Dynamic optimization scheduling method and system for green electricity energy storage system

Abstract

The invention provides a dynamic optimization scheduling method and system for a green electricity energy storage system, and the method comprises the steps: collecting the operation data of the energy storage system, carrying out the standardization processing and abnormal value elimination, and generating a standardized data set; environment data time-varying features are extracted based on the data set, a wind power prediction model is constructed, charge and discharge efficiency nonlinear characteristics and capacity dynamic changes of the energy storage system are considered, and a feasible operation interval is determined; a collaborative optimization objective function based on power grid power smoothness maximization, wind power consumption rate maximization and energy storage capacity utilization rate optimization is established in combination with the prediction model and the feasible interval; and solving an objective function by adopting a multi-objective optimization algorithm, constructing a constraint set, establishing a state observation equation, and dynamically updating control input by adopting a self-adaptive control method based on real-time data feedback to realize optimal scheduling. The method can significantly improve prediction precision and operation efficiency, realizes multi-target collaborative optimization, and has important application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage system scheduling, and specifically relates to a method and system for dynamic optimization scheduling of a green electricity energy storage system. Background Art

[0002] With the advancement of energy structure transformation, the installed capacity of renewable energy power generation represented by wind power continues to grow rapidly; energy storage systems, as an important means of regulation, can effectively improve the absorption capacity of wind power and smooth power fluctuations; however, the intermittent, volatile and random characteristics of wind power have brought severe challenges to the safe and stable operation of the power system, and more advanced energy storage system scheduling technologies need to be developed.

[0003] Existing wind power prediction methods are mainly based on static characteristic models, which fail to fully consider the dynamic changes of environmental factors, resulting in insufficient prediction accuracy, especially in complex terrain and extreme weather conditions, where the prediction error increases significantly. Traditional energy storage system scheduling strategies often adopt simplified linear efficiency models, ignoring the nonlinear characteristics of energy storage equipment during actual operation, such as the nonlinear changes in charging and discharging efficiency with power and temperature, the attenuation characteristics of energy storage capacity with cycle number and usage time, and the impact of charge state and discharge depth on life. This leads to a large deviation between actual scheduling effects and theoretical calculations. Current energy storage optimization scheduling methods generally adopt a single optimization objective, which fails to coordinately consider multiple key indicators such as grid power smoothness, wind power consumption rate and energy storage capacity utilization rate, and the control strategy is fixed and cannot be adaptively adjusted according to real-time operating conditions.

[0004] Therefore, there is an urgent need for a new method for dynamic optimization and scheduling of green electricity energy storage systems, so that the system can be flexibly adjusted according to the real-time operating status. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for dynamic optimization scheduling of a green electricity energy storage system. By constructing a wind power prediction model with time-varying characteristics, the nonlinear characteristics and dynamic constraints of the energy storage system are fully considered, a multi-objective collaborative optimization framework is established, and an adaptive control strategy is adopted to achieve the optimal scheduling of the energy storage system.

[0006] In a first aspect, the present invention provides a green power energy storage system dynamic optimization scheduling method for adaptively optimizing the power grid power smoothness, wind power consumption rate and energy storage capacity utilization rate in a wind farm-based energy storage system, the method comprising the following steps:

[0007] Step 1: Collect the operating data of the green electricity storage system, standardize the operating data and remove outliers to generate a standardized data set.

[0008] The operating data include: wind farm environment data, wind turbine output, energy storage system parameters and grid load data; the wind farm environment data include: wind speed, wind direction, temperature, air pressure and turbulence intensity.

[0009] Step 2: Based on the standardized data set, the time-varying characteristics of the environmental data are extracted to construct a wind power prediction model with time-varying parameters; considering the nonlinear characteristics of the charging and discharging efficiency of the energy storage system and the dynamic changes in the capacity, the feasible operating range of the energy storage system is determined according to the system safety constraints.

[0010] Step three, combining the wind power prediction model and the feasible operating range of the energy storage system, establish the energy storage system collaborative optimization objective function.

[0011] The energy storage system collaborative optimization objective function is based on three collaborative objectives: maximizing grid power smoothness, maximizing wind power consumption rate, and optimizing energy storage capacity utilization.

[0012] Step 4: Use a multi-objective optimization algorithm to solve the collaborative optimization objective function of the energy storage system, build a set of system operation constraints, establish the state observation equation of the energy storage system, and based on real-time data feedback, use an adaptive control method to dynamically update the control input of the energy storage system to achieve the optimal scheduling of the energy storage system.

[0013] Furthermore, in step 2, based on the standardized data set, extracting the time-varying characteristics of the environmental data and constructing a wind power prediction model with time-varying parameters includes:

[0014] The time-varying characteristics of the environmental data are extracted, F(t)=X(t)·M(t)+D(t), where t is the time variable, unit: s; F(t) is the environmental feature vector at time t; X(t) is the environmental state matrix at time t, M(t) is the time-varying mapping matrix at time t, and D(t) is the environmental disturbance vector at time t.

[0015] The expression of the environmental state matrix X(t) at time t is: X(t) = [v(t), d(t), h(t), p(t), i(t)], where v(t) is the wind speed, unit: d(t) is the wind direction angle, h(t) is the temperature, unit: ℃; p(t) is the air pressure, unit: hPa; i(t) is the turbulence intensity, unit: %.

[0016] The expression of the variable mapping matrix M(t) at time t is: M(t)=M(t-1)+α·e(t), where α is the learning rate parameter with a value range of (0, 1), and e(t) is the characteristic error vector.

[0017] The wind power prediction model based on time-varying characteristics is expressed as: Pr(t)=F(t)·V(t)·Pb; where Pr(t) is the predicted power, unit: kW.

[0018] V(t) is the time-varying characteristic influence vector, V(t) = [v1(t), v2(t), v3(t), v4(t), v5(t)], v1(t), v2(t), v3(t), v4(t), v5(t) represent the wind speed characteristic influence coefficient, wind direction characteristic influence coefficient, temperature characteristic influence coefficient, air pressure characteristic influence coefficient and turbulence characteristic influence coefficient respectively;

[0019]

[0020] Among them, vn is the rated wind speed, d0 is the optimal wind direction angle, h0 is the reference temperature, which is 20℃, kh is the temperature influence coefficient, and its value range is (0, 1), p0 is the standard atmospheric pressure, and ki is the turbulence influence coefficient, and its value range is (0, 1).

[0021] Furthermore, in step 2, the nonlinear characteristics of the energy storage system's charging and discharging efficiency and the dynamic change of its capacity are considered, and the feasible operating range of the energy storage system is determined according to the system safety constraints, including:

[0022] The nonlinear characteristics of the energy storage system efficiency during the charging and discharging processes are expressed as:

[0023]

[0024] Wherein, η0 is the nominal efficiency, Pc(t) is the charging power at time t, Pd(t) is the discharging power at time t, Pn is the rated power, kc and kd are the power attenuation coefficients, ranging from (0 to 1); kt is the temperature influence coefficient;

[0025] The dynamic change of energy storage system capacity is expressed as: C(t) = C0.e ((-kn·N(t))) ·[1-kd·|D(t)-D0|]; where C0 is the initial capacity, kn is the cycle life attenuation coefficient, ranging from (0, 1), N(t) is the equivalent number of cycles, D(t) is the discharge depth at time t, and D0 is the reference discharge depth.

[0026] Based on the nonlinear characteristics of energy storage system efficiency and dynamic changes in capacity, the safety constraints that the feasible operation range of the energy storage system must meet are:

[0027] Charge and discharge power constraints, |Pc(t)|≤Pc| and Pd(t)|≤Pd;

[0028] State of charge constraints, SOCm≤SOC(t)≤SOCM| and dSOC(t) / dt|≤SOCr;

[0029] Cycle life constraints, N(t)≤Nm and Dm≤D(t)≤DM;

[0030] Temperature constraint, hm≤h(t)≤hM;

[0031] Wherein, Pc is the maximum charging power, Pd is the maximum discharging power, SOCm is the minimum state of charge, SOC(t) is the charge at time t, SOCM is the maximum state of charge, SOCr is the maximum rate of change of state of charge; Nm is the maximum number of cycles, Dm is the minimum depth of discharge, DM is the maximum depth of discharge; hm is the minimum operating temperature, and hM is the maximum operating temperature.

[0032] Furthermore, the energy storage system collaborative optimization objective function must satisfy the safety constraints that the feasible operation range of the energy storage system must satisfy, and the collaborative optimization objective function is based on the power smoothness of the power grid, the wind power consumption rate and the energy storage capacity utilization rate;

[0033] Grid power smoothness index: Among them, AP(t) is the power change rate of the power grid at time t; wind power consumption rate index: Among them, Pa(t) is the actual wind power output at time t, unit: kW; energy storage capacity utilization index: Among them, S0(t) is the optimal state of charge at time t.

[0034] Coordinated optimization objective function: L(t)=∑[w1·S(t+k)+w2·U(t+k)+w3·E(t+k)], k=0, 1, ..., N; where N is the optimization time domain length, w1, w2, w3 are weight coefficients, and w1+w2+w3=1, S(t+k) is the power smoothness index of the power grid predicted in k steps, U(t+k) is the wind power consumption rate index predicted in k steps, and E(t+k) is the energy storage capacity utilization index predicted in k steps.

[0035] Furthermore, a multi-objective optimization algorithm is used to solve the energy storage system collaborative optimization objective function, and the system operation constraint set is expressed as:

[0036] G(x)=g1(x), g2(x), g3(x), g4(x), g5(x)≤0

[0037] Among them, g1(x), g2(x), g3(x), g4(x) and g5(x) correspond to the safety constraints that must be met in the feasible operating range of the energy storage system: g1(x): SOCm≤SOC(t)≤SOCM; g2(x): |Pc(t)|≤Pc; g3(x): |Pd(t)|≤Pd; g4(x): g5(x):hm≤h(t)≤hM;

[0038] State observation equation of energy storage system: x(t+1)=A(t)·x(t)+B(t)·u(t)+K(t)·e(t)

[0039] Among them, x(t) is the system state vector, including S(t), Pa(t), and P(t) state quantities, u(t) is the control input vector, including Pc(t), Pd(t) and other control quantities, e(t) is the observation error, K(t) is the adaptive gain matrix, and A(t) and B(t) are time-varying system matrices;

[0040] Adaptive control method is used to dynamically update the control input of the energy storage system:

[0041] u(t)=u0(t)+Δu(t);

[0042]

[0043] Among them, Δu(t) is the compensation control amount, β is the control gain coefficient, and its value range is (0, 1). is the gradient of the objective function L(t) with respect to the control quantity.

[0044] In the second aspect, based on the same inventive concept, the present invention provides a dynamic optimization scheduling system for a green electricity energy storage system, which is used to execute the method described in the first aspect. The scheduling system includes: a data acquisition and preprocessing module, a wind power prediction and energy storage system constraint analysis module, an optimization target construction module and an adaptive optimization control module connected in sequence.

[0045] The data acquisition and preprocessing module is used to collect the operating data of the green power energy storage system, and standardize the operating data and eliminate outliers to generate a standardized data set; the operating data includes wind farm environmental data, wind turbine output, energy storage system parameters and grid load data; the wind farm environmental data includes wind speed, wind direction, temperature, air pressure and turbulence intensity.

[0046] The wind power prediction and energy storage system constraint analysis module is used to extract the time-varying characteristics of environmental data based on the standardized data set, and construct a wind power prediction model with time-varying parameters; considering the nonlinear characteristics of the energy storage system's charging and discharging efficiency and the dynamic changes in capacity, the feasible operating range of the energy storage system is determined according to the system safety constraints.

[0047] The optimization target construction module is used to combine the wind power prediction model and the feasible operation range of the energy storage system to establish a collaborative optimization objective function of the energy storage system based on maximizing the smoothness of the power grid, maximizing the wind power consumption rate and optimizing the energy storage capacity utilization.

[0048] An adaptive optimization control module, used for solving the energy storage system collaborative optimization objective function by adopting a multi-objective optimization algorithm;

[0049] Construct a set of system operation constraints, establish the state observation equation of the energy storage system, and based on real-time data feedback, use adaptive control methods to dynamically update the control input of the energy storage system to achieve optimal scheduling of the energy storage system.

[0050] The beneficial effects of the present invention are as follows:

[0051] The wind power prediction model constructed by the present invention by introducing a time-varying mapping matrix and an environmental disturbance vector can effectively capture the dynamic change law of environmental factors, and adopts a characteristic error vector for online updating, which significantly improves the wind power prediction accuracy; the nonlinear characteristics of the charging and discharging efficiency and the dynamic change characteristics of the capacity of the energy storage system are fully considered, and the feasible operation range is determined in combination with multi-dimensional safety constraints, which effectively prolongs the service life of the energy storage system; the collaborative optimization objective function of the power smoothness of the power grid, the wind power consumption rate and the energy storage capacity utilization rate is innovatively constructed, and the multi-objective dynamic balance is achieved by introducing weight coefficients, and the control input is dynamically updated by an adaptive control method, which ensures the robustness and adaptability of the system scheduling.

[0052] The present invention can improve the power smoothness of the power grid by 15%-25%, the wind power consumption rate by 10%-20%, and the energy storage capacity utilization rate by 20%-30%. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the green electricity energy storage system dynamic optimization scheduling method of the present invention;

[0054] Figure 2 It is a schematic diagram of the composition of the dynamic optimization scheduling system of the green electricity energy storage system of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example 1

[0057] like Figure 1 As shown, the green electricity energy storage system dynamic optimization scheduling method of the present invention is used to adaptively optimize the grid power smoothness, wind power consumption rate and energy storage capacity utilization rate in the wind farm-based energy storage system.

[0058] The method comprises the following steps:

[0059] Step 1: Collect the operating data of the green electricity storage system, standardize the operating data and remove outliers to generate a standardized data set.

[0060] Specifically, taking the actual operation data of a wind farm as an example, the collected operation data are processed as follows: the wind speed data is normalized, and the wind speed value of 0-30m / s is mapped to the interval of 0-1; the temperature data is standardized to make its mean 0 and variance 1; outliers are eliminated by the 3σ criterion, that is, data points that exceed the range of ±3 times the standard deviation of the mean value are marked as outliers and eliminated.

[0061] The operating data include: wind farm environment data, wind turbine output, energy storage system parameters and grid load data; the wind farm environment data include: wind speed, wind direction, temperature, air pressure and turbulence intensity.

[0062] Step 2: Based on the standardized data set, the time-varying characteristics of the environmental data are extracted to construct a wind power prediction model with time-varying parameters; considering the nonlinear characteristics of the charging and discharging efficiency of the energy storage system and the dynamic changes in the capacity, the feasible operating range of the energy storage system is determined according to the system safety constraints.

[0063] Based on the standardized data set, extracting the time-varying characteristics of the environmental data and constructing a wind power prediction model with time-varying parameters include:

[0064] The time-varying characteristics of the environmental data are extracted, F(t)=X(t)·M(t)+D(t), where t is the time variable, unit: s; F(t) is the environmental feature vector at time t; X(t) is the environmental state matrix at time t, M(t) is the time-varying mapping matrix at time t, and D(t) is the environmental disturbance vector at time t.

[0065] The expression of the environmental state matrix X(t) at time t is: X(t) = [v(t), d(t), h(t), p(t), i(t)], where v(t) is the wind speed, unit: d(t) is the wind direction angle, h(t) is the temperature, unit: ℃; p(t) is the air pressure, unit: hPa; i(t) is the turbulence intensity, unit: %.

[0066] The expression of the variable mapping matrix M(t) at time t is: M(t)=M(t-1)+α·e(t), where α is the learning rate parameter with a value range of (Θ, 1), and e(t) is the characteristic error vector.

[0067] The wind power prediction model based on time-varying characteristics is expressed as: Pr(t)=F(t)·V(t)·Pb; where Pr(t) is the predicted power, unit: kW.

[0068] V(t) is the time-varying characteristic influence vector, V(t) = [v1(t), v2(t), v3(t), v4(t), v5(t)], v1(t), v2(t), v3(t), v4(t), v5(t) represent the wind speed characteristic influence coefficient, wind direction characteristic influence coefficient, temperature characteristic influence coefficient, air pressure characteristic influence coefficient and turbulence characteristic influence coefficient respectively;

[0069]

[0070] Among them, vn is the rated wind speed, d0 is the optimal wind direction angle, h0 is the reference temperature, which is 20℃, kh is the temperature influence coefficient, and its value range is (0, 1), p0 is the standard atmospheric pressure, and ki is the turbulence influence coefficient, and its value range is (0, 1).

[0071] Taking actual wind farm data as an example, when wind speed v(t) = 12m / s and rated wind speed vn = 15m / s, the wind speed characteristic influence coefficient v1(t) = 0.85; when the deviation between the wind direction angle d(t) and the optimal wind direction angle d0 is 30°, the wind direction characteristic influence coefficient v2(t) = 0.866; when the temperature h(t) = 25°C and the temperature influence coefficient kh = 0.5, the temperature characteristic influence coefficient v3(t) = 0.875.

[0072] Considering the nonlinear characteristics of the energy storage system's charging and discharging efficiency and the dynamic changes in capacity, the feasible operating range of the energy storage system is determined according to the system safety constraints, including:

[0073] The nonlinear characteristics of the energy storage system efficiency during the charging and discharging processes are expressed as:

[0074]

[0075] Among them, η0 is the nominal efficiency, Pc(t) is the charging power at time t, Pd(t) is the discharging power at time t, Pn is the rated power, kc and kd are the power attenuation coefficients, and their value range is (0, 1); kt is the temperature influence coefficient; for example, the nominal efficiency of a certain energy storage system η0 = 0.95, when the charging power is 80% of the rated power, the power attenuation coefficient kc = 0.2, the ambient temperature is 5°C higher than the reference temperature, and the temperature influence coefficient kt = 0.01, the charging efficiency ηc(t) = 0.92.

[0076] The dynamic change of energy storage system capacity is expressed as: C(t) = C0·e ((-kn·N(t))) ·[1-kd·|D(t)-D0|]; where C0 is the initial capacity, kn is the cycle life attenuation coefficient, ranging from (0, 1), N(t) is the equivalent number of cycles, D(t) is the discharge depth at time t, and D0 is the reference discharge depth.

[0077] Based on the nonlinear characteristics of energy storage system efficiency and dynamic changes in capacity, the safety constraints that the feasible operation range of the energy storage system must meet are:

[0078] Charge and discharge power constraints, |Pc(t)|≤Pc| and Pd(t)|≤Pd;

[0079] State of charge constraints, SOCm≤SOC(t)≤SOCM| and dSOC(t) / dt|≤SOCr;

[0080] Cycle life constraints, N(t)≤Nm and Dm≤D(t)≤DM;

[0081] Temperature constraint, hm≤h(t)≤hM;

[0082] Wherein, Pc is the maximum charging power, Pd is the maximum discharging power, SOCm is the minimum state of charge, SOC(t) is the charge at time t, SOCM is the maximum state of charge, SOCr is the maximum rate of change of state of charge; Nm is the maximum number of cycles, Dm is the minimum depth of discharge, DM is the maximum depth of discharge; hm is the minimum operating temperature, and hM is the maximum operating temperature.

[0083] Step three, combining the wind power prediction model and the feasible operating range of the energy storage system, establish the energy storage system collaborative optimization objective function.

[0084] The energy storage system collaborative optimization objective function is based on three collaborative objectives: maximizing grid power smoothness, maximizing wind power consumption rate, and optimizing energy storage capacity utilization.

[0085] The energy storage system collaborative optimization objective function must satisfy the safety constraints that the feasible operation range of the energy storage system must satisfy, and the collaborative optimization objective function is based on the power smoothness of the power grid, the wind power consumption rate and the energy storage capacity utilization rate;

[0086] Grid power smoothness index: Among them, AP(t) is the power change rate of the power grid at time t; wind power consumption rate index: Among them, Pa(t) is the actual wind power output at time t, unit: kW; energy storage capacity utilization index: Among them, S0(t) is the optimal state of charge at time t.

[0087] Coordinated optimization objective function: L(t)=∑[w1·S(t+k)+w2·U(t+k)+w3·E(t+k)], k=0, 1, ..., N; where N is the optimization time domain length, w1, w2, w3 are weight coefficients, and w1+w2+w3=1, S(t+k) is the power smoothness index of the power grid predicted in k steps, U(t+k) is the wind power consumption rate index predicted in k steps, and E(t+k) is the energy storage capacity utilization index predicted in k steps.

[0088] Step 4: Use a multi-objective optimization algorithm to solve the collaborative optimization objective function of the energy storage system, build a set of system operation constraints, establish the state observation equation of the energy storage system, and based on real-time data feedback, use an adaptive control method to dynamically update the control input of the energy storage system to achieve the optimal scheduling of the energy storage system.

[0089] The multi-objective optimization algorithm is used to solve the energy storage system collaborative optimization objective function, and the system operation constraint set is expressed as:

[0090] G(x)=g1(x), g g2(x), g g3(x), g g4(x),g5(x)≤0

[0091] Among them, g1(x), g2(x), g3(x), g4(x) and g5(x) correspond to the safety constraints that must be met in the feasible operating range of the energy storage system: g1(x): SOCm≤SOC(t)≤SOCM; g2(x): |Pc(t)|≤Pc; g3(x): |Pd(t)|≤Pd; g4(x): g5(x):hm≤h(t)≤hM;

[0092] State observation equation of energy storage system: x(t+1)=A(t)·x(t)+B(t)·u(t)+K(t)·e(t)

[0093] Among them, x(t) is the system state vector, including S(t), Pa(t), and P(t) state quantities, u(t) is the control input vector, including Pc(t), Pd(t) and other control quantities, e(t) is the observation error, K(t) is the adaptive gain matrix, and A(t) and B(t) are time-varying system matrices;

[0094] Adaptive control method is used to dynamically update the control input of the energy storage system:

[0095] u(t)=u0(t)+Δu(t);

[0096]

[0097] Among them, Δu(t) is the compensation control amount, β is the control gain coefficient, and its value range is (0, 1). is the gradient of the objective function L(t) with respect to the control quantity.

[0098] In order to verify the effectiveness of the present invention, a wind farm energy storage system was selected for actual application testing. The installed capacity of the wind farm is 100MW, the supporting energy storage system capacity is 20MWh, and the test period is from January 2023 to December 2023. The specific experimental data and results are as follows:

[0099] Wind farm environmental conditions: annual average wind speed 8.5m / s, dominant wind direction northwesterly, annual average temperature 15℃, average turbulence intensity 12%;

[0100] Energy storage system parameters: rated power 10MW, nominal efficiency 0.95, reference discharge depth 0.8, cycle life 10,000 times;

[0101] System weight parameters: w 1=0.4, w2=0.3, w3=0.3, optimized time domain length N=24.

[0102] By comparing the system operation data before and after adopting the method of the present invention:

[0103] 1) Grid power smoothness: The original average fluctuation rate was 22.5%, which was reduced to 16.8% after adopting this method, with an improvement of 25.3%;

[0104] 2) Wind power consumption rate: The original average consumption rate was 82.3%, which was increased to 96.8% after adopting this method, with an improvement of 17.6%;

[0105] 3) Energy storage capacity utilization rate: The original average utilization rate was 65.2%, which was increased to 83.1% after adopting this method, with an improvement of 27.5%.

[0106] Based on the above operating results, the annual wind power grid-connected power can be increased by about 18.5 million kWh. Calculated at 0.3 yuan / kWh, the economic benefits can be increased by about 5.55 million yuan. At the same time, due to the extended service life of the energy storage system, it is estimated that the equipment replacement cost can be saved by about 2 million yuan. The above verification cases fully demonstrate the remarkable effect of the present invention in improving the power smoothness of the power grid, the wind power consumption rate and the energy storage capacity utilization rate.

[0107] Example 2

[0108] like Figure 2 As shown, it is a schematic diagram of the composition of the dynamic optimization scheduling system of the green electricity energy storage system of the present invention, which is used to execute the method of Example 1. The scheduling system includes: a data acquisition and preprocessing module, a wind power prediction and energy storage system constraint analysis module, an optimization target construction module and an adaptive optimization control module connected in sequence.

[0109] The module adopts a distributed data acquisition system, which includes multiple field data acquisition units with a sampling period of 1s. It transmits data through industrial Ethernet to achieve real-time collection and processing of wind farm operation data.

[0110] The data acquisition and preprocessing module is used to collect the operating data of the green power energy storage system, and standardize the operating data and eliminate outliers to generate a standardized data set; the operating data includes wind farm environmental data, wind turbine output, energy storage system parameters and grid load data; the wind farm environmental data includes wind speed, wind direction, temperature, air pressure and turbulence intensity.

[0111] The wind power prediction and energy storage system constraint analysis module is used to extract the time-varying characteristics of environmental data based on the standardized data set, and construct a wind power prediction model with time-varying parameters; considering the nonlinear characteristics of the energy storage system's charging and discharging efficiency and the dynamic changes in capacity, the feasible operating range of the energy storage system is determined according to the system safety constraints. This module adopts a rolling prediction method with a prediction time scale of 48 hours and a prediction step of 15 minutes. By updating the time-varying mapping matrix M(t) in real time, the prediction model can adapt to environmental changes. In practical applications, the prediction accuracy of this module can reach 85%-90%, providing reliable data support for the optimal scheduling of the energy storage system.

[0112] The optimization target construction module is used to combine the wind power prediction model and the feasible operation range of the energy storage system to establish a collaborative optimization objective function of the energy storage system based on maximizing the smoothness of the power grid, maximizing the wind power consumption rate and optimizing the energy storage capacity utilization.

[0113] The adaptive optimization control module is used to solve the energy storage system collaborative optimization objective function by using a multi-objective optimization algorithm; construct a system operation constraint set, establish the energy storage system state observation equation, and dynamically update the control input of the energy storage system based on real-time data feedback using an adaptive control method to achieve the optimal scheduling of the energy storage system; this module is based on the model predictive control (MPC) principle, and the control cycle is 1 minute. In each control cycle, the current state of the system is first obtained based on the state observation equation, and then the optimal control sequence is obtained by solving the multi-objective optimization problem, and finally the control quantity at the current moment is executed. For example, when it is observed that the state of charge SOC(t) of the energy storage system deviates from the target value, the control module will automatically adjust the charging and discharging power so that the system gradually tends to the optimal operating state.

[0114] In practical applications, the weight coefficient can be dynamically adjusted according to the grid operation requirements and the characteristics of the energy storage system. For example, when the grid fluctuates greatly, the power smoothness weight w1 is increased; when the wind power output is high, the absorption rate weight w2 is increased; when the energy storage capacity is close to the limit, the capacity utilization weight w3 is increased.

[0115] Data exchange is achieved between functional modules through industrial communication networks, and a distributed architecture is used to improve the reliability and real-time performance of the system. The system is configured with dual-machine hot backup. When the main controller fails, the backup controller can complete disturbance-free switching within 100ms to ensure the continuous and stable operation of the energy storage system.

[0116] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A green power energy storage system dynamic optimization scheduling method, which is used to adaptively optimize the grid power smoothness, wind power consumption rate and energy storage capacity utilization rate in a wind farm-based energy storage system, characterized in that: The method comprises the following steps: Step 1: Collect the operating data of the green power storage system, standardize the operating data and remove outliers to generate a standardized data set; The operation data include: wind farm environment data, wind turbine output, energy storage system parameters and grid load data; the wind farm environment data include: wind speed, wind direction, temperature, air pressure and turbulence intensity; Step 2: Based on the standardized data set, extract the time-varying characteristics of the environmental data and construct a wind power prediction model with time-varying parameters; consider the nonlinear characteristics of the energy storage system's charging and discharging efficiency and the dynamic changes in capacity, and determine the feasible operating range of the energy storage system according to the system safety constraints; Step 3: Combine the wind power prediction model and the feasible operation range of the energy storage system to establish the energy storage system collaborative optimization objective function; The energy storage system collaborative optimization objective function is based on three collaborative objectives: maximizing grid power smoothness, maximizing wind power consumption rate, and optimizing energy storage capacity utilization; Step 4: Use a multi-objective optimization algorithm to solve the collaborative optimization objective function of the energy storage system, build a set of system operation constraints, establish the state observation equation of the energy storage system, and based on real-time data feedback, use an adaptive control method to dynamically update the control input of the energy storage system to achieve the optimal scheduling of the energy storage system.

2. The green electricity energy storage system dynamic optimization scheduling method according to claim 1 is characterized in that: In the step 2, based on the standardized data set, extracting the time-varying characteristics of the environmental data and constructing a wind power prediction model with time-varying parameters includes: Extract the time-varying features of environmental data, F(t) = X(t)·M(t)+D(t), where t is the time variable, unit: s; F(t) is the environmental feature vector at time t; X(t) is the environmental state matrix at time t, M(t) is the time-varying mapping matrix at time t, and D(t) is the environmental disturbance vector at time t; The expression of the environmental state matrix X(t) at time t is: X(t) = [v(t), d(t), h(t), p(t), i(t)], where v(t) is the wind speed, unit: d(t) is the wind direction angle, h(t) is the temperature, unit: °C; p(t) is the air pressure, unit: hPa; i(t) is the turbulence intensity, unit: %; The expression of the variable mapping matrix M(t) at time t is: M(t) = M(t-1) + α·e(t), where α is the learning rate parameter with a value range of (θ, 1), and e(t) is the characteristic error vector; The wind power prediction model based on time-varying characteristics is expressed as: Pr(t) = F(t)·V(t)·Pb; where Pr(t) is the predicted power, unit: kW; V(t) is the time-varying characteristic influence vector, V(t) = [v1(t), v2(t), v3(t), v4(t), v5(t)], v1(t), v2(t), v3(t), v4(t), v5(t) represent the wind speed characteristic influence coefficient, wind direction characteristic influence coefficient, temperature characteristic influence coefficient, air pressure characteristic influence coefficient and turbulence characteristic influence coefficient respectively; Among them, vn is the rated wind speed, d0 is the optimal wind direction angle, h0 is the reference temperature, which is 20℃, kh is the temperature influence coefficient, and its value range is (0, 1), p0 is the standard atmospheric pressure, and ki is the turbulence influence coefficient, and its value range is (0, 1).

3. The green electricity energy storage system dynamic optimization scheduling method according to claim 2 is characterized in that: In step 2, the nonlinear characteristics of the energy storage system's charging and discharging efficiency and the dynamic change of its capacity are considered, and the feasible operating range of the energy storage system is determined according to the system safety constraints, including: The nonlinear characteristics of the energy storage system efficiency during the charging and discharging processes are expressed as: Wherein, η0 is the nominal efficiency, Pc(t) is the charging power at time t, Pd(t) is the discharging power at time t, Pn is the rated power, kc and kd are the power attenuation coefficients, ranging from (0 to 1); kt is the temperature influence coefficient; The dynamic change of energy storage system capacity is expressed as: C(t) = C0.e ((-kn·N(t))) .[1-kd·|D(t)-D0|]; where C0 is the initial capacity, kn is the cycle life attenuation coefficient, ranging from (0, 1), N(t) is the equivalent number of cycles, D(t) is the discharge depth at time t, and D0 is the reference discharge depth; Based on the nonlinear characteristics of energy storage system efficiency and dynamic changes in capacity, the safety constraints that the feasible operation range of the energy storage system must meet are: Charge and discharge power constraints, |Pc(t)|≤Pc| and Pd(t)|≤Pd; State of charge constraints, SOCm≤SOC(t)≤SOCM| and dSOC(t) / dt|≤SOCr; Cycle life constraints, N(t)≤Nm and Dm≤D(t)≤DM; Temperature constraint, hm≤h(t)≤hM; Wherein, Pc is the maximum charging power, Pd is the maximum discharging power, SOCm is the minimum state of charge, SOC(t) is the charge at time t, SOCM is the maximum state of charge, SOCr is the maximum rate of change of state of charge; Nm is the maximum number of cycles, Dm is the minimum depth of discharge, DM is the maximum depth of discharge; hm is the minimum operating temperature, and hM is the maximum operating temperature.

4. The green electricity energy storage system dynamic optimization scheduling method according to claim 3 is characterized in that: The energy storage system collaborative optimization objective function must satisfy the safety constraints that the feasible operation range of the energy storage system must satisfy, and the collaborative optimization objective function is based on the power smoothness of the power grid, the wind power consumption rate and the energy storage capacity utilization rate; Grid power smoothness index: Among them, AP(t) is the power change rate of the power grid at time t; wind power consumption rate index: Among them, Pa(t) is the actual wind power output at time t, unit: kW; energy storage capacity utilization index: Among them, S0(t) is the optimal state of charge at time t; Coordinated optimization objective function: L(t)=∑[w1·S(t+k)+w2·U(t+k)+w3·E(t+k)], k=0, 1, ..., N; where N is the optimization time domain length, w1, w2, w3 are weight coefficients, and w1+w2+w3=1, S(t+k) is the power smoothness index of the power grid predicted in k steps, U(t+k) is the wind power consumption rate index predicted in k steps, and E(t+k) is the energy storage capacity utilization index predicted in k steps.

5. The green electricity energy storage system dynamic optimization scheduling method according to claim 4 is characterized in that: The multi-objective optimization algorithm is used to solve the energy storage system collaborative optimization objective function, and the system operation constraint set is expressed as: G(x)=g1(x), g2(x), g3(x), g4(x), g5(x)≤0 Among them, g1(x), g2(x), g3(x), g4(x) and g5(x) correspond to the safety constraints that must be met in the feasible operating range of the energy storage system: g1(x): SOCm≤SOC(t)≤SOCM; g2(x): |Pc(t)|≤Pc; g3(x): |Pd(t)|≤Pd; g4(x): g5(x):hm≤h(t)≤hM; State observation equation of energy storage system: x(t+1)=A(t)·x(t)+B(t)·u(t)+K(t)·e(t) Among them, x(t) is the system state vector, including S(t), Pa(t), and P(t) state quantities, u(t) is the control input vector, including Pc(t), Pd(t) and other control quantities, e(t) is the observation error, K(t) is the adaptive gain matrix, and A(t) and B(t) are time-varying system matrices; Adaptive control method is used to dynamically update the control input of the energy storage system: u(t)=u0(t)+Δu(t); Among them, Δu(t) is the compensation control amount, β is the control gain coefficient, and its value range is (0, 1). is the gradient of the objective function L(t) with respect to the control quantity.

6. A green electricity energy storage system dynamic optimization scheduling system, used to execute the method according to any one of claims 1 to 5, characterized in that: The dispatching system comprises: The data acquisition and preprocessing module is used to collect the operating data of the green power energy storage system, and perform standardization processing and outlier elimination on the operating data to generate a standardized data set; the operating data includes wind farm environmental data, wind turbine output, energy storage system parameters and grid load data; the wind farm environmental data includes wind speed, wind direction, temperature, air pressure and turbulence intensity; The wind power prediction and energy storage system constraint analysis module is used to extract the time-varying characteristics of environmental data based on the standardized data set and construct a wind power prediction model with time-varying parameters; considering the nonlinear characteristics of the energy storage system's charging and discharging efficiency and the dynamic changes in capacity, the feasible operation range of the energy storage system is determined according to the system safety constraints; An optimization target building module is used to combine the wind power prediction model and the feasible operation range of the energy storage system to establish an energy storage system collaborative optimization objective function based on maximizing grid power smoothness, maximizing wind power consumption rate and optimizing energy storage capacity utilization; An adaptive optimization control module, used for solving the energy storage system collaborative optimization objective function by adopting a multi-objective optimization algorithm; Construct a set of system operation constraints, establish the state observation equation of the energy storage system, and based on real-time data feedback, use adaptive control methods to dynamically update the control input of the energy storage system to achieve optimal scheduling of the energy storage system.

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