmospheric conditions, the light intensity and the ambient temperature in combination with the irradiance of the working point and the battery surface temperature of the working point as follows: (5) wherein the relationship between the battery surface temperature and the atmospheric temperature is expressed as: (6) wherein , , is a constant coefficient; (3) Based on the cycle life model of lithium battery energy storage, considering the influence of charging and discharging on capacity attenuation, the energy storage capacity attenuation cost is quantified by the following formula: (7) wherein, is the total energy storage attenuation cost; is the time step; is the time period; is the cost coefficient of a single charge-discharge cycle; represents the discharge capacity at time t; is the nominal cycle depth, used for standardizing cycle number calculation; is the nominal capacity of the energy storage system; is the cycle number at the nominal cycle depth; is the replacement cost of the energy storage system.

[0010] Further, in step S2, the wind power, photovoltaic output data and daily load data of the whole year are clustered using the K-means clustering algorithm to obtain typical curves of wind power, photovoltaic output and load, which are used as the scenario set of lower-level power regulation.

[0011] Further, in step S3, based on the dynamic mathematical model constructed in step S1 and the typical scenario set of wind power output and load generated in step S2, the day-ahead scheduling optimization based on 24-hour price fluctuation is carried out with an hourly time scale as the optimization target, and the specific implementation is as follows: (1) Obtain the 24-hour price curve of the electricity market, combine the typical scenario set of step S2, and determine the market revenue potential of each hour; (2) Take maximizing total revenue as the goal, construct an hourly optimization model, comprehensively consider the predicted output of wind power and photovoltaic and the charging and discharging plan of the energy storage system considering capacity attenuation cost, and at the same time improve the constraints of safe operation of power grid, including power balance, upper and lower limits of state of charge SOC of energy storage and output range of microgrid; (3) Optimize the allocation of energy storage charging and discharging and wind and solar power output according to the peak and off-peak electricity price periods: During the off-peak electricity price period, prioritize the use of wind and solar power generation to meet load demand and charge the energy storage system; During the peak electricity price period, coordinate energy storage discharging and wind and solar power output to maximize market electricity sales revenue and participate in frequency response services; Optimize the power dispatch scheme for each hour through linear programming or mixed integer programming to ensure maximum revenue. (4) Generate hourly power scheduling schemes, including output plans for wind power, photovoltaic and energy storage systems and energy storage charging and discharging strategies, to provide a basis for minute-level real-time control in steps S4-S6.

[0012] Furthermore, in step S4, the frequency modulation power demand of the main grid, i.e., the power response demand... Represented as: (8) In the formula, For actual frequency, For the rated frequency, The droop coefficient is... This represents the ratio of the maximum permissible frequency deviation to the maximum adjustable power range of the unit.

[0013] Furthermore, in step S5, a power demand sequence on a minute-level timescale is generated, including coordinated output plans for wind power, photovoltaic, and energy storage systems, as well as energy storage charging and discharging strategies. This ensures that the real-time control in the second stage remains unchanged while maintaining the arbitrage profits from the first stage. Therefore, the frequency response power provided on a second-level timescale is: (9) In the formula, , , , Real-time photovoltaic power output, wind turbine power output, energy storage discharge and charging power; , , , The photovoltaic output, wind turbine output, and energy storage discharge and charging power obtained from the upper-level scheduling in step S3 are: The benefits of microgrid participation in frequency response are: (10) in, The frequency response benefit throughout the day; The frequency response quantity provided to the microgrid in real time; This is the frequency response error penalty coefficient, the magnitude of which depends on the amount of frequency provided by the microgrid in real time. Frequency response in agreement with the main power grid percentage error, This refers to the real-time frequency response electricity price.

[0014] Further, in step S6, a multivariate large-scale mixed integer optimization model is constructed with the maximum of the sum of the first-stage arbitrage revenue and the second-stage participation frequency response service market revenue as the optimization target, and the optimization model is solved by means of a PSO optimization algorithm to obtain a real-time power regulation scheme.

[0015] The present application has the beneficial effects that: 1. Multi-time scale collaborative optimization: The hourly scheduling and minute-level regulation are coordinated through two-level optimization to maximize the arbitrage and frequency response revenue. The multi-time scale optimization deficiency of the prior art is effectively made up, and the comprehensive efficiency of the wind-solar-storage micro-grid is improved.

[0016] 2. Energy storage attenuation cost optimization: The energy storage capacity attenuation cost formula is integrated, and the charging and discharging strategy is optimized to prolong the service life of the energy storage. The rapid frequency response and economy are balanced, and the market revenue quantification deficiency problem is solved.

[0017] 3. Efficient frequency response capability: High-frequency real-time frequency data and frequency response market mode based on droop characteristics are used to accurately quantify the power demand. The PSO algorithm is used to optimize the regulation scheme, and the frequency regulation efficiency and grid safety are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 Fig. 1 is a schematic diagram of a wind-solar-storage micro-grid participating in a frequency response service market power regulation method; Figure 2 Fig. 2 is an effect diagram of the first-stage day-ahead scheduling in the embodiment; Figure 3 Fig. 3 is a schematic diagram of the change of the energy storage SOC in the second-stage real-time regulation in the embodiment; Figure 4 Fig. 4 is a schematic diagram of the output of the photovoltaic in the scheduling stage and the real-time regulation stage in the embodiment; Figure 5 Fig. 5 is a schematic diagram of the output of the wind power in the scheduling stage and the real-time regulation stage in the embodiment. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] The application provides a power regulation method for a wind-solar-storage micro-grid participating in a frequency response service market, aiming to optimize comprehensive benefits and meet safety operation constraints of the power grid. The method fully considers a capacity attenuation cost of a storage system, realizes efficient power regulation of the wind-solar-storage micro-grid by constructing a two-level optimization structure, and the upper layer is a scheduling layer, the optimization target is a time scale of an hour, price arbitrage is carried out based on 24-hour price differences, and market benefits are maximized, and the lower layer is a real-time regulation layer, focuses on minute-level optimization, uses adjustable capacity of wind power and photovoltaic and fast response capability of the storage system, and meets frequency regulation requirements of the main power grid. Particle swarm optimization (PSO) and other optimization algorithms are used for problem solving, and the efficiency and feasibility of the optimization scheme are ensured.

[0021] Specifically, referring to Figures 1-5 , the application provides a power regulation method for a wind-solar-storage micro-grid participating in a frequency response service market, and the method is specifically as follows: Step S1, a dynamic mathematical model of wind power, photovoltaic and storage is constructed, and capacity attenuation characteristics are considered; Step S2, a typical scenario set of wind-solar load is generated through clustering; Step S3, hour-level scheduling is optimized based on price arbitrage; Step S4, frequency response requirements are determined in combination with real-time frequency data and frequency service modes; Step S5, a minute-level regulation model is constructed, and safety constraints and attenuation costs are integrated; Step S6, a particle swarm optimization algorithm is used to solve the optimization model to obtain a regulation scheme.

[0022] In step S1, (1) when the wind speed is , the output power of the wind turbine unit is approximately expressed by a piecewise function as follows: (1) In the formula, is the rated output power of the wind turbine unit; is the cut-in wind speed; is the cut-out wind speed; is the rated wind speed; wherein the rated output power of the wind turbine unit is obtained by combining air density , wind energy utilization coefficient , tip speed ratio , pitch angle , the number of wind turbine units and the angular velocity of the wind turbine rotation in combination with a wind turbine dynamics model: (2) wherein the tip speed ratio and wind energy utilization coefficient is expressed as: (3) (4) (2) the output power of the photovoltaic array from the output power under standard atmospheric conditions , the light intensity and the ambient temperature The irradiance combined with the working point and the cell surface temperature of the working point is obtained: (5) wherein the relationship between the cell surface temperature and the atmospheric temperature is expressed as: (6) In the formula , , is a constant coefficient; (3) Based on the cycle life model of lithium battery energy storage, considering the influence of charging and discharging on capacity attenuation, the energy storage capacity attenuation cost is quantified by the following formula: (7) In the formula, is the total energy storage attenuation cost; is the time step; is the time period; is the cost coefficient of a single charge-discharge cycle; denotes the discharge amount at time t; is the nominal cycle depth, used for standardizing cycle number calculation; is the nominal capacity of the energy storage system; is the cycle number at the nominal cycle depth; is the replacement cost of the energy storage system.

[0023] In step S2, the wind power, photovoltaic output data and daily load data of the whole year are clustered using the K-means clustering algorithm to obtain the typical curves of wind power, photovoltaic output and load, which are used as the scene set of the lower power regulation.

[0024] In step S3, based on the dynamic mathematical model constructed in step S1 and the typical scene set of wind power output and load generated in step S2, the day-ahead scheduling optimization based on 24-hour price fluctuation is carried out with the hourly time scale as the optimization target, and the specific implementation is as follows: (1) Obtain the 24-hour electricity price curve of the electricity market, and determine the market revenue potential for each hour by combining the typical scenario set in step S2; (2) To maximize total revenue, an hourly optimization model is constructed, taking into account the predicted output of wind power and photovoltaic power and the charging and discharging plan of the energy storage system that includes capacity decay costs. At the same time, the constraints on grid safety operation are improved, including power balance, upper and lower limits of energy storage state of charge (SOC) and microgrid output range. (3) Optimize the allocation of energy storage charging and discharging and wind and solar power output according to the peak and off-peak electricity price periods: During the off-peak electricity price period, prioritize the use of wind and solar power generation to meet load demand and charge the energy storage system; During the peak electricity price period, coordinate energy storage discharging and wind and solar power output to maximize market electricity sales revenue and participate in frequency response services; Optimize the power dispatch scheme for each hour through linear programming or mixed integer programming to ensure maximum revenue. (4) Generate hourly power scheduling schemes, including output plans for wind power, photovoltaic and energy storage systems and energy storage charging and discharging strategies, to provide a basis for minute-level real-time control in steps S4-S6.

[0025] In step S4, the power system frequency directly reflects the relationship between power generation and load. When load consumption exceeds power generation, the system frequency decreases; when load consumption is less than power generation, the system frequency increases. This is the main content of the active power-frequency droop characteristic, which is the core mechanism for primary frequency regulation in a power system. It enables generators or inverters to automatically adjust their active power output according to changes in system frequency, thereby maintaining system frequency stability. The frequency regulation power demand of the main grid is also known as the power response demand. Represented as: (8) In the formula, For actual frequency, For the rated frequency, The droop coefficient is... This represents the ratio of the maximum permissible frequency deviation to the maximum adjustable power range of the unit.

[0026] In step S5, the state of charge (SOC) and energy state management rules of the energy storage system are evaluated based on the dynamic mathematical model from step S1. This ensures that the response power demand does not exceed the available energy storage capacity, and considers degradation costs to optimize energy storage output allocation. A power demand sequence on a minute-scale timescale is generated, including coordinated output plans for wind power, photovoltaics, and energy storage systems, as well as energy storage charging and discharging strategies (here, to increase the available capacity participating in frequency response, some restrictions are typically placed on wind and solar power generation in S3, reserving spare capacity for second-level adjustments in stage S5), ensuring that the electricity price arbitrage revenue in the first stage remains unchanged (grid purchase of electricity). and selling electricity to the grid If the second stage of real-time regulation is performed under the condition that the frequency response power provided is constant (i.e., the frequency response power provided is not changed), then the frequency response power provided on the second time scale is: (9) In the formula, , , , is the real-time photovoltaic output, wind turbine output, and energy storage discharge and charging power; , , , is the photovoltaic output, wind turbine output, and energy storage discharge and charging power obtained by the upper-layer scheduling of step S3; and the benefit of the microgrid participating in the frequency response is: (10) In the formula, is the frequency response benefit in a day; is the amount of frequency response provided by the microgrid in real time; is a frequency response error penalty coefficient, and the size of the coefficient depends on the percentage error of the amount of frequency response provided by the microgrid in real time and the amount of frequency response agreed with the main grid , is the real-time frequency response price.

[0027] In step S6, a multivariable large-scale mixed integer optimization model is constructed with the sum of the first-stage arbitrage benefit and the second-stage benefit of participating in the frequency response service market as the optimization objective, the optimization model is solved by means of the PSO optimization algorithm, and a real-time power regulation scheme is obtained.

[0028] Although the present application has been disclosed with reference to the preferred embodiments above, it is not intended to limit the present application, and any person skilled in the art can make various modifications and modifications without departing from the spirit and scope of the present application, and therefore the protection scope of the present application should be defined by the claims.

Claims

1. A power regulation method for a wind-solar-storage microgrid participating in a frequency response service market, characterized in that, The method is specifically: Step S1, construct a dynamic mathematical model of wind power, photovoltaic and energy storage, considering the capacity attenuation characteristics; Step S2, generate a typical scenario set of wind and light load by clustering; Step S3, optimize the hourly scheduling based on electricity price arbitrage; Step S4, determine the frequency response demand combined with real-time frequency data and frequency service mode; Step S5, construct a minute-level regulation model, integrate safety constraints and attenuation cost; Step S6, solve the optimization model by particle swarm optimization algorithm to obtain the regulation scheme.

2. The method of claim 1, wherein, In step S1, (1) When the wind speed is , the output power of the wind turbine unit is approximately expressed by a piecewise function as follows: (1) In the formula: is the rated output power of the wind turbine generator set; is the cut-in wind speed; is the cut-out wind speed; is the rated wind speed; wherein the rated output power of the wind turbine generator set is derived from the air density , the wind energy utilization coefficient , the tip speed ratio , the pitch angle , the number of wind turbine generators and the angular velocity of the wind turbine generator rotation is derived in combination with the wind turbine dynamics model. (2) wherein the tip speed ratio and the wind energy utilization coefficient is expressed as: (3) (4) (2) the output power of the photovoltaic array from the output power under standard atmospheric conditions , the intensity of the light and the ambient temperature in combination with the irradiance of the operating point and the cell surface temperature of the operating point : (5) wherein the relationship between the battery surface temperature and the atmospheric temperature is expressed as: (6) In the formula , , are constants; (3) Based on the cycle life model of lithium battery energy storage, considering the influence of charging and discharging on capacity attenuation, the energy storage capacity attenuation cost is quantified by the following formula: (7) wherein, is the total energy storage decay cost; is the time step; is the time period; is the cost coefficient for a single charge-discharge cycle; denotes the discharge amount at time t; is the nominal cycle depth, used for standardizing cycle number calculation; is the nominal capacity of the energy storage system; is the cycle number at the nominal cycle depth; is the energy storage system replacement cost.

3. The method of claim 2, wherein, In step S2, the wind power, photovoltaic output data and daily load data of the whole year are clustered by K-means clustering algorithm to obtain the typical curve of wind power, photovoltaic output and load, which is used as the scenario set of lower power regulation.

4. The method of claim 3, wherein, In step S3, based on the dynamic mathematical model constructed in step S1 and the typical scenario set of wind power output and load generated in step S2, the day-ahead scheduling optimization based on 24-hour electricity price fluctuation is carried out with hourly time scale as the optimization target, and the specific implementation is as follows: (1) Obtain the 24-hour electricity price curve of the power market, and determine the market revenue potential of each hour combined with the typical scenario set in step S2; (2) Construct an hourly optimization model to maximize total revenue, considering the predicted output of wind power and photovoltaic and the charging and discharging plan of energy storage system with capacity attenuation cost, while improving the safety operation constraints of power grid, including power balance, upper and lower limits of state of charge (SOC) of energy storage and output range of microgrid; (3) According to the peak and valley period of electricity price, optimize the charging and discharging of energy storage and the distribution of wind and light output: in the valley period of electricity price, wind and light power is preferentially used to meet the load demand, and the energy storage system is charged; in the peak period of electricity price, the energy storage is discharged and the wind and light output is coordinated to maximize the market electricity selling revenue, while participating in frequency response service; through linear programming or mixed integer programming, the power scheduling scheme of each hour is optimized to maximize the revenue; (4) Generate the hourly power scheduling scheme, including the output plan of wind power, photovoltaic and energy storage system and the charging and discharging strategy of energy storage, which provides the basis for the minute-level real-time regulation in steps S4-S6.

5. The method of claim 4, wherein, In step S4, the frequency modulation power demand of the main grid, i.e. the power response demand is represented as: (8) wherein is the actual frequency, is the rated frequency, is the droop coefficient, represents the ratio of the maximum allowed frequency deviation and the maximum adjustable power range of the unit.

6. The method of claim 5, wherein, In step S5, the power demand sequence of minute-level time scale is generated, including the coordinated output plan of wind power, photovoltaic and energy storage system and the charging and discharging strategy of energy storage, which ensures that the real-time regulation in the second stage is carried out under the condition that the arbitrage revenue of the first stage electricity price is unchanged, and the frequency response power provided in the second stage is: (9) In the formula, , , , is the real-time photovoltaic output, fan output, energy storage discharge and charging power; , , , is the photovoltaic output, fan output, energy storage discharge and charging power obtained by the upper layer scheduling of step S3; the benefit of the microgrid participating in the frequency response is: (10) wherein, is the frequency response benefit for a day; is the amount of frequency response provided by the microgrid in real time; is the frequency response error penalty coefficient, which depends on the amount of frequency response provided by the microgrid in real time is the amount of frequency response agreed with the main grid is the percentage error of the amount of frequency response agreed with the main grid, is the real-time frequency response price.

7. The method of claim 6, wherein, In step S6, a multivariate large-scale mixed integer optimization model is constructed with the maximum sum of the two-stage revenue of the first-stage electricity price arbitrage revenue and the second-stage market revenue of participating in frequency response service as the optimization target, and the optimization model is solved by PSO optimization algorithm to obtain the real-time power regulation scheme.

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