Day-ahead and intra-day dual-time scale power system voltage regulation method and system combining new energy station with energy storage

Abstract

The invention is suitable for the technical field of power system optimization, and provides a day-ahead and intra-day dual-time scale power system voltage regulation method and system combining a new energy station with energy storage, and the method comprises the steps: constructing a new energy station model and a power system network model respectively, respectively calculating predicted values of new energy generation power and load power according to the new energy station model and the power system network model, and obtaining predicted values of node voltage amplitude and phase angle of the day-ahead prospective dispatching power system based on the predicted values; constructing an optimization model of day-ahead look-ahead scheduling; according to the new energy station model and the power system network model, real-time values of new energy generation power and load power are calculated respectively, and predicted values of node voltage amplitude and phase angle of the intra-day rolling scheduling power system are obtained based on the real-time values; constructing an optimization model of intra-day rolling scheduling; and a day-ahead and intra-day dual-time scale voltage optimization scheme of the power system is determined, so that the problem of unstable pressure of the power system is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a method and system for voltage regulation of a power system with dual time scales day-ahead and day-intraday combining a new energy station with energy storage. Background Art

[0002] The safe operation of the power system includes the stability of the voltage amplitude, and the reactive transmission in the system causes the voltage to drop, so it is necessary to compensate the system for reactive power to maintain its voltage amplitude level. Traditional voltage optimization methods generally use reactive compensation equipment to compensate for the lack of reactive power in the system to maintain reactive balance and thus maintain the voltage. As energy storage technology matures, the system uses it more and more, and its ability to provide reactive compensation while charging and discharging active power has also attracted attention. However, existing voltage optimization methods generally use a single time scale optimization operation model, rather than establishing multi-time scale optimization operations based on the characteristics of various types of equipment, which limits the accuracy of the voltage and leads to voltage instability in the operation of the power system.

[0003] In view of this, a method and system for regulating power system voltage on dual time scales day-ahead and day-intraday by combining new energy stations with energy storage is needed. Summary of the invention

[0004] The embodiment of the present application provides a dual-time-scale power system voltage regulation method for the day before and within the day combining a new energy station with energy storage, which is used to solve the problem of voltage instability in power system operation.

[0005] The first aspect of the embodiment of the present application provides a method for regulating voltage of a power system with dual time scales of the day before and within the day in combination with a new energy station and energy storage, comprising:

[0006] A new energy station model and a power system network model are constructed respectively, wherein the new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model, and the power system network model includes a transmission line model and a load model;

[0007] According to the new energy station model and the power system network model, the predicted values ​​of the new energy power generation power and the load power are calculated respectively, and the predicted values ​​of the node voltage amplitude and phase angle of the day-ahead forward dispatching power system are obtained based on the predicted values;

[0008] Building an optimization model for day-ahead forward scheduling based on the predicted values ​​of node voltage amplitudes and phase angles of the day-ahead forward scheduling power system;

[0009] According to the new energy station model and the power system network model, the real-time values ​​of the new energy power generation power and the load power are calculated respectively, and the predicted values ​​of the node voltage amplitude and phase angle of the intraday rolling dispatching power system are obtained based on the real-time values;

[0010] Constructing an optimization model for intraday rolling dispatch based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system;

[0011] Based on the optimization model of the day-ahead forward scheduling and the optimization model of the intraday rolling scheduling, a dual-time scale voltage optimization scheme for the power system day-ahead and intraday is determined.

[0012] Furthermore, the new energy station model and the power system network model are constructed separately, the new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model, and the power system network model includes a transmission line model and a load model, including:

[0013] Wind turbine power model:

[0014]

[0015] Where: P w (v) is the predicted power generation value of the wind farm, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the cut-out wind speed, a, b and c are the curve fitting coefficients, P r is the rated power;

[0016] Photovoltaic cell power model:

[0017]

[0018] Where: P pv is the predicted value of the power generation of the photovoltaic power plant, P STC is the power under standard test conditions, I STC is the standard light intensity, k is the temperature coefficient, and T is the photovoltaic cell temperature;

[0019] Energy storage output power model:

[0020]

[0021] Where: SOC(t+1) is the energy storage charge state at time t+1, η dis is the discharge efficiency, η ch is the charging efficiency, P dis (t) is the discharge power at time t, P ch (t) is the charging power at time t, E cap is the capacity of the energy storage system, and Δt is the time step.

[0022] Furthermore, the new energy station model and the power system network model are constructed separately, the new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model, and the power system network model includes a transmission line model and a load model, including:

[0023] Transmission line model:

[0024] P ij =V i V j (G ij cosθ ij +B ij sinθ ij )

[0025] Q ij =V i V j (G ij sinθ ij ―B ij cosθ ij )

[0026] Where: V i and V j is the voltage amplitude of the nodes at both ends of the line, G ij is the line conductance, B ij is the line susceptance, θ ij is the node voltage phase angle difference;

[0027] Load Model:

[0028] P L =P L0

[0029] Q L =Q L0

[0030] Where: P L and Q L are the active power and reactive power of the load, P L0 and Q L0 They are the set values ​​of the active power and reactive power of the load respectively.

[0031] Furthermore, the optimization model of the day-ahead forward scheduling is constructed based on the predicted values ​​of the node voltage amplitude and phase angle of the day-ahead forward scheduling power system, including:

[0032] The objective function of the optimization model for day-ahead scheduling is:

[0033]

[0034] Among them: F eis the voltage deviation, C g is the conventional power generation cost, C z is the cost of energy storage equipment, C q is the reactive power compensation cost, α, β and γ are the corresponding weight coefficients, n is the number of nodes, V j is the voltage amplitude at node j, is the reference voltage amplitude of node j.

[0035] Furthermore, the optimization model of the day-ahead forward scheduling is constructed based on the predicted values ​​of the node voltage amplitude and phase angle of the day-ahead forward scheduling power system, including:

[0036] Power balance constraints:

[0037]

[0038] Where: P gi and Q gi are respectively the active output and reactive output of the i-th conventional generator, P wk and Q wk are the active power and reactive power of the kth wind farm, respectively, P pvl and Q pvl are respectively the active power and reactive power of the lth photovoltaic power plant, P dm and Q dm are the active power demand and reactive power demand of the mth load node, respectively, P loss and Q loss are the active power loss and reactive power loss of the system respectively;

[0039] Constraints on renewable energy generation:

[0040] 0≤P wk ≤P wk,max

[0041] 0≤P pvl ≤P pvl,max

[0042] Where: p wk,max and P pvl,max are the maximum power generation of the kth wind farm and the lth photovoltaic farm respectively;

[0043] Energy storage system constraints:

[0044] P ESS,min ≤P ESS (t)≤P ESS,max

[0045] SOC min ≤SOC(t)≤SOC max

[0046]

[0047] Where: P ESS,min and P ESS,max are the lower and upper limits of the energy storage system output power, SOC(t) is the state of charge of the energy storage system at time t, P ESS (t) is the output power of the energy storage system at time t, SOC min and SOC max are the lower and upper limits of the state of charge, respectively, ch and η dis are respectively the charge and discharge efficiency, P ch (t) and P dis (t) is the charge and discharge power at time t, E cap is the capacity of the energy storage system, Δt is the time step;

[0048] Conventional generator output constraints:

[0049] P gi,min ≤P gi ≤P gi,max

[0050] Q gi,min ≤Q gi ≤Q gi,max

[0051] Where: P gi,min and P gi,max are the lower and upper limits of the active output of the i-th conventional generator, Q gi,min and Q gi,max are the lower and upper limits of the reactive power output of the i-th conventional generator respectively.

[0052] Furthermore, the optimization model for intraday rolling dispatching is constructed based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatching power system, including:

[0053]

[0054] Where: W p is the weight coefficient of voltage deviation, V n is the voltage at node n, ΔP gen,i and ΔQ gen,i are the output changes of active power and reactive power of generator set i compared with the day-ahead dispatching layer, PT is the size of the control time window, ΔP gen,i (k+d|k) is the output change of the active power of generator set i in the optimization window k to k+d compared with the day-ahead dispatching layer, ΔQ gen,i(k+d|k) is the output change of the reactive power of generator set i in the optimization time window from k to k+d compared with the day-ahead dispatching layer.

[0055] Furthermore, the optimization model for intraday rolling dispatching is constructed based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatching power system, including:

[0056] Voltage range safety constraints:

[0057] V n,min ≤V n (k+d|k)≤V n,max

[0058] Where: V n,min and V n,max is the maximum and minimum voltage at node n, V n (k+d|k) means that within the optimization time window from k to k+d, the voltage of node n meets the safety constraint;

[0059] Constraints on the change in generator output and energy storage device output:

[0060] ΔP gen,i,min ≤ΔP gen,i (k+d|k)≤ΔP gen,i,max

[0061] ΔQ gen,i,min ≤ΔQ gen,i (k+d|k)≤ΔQ gen,i,max

[0062] ΔP ESS,i,min ≤ΔP ESS,i (k+d|k)≤ΔP ESS,i,max

[0063] ΔQ ESS,i,min ≤ΔQ ESS,i (k+d|k)≤ΔQ ESS,i,max

[0064] Where: ΔP gen,i and ΔQ gen,i are the output changes of the active and reactive power of the generator set compared with the day-ahead dispatching layer, ΔP ESS,i and Q ESS,i are the output changes of active power and reactive power of energy storage device i compared with the day-ahead dispatching layer, ΔP gen,i,min and ΔP gen,i,max are the lower and upper limits of the change in the active power of generator set i compared to the output of the day-ahead dispatching layer; ΔQ gen,i,min and ΔQ gen,i,max are the lower and upper limits of the reactive power change of generator set i compared with the output of the day-ahead dispatching layer; ΔPESS,i,min and ΔP ESS,i,max are the lower and upper limits of the change in the output of energy storage device i compared to the day-ahead dispatching layer; ΔQ ESS,i,min and ΔQ ESS,i,max are the lower and upper limits of the reactive power change of energy storage device i compared to the output of the day-ahead dispatching layer; ΔP ESS,i (k+d|k) is the output change of the active power of energy storage device i in the optimization time window from k to k+d compared with the day-ahead scheduling layer; ΔQ ESS,i (k+d|k) is the output change of the reactive power of energy storage device i in the optimization time window from k to k+d compared with the day-ahead dispatching layer.

[0065] Power variation balance constraint:

[0066]

[0067] Where: G s and G are the number of energy storage systems and generators, respectively, ΔP ESS,i (k+d|k) and ΔQ ESS,i (k+d|k) is the active power variable and reactive power variable of the i-th energy storage system from time k to k+d, ΔP gen,i (k+d|k) and ΔQ gen,i (k+d|k) is the active power variable and reactive power variable of the i-th generator from time k to k+d, ΔP d (k+d|k) and ΔQ d (k+d|k) is the change in active and reactive power of the intraday load from time k to k+d compared with the day-ahead dispatching layer and the change in reactive power of the intraday load relative to the day-ahead dispatching layer, ΔP w (k+d|k) and ΔQ w (k+d|k) is the active power change and reactive power variable of the wind turbine from time k to k+d, ΔP pv (k+d|k) and ΔQ pv (k+d|k) is the active power change and reactive power variable of the photovoltaic unit from time k to k+d.

[0068] A second aspect of the embodiment of the present application provides a dual-time-scale power system voltage regulation system for a new energy station combined with energy storage, including:

[0069] A new energy station model and a power system network model construction unit, used to respectively construct a new energy station model and a power system network model, wherein the new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model, and the power system network model includes a transmission line model and a load model;

[0070] A day-ahead predicted value determination unit for node voltage amplitude and phase angle, used to calculate predicted values ​​of renewable energy generation power and load power according to the renewable energy site model and the power system network model, and obtain predicted values ​​of node voltage amplitude and phase angle of the day-ahead forward dispatching power system based on the predicted values;

[0071] An optimization model building unit for day-ahead forward scheduling, used to build an optimization model for day-ahead forward scheduling based on the predicted values ​​of node voltage amplitudes and phase angles of the day-ahead forward scheduling power system;

[0072] A unit for determining intraday forecast values ​​of node voltage amplitude and phase angle, for calculating the real-time values ​​of the renewable energy power generation and load power according to the renewable energy site model and the power system network model, and obtaining forecast values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system based on the real-time values;

[0073] An optimization model building unit for intraday rolling dispatch, used for building an optimization model for intraday rolling dispatch based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system;

[0074] The power system day-ahead intraday dual-time-scale voltage optimization scheme determination unit is used to determine the power system day-ahead intraday dual-time-scale voltage optimization scheme based on the optimization model of the day-ahead forward scheduling and the optimization model of the intraday rolling scheduling.

[0075] A third aspect of the embodiments of the present application provides a computer device, including:

[0076] memories, transceivers, processors, and bus systems;

[0077] Wherein, the memory is used to store programs;

[0078] The processor is used to execute the program in the memory, including executing the dual-time-scale power system voltage regulation method for the day-ahead and intra-day combined with new energy stations and energy storage as described above;

[0079] The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

[0080] A fourth aspect of an embodiment of the present application provides a readable storage medium, including instructions, which, when executed on a computer, enables the computer to execute the steps of the method for voltage regulation of a dual-time-scale power system in the day-ahead and intra-day combining new energy stations with energy storage as described above.

[0081] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0082] The present invention constructs a new energy station model including a wind turbine power model, a photovoltaic cell power model and an energy storage output power model, as well as a power system network model including a transmission line model and a load model, calculates the predicted values ​​of the new energy power generation power and the load power, obtains the predicted values ​​of the node voltage amplitude and the phase angle of the power system for forward-looking scheduling, and constructs a forward-looking scheduling optimization model for the day before; calculates the real-time values ​​of the new energy power generation power and the load power according to the same model, obtains the predicted values ​​of the node voltage amplitude and the phase angle of the power system for intraday rolling scheduling and constructs its optimization model; determines the dual-time scale voltage optimization scheme of the power system for the day before and within the day based on the optimization models of these two time scales, effectively integrates the uncertainty of the new energy station and the operating characteristics of the power system, gives full play to the synergistic effect of each model and optimization strategy, realizes the precise voltage regulation of the power system at different time scales, significantly improves the stability and economy of the system operation, and effectively guarantees the high quality and sustainability of the power supply.

[0083] Other advantages, objectives, and features of the present invention will be set forth in part in the following description, and in part will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 The present invention is a flowchart of an embodiment of a method for voltage regulation of a power system with dual time scales, day-ahead and day-intraday, combining a new energy station with energy storage. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0086] In this embodiment, the dual-time scale power system voltage regulation method of the day-ahead and intraday combined with the new energy station and energy storage is used to realize the precise voltage regulation of the power system voltage at different time scales. The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, without specific limitation.

[0087] Embodiment 1

[0088] See also Figure 1 In the present invention, an embodiment of a method for regulating voltage of a dual-time-scale power system in a day-ahead and day-intraday manner combining a new energy station with energy storage comprises the following steps:

[0089] S11. Construct a new energy station model and a power system network model respectively. The new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model. The power system network model includes a transmission line model and a load model.

[0090] In this embodiment, the new energy station model includes a wind farm model, in which wind speed prediction is performed to obtain a wind turbine power model. Specifically, an autoregressive moving average (ARMA) model is used to analyze and fit historical wind speed data, and the model order and other correlation coefficients are determined, so that the future wind speed sequence v(t) can be predicted based on past wind speed information. The time step can be set according to demand, for example, in hours for day-ahead prediction and in minutes for intraday prediction.

[0091] The expression of the wind turbine power model is as follows:

[0092]

[0093] Where: P w (v) is the predicted power generation value of the wind farm, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the cut-out wind speed, a, b and c are the curve fitting coefficients, P r is the rated power.

[0094] The new energy station model also includes a photovoltaic power generation field model. First, the time series decomposition method is used to decompose the light intensity sequence into trend terms, seasonal terms and random terms. The trend term uses linear regression or polynomial fitting to determine its changing law; the seasonal term is constructed based on the periodic change law of light intensity; the random term is described by the autoregressive (AR) model, and each part is predicted and summed up to obtain the light intensity prediction value I(t). The expression of the photovoltaic cell power model is as follows:

[0095]

[0096] Where: P pv is the predicted value of the power generation of the photovoltaic power plant, P STC is the power under standard test conditions, I STC is the standard light intensity, k is the temperature coefficient, and T is the photovoltaic cell temperature.

[0097] The new energy station model also includes an energy storage system model. A simple equivalent circuit model is used here, and its charge state change equation is:

[0098]

[0099] Where: SOC(t+1) is the energy storage charge state at time t+1, η dis is the discharge efficiency, η ch is the charging efficiency, P dis (t) is the discharge power at time t, P ch (t) is the charging power at time t, E cap is the capacity of the energy storage system, and Δt is the time step.

[0100] In this embodiment, the transmission line model adopts a π-type equivalent circuit model, and the power equation at both ends is:

[0101] P ij =V i V j (G ij cosθ ij )+B ij sinθ ij )

[0102] Q ij =V i V j (G ij sinθ ij ―B ij cosθ ij )

[0103] Where: V i and V j is the voltage amplitude of the nodes at both ends of the line, G ij is the line conductance, B ij is the line susceptance, θ ij is the node voltage phase angle difference.

[0104] The load model adopts a constant power load model:

[0105] P L =P L0

[0106] Q L =Q L0

[0107] Where: P L and Q L are the active power and reactive power of the load, P L0 and Q L0 They are the set values ​​of the active power and reactive power of the load respectively.

[0108] After determining the above-mentioned new energy station model and power system network model, the state variables and parameters are initialized, and the power generation power P of the initialized new energy station is set according to the current actual power generation situation or the preliminary prediction value. w (0), P pv(0). Initialize the energy storage system SOC(0), P ch (0), P dis (0), for example, the initial state of charge is set within a reasonable range (such as about 0.5), the initial charge and discharge power P ch (0) and P dis (0) can be set to 0. Initialize the voltage amplitude V of the power system network node i (0) and phase angle θ i (0), the voltage amplitude can usually be initialized to the rated value (such as 1.0pu) and the phase angle can be initialized to 0.

[0109] S12. Calculate the forecast values ​​of the renewable energy power generation and load power according to the renewable energy station model and the power system network model, and obtain the forecast values ​​of the node voltage amplitude and phase angle of the forward-looking dispatching power system based on the forecast values;

[0110] The wind speed model and light intensity model are used to predict the wind speed v(t) and light intensity I(t) for the next day (T = 24 hours, time step Δt = 1 hour), and then they are substituted into the wind turbine power model and photovoltaic cell power model respectively to calculate the predicted power generation value P of the new energy station. w (t) and P pv (t)(t=1,2,…T). The ARIMA time series forecasting method is used to predict. By analyzing the historical load data and fitting the model, the parameters of the ARIMA model are determined to predict the load active power P for the next day. L (t) and reactive power Q L (t)(t=1,2,…T). The predicted renewable energy power generation and load power are combined with the power system network model to perform power flow calculation. Here, the Newton-Raphson power flow calculation method is used to construct the active power unbalance equation and reactive power unbalance equation:

[0111]

[0112] Where: P gi is the active power output of conventional generators. In the system of new energy stations combined with energy storage, the active power output of conventional generators can be set according to the plan or initial setting. Here it is set to P g0 .

[0113] Construct the Jacobian matrix J and solve it by iteration

[0114]

[0115] Get the node voltage amplitude V for each hour in the next day i (t) and phase angle θ iThe predicted value of (t)(i=1,2,…,n)(t=1,2,…,t).

[0116] S13. Construct an optimization model for the day-ahead forward dispatch based on the predicted values ​​of node voltage amplitude and phase angle of the day-ahead forward dispatch power system;

[0117] The day-ahead forward dispatch aims at economic optimization and solves the economic optimization problem on a slow time scale with hourly granularity to ensure the economic operation of the units and energy storage equipment while minimizing the voltage deviation. The expression of the objective function is as follows:

[0118]

[0119] Among them: Among them: F e is the voltage deviation, C g is the conventional power generation cost, C z is the cost of energy storage equipment, C q is the reactive power compensation cost, α, β and γ are the corresponding weight coefficients, n is the number of nodes, V j is the voltage amplitude at node j, is the reference voltage amplitude of node j.

[0120] The constraints are:

[0121] Power balance constraints:

[0122]

[0123] Where: P gi and Q gi are respectively the active output and reactive output of the i-th conventional generator, P wk and Q wk are the active power and reactive power of the kth wind farm, respectively, P pvl and Q pvl are respectively the active power and reactive power of the lth photovoltaic power plant, P dm and Q dm are the active power demand and reactive power demand of the mth load node, respectively, P loss and Q loss are the active power loss and reactive power loss of the system respectively;

[0124] Constraints on renewable energy generation:

[0125] 0≤P wk ≤P wk,max

[0126] 0≤P pvl ≤P pvl,max

[0127] Where: p wk,max and P pvl,max are the maximum power generation of the kth wind farm and the lth photovoltaic farm respectively;

[0128] Energy storage system constraints:

[0129] P ESS,min ≤P ESS (t)≤P ESS,max

[0130] SOC min ≤SOC(t)≤SOC max

[0131]

[0132] Where: P ESS,min and P ESS,max are the lower and upper limits of the energy storage system output power, SOC(t) is the state of charge of the energy storage system at time t, P ESS (t) is the output power of the energy storage system at time t, SOC min and SOC max are the lower and upper limits of the state of charge, respectively, ch and η dis are respectively the charge and discharge efficiency, P ch (t) and P dis (t) is the charge and discharge power at time t, E cap is the capacity of the energy storage system, Δt is the time step;

[0133] Conventional generator output constraints:

[0134] P gi,min ≤P gi ≤P gi,max

[0135] Q gi,min ≤Q gi ≤Q gi,max

[0136] Where: P gi,min and P gi,max are the lower and upper limits of the active output of the i-th conventional generator, Q gi,min and Q gi,max are the lower and upper limits of the reactive power output of the i-th conventional generator respectively.

[0137] The objective function and constraints are converted into the form of interior point method processing by using the interior point method nonlinear programming solver. The optimization result of the day-ahead forward scheduling is obtained through iterative solution, including the power generation plan of conventional generators. Energy storage system charging and discharging plan And the target value of each node voltage and phase angle

[0138] S14. Calculate the real-time values ​​of the renewable energy power generation and load power according to the renewable energy station model and the power system network model, and obtain the predicted values ​​of the node voltage amplitude and phase angle of the intraday rolling dispatching power system based on the real-time values;

[0139] Use the real-time monitoring data of new energy stations, such as wind speed sensor and light intensity sensor data, to update the new energy power generation forecast. For wind farms, according to the real-time wind speed v real (k) and power curve to calculate the real-time power generation P w,real (k), and use the exponential smoothing method to update the power generation forecast in the short term, such as P w (k+d)=αP w (k+d―1)+(1―α)P w,real (k)(d=1,2,…,D)where D is the time step in the prediction domain, α is the smoothing coefficient, and for photovoltaic power plants, the power generation prediction is similarly updated according to the real-time light intensity.

[0140] The weighted moving average method is used to utilize real-time load measurement data P L,real (k) and Q L,real (k) Update the load power forecast, such as P L (k+d)=αP L (k+d―1)+(1―α)P L,real (k) and Q L (k+d)=αQ L (k+d―1)+(1―α)Q L,real (k)(d=1,2,…,D).

[0141] According to the updated renewable energy power generation and load power, combined with the power system network model, the power flow calculation is carried out, and the fast decoupling power flow calculation method is adopted to construct the active power unbalance equation approximated as follows: The reactive power unbalance equation is approximated as By iteratively solving these two equations, we can obtain the node voltage amplitude V of the system in the short future time. Assume that the prediction time domain is D time steps, and the time step is Δt′, such as N = 10, Δt′ = 5 minutes. i (k+m) and phase angle θ i The predicted value of (k+m)(i=1,2,…,n)(m=1,2,…,T).

[0142] S15. Construct an optimization model for intraday rolling dispatch based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system;

[0143] The objective function of the optimization model of inner rolling scheduling is:

[0144]

[0145] Where: W p is the weight coefficient of voltage deviation, V n is the voltage at node n, ΔP gen,i and ΔQ gen,i are the output changes of active power and reactive power of generator set i compared with the day-ahead dispatching layer, PT is the size of the control time window, ΔP gen,i (k+d|k) is the output change of the active power of generator set i in the optimization window k to k+d compared with the day-ahead dispatching layer, ΔQ gen,i (k+d|k) is the output change of the reactive power of generator set i in the optimization time window from k to k+d compared with the day-ahead dispatching layer.

[0146] Voltage range safety constraints:

[0147] V n,min ≤V n (k+d|k)≤V n,max

[0148] Where: V n,min and V n,max is the maximum and minimum voltage at node n, V n (k+d|k) means that within the optimization time window from k to k+d, the voltage of node n meets the safety constraint;

[0149] Constraints on the change in generator output and energy storage device output:

[0150] ΔP gen,i,min ≤ΔP gen,i (k+d|k)≤ΔP gen,i,max

[0151] ΔQ gen,i,min ≤ΔQ gen,i (k+d|k)≤ΔQ gen,i,max

[0152] ΔP ESS,i,min ≤ΔP ESS,i (k+d|k)≤ΔP ESS,i,max

[0153] ΔQ ESS,i,min ≤ΔQ ESS,i (k+d|k)≤ΔQ ESS,i,max

[0154] Where: ΔP gen,i and ΔQ gen,i are the output changes of the active and reactive power of the generator set compared with the day-ahead dispatching layer, ΔP ESS,i and Q ESS,i are the output changes of active power and reactive power of energy storage device i compared with the day-ahead dispatching layer, ΔP gen,i,min and ΔP gen,i,max are the lower and upper limits of the change in the active power of generator set i compared to the output of the day-ahead dispatching layer; ΔQ gen,i,min and ΔQ gen,i,max are the lower and upper limits of the reactive power change of generator set i compared with the output of the day-ahead dispatching layer; ΔP ESS,i,min and ΔP ESS,i,max are the lower and upper limits of the change in the output of energy storage device i compared to the day-ahead dispatching layer; ΔQ ESS,i,min and ΔQ ESS,i,max are the lower and upper limits of the reactive power change of energy storage device i compared to the output of the day-ahead dispatching layer; ΔP ESS,i (k+d|k) is the output change of the active power of energy storage device i in the optimization time window from k to k+d compared with the day-ahead scheduling layer; ΔQ ESS,i (k+d|k) is the output change of the energy storage device i in the optimization window from k to k+d compared with the day-ahead dispatching layer;

[0155] Power variation balance constraint:

[0156]

[0157] Where: G s and G are the number of energy storage systems and generators, respectively, ΔP ESS,i (k+d|k) and ΔQ ESS,i (k+d|k) is the active power variable and reactive power variable of the i-th energy storage system from time k to k+d, ΔP gen,i (k+d|k) and ΔQ gen,i (k+d|k) is the active power variable and reactive power variable of the i-th generator from time k to k+d, ΔP d (k+d|k) and ΔQ d (k+d|k) is the change in active and reactive power of the intraday load from time k to k+d compared with the day-ahead dispatching layer and the change in reactive power of the intraday load relative to the day-ahead dispatching layer, ΔP w (k+d|k) and ΔQ w (k+d|k) is the active power change and reactive power variable of the wind turbine from time k to k+d, ΔP pv(k+d|k) and ΔQ pv (k+d|k) is the active power change and reactive power variable of the photovoltaic unit from time k to k+d.

[0158] S16. Determine the dual-time-scale voltage optimization scheme for the power system day-ahead and day-intraday based on the optimization model of the day-ahead forward scheduling and the optimization model of the intraday rolling scheduling.

[0159] By deeply integrating the day-ahead forward dispatch optimization model and the intraday rolling dispatch optimization model, a comprehensive day-ahead and intraday dual-time scale voltage optimization scheme for the power system is constructed. In this scheme, the day-ahead forward dispatch optimization model calculates the accurate forecast values ​​of renewable energy power generation and load power in the future for a long period of time (such as 24 hours), comprehensively considers multiple objectives such as power generation cost, energy storage cost, reactive power compensation cost and voltage deviation, and determines the basic framework of conventional generator power generation plan, energy storage system charging and discharging strategy and preliminary target value of voltage at each node under a series of complex conditions such as power balance, renewable energy power generation constraints, energy storage system constraints, conventional generator output constraints and network flow constraints. The intraday rolling dispatch optimization model is based on a short time scale (such as 5-15 minutes), based on the real-time data update of renewable energy stations and loads, and corrects the system state prediction in real time, with the goal of minimizing voltage deviation and control equipment adjustment. Under the constraints of voltage safety range, real-time restrictions on renewable energy power generation, dynamic constraints of energy storage systems, flexible adjustment constraints of conventional generators and precise balance of power changes, the day-ahead plan is quickly and dynamically adjusted to determine the precise control strategy in a short time. This enables the power system to effectively cope with the intermittent and volatile nature of renewable energy generation, random changes in loads, and uncertainty in system operation, achieving continuous optimization and precise and stable control of voltage at dual time scales, greatly improving the economy, reliability, and safety of power system operation, and effectively solving the problem of unstable voltage in the power system.

[0160] Embodiment 2

[0161] An embodiment of a dual-time-scale power system voltage regulation system combining a new energy station with energy storage in the day-ahead and day-ahead includes the following steps:

[0162] A new energy station model and a power system network model construction unit, which are used to respectively construct a new energy station model and a power system network model. The new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model. The power system network model includes a transmission line model and a load model.

[0163] A day-ahead predicted value determination unit for node voltage amplitude and phase angle, used to calculate predicted values ​​of renewable energy generation power and load power according to a renewable energy site model and a power system network model, and obtain predicted values ​​of node voltage amplitude and phase angle for forward-looking dispatch of the power system based on the predicted values;

[0164] An optimization model building unit for day-ahead forward dispatching, used for building an optimization model for day-ahead forward dispatching based on predicted values ​​of node voltage amplitude and phase angle of the day-ahead forward dispatching power system;

[0165] A unit for determining intraday forecast values ​​of node voltage amplitude and phase angle, which is used to calculate the real-time values ​​of renewable energy power generation and load power according to the renewable energy station model and the power system network model, and obtain forecast values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system based on the real-time values;

[0166] An optimization model building unit for intraday rolling dispatch, used to build an optimization model for intraday rolling dispatch based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system;

[0167] The unit for determining the dual-time-scale voltage optimization scheme for the day before the power system is used to determine the dual-time-scale voltage optimization scheme for the day before the power system based on the optimization model of the day-ahead forward scheduling and the optimization model of the intraday rolling scheduling.

[0168] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0169] Embodiment 3

[0170] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0171] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0172] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0173] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0174] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A dual-time scale power system voltage regulation method for a new energy station combined with energy storage, characterized in that: include: A new energy station model and a power system network model are constructed respectively, wherein the new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model, and the power system network model includes a transmission line model and a load model; According to the new energy station model and the power system network model, the predicted values ​​of the new energy power generation power and the load power are calculated respectively, and the predicted values ​​of the node voltage amplitude and phase angle of the day-ahead forward dispatching power system are obtained based on the predicted values; Building an optimization model for day-ahead forward scheduling based on the predicted values ​​of node voltage amplitudes and phase angles of the day-ahead forward scheduling power system; According to the new energy station model and the power system network model, the real-time values ​​of the new energy power generation power and the load power are calculated respectively, and the predicted values ​​of the node voltage amplitude and phase angle of the intraday rolling dispatching power system are obtained based on the real-time values; Constructing an optimization model for intraday rolling dispatch based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system; Based on the optimization model of the day-ahead forward scheduling and the optimization model of the intraday rolling scheduling, a dual-time scale voltage optimization scheme for the power system day-ahead and intraday is determined.

2. The method for voltage regulation of a dual-time scale power system of a new energy station combined with energy storage in the day before and within the day according to claim 1 is characterized in that: The new energy station model and the power system network model are constructed separately, wherein the new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model, and the power system network model includes a transmission line model and a load model, including: Wind turbine power model: Where: P w (v) is the predicted power generation value of the wind farm, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the cut-out wind speed, a, b and c are the curve fitting coefficients, P r is the rated power; Photovoltaic cell power model: Where: P pv is the predicted value of the power generation of the photovoltaic power plant, P STC is the power under standard test conditions, I STC is the standard light intensity, k is the temperature coefficient, and T is the photovoltaic cell temperature; Energy storage output power model: Where: SOC(t+1) is the energy storage charge state at time t+1, η dis is the discharge efficiency, η ch is the charging efficiency, P dis (t) is the discharge power at time t, P ch (t) is the charging power at time t, E cap is the capacity of the energy storage system, and Δt is the time step.

3. The method for voltage regulation of a power system with dual time scales of the day-ahead and intraday combined with a new energy station according to claim 2 is characterized in that: The new energy station model and the power system network model are constructed separately, wherein the new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model, and the power system network model includes a transmission line model and a load model, including: Transmission line model: P ij =V i V j (G ij cosθ ij +B ij sinθ ij ) Q ij =V i V j (G ij sinθ ij ―B ij cosθ ij ) Where: V i and V j is the voltage amplitude of the nodes at both ends of the line, G ij is the line conductance, B ij is the line susceptance, θ ij is the node voltage phase angle difference; Load Model: P L =P L0 Q L =Q L0 Where: P L and Q L are the active power and reactive power of the load, P L0 and Q L0 They are the set values ​​of the active power and reactive power of the load respectively.

4. The method for voltage regulation of a power system with dual time scales of day-ahead and day-intraday combining a new energy station with energy storage according to claim 1 is characterized in that: The optimization model for the day-ahead forward scheduling is constructed based on the predicted values ​​of the node voltage amplitude and phase angle of the day-ahead forward scheduling power system, including: The objective function of the optimization model for day-ahead scheduling is: Among them: F e is the voltage deviation, C g is the conventional power generation cost, C z is the cost of energy storage equipment, C q is the reactive power compensation cost, α, β and γ are the corresponding weight coefficients, n is the number of nodes, V j is the voltage amplitude at node j, is the reference voltage amplitude of node j.

5. The method for voltage regulation of a power system with dual time scales of the day-ahead and intraday combined with a new energy station according to claim 4 is characterized in that: The optimization model for the day-ahead forward scheduling is constructed based on the predicted values ​​of the node voltage amplitude and phase angle of the day-ahead forward scheduling power system, including: Power balance constraints: Where: P gi and Q gi are respectively the active output and reactive output of the i-th conventional generator, P wk and Q wk are the active power and reactive power of the kth wind farm, respectively, P pvl and Q pvl are respectively the active power and reactive power of the lth photovoltaic power plant, P dm and Q dm are the active power demand and reactive power demand of the mth load node, respectively, P loss and Q loss are the active power loss and reactive power loss of the system respectively; Constraints on renewable energy generation: 0≤P wk ≤P wk,max 0≤P pvl ≤P pvl,max Where: P wk,max and P pvl,max are the maximum power generation of the kth wind farm and the lth photovoltaic farm respectively; Energy storage system constraints: P ESS,min ≤P ESS (t)≤P ESS,max SOC min ≤SOC(t)≤SOC max Where: P ESS,min and P ESS,max are the lower and upper limits of the energy storage system output power, SOC(t) is the state of charge of the energy storage system at time t, P ESS (t) is the output power of the energy storage system at time t, SOC min and SOC max are the lower and upper limits of the state of charge, respectively, ch and η dis are respectively the charge and discharge efficiency, P ch (t) and P dis (t) is the charge and discharge power at time t, E cap is the capacity of the energy storage system, Δt is the time step; Conventional generator output constraints: P gi,min ≤P gi ≤P gi,max Q gi,min ≤Q gi ≤Q gi,max Where: P gi,min and P gi,max are the lower and upper limits of the active output of the i-th conventional generator, Q gi,min and Q gi,max are the lower and upper limits of the reactive power output of the i-th conventional generator respectively.

6. The method for voltage regulation of a power system with dual time scales of day-ahead and day-intraday combining new energy stations with energy storage according to claim 1 is characterized in that: The optimization model for intraday rolling dispatching is constructed based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatching power system, including: The objective function of the optimization model of inner rolling scheduling is: Where: W p is the weight coefficient of voltage deviation, V n is the voltage at node n, ΔP gen,i and ΔQ gen,i are the output changes of active power and reactive power of generator set i compared with the day-ahead dispatching layer, PT is the size of the control time window, ΔP gen,i (k+d|k) is the output change of the active power of generator set i in the optimization window k to k+d compared with the day-ahead dispatching layer, ΔQ gen,i (k+d|k) is the output change of the reactive power of generator set i in the optimization time window from k to k+d compared with the day-ahead dispatching layer.

7. The method for voltage regulation of a power system with dual time scales of day-ahead and day-intraday combining new energy stations with energy storage according to claim 6 is characterized in that: The optimization model for intraday rolling dispatching is constructed based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatching power system, including: Voltage range safety constraints: V n,min ≤V n (k+d∣k)≤V n,max Where: V n,min and V n,max is the maximum and minimum voltage at node n, V n (k+d|k) means that within the optimization time window from k to k+d, the voltage of node n meets the safety constraint; Constraints on the change in generator output and energy storage device output: ΔP gen,i,min ≤ΔP gen,i (k+d∣k)≤ΔP gen,i,max ΔQ gen,i,min ≤ΔQ gen,i (k+d∣k)≤ΔQ gen,i,max ΔP ESS,i,min ≤ΔP ESS,i (k+d∣k)≤i ,i,max ΔQ ESS,i,min ≤ΔQ ESS,i (k+d∣k)≤ΔQ ESS,i,max Where: ΔP gen,i and ΔQ gen,i are the output changes of the active and reactive power of the generator set compared with the day-ahead dispatching layer, ΔP ESS,i and Q ESS,i are the output changes of active power and reactive power of energy storage device i compared with the day-ahead dispatching layer, ΔP gen,i,min and ΔP gen,i,max are the lower and upper limits of the change in the active power of generator set i compared to the output of the day-ahead dispatching layer; ΔQ gen,i,min and ΔQ gen,i,max are the lower and upper limits of the reactive power change of generator set i compared with the output of the day-ahead dispatching layer; ΔP ESS,i,min and ΔP ESS,i,max are the lower and upper limits of the change in the output of energy storage device i compared to the day-ahead dispatching layer; ΔQ ESS,i,min and ΔQ ESS,i,max are the lower and upper limits of the reactive power change of energy storage device i compared to the output of the day-ahead dispatching layer; ΔP ESS,i (k+d|k) is the output change of the active power of energy storage device i in the optimization time window from k to k+d compared with the day-ahead scheduling layer; ΔQ ESS,i (k+d|k) is the output change of the energy storage device i in the optimization window from k to k+d compared with the day-ahead dispatching layer; Power variation balance constraint: Where: G s and G are the number of energy storage systems and generators, respectively, ΔP ESS,i (k+d|k) and ΔQ ESS,i (k+d|k) is the active power variable and reactive power variable of the i-th energy storage system from time k to k+d, ΔP gen,i (k+d|k) and ΔQ gen,i (k+d|k) is the active power variable and reactive power variable of the i-th generator from time k to k+d, ΔP d (k+d|k) and ΔQ d (k+d|k) is the change in active and reactive power of the intraday load from time k to k+d compared with the day-ahead dispatching layer and the change in reactive power of the intraday load relative to the day-ahead dispatching layer, ΔP w (k+d|k) and ΔQ w (k+d|k) is the active power change and reactive power variable of the wind turbine from time k to k+d, ΔP pv (k+d|k) and ΔQ pv (k+d|k) is the active power change and reactive power variable of the photovoltaic unit from time k to k+d.

8. A dual-time scale power system voltage regulation system combining new energy stations with energy storage, characterized in that: include: A new energy station model and a power system network model construction unit, used to respectively construct a new energy station model and a power system network model, wherein the new energy station model includes a wind turbine power model, a photovoltaic battery power model and an energy storage output power model, and the power system network model includes a transmission line model and a load model; A day-ahead predicted value determination unit for node voltage amplitude and phase angle, used to calculate predicted values ​​of renewable energy generation power and load power according to the renewable energy site model and the power system network model, and obtain predicted values ​​of node voltage amplitude and phase angle of the day-ahead forward dispatching power system based on the predicted values; An optimization model building unit for day-ahead forward scheduling, used to build an optimization model for day-ahead forward scheduling based on the predicted values ​​of node voltage amplitudes and phase angles of the day-ahead forward scheduling power system; A unit for determining intraday forecast values ​​of node voltage amplitude and phase angle, for calculating the real-time values ​​of the renewable energy power generation and load power according to the renewable energy site model and the power system network model, and obtaining forecast values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system based on the real-time values; An optimization model building unit for intraday rolling dispatch, used for building an optimization model for intraday rolling dispatch based on the predicted values ​​of node voltage amplitude and phase angle of the intraday rolling dispatch power system; The power system day-ahead intraday dual-time-scale voltage optimization scheme determination unit is used to determine the power system day-ahead intraday dual-time-scale voltage optimization scheme based on the optimization model of the day-ahead forward scheduling and the optimization model of the intraday rolling scheduling.

9. A computer device, characterized in that: include: memories, transceivers, processors, and bus systems; Wherein, the memory is used to store programs; The processor is used to execute the program in the memory, including executing the dual-time-scale power system voltage regulation method for the day-ahead and intra-day combined with energy storage of a new energy station according to any one of claims 1 to 7; The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

10. A readable storage medium, characterized in that: It includes instructions, which, when executed on a computer, enable the computer to execute the steps of the method for voltage regulation of a dual-time-scale power system in the day-ahead and intra-day combining a new energy station with energy storage as described in any one of claims 1 to 7.

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