Short-term Optimal Scheduling Method for Wind-PV-Pumped Storage System Considering Dynamic Frequency Response

Abstract

The present invention discloses a short-term optimal scheduling method for a wind-solar-pumped storage system considering dynamic frequency response, and the steps are as follows: Establish an equivalent model of the dynamic frequency response of the wind-solar-pumped storage system in advance; Derive an analytical expression of the system frequency index from the equivalent model of the system dynamic frequency response and convert it into an inequality constraint; Establish a short-term optimal scheduling model for the wind-solar-pumped storage system considering dynamic frequency response in advance; When the installed capacity of the pumped storage unit increases and the system dynamic frequency constraint is considered, the short-term optimal scheduling model achieves the goals of increasing the system's on-grid power, reducing the curtailment of wind and photovoltaic power, and enhancing the system's frequency response ability. The present invention can improve the accommodation level of wind power and photovoltaic power, effectively suppress the rapid change of the system frequency under disturbances, enhance the system's inertia response ability and frequency regulation ability by putting into new operating units, limit the frequency offset within the normal range, and ensure the safe and stable operation of the system.

Description

Technical Field

[0001] The present invention relates to power dispatching, and in particular to a short-term optimal dispatching method for a wind-solar-pumped storage system considering dynamic frequency response. Background Art

[0002] The inherent volatility, intermittency, and uncertainty characteristics of wind energy and solar energy pose great challenges to the safe and stable operation of the system and the accommodation of new energy. In addition, due to power decoupling control, wind farms and photovoltaic power stations lack inertial response capabilities. Therefore, as conventional energy is gradually replaced by new energy, the system frequency response ability will be further weakened.

[0003] The relevant technical regulations issued in China regarding the grid connection of wind farms and photovoltaic power stations clearly state that wind farms and photovoltaic power stations should have the ability to participate in system frequency modulation, peak shaving, and standby. Research shows that wind turbines can perform simulated inertia control and droop control like conventional units, and thus participate in frequency regulation. By adopting a virtual synchronous machine control strategy, photovoltaic units can operate in a frequency droop control mode, thereby providing frequency support for the system. In addition, wind farms and photovoltaic power stations can also provide standby by partially curtailing wind and light. However, most of the literature considers a thermal-wind complementary power generation system in the model, and does not consider the frequency regulation effects of photovoltaic power stations and energy storage power stations. Moreover, the ability of wind power and photovoltaic to provide standby is not taken into account. With the increase in the penetration rate of renewable energy, it is required that pumped storage units should also have the ability to participate in grid frequency regulation. Some literature has studied the participation of wind-pumped storage complementary or photovoltaic-pumped storage complementary systems in frequency modulation, but few literature has studied the dynamic frequency response problem of wind-solar-pumped storage systems. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide a short-term optimal dispatching method for a wind-solar-pumped storage system considering dynamic frequency response, so as to solve the problems of increased system uncertainty and weakened frequency modulation ability under the background of high-proportion new energy grid connection, and improve the accommodation level of renewable energy.

[0005] Technical Solution: A short-term optimal dispatching method for a wind-solar-pumped storage system considering dynamic frequency response according to the present invention includes the following steps:

[0006] (1) Pre-establish a dynamic frequency response equivalent model of the wind-solar-pumped storage system, derive an analytical expression of the system frequency index from the system dynamic frequency response equivalent model, and convert it into an inequality constraint, where the frequency index includes the frequency change rate, the lowest point frequency deviation, and the quasi-steady state frequency deviation.

[0007] (2) Establish a short-term optimal scheduling model for a wind-solar-pumped storage system considering dynamic frequency response in advance; the short-term optimal scheduling model includes an objective function with the maximum system power generation as the goal and corresponding constraint conditions.

[0008] (3) When the installed capacity of the pumped storage unit increases and the system dynamic frequency constraint is considered, the short-term optimal scheduling model achieves the goals of increasing the system power generation, reducing the curtailment of wind and photovoltaic power, and enhancing the system frequency response ability. The pumped storage unit shares the task of reserving standby power for the wind farm and photovoltaic power station, reducing the curtailment of wind and photovoltaic power in the system, effectively improving the consumption level of renewable energy. Moreover, as the installed capacity of the pumped storage power station increases, the system power generation increases accordingly. After considering the dynamic frequency response, through a reasonable unit start-stop plan, it can effectively respond to the rapid drop of the system frequency under disturbances, and can limit the lowest point frequency deviation and quasi-steady state frequency deviation within the allowable range, reducing the possibility of under-frequency load shedding, which is conducive to the system restoring frequency stability faster.

[0009] The dynamic frequency response equivalent model includes a dynamic frequency response equivalent model under power generation conditions and a dynamic frequency response equivalent model under pumping conditions;

[0010] Under pumping conditions, the time-domain expression of the frequency response of the wind-solar-pumped storage system is:

[0011]

[0012] Under power generation conditions, the time-domain expression of the frequency response of the wind-solar-pumped storage system is:

[0013]

[0014] Among them, H w , H v respectively represent the virtual inertia time constants of the wind farm and photovoltaic power station; H s represents the system inertia time constant; K W , K V , K J respectively represent the mechanical power gains of the wind farm, photovoltaic power station, and pumped storage power station at time t; R j represents the droop coefficient of the governor; T w represents the water flow time constant of the pumped storage unit; R w , R v respectively represent the droop coefficients of the wind farm and photovoltaic power station; D represents the system damping coefficient; ΔP represents the system power deficit at time t.

[0015] The specific frequency index described in step (1) is:

[0016] Rate of change of frequency:

[0017] Frequency deviation at the lowest point under pumping condition:

[0018]

[0019] Frequency deviation at the lowest point under power generation condition:

[0020]

[0021] Quasi-steady state frequency deviation under pumping condition:

[0022] Quasi-steady state frequency deviation under power generation condition:

[0023] When power perturbation occurs in the system, the above indicators shall meet the following constraints:

[0024]

[0025] Among them, H w 、H v represent the virtual inertia time constants of the wind farm and the PV power station respectively; H s represents the system inertia time constant; K W 、K V 、K J represent the mechanical power gains of the wind farm, the PV power station and the pumped storage power station at time t respectively; R j represents the droop coefficient of the governor; T w represents the water flow time constant of the pumped storage unit; R w 、R v represent the droop coefficients of the wind farm and the PV power station respectively; D represents the system damping coefficient; f b represents the rated frequency; represents the maximum allowable frequency change rate; Δf lim represents the low frequency load shedding trigger frequency; Δf ss,lim represents the maximum allowable quasi-steady state frequency deviation; ΔP represents the system power deficit at time t.

[0026] The objective function described in step (2) is:

[0027]

[0028] The constraint conditions include:

[0029] Wind and solar power output constraint: 0 ≤ P i,t ≤ P i,N

[0030] Pu i,t =μ i,t +1.96σ i,t

[0031] Pl i,t = μ i,t -1.96σ i,t

[0032] P i,max,t = min{Pu i,t , P i,N}

[0033] P i,min,t = max{0, Pl i,t}

[0034] P i,min,t ≤ P i,t ≤ P i,max,t

[0035]

[0036] Pumped-storage unit output constraint:

[0037] Pumped-storage unit operating condition constraint:

[0038]

[0039] Pumped-storage unit reserve constraint:

[0040]

[0041] Pumped-storage power station water volume constraint: X min ≤ X t ≤ X max X NT = X 0

[0042] Reserve demand constraint:

[0043]

[0044] Dynamic frequency response constraint:

[0045]

[0046] Under pumping condition:

[0047] Under power generation condition:

[0048]

[0049] Among them, \(t\) represents the time period number; \(NT\) represents the total number of time periods; \(i\) represents the index of the wind farm / photovoltaic power station; \(j\) represents the number of pumped storage units; \(NW\), \(NV\), and \(NJ\) respectively represent the total numbers of wind farms, photovoltaic power stations, and pumped storage units; \(P\) i,t represents the planned output of the wind farm / photovoltaic power station in the \(t\)th time period; represents the planned power generation power and planned pumping power of the pumped storage unit \(j\) in the \(t\)th time period; \(P\) i,N represents the installed capacity of the wind farm / photovoltaic power station; \(Pu\) i,t , \(Pl\) i,t respectively represent the upper and lower limits of the output of the wind farm / photovoltaic power station in the \(t\)th time period; \(\mu\) i,t , \(\sigma\) i,t respectively represent the mean and standard deviation of the predicted output probability distribution of the wind farm / photovoltaic power station in the \(t\)th time period; \(P\) i,max,t , \(P\) i,min,t respectively represent the upper and lower limits of the available power of the wind farm / photovoltaic power station in the \(t\)th time period; respectively represent the upper and lower regulation reserve capacities provided by the wind farm / photovoltaic power station in the \(t\)th time period; respectively represent the operating status of the pumped storage unit \(j\) in the \(t\)th time period, then the pumped storage unit \(j\) is working in the power generation state; then the pumped storage unit \(j\) is working in the pumping state; then the pumped storage unit \(j\) is in the idle state; respectively represent the maximum and minimum power generation powers of the pumped storage unit \(j\); respectively represent the maximum and minimum pumping powers of the pumped storage unit \(j\); respectively represent the upper and lower reserve capacities that the pumped storage unit \(j\) can provide under the power generation condition in the \(t\)th time period; respectively represent the upper and lower reserve capacities that the pumped storage unit \(j\) can provide under the pumping condition in the \(t\)th time period; \(X\) t represents the upper reservoir storage capacity in the \(t\)th time period; respectively represent the average water / electricity conversion coefficients when the pumped storage unit \(j\) pumps water and generates electricity; \(X\) max , \(X\) min respectively represent the maximum and minimum water storage capacities of the upper reservoir; \(X\) 0 represents the initial storage capacity of the upper reservoir; \(X\) NT represents the upper reservoir storage capacity at the end of the dispatching period; \(\alpha\) and \(\beta\) respectively represent the constant coefficients of the uncertainty of wind power and photovoltaic power outputs, approximately 15% - 25%; \(H\) s,t represents the equivalent inertia time constant of the system in the \(t\)th time period; \(H\) j represents the inertia time constant of the pumped storage unit; \(H\) W , \(H\) V respectively represent the equivalent virtual inertia time constants of the wind farm and the photovoltaic power station; \(H\) i represents the inertia time constant of the wind turbine / photovoltaic unit; \(K\) W,t , \(K\)V,t , K J,t respectively represent the mechanical power gains of the wind farm, photovoltaic power station, and pumped storage power station during the t period; R H,t represents the equivalent droop coefficient under the power generation condition of the pumped storage power station; R j represents the droop coefficient of the governor; T w,t represents the equivalent water flow time constant of the water turbine; T w,j represents the water flow time constant of the pumped storage unit; R W , R V respectively represent the equivalent droop coefficients of the wind farm and photovoltaic power station; R i represents the droop coefficient of the wind turbine / photovoltaic unit; D represents the system damping coefficient; f b represents the rated frequency; represents the maximum allowable frequency change rate; Δf lim represents the low-frequency load shedding trigger frequency; Δf ss,lim represents the maximum allowable quasi-steady state frequency deviation; ΔP t represents the system power deficit during the t period.

[0050] A computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-mentioned short-term optimal scheduling method for a wind-solar-pumped storage system considering dynamic frequency response.

[0051] A computer device, including a storage, a processor, and a computer program stored on the storage and executable on the processor, and when the processor executes the computer program, it implements the above-mentioned short-term optimal scheduling method for a wind-solar-pumped storage system considering dynamic frequency response.

[0052] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0053] 1. When the pumped storage power station participates in scheduling, it can jointly undertake the task of reserved standby with wind and light, greatly reducing the curtailment of wind and photovoltaic power, and improving the accommodation level of wind power and photovoltaic power; as the installed capacity of the pumped storage increases continuously, the reserved standby capacity it can provide will also increase, reducing the reserved standby of wind power and photovoltaic power, and the planned grid-connected electricity of the system will gradually increase.

[0054] 2. After considering the dynamic frequency constraint, frequency indicators such as the system frequency change rate, the lowest point frequency deviation, and the quasi-steady state frequency deviation can all change within the operating range. Therefore, the model considering frequency response has more obvious advantages, can effectively suppress the rapid change of the system frequency under disturbances, enhance the system inertia response ability and frequency regulation ability by putting into new operating units, limit the frequency offset within the normal range, and ensure the safe and stable operation of the system;

[0055] 3. From an economic perspective, it costs nothing for pumped-storage units to provide backup power. If wind and solar power provide backup power, there will be curtailment penalty costs for wind and solar power. Description of the Drawings

[0056] Figure 1 is the flowchart of the steps of the present invention;

[0057] Figure 2 is a schematic diagram of the equivalent model of the dynamic frequency response of the wind-solar-pumped storage system under the pumping condition;

[0058] Figure 3 is a schematic diagram of the equivalent model of the dynamic frequency response of the wind-solar-pumped storage system under the power generation condition;

[0059] Figure 4 is a schematic diagram of the optimization results of the reserve capacity under different operation modes. Among them, Figure 4 (a) is a schematic diagram of the optimization results of the reserve capacity in Mode A, Figure 4 (b) is a schematic diagram of the optimization results of the reserve capacity in Mode B, Figure 4 (c) is a schematic diagram of the optimization results of the reserve capacity in Mode C;

[0060] Figure 5 is a schematic diagram of the unit start-stop under different operation modes. Among them, Figure 5 (a) is a schematic diagram of the unit start-stop in Mode C, Figure 5 (b) is a schematic diagram of the unit start-stop in Mode D;

[0061] Figure 6 is a schematic diagram of the dynamic frequency indexes under different operation modes. Among them, Figure 6 (a) is a schematic diagram of the frequency change rate under different operation modes, Figure 6 (b) is a schematic diagram of the lowest point frequency deviation under different operation modes, Figure 6 (c) is a schematic diagram of the quasi-steady state frequency deviation under different operation modes. Detailed Embodiment

[0062] The technical solution of the present invention will be further described below in conjunction with the drawings.

[0063] As Figure 1 shown, a short-term optimal scheduling method for a wind-solar-pumped storage system considering dynamic frequency response includes the following steps:

[0064] (1) First, establish an equivalent model of the dynamic frequency response of the wind-solar-pumped storage system, derive an analytical expression of the system frequency index from the equivalent model of the system dynamic frequency response, and convert it into an inequality constraint. The frequency indexes include the frequency change rate, the lowest point frequency deviation, and the quasi-steady state frequency deviation.

[0065] (2) Establish a short-term optimal scheduling model for a wind-solar-pumped storage system considering dynamic frequency response in advance; the short-term optimal scheduling model includes an objective function with the maximum system on-grid power as the goal and corresponding constraint conditions.

[0066] (3) When the installed capacity of the pumped storage unit increases and the system dynamic frequency constraint is considered, the short-term optimal scheduling model achieves the goals of increasing the system on-grid power, reducing the curtailment of wind and photovoltaic power, and enhancing the system frequency response ability. The pumped storage unit shares the task of reserved standby for the wind farm and photovoltaic power station, reducing the curtailment of wind and photovoltaic power in the system, effectively improving the consumption level of renewable energy. Moreover, as the installed capacity of the pumped storage power station increases, the system on-grid power increases accordingly. After considering the dynamic frequency response, through a reasonable unit start-stop plan, it can effectively cope with the rapid drop of the system frequency under disturbance, and can limit the lowest point frequency deviation and quasi-steady state frequency deviation within the allowable range, reducing the possibility of under-frequency load shedding, which is conducive to the system to recover frequency stability faster.

[0067] The mathematical model for the short-term optimal scheduling of the wind-solar-pumped storage system described in the present invention includes an equivalent model of the dynamic frequency response of the wind-solar-pumped storage system and a short-term optimal scheduling model of the wind-solar-pumped storage system considering dynamic frequency response.

[0068] (I) Equivalent model of the dynamic frequency response of the wind-solar-pumped storage system:

[0069] Under the pumping condition, the time-domain expression of the frequency response of the wind-solar-pumped storage system is:

[0070]

[0071] Under the power generation condition, the time-domain expression of the frequency response of the wind-solar-pumped storage system is:

[0072]

[0073] Based on the time-domain expression of the system frequency response, determine the frequency change rate, the lowest point frequency deviation, and the quasi-steady state frequency deviation of the system.

[0074] Frequency change rate:

[0075] Lowest point frequency deviation under the pumping condition:

[0076]

[0077] Lowest point frequency deviation under the power generation condition:

[0078]

[0079] Quasi-steady state frequency deviation under the pumping condition:

[0080] Quasi-steady state frequency deviation under power generation condition:

[0081] When power disturbance occurs in the system, the above indicators shall meet the following constraints:

[0082]

[0083] Among them, H w and H v represent the virtual inertia time constants of the wind farm and the PV power station respectively; H s represents the system inertia time constant; K W and K V and K J represent the mechanical power gains of the wind farm, the PV power station and the pumped storage power station at time t respectively; R j represents the droop coefficient of the governor; T w represents the water flow time constant of the pumped storage unit; R w and R v represent the droop coefficients of the wind farm and the PV power station respectively; D represents the system damping coefficient; f b represents the rated frequency; represents the maximum allowable frequency change rate; Δf lim represents the low-frequency load shedding trigger frequency; Δf ss,lim represents the maximum allowable quasi-steady state frequency deviation; ΔP represents the system power deficit at time t.

[0084] (2) Short-term optimal scheduling model of wind-PV-pumped storage system considering dynamic frequency constraints:

[0085] The objective function is:

[0086]

[0087] The constraint conditions include:

[0088] Wind and PV power output constraints: 0 ≤ P i,t ≤ P i,N

[0089] Pu i,t = μ i,t + 1.96σ i,t

[0090] Pl i,t = μ i,t - 1.96σ i,t

[0091] P i,max,t = min{Pu i,t , P i,N}

[0092] P i,min,t = max{0, Pl i,t}

[0093] P i,min,t ≤ P i,t ≤ P i,max,t

[0094]

[0095] Pumped - storage unit output constraint:

[0096]

[0097] Pumped - storage unit operating condition constraint:

[0098]

[0099] Pumped - storage unit reserve constraint:

[0100]

[0101] Pumped - storage power station water volume constraint: X min ≤ X t ≤ X max X NT = X 0

[0102] Reserve demand constraint:

[0103]

[0104] Dynamic frequency response constraint:

[0105]

[0106]

[0107] Under pumping condition:

[0108]

[0109] Among them, t represents the time - period number; NT represents the total number of time - periods; i represents the index of the wind farm / photovoltaic power station; j represents the pumped - storage unit number; NW, NV, NJ represent the total numbers of wind farms, photovoltaic power stations, and pumped - storage units respectively; P i,t represents the planned output of the wind farm / photovoltaic power station in time - period t; represents the planned power generation power and planned pumping power of pumped - storage unit j in time - period t; P i,N represents the installed capacity of the wind farm / photovoltaic power station; Pui,t and Pl i,t represent the upper and lower limits of the output of the wind farm / photovoltaic power station in period t, respectively; μ i,t and σ i,t represent the mean and standard deviation of the predicted output probability distribution of the wind farm / photovoltaic power station in period t, respectively; P i,max,t and P i,min,t represent the upper and lower limits of the available power of the wind farm / photovoltaic power station in period t, respectively; represent the upper and lower regulation reserve capacities provided by the wind farm / photovoltaic power station in period t, respectively; represent the operating states of the pumped-storage unit j in period t, then the pumped-storage unit j is operating in the power generation state; then the pumped-storage unit j is operating in the pumping state; then the pumped-storage unit j is in the idle state; represent the maximum and minimum power generation powers of the pumped-storage unit j, respectively; represent the maximum and minimum pumping powers of the pumped-storage unit j, respectively; represent the upper and lower reserve capacities that the pumped-storage unit j can provide in period t under the power generation condition, respectively; represent the upper and lower reserve capacities that the pumped-storage unit j can provide in period t under the pumping condition, respectively; X t represents the upper reservoir storage capacity in period t; represent the average water / electricity conversion coefficients of the pumped-storage unit j during pumping and power generation, respectively; X max and X min represent the maximum and minimum water storage volumes of the upper reservoir, respectively; X 0 represents the initial storage capacity of the upper reservoir; X NT represents the upper reservoir storage capacity at the end of the dispatching period; α and β represent the uncertainty constant coefficients of wind power and photovoltaic power output, approximately 15% - 25%; H s,t represents the equivalent inertia time constant of the system in period t; H j represents the inertia time constant of the pumped-storage unit; H W and H V represent the equivalent virtual inertia time constants of the wind farm and the photovoltaic power station, respectively; H i represents the inertia time constant of the wind turbine / photovoltaic unit; K W,t and K V,t and K J,t represent the mechanical power gains of the wind farm, photovoltaic power station, and pumped-storage power station in period t, respectively; R H,t represents the equivalent droop coefficient of the pumped-storage power station under the power generation condition; R j represents the droop coefficient of the governor; T w,t represents the equivalent water flow time constant of the water turbine; T w,j represents the water flow time constant of the pumped-storage unit; R W, R V respectively represent the equivalent droop coefficients of the wind farm and the PV power station; R i represents the droop coefficient of the wind turbine / PV unit; D represents the system damping coefficient; f b represents the rated frequency; represents the maximum allowable frequency change rate; Δf lim represents the low-frequency load shedding trigger frequency; Δf ss,lim represents the maximum allowable quasi-steady state frequency deviation; ΔP t represents the system power deficit at time t.

[0110] To more clearly illustrate the embodiments of the present application, the following is Figures 2 - 6 described.

[0111] (1) The equivalent dynamic frequency response models of the wind-solar-pumped storage system under the pumping condition and the power generation condition are respectively as Figure 2 and Figure 3 shown. From Figure 2 and Figure 3 the system transfer function is obtained, and its inverse Laplace transform is performed to obtain the time-domain expression of the system frequency response.

[0112] (2) Taking the Zhangbei wind-solar-storage demonstration project as an example, the short-term optimal scheduling model of the wind-solar-pumped storage system is analyzed and calculated. The total installed capacity of wind power and PV power is 3300 MW, and the capacity ratio is 2:1. The pumped storage power station includes 6 variable-speed constant-frequency units, with a single-unit rated capacity of 300 MW. The pumping power is continuously adjustable within the power range of 10% to 100%. The specific parameters are shown in Table 1. The inertia time constants of the wind farm and the PV power station are taken as 6 s and 4 s respectively, the droop control coefficients are 0.02 and 0.04 respectively, the rated frequency is 50 Hz, the maximum allowable frequency change rate is 0.25 Hz / s, the low-frequency load shedding trigger frequency is 49.8 Hz, and the maximum allowable steady-state frequency deviation is 0.2 Hz. The probability distribution parameters of the wind power and PV power outputs are shown in Table 2 and Table 3 respectively, and the system power deficit is shown in Table 4.

[0113] Table 1 Parameters of the pumped storage power station

[0114]

[0115]

[0116] Table 2 Probability distribution parameters of PV power output

[0117] Time period μ σ Time period μ σ Time period μ σ Time period μ σ 7 55.57 3.96 10 551.54 21.56 13 910.45 35.20 16 377.78 15.40 8 202.53 11.44 11 715.09 26.84 14 810.52 31.24 17 227.35 11.88 9 397.36 16.72 12 852.94 33.44 15 575.64 22.44 18 150.17 7.04

[0118] Table 3 Probability distribution parameters of wind power output

[0119] Time period μ σ Time period μ σ Time period μ σ Time period μ σ 1 1913 56 7 1790 76 13 970 141 19 1813 65 2 1900 59 8 1480 91 14 1000 129 20 1839 62 3 1915 59 9 1364 109 15 1180 112 21 1900 59 4 1904 62 10 1176 126 16 1458 94 22 1910 62 5 1874 62 11 1018 138 17 1590 79 23 1915 56 6 1819 65 12 877 144 18 1670 67 24 1918 59

[0120] Table 4 System power deficit

[0121] Time period 1 2 3 4 5 6 7 8 9 10 11 12 ΔP 0.1 0.12 0.13 0.14 0.13 0.125 0.132 0.13 0.135 0.14 0.142 0.145 Time period 13 14 15 16 17 18 19 20 21 22 23 24 ΔP 0.135 0.15 0.13 0.14 0.14 0.125 0.13 0.13 0.14 0.132 0.12 0.1

[0122] (3) Analyze the impact of the installed capacity of pumped-storage units on the system optimal dispatching. Without considering the system dynamic frequency response, analyze the following three operation modes. Mode A: No pumped-storage units participate in dispatching; Mode B: One pumped-storage unit participates in dispatching; Mode C: Six pumped-storage units participate in dispatching.

[0123] Table 5 Optimal results under different operation modes

[0124] Operating mode Mode A Mode B Mode C On - grid power / MW 45629.402 48301.739 48338.417 Discarded power / MW 2810.09 45.10 0

[0125] Table 5 shows the optimal results under three operation modes. Figure 4 Compare the reserved spare capacities of wind power, photovoltaic power and pumped-storage under different operation modes. After analysis, it is found that:

[0126] When there is no pumped-storage, on the one hand, the randomness and intermittency of wind and photovoltaic power output threaten the safe and stable operation of the system, making it a spare demander; on the other hand, wind power and photovoltaic power become spare providers by reducing their grid-connected power. Therefore, when only wind and photovoltaic power are the main power generation sources in Mode A, all the spare capacity required by the system is provided by wind power and photovoltaic power, resulting in a large amount of curtailed wind and photovoltaic power.

[0127] When pumped-storage units participate in dispatching, the pumped-storage units can jointly undertake the task of reserving spare capacity with wind and photovoltaic power. Figure 4 It is not difficult to find that in Mode B and Mode C, the pumped-storage units mainly provide upward regulation spare capacity, while wind power and photovoltaic power mainly provide downward regulation spare capacity. The reason is that through curtailment of wind and photovoltaic power, the real-time output of wind and photovoltaic power is less than the planned output, and the upward adjustable surplus of wind and photovoltaic power can be regarded as upward regulation spare capacity. However, in order to maximize the system grid-connected power, curtailment of power should be avoided as much as possible, so the upward regulation spare can only be provided by pumped-storage units. The downward regulation spare capacity that wind and photovoltaic power can provide is related to their real-time output. As the acceptance level of wind and photovoltaic power increases, the downward regulation spare capacity they can provide naturally increases, so they are the main providers of downward regulation spare capacity.

[0128] As the installed capacity of pumped-storage units increases, the system grid-connected power increases, and the curtailed wind and photovoltaic power decreases significantly, improving the consumption level of wind power and photovoltaic power. However, comparing Mode A and Mode B, the system grid-connected power only increases by 4.6%. This is because the main role of the pumped-storage units after they are added is to provide spare capacity and they are not used for power generation. On the one hand, wind and photovoltaic power need to curtail wind and photovoltaic power to provide spare capacity. If they are used as the providers of spare capacity, it will conflict with the goal of maximizing the system grid-connected power. On the other hand, it costs nothing for pumped-storage units to provide spare capacity, while if wind and photovoltaic power provide spare capacity, there will be curtailment penalty costs.

[0129] Table 6 Optimization Results under Different Pumped-Storage Installed Capacities

[0130] Pumped - storage installed capacity / MW On - grid power / MW 300 48301.739 600 48338.417 900 48338.417

[0131] Table 6 analyzes the optimization results of pumped-storage power stations under other installed capacities. It is not difficult to find that with the continuous increase of the pumped-storage installed capacity, the reserved spare capacity will increase, reducing the reserved spare capacity of wind power and photovoltaic power, and the planned grid-connected power of the system will gradually increase.

[0132] (4) Analyze the impact of dynamic frequency response on the optimal dispatching of the system. When there is a power deficit in the system, there should be enough generating units to provide inertia support to resist the rapid change of frequency. Therefore, on the basis of operation mode C, considering the dynamic frequency response constraint, its impact on the system dispatching decision is verified, denoted as operation mode D. Figure 5 Compare the start-stop plans of the units under the two operation modes Indicates that the pumped-storage unit is operating in the pumping condition. The analysis finds that

[0133] To ensure the stable operation of the system and improve the system frequency regulation ability, when considering the dynamic frequency constraint, more units need to be dispatched to operate online.

[0134] Most of the units operate in the power generation condition. The reason is that after considering the dynamic frequency constraint, during the pumping condition, the quasi-steady-state frequency deviation of the system is mainly related to the droop control of wind farms, photovoltaic power stations and the number of online units; while during the power generation condition, the quasi-steady-state frequency deviation of the system is also affected by the turbine regulation coefficient. From the quasi-steady-state frequency deviation formula, it can be seen that after the action of the turbine speed control system, the quasi-steady-state frequency deviation of the system is smaller than that during the pumping condition. Therefore, during period 1-2, the power deficit of the system is small, and the frequency constraint can be met by increasing the number of online units during the pumping condition; during period 3-22, as the power deficit of the system increases, the units need to operate in the power generation condition to meet the frequency deviation constraint; while for the units during period 23-24, pumping is mainly affected by the reservoir storage capacity constraint at the end of the dispatching period, used to adjust the balance of the reservoir storage capacity at the end of the dispatching period.

[0135] Figure 6 Compare the frequency change rate, the lowest point frequency deviation and the quasi-steady-state frequency deviation under the two operation modes. Through analysis, it is found that

[0136] When not considering the dynamic frequency constraint, the frequency change rate, the lowest point frequency deviation and the quasi-steady-state frequency deviation of the system exceed the limit values in most periods. Therefore, with the gradual penetration of wind power and photovoltaic power, the system inertia further decreases and the frequency regulation ability weakens, seriously threatening the frequency stability of the system.

[0137] When considering dynamic frequency constraints, the system frequency change rate, the lowest point frequency deviation, and the quasi-steady state frequency deviation can all vary within the operating range. Therefore, the model advantages considering frequency response are more obvious, which can effectively suppress the rapid change of the system frequency under disturbances. By putting new operating units into operation, the system inertia response ability and frequency regulation ability are enhanced, and the frequency offset is restricted to remain within the normal range.

[0138] Table 7 Optimization results under different operating modes

[0139] Operating mode Mode C Mode D On - grid power / MW 48338.417 48220.494

[0140] Table 7 compares the dispatching decision results before and after considering dynamic frequency constraints. It can be seen from the table that after considering dynamic frequency constraints, the system's on-grid electricity does not increase but decreases. The reason is that most of the newly added generating units participate in the dispatching with the minimum output power to provide inertia support for the system. On the other hand, to meet the upper regulation reserve demand, the output of the pumped storage units increases during the pumping condition, and the system's on-grid electricity decreases accordingly.

Claims

1. A short-term optimal scheduling method for a wind-solar-pumped storage system considering dynamic frequency response, characterized in that, it includes the following steps: (1) Establish a dynamic frequency response equivalent model of the wind-solar-pumped storage system in advance, derive an analytical expression of the system frequency index from the system dynamic frequency response equivalent model, and convert it into inequality constraints. The frequency indexes include the rate of change of frequency, the lowest point frequency deviation, and the quasi-steady state frequency deviation; The dynamic frequency response equivalent model includes a dynamic frequency response equivalent model under power generation conditions and a dynamic frequency response equivalent model under pumping conditions; Under pumping conditions, the time-domain expression of the frequency response of the wind-solar-pumped storage system is: Under power generation conditions, the time-domain expression of the frequency response of the wind-solar-pumped storage system is: Among them, H w and H v respectively represent the virtual inertia time constants of the wind farm and the photovoltaic power station; H s represents the system inertia time constant; K W and K V and K J respectively represent the mechanical power gains of the wind farm, the photovoltaic power station, and the pumped storage power station at time t; R j represents the droop coefficient of the governor; T w represents the water flow time constant of the pumped storage unit; R w and R v respectively represent the droop coefficients of the wind farm and the PV power station; D represents the system damping coefficient; ΔP represents the system power deficit; The specific frequency indexes are: Rate of change of frequency: The lowest point frequency deviation under pumping conditions: The lowest point frequency deviation under power generation conditions: Quasi-steady state frequency deviation under pumping condition: Quasi-steady state frequency deviation under power generation conditions: When a power disturbance occurs in the system, the above indexes need to satisfy the following constraints: Among them, H w and H v represent the virtual inertia time constants of the wind farm and the photovoltaic power station respectively; H s represents the system inertia time constant; K W and K V and K J represent the mechanical power gains of the wind farm, the photovoltaic power station, and the pumped-storage power station at time t respectively; R j represents the droop coefficient of the governor; T w represents the water flow time constant of the pumped-storage unit; R w and R v represent the droop coefficients of the wind farm and the photovoltaic power station respectively; D represents the system damping coefficient; f b represents the rated frequency; represents the maximum allowable frequency change rate; Δf lim represents the under-frequency load shedding trigger frequency; Δf ss,lim represents the maximum allowable quasi-steady state frequency deviation; ΔP represents the system power deficit; (2) Establish a short-term optimal scheduling model for the wind-solar-pumped storage system considering dynamic frequency response in advance; the short-term optimal scheduling model includes an objective function with the maximum system on-grid power as the goal and corresponding constraint conditions; (3) When the installed capacity of the pumped storage unit increases and the system dynamic frequency constraint is considered, the short-term optimal scheduling model achieves the goals of increasing the system on-grid power, reducing the curtailment of wind and solar power, and enhancing the system frequency response ability.

2. A short-term optimal scheduling method for a wind-solar-pumped storage system considering dynamic frequency response according to claim 1, characterized in that, the objective function described in step (2) is: The constraint conditions include: Renewable power output constraint: 0 ≤ P i,t ≤ P i,N Pu i,t = μ i,t + 1.96σ i,t Pl i,t = μ i,t - 1.96σ i,t P i,max,t = min{Pu i,t , P i,N} P i,min,t = max{0, Pl i,t} P i,min,t ≤P i,t ≤P i,max,t Output constraint of pumped-storage unit: Operating condition constraints of pumped-storage units: Pumped storage unit standby constraint: Water quantity constraint of pumped-storage power station: X min ≤X t ≤X max X NT = X 0 Standby requirement constraint: Dynamic frequency response constraint: Under pumping conditions: Under power generation conditions: Among them, t represents the time period number; NT represents the total number of time periods; i represents the index of the wind farm / photovoltaic power station; j represents the pumped-storage unit number; NW, NV, and NJ respectively represent the total numbers of wind farms, photovoltaic power stations, and pumped-storage units; P i,t represents the planned output of the wind farm / photovoltaic power station in time period t; represents the planned power generation power and planned pumping power of pumped-storage unit j in time period t; P i,N represents the installed capacity of the wind farm / photovoltaic power station; Pu i,t , Pl i,t respectively represent the upper and lower limits of the output of the wind farm / photovoltaic power station in time period t; μ i,t , σ i,t respectively represent the mean and standard deviation of the predicted output probability distribution of the wind farm / photovoltaic power station in time period t; P i,max,t , P i,min,t respectively represent the upper and lower limits of the available power of the wind farm / photovoltaic power station in time period t; respectively represent the upper and lower regulation reserve capacities provided by the wind farm / photovoltaic power station in time period t; respectively represent the operating status of pumped-storage unit j in time period t, then pumped-storage unit j is working in the power generation state; then pumped-storage unit j is working in the pumping state; then pumped-storage unit j is in the idle state; respectively represent the maximum and minimum power generation powers of pumped-storage unit j; respectively represent the maximum and minimum pumping powers of pumped-storage unit j; respectively represent the upper and lower reserve capacities that pumped-storage unit j can provide in time period t under the power generation condition; respectively represent the upper and lower reserve capacities that pumped-storage unit j can provide in time period t under the pumping condition; X t represents the planned reservoir capacity of the upper reservoir in time period t; respectively represent the average water / electricity conversion coefficients when pumped-storage unit j pumps water and generates electricity; X max , X min respectively represent the maximum and minimum water storage capacities of the upper reservoir; X 0 represents the initial reservoir capacity of the upper reservoir; X NT represents the reservoir capacity of the upper reservoir at the end of the dispatching period; α and β respectively represent the constant coefficients of the uncertainties of wind power and photovoltaic power outputs, about 15%-25%; H s,t represents the equivalent inertia time constant of the system in time period t; H j represents the inertia time constant of the pumped-storage unit; H W , H V respectively represent the equivalent virtual inertia time constants of the wind farm and the photovoltaic power station; H i represents the inertia time constant of the wind turbine / photovoltaic unit; K W,t , K V,t , K J,t respectively represent the mechanical power gains of the wind farm, photovoltaic power station, and pumped-storage power station during the t period; R H,t represents the equivalent droop coefficient under the power generation condition of the pumped-storage power station; R j represents the droop coefficient of the governor; T w,t represents the equivalent water flow time constant of the water turbine; T w,j represents the water flow time constant of the pumped-storage unit; R W , R V respectively represent the equivalent droop coefficients of the wind farm and photovoltaic power station; R i represents the droop coefficient of the wind turbine / photovoltaic unit; D represents the system damping coefficient; f b represents the rated frequency; represents the maximum allowable frequency change rate; Δf lim represents the low-frequency load shedding trigger frequency; Δf ss,lim represents the maximum allowable quasi-steady state frequency deviation; ΔP t represents the system power deficit during the t period.

3. A computer storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, it implements a short-term optimal scheduling method for a wind-solar-pumped storage system considering dynamic frequency response as described in any one of claims 1-2.

4. A computer device, including a storage, a processor, and a computer program stored on the storage and operable on the processor, characterized in that, when the processor executes the computer program, it implements a short-term optimal scheduling method for a wind-solar-pumped storage system considering dynamic frequency response as described in any one of claims 1-2.

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