Scheduling method and device of water-wind-light complementary power system based on cascade reservoirs

Abstract

The invention relates to a cascade reservoir-based dispatching method and device for a water-wind-light complementary power system, and the method comprises the following steps: screening out a hydropower station, a wind power station and a photovoltaic power station which participate in dispatching according to the working scales of the hydropower station, the wind power station and the photovoltaic power station in the power system; working state constraint conditions are set for the hydropower station, the wind power station and the photovoltaic power station participating in scheduling; constructing a power system scheduling strategy optimization model based on the working state constraint conditions of the hydropower station, the wind power station and the photovoltaic power station participating in scheduling; and solving the power system scheduling strategy optimization model to obtain a scheduling optimization result. Effective complementation of new energy volatility is realized by optimizing a water-wind-light intra-day scheduling strategy, the utilization rate of clean energy is improved, and safe and stable operation of a power grid system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a dispatching method and device for a water-wind-solar complementary power system based on cascade reservoirs. Background Art

[0002] Developing clean energy generation such as wind power and photovoltaics is an important way to achieve zero carbon emissions. However, both wind power and photovoltaic power generation have strong randomness, volatility and intermittency. The large-scale connection of wind power and photovoltaics to the grid poses a severe test to the stable operation of the power system.

[0003] As a power source with large scale and good regulation performance, hydropower stations can effectively smooth out the volatility and randomness of photovoltaic power generation, especially when wind and solar resources are unstable, and ensure the stable operation of the power system. This water-wind-solar complementary model not only improves energy utilization efficiency, but also helps to achieve large-scale application and sustainable development of clean energy, and improve the grid's ability to absorb it.

[0004] The hydropower-wind-photovoltaic complementary power system needs to optimize the scheduling strategy to achieve a more economical, efficient and safe improvement of the power dynamic balance capability of the power system. However, the existing hydropower-wind-photovoltaic complementary power scheduling optimization strategy considers a single optimization target and does not take into account the research on reservoir scheduling at the intraday scale, especially the impact of downstream power stations on the fluctuation of the power system. Summary of the invention

[0005] The present invention provides a dispatching method and device for a water-wind-solar complementary power system based on cascade reservoirs, which optimizes the intraday dispatching strategy of water-wind-solar to achieve effective complementarity with the volatility of new energy, improve the utilization rate of clean energy, and realize safe and stable operation of the power grid system.

[0006] The present invention solves the above technical problems in the following way: A dispatching method for a water-wind-solar hybrid power system based on cascade reservoirs comprises the following steps:

[0007] Compare the power generation of the hydropower station in the power system with the power generation threshold, and select the hydropower station with a power generation threshold greater than the power generation threshold to participate in the dispatch; all wind power stations in the power system are equivalent to a virtual wind power station for dispatch; all photovoltaic power stations in the power system are equivalent to a virtual photovoltaic power station for dispatch;

[0008] Establish working status constraints for hydropower stations, virtual wind power stations, and virtual photovoltaic power stations participating in the dispatch;

[0009] Based on the working status constraints of the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations involved in the dispatch, a power system dispatch strategy optimization model is constructed;

[0010] The power system dispatch strategy optimization model is solved to obtain the dispatch optimization results.

[0011] Preferably, the setting of working state constraints for the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations participating in the scheduling includes:

[0012] The output constraints of the virtual wind power station are as follows:

[0013]

[0014] Where: is the installed capacity of the lth wind power station; g l,t is the output coefficient of the lth virtual wind power station in period t; is the minimum output of the lth virtual wind power station in period t; is the maximum output of the lth virtual wind power station in period t;

[0015] The output constraints of the virtual photovoltaic power station are as follows:

[0016]

[0017] Where: is the installed capacity of the kth virtual photovoltaic power station; f k,t is the output coefficient of the kth virtual photovoltaic power station in period t; is the minimum output of the kth virtual photovoltaic power station during period t; is the maximum output of the kth virtual PV power station during period t;

[0018] The output constraints of the hydropower station are as follows:

[0019] Output constraints of hydropower station units:

[0020]

[0021]

[0022] Where: are the minimum and maximum outputs allowed for the jth power station in the tth period, respectively; is the power generation flow of the jth hydropower station in the tth period; δ j is the water consumption rate of the ith hydropower station; k is the conversion coefficient;

[0023] Reservoir water level constraints:

[0024]

[0025] Where: Z j,t is the water level of the jth hydropower station at the end of the tth period; is the upper limit of the water level of the jth hydropower station at the end of the tth period; is the lower limit of the water level of the jth hydropower station at the end of the tth period;

[0026] Reservoir water level daily fluctuation constraints:

[0027]

[0028] Where: Z j,end is the end-of-day water level of the jth reservoir; Z j,begin is the initial water level of the jth reservoir, Z j,allow is the daily water level fluctuation allowed for the jth reservoir;

[0029] Downflow flow restriction:

[0030]

[0031] Where: Q j,t is the discharge flow of the j-th hydropower station in the t-th period; is the maximum discharge flow of the jth hydropower station in the tth period; is the minimum discharge flow of the j-th hydropower station in the t-th period.

[0032] Power generation flow constraints:

[0033]

[0034] Where: is the maximum power generation flow of the jth hydropower station in the tth period; is the minimum power generation flow of the jth hydropower station in the tth period;

[0035] Water balance constraints in the time dimension:

[0036]

[0037] Where: V j,t V is the final storage capacity of the jth hydropower station at the tth period; j,t-1 is the initial storage capacity of the jth hydropower station in the tth period; R j,t is the inflow of the jth hydropower station in the tth period;

[0038] Water balance constraints in the spatial dimension:

[0039]

[0040] Where: I j,t is the interval flow of the jth hydropower station in the tth period; τ j-1 is the time it takes for the outflow from the j-1th hydropower station to reach the jth member reservoir;

[0041] Water level storage capacity curve:

[0042]

[0043] Where: f v,z () is the reservoir water level and storage capacity interpolation function;

[0044] Water level flow curve:

[0045]

[0046] Where: f Q,z () is the interpolation function of the discharge flow at the reservoir tailwater level;

[0047] The transmission channel constraints between different sections in the power system are as follows:

[0048]

[0049] Where: is the transmission capacity of the dth transmission section; wN l,t is the output of the lth virtual wind power station in the tth period; sN k,t hN is the output of the kth virtual photovoltaic power station in the tth period; j,t is the output of the j-th hydropower station in the t-th period.

[0050] Preferably, the power system dispatch strategy optimization model includes an upper-level dispatch model and a lower-level dispatch model;

[0051] The upper-level scheduling model takes the highest matching degree among hydropower, wind power and photovoltaic power generation and the maximization of daily power generation as the objective function;

[0052] The objective function of the lower-level dispatching model is to minimize the output fluctuation of the downstream hydropower stations.

[0053] Preferably, the upper-level scheduling model takes the highest matching degree of hydropower, wind power and photovoltaic power generation and the maximization of daily power generation as the objective function:

[0054]

[0055]

[0056] Where: ρ is the load tracking coefficient, t is the time period variable, T is the total number of time periods, N r,t is the power generation output in the tth period, L t is the power load in the tth period, is the average load level, l, k, j are the numbers of wind power, photovoltaic power generation and hydropower stations respectively; L, K, J are the total number of wind power, photovoltaic power generation and hydropower stations respectively; wN l,t is the output of the lth virtual wind power station in the tth period; sN k,thN is the output of the kth virtual photovoltaic power station in the tth period; j,t is the output of the jth hydropower station in the tth period; E is the total power generation in the day; m t is the number of hours in period t.

[0057] By utilizing the regulation capacity of hydropower, the intermittency, randomness and volatility of wind power and photovoltaic power generation can be smoothed to the greatest extent possible, so that the total output power is closer to the grid load level and the impact on grid operation is minimized. The load tracking coefficient ρ is defined to quantitatively evaluate the characteristics of the power source tracking the load of the hydro-wind-solar complementary power system. The closer the load tracking coefficient is to 1, the more consistent the change characteristics of the complementary power generation power of multiple energy sources and the load power within the time scale of the investigation, and the better the output power of the power generation system tracks the load.

[0058] Preferably, the lower-level dispatching model takes minimizing the output fluctuation of the downstream hydropower station as the objective function:

[0059]

[0060] Where: F is the output fluctuation index of the downstream hydropower station; is the average output of downstream hydropower stations for external transmission; β is the proportion of electricity transmitted by downstream hydropower stations; k h is the conversion coefficient of the hth downstream hydropower station, is the power generation flow of the hth downstream hydropower station, is the net water head of the hth downstream hydropower station.

[0061] The downstream hydropower station, which only has daily regulation performance, has a close hydroelectric connection with the upstream power station. If the two do not transmit to the same power grid, they will face different needs. The downstream power station mainly transmits to other channels and only occupies a small part of the channel. Considering the impact of its output fluctuation on the channel, since it only has daily regulation performance, it needs the cooperation of the upstream power station to minimize its output fluctuation to ensure the stability of the transmitted power.

[0062] Preferably, solving the power system dispatch strategy optimization model to obtain the dispatch optimization result specifically includes:

[0063] The upper-layer scheduling model is solved by the PSO algorithm of the Sigmoid function to obtain the upper-layer stage scheduling optimization result;

[0064] Based on the optimization results of the upper-level scheduling, the MSPOA algorithm is used to solve the lower-level scheduling model to obtain the reservoir water level operation plan of the hydropower station. If the water level of the reservoir water level of the downstream hydropower station in the reservoir water level operation plan exceeds the set range at any time, the upper-level scheduling model is re-solved until the reservoir water level of the downstream hydropower station meets the set range at any time of operation.

[0065] Preferably, the upper scheduling model is solved by the PSO algorithm of the Sigmoid function to obtain the upper stage scheduling optimization result; based on the upper stage scheduling optimization result, the lower scheduling model is solved by the MSPOA algorithm to obtain the hydropower station reservoir water level operation plan, if the reservoir water level of the downstream hydropower station in the hydropower station reservoir water level operation plan exceeds the set range at any time of operation, the upper scheduling model is re-solved until the reservoir water level of the downstream hydropower station meets the set range at any time of operation, specifically including:

[0066] Input the load demand of the power system and the output forecast curves obtained from the hydropower station, virtual wind power station, and virtual photovoltaic power station into the PSO algorithm of the Sigmoid function;

[0067] Based on the Sigmoid function, the water storage curve of the hydropower station reservoir is initialized in the variable domain, and the water level is obtained as the initial state of the particle swarm;

[0068] Calculate the initial fitness of particles and determine the initial global extreme value and initial individual extreme value;

[0069] Based on the particle swarm update formula, the position and velocity of the particles are updated until the hydropower station, virtual wind power station, and virtual photovoltaic power station meet the working state constraints;

[0070] Calculate the fitness value of the particle position and speed after updating, compare with the historical individual extreme value and the global extreme value, determine the current individual optimal position and the current global optimal position, output the obtained results and put them into the result cluster; repeatedly update the particle position and speed until the maximum number of cycles is reached;

[0071] After the cycle is completed, the best result is selected from the result cluster to obtain the scheduling optimization result;

[0072] Formulate an initial trajectory within the feasible solution domain under the total output process of the hydropower station that meets the optimization results of the upper stage dispatch;

[0073] Determine the parameters of the MSPOA algorithm;

[0074] The POA algorithm is used to solve the lower-level dispatch model, and the time step is gradually refined in stages to optimize the water level dispatch of the cascade power stations until the accuracy requirements are met. The calculated operation plan of the hydropower station reservoir water level is output, as follows:

[0075] Set the time step to 4h, and use the POA algorithm to solve the lower scheduling model in the order of the cascade hydropower stations. (Set the water levels at both ends of the first period of the first power station unchanged, adjust the water level in the middle, solve the upper and lower scheduling models one by one, select the optimal solution as the new water level in the middle of the first period of the power station, and update the water level trajectory. After traversing all power stations in the same way, push back one calculation period at a time until all periods are traversed, and proceed to the next step when the maximum number of cycles is met).

[0076] Set the time step to 2h, and use the POA algorithm with a time step of 4h to solve the new water level at the end of each time period as the new initial trajectory. Use the POA algorithm to find the optimal value, and proceed to the next step when the maximum number of cycles is met.

[0077] The time step is set to 1h, and the new water level at the end of each time period obtained by solving the POA algorithm with a time step of 2h is used as the new initial trajectory. The POA algorithm is used for optimization and stops when the optimization accuracy is met.

[0078] If the reservoir water level of the downstream hydropower station in the reservoir water level operation plan exceeds the set range at any time of operation, the initialization of the reservoir water storage curve based on the Sigmoid function in the variable domain is readjusted, and the new water level is recalculated as the initial state of the particle swarm, and the upper scheduling model is solved again until the reservoir water level of the downstream hydropower station meets the set range at any time of operation.

[0079] The dispatch optimization results include the dispatch optimization results of the upper stage and the water level operation plan of the hydropower station reservoir. The dispatch optimization results of the upper stage include the daily water level application mode of the hydropower reservoir, the daily water level control range, the daily power generation output plan of the hydropower unit, and the daily output plan of the system wind power and photovoltaic power.

[0080] The present invention also provides a dispatching device for a water-wind-solar complementary power system based on cascade reservoirs, comprising:

[0081] The scheduling object screening module is used to compare the power generation of the hydropower station in the power system with the power generation threshold, and select the hydropower station with a power generation threshold greater than the power generation threshold to participate in the scheduling; all wind power stations in the power system are equivalent to a virtual wind power station for scheduling; all photovoltaic power stations in the power system are equivalent to a virtual photovoltaic power station for scheduling;

[0082] The constraint condition establishment module is used to establish working status constraint conditions for the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations participating in the dispatch;

[0083] The strategy model building module is used to build a power system dispatch strategy optimization model based on the working status constraints of the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations involved in the dispatch;

[0084] The optimization dispatch generation module is used to solve the power system dispatch strategy optimization model and obtain the dispatch optimization results.

[0085] A computer storage medium, wherein the computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the scheduling method of the water-wind-solar complementary power system based on cascade reservoirs as described above are implemented.

[0086] An electronic device comprises a memory and a processor: the memory is used to store computer executable instructions, the processor is used to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the scheduling method of the water-wind-solar complementary power system based on cascade reservoirs as described above.

[0087] The beneficial effects of the present invention are as follows: based on establishing the optimization object of the water-wind-solar complementary power system, the present invention constructs a collaborative scheduling optimization model on the intraday time scale with the objective functions of maximizing the load matching degree of the water-wind-solar complementary power system, maximizing the intraday power generation of water, wind and solar, and minimizing the output fluctuation of the downstream hydropower station. Solving the model can obtain the intraday collaborative scheduling optimization plan of the water-wind-solar complementary power system, and can derive a power generation plan of the hydropower-wind-photovoltaic complementary power system that meets the constraints, improve the load matching degree of the water-wind-solar complementary power generation system, the utilization rate of water, wind and solar energy, and the stability of the output of the downstream power station, so as to realize the resource integration, stable power supply and safe and stable operation of the water-wind-solar complementary power generation system.

[0088] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0090] Figure 1 This is a flow chart of a dispatching method for a water-wind-solar complementary power system based on cascade reservoirs according to Example 1;

[0091] Figure 2 This is a module diagram of a dispatching device for a water-wind-solar complementary power system based on cascade reservoirs according to Example 2. DETAILED DESCRIPTION

[0092] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0093] like Figure 1 As shown, the present invention provides a dispatching method for a water-wind-solar complementary power system based on cascade reservoirs, comprising the following steps:

[0094] S1. By comparing the power generation power of the hydropower station in the power system with the power generation power threshold, the hydropower station with a power generation power greater than the power generation power threshold is selected to participate in the dispatch; all wind power stations in the power system are equivalent to a virtual wind power station for dispatch; all photovoltaic power stations in the power system are equivalent to a virtual photovoltaic power station for dispatch.

[0095] In order to reduce the complexity of the optimization model and the difficulty of calculation, in this embodiment, the hydropower station with an installed capacity of more than 300,000 kW in the water-wind-solar complementary power system is optimized as the adjustment object, and the small hydropower station with an installed capacity of less than 300,000 kW is equivalent to a virtual hydropower station, and only its power generation efficiency is considered, and no scheduling optimization is performed. All wind turbines in the power generation system are equivalent to one unit modeling; all photovoltaic power generation is equivalent to one unit modeling.

[0096] S2. Establish working status constraints for the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations participating in the dispatch, as follows:

[0097] 1) The output constraints of the virtual wind power station are as follows:

[0098]

[0099] Where: is the installed capacity of the lth wind power station; g l,t is the output coefficient of the lth virtual wind power station in period t; is the minimum output of the lth virtual wind power station in period t; is the maximum output of the lth virtual wind power station in period t;

[0100] 2) The output constraints of the virtual photovoltaic power station are as follows:

[0101]

[0102] Where: is the installed capacity of the kth virtual photovoltaic power station; f k,tis the output coefficient of the kth virtual photovoltaic power station in period t; is the minimum output of the kth virtual photovoltaic power station during period t; is the maximum output of the kth virtual PV power station during period t;

[0103] 3) The output constraints of the hydropower station are as follows:

[0104] Output constraints of hydropower station units:

[0105]

[0106] Where: are the minimum and maximum outputs allowed for the jth power station in the tth period, respectively; is the power generation flow of the jth hydropower station in the tth period; δ j is the water consumption rate of the ith hydropower station; k is the conversion coefficient;

[0107] Reservoir water level constraints:

[0108]

[0109] Where: Z j,t is the water level of the jth hydropower station at the end of the tth period; is the upper limit of the water level of the jth hydropower station at the end of the tth period; is the lower limit of the water level of the jth hydropower station at the end of the tth period;

[0110] Reservoir water level daily fluctuation constraints:

[0111]

[0112] Where: Z j,end is the end-of-day water level of the jth reservoir; Z j,begin is the initial water level of the jth reservoir, Z j,allow is the daily water level fluctuation allowed for the jth reservoir;

[0113] Downflow flow restriction:

[0114]

[0115] Where: Q j,t is the discharge flow of the j-th hydropower station in the t-th period; is the maximum discharge flow of the jth hydropower station in the tth period; is the minimum discharge flow of the j-th hydropower station in the t-th period.

[0116] Power generation flow constraints:

[0117]

[0118] Where: is the maximum power generation flow of the jth hydropower station in the tth period; is the minimum power generation flow of the jth hydropower station in the tth period;

[0119] Water balance constraints in the time dimension:

[0120]

[0121] Where: V j,t V is the final storage capacity of the jth hydropower station at the tth period; j,t-1 is the initial storage capacity of the jth hydropower station in the tth period; R j,t is the inflow of the jth hydropower station in the tth period;

[0122] Water balance constraints in the spatial dimension:

[0123]

[0124] Where: I j,t is the interval flow of the jth hydropower station in the tth period; τ j-1 is the time it takes for the outflow from the j-1th hydropower station to reach the jth member reservoir;

[0125] Water level storage capacity curve:

[0126]

[0127] Where: f v,z () is the reservoir water level and storage capacity interpolation function;

[0128] Water level flow curve:

[0129]

[0130] Where: f Q,z () is the interpolation function of the discharge flow at the reservoir tailwater level;

[0131] 4) The transmission channel constraints between different sections in the power system are as follows:

[0132]

[0133] Where: is the transmission capacity of the dth transmission section; wN l,t is the output of the lth virtual wind power station in the tth period; sN k,t hN is the output of the kth virtual photovoltaic power station in the tth period; j,t is the output of the j-th hydropower station in the t-th period.

[0134] S3. Based on the working status constraints of the hydropower stations, virtual wind power stations and virtual photovoltaic power stations involved in the dispatch, a power system dispatch strategy optimization model is constructed;

[0135] The power system dispatch strategy optimization model includes an upper dispatch model and a lower dispatch model;

[0136] The upper-level scheduling model takes the highest matching degree of hydropower, wind power and photovoltaic power generation and the maximization of daily power generation as the objective function:

[0137]

[0138] Where: ρ is the load tracking coefficient, t is the time period variable, T is the total number of time periods, N r,t is the power generation output in the tth period, L t is the power load in the tth period, is the average load level, l, k, j are the numbers of wind power, photovoltaic power generation and hydropower stations respectively; L, K, J are the total number of wind power, photovoltaic power generation and hydropower stations respectively; wN l,t is the output of the lth virtual wind power station in the tth period; sN k,t hN is the output of the kth virtual photovoltaic power station in the tth period; j,t is the output of the jth hydropower station in the tth period; E is the total power generation in the day; m t is the number of hours in period t.

[0139] The objective function of the lower-level dispatching model is to minimize the output fluctuation of downstream hydropower stations:

[0140]

[0141] Where: F is the output fluctuation index of the downstream hydropower station; is the average output of downstream hydropower stations for external transmission; β is the proportion of electricity transmitted by downstream hydropower stations; k h is the conversion coefficient of the hth downstream hydropower station, is the power generation flow of the hth downstream hydropower station, is the net water head of the hth downstream hydropower station.

[0142] S4. Solve the power system dispatch strategy optimization model to obtain the dispatch optimization results, including:

[0143] S41, solving the upper-layer scheduling model by using the PSO algorithm of the Sigmoid function to obtain the upper-layer stage scheduling optimization result, the specific steps are as follows:

[0144] Input the load demand of the power system and the output forecast curves obtained from the hydropower station, virtual wind power station, and virtual photovoltaic power station into the PSO algorithm of the Sigmoid function;

[0145] Based on the Sigmoid function, the water storage curve of the hydropower station reservoir is initialized in the variable domain, and the water level is obtained as the initial state of the particle swarm;

[0146] Calculate the initial fitness of particles and determine the initial global extreme value and initial individual extreme value;

[0147] Based on the particle swarm update formula, the position and velocity of the particles are updated until the hydropower station, virtual wind power station, and virtual photovoltaic power station meet the working state constraints;

[0148] Calculate the fitness value of the particle position and speed after updating, compare with the historical individual extreme value and the global extreme value, determine the current individual optimal position and the current global optimal position, output the obtained results and put them into the result cluster; repeatedly update the particle position and speed until the maximum number of cycles is reached;

[0149] After the cycle is completed, the best result is selected from the result cluster to obtain the scheduling optimization result of the upper stage.

[0150] S42, based on the optimization result of the upper-level stage scheduling, the lower-level scheduling model is solved by using the MSPOA algorithm to obtain a hydropower station reservoir water level operation plan. If the reservoir water level of the downstream hydropower station in the hydropower station reservoir water level operation plan exceeds the set range at any time of operation, the upper-level scheduling model is re-solved until the reservoir water level of the downstream hydropower station meets the set range at any time of operation. The specific steps are as follows:

[0151] Formulate an initial trajectory within the feasible solution domain under the total output process of the hydropower station that meets the optimization results of the upper stage dispatch;

[0152] Determine the parameters of the MSPOA algorithm;

[0153] The POA algorithm is used to solve the lower-level dispatch model, and the time step is gradually refined in stages to optimize the water level dispatch of the cascade power stations until the accuracy requirements are met. The calculated operation plan of the hydropower station reservoir water level is output, as follows:

[0154] Set the time step to 4h, and use the POA algorithm to solve the lower scheduling model in the order of the cascade power stations. (Set the water levels at both ends of the first period of the first power station unchanged, adjust the water level in the middle, solve the upper and lower scheduling models one by one, select the optimal solution as the new water level in the middle of the first period of the power station, and update the water level trajectory. After traversing all power stations in the same way, push back one calculation period at a time until all periods are traversed, and proceed to the next step when the maximum number of cycles is met).

[0155] Set the time step to 2h, and use the POA algorithm with a time step of 4h to solve the new water level at the end of each time period as the new initial trajectory. Use the POA algorithm to find the optimal value, and proceed to the next step when the maximum number of cycles is met.

[0156] The time step is set to 1h, and the new water level at the end of each time period obtained by solving the POA algorithm with a time step of 2h is used as the new initial trajectory. The POA algorithm is used for optimization and stops when the optimization accuracy is met.

[0157] If the reservoir water level of the downstream hydropower station in the reservoir water level operation plan exceeds the set range at any time of operation, the initialization of the reservoir water storage curve based on the Sigmoid function in the variable domain is readjusted, and the new water level is recalculated as the initial state of the particle swarm, and the upper scheduling model is solved again until the reservoir water level of the downstream hydropower station meets the set range at any time of operation.

[0158] The dispatch optimization results include the dispatch optimization results of the upper stage and the water level operation plan of the hydropower station reservoir. The dispatch optimization results of the upper stage include the daily water level application mode of the hydropower reservoir, the daily water level control range, the daily power generation output plan of the hydropower unit, and the daily output plan of the system wind power and photovoltaic power.

[0159] Example 2

[0160] This embodiment provides a dispatching device for a water-wind-solar hybrid power system based on cascade reservoirs, including:

[0161] The scheduling object screening module is used to compare the power generation of the hydropower station in the power system with the power generation threshold, and select the hydropower station with a power generation threshold greater than the power generation threshold to participate in the scheduling; all wind power stations in the power system are equivalent to a virtual wind power station for scheduling; all photovoltaic power stations in the power system are equivalent to a virtual photovoltaic power station for scheduling;

[0162] The constraint condition establishment module is used to establish working status constraint conditions for the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations participating in the dispatch;

[0163] The strategy model building module is used to build a power system dispatch strategy optimization model based on the working status constraints of the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations involved in the dispatch;

[0164] The optimization dispatch generation module is used to solve the power system dispatch strategy optimization model and obtain the dispatch optimization results.

[0165] Example 3

[0166] A computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the dispatching method of a water-wind-solar complementary power system based on cascade reservoirs as described in Example 1 are implemented.

[0167] Example 4

[0168] An electronic device comprises a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the dispatching method of a water-wind-solar complementary power system based on cascade reservoirs as described in Example 1 are implemented.

[0169] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A dispatching method for a water-wind-solar hybrid power system based on cascade reservoirs, characterized in that: The following steps are involved: Compare the power generation of the hydropower station in the power system with the power generation threshold, and select the hydropower station with a power generation threshold greater than the power generation threshold to participate in the dispatch; all wind power stations in the power system are equivalent to a virtual wind power station for dispatch; all photovoltaic power stations in the power system are equivalent to a virtual photovoltaic power station for dispatch; Establish working status constraints for hydropower stations, virtual wind power stations, and virtual photovoltaic power stations participating in the dispatch; Based on the working status constraints of the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations involved in the dispatch, a power system dispatch strategy optimization model is constructed; The power system dispatch strategy optimization model is solved to obtain the dispatch optimization results.

2. According to claim 1, a dispatching method for a water-wind-solar hybrid power system based on cascade reservoirs is characterized in that: The working state constraints set for the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations participating in the dispatching include: The output constraints of the virtual wind power station are as follows: Where: is the installed capacity of the lth wind power station; g l,t is the output coefficient of the lth virtual wind power station in period t; is the minimum output of the lth virtual wind power station in period t; is the maximum output of the lth virtual wind power station in period t; The output constraints of the virtual photovoltaic power station are as follows: Where: is the installed capacity of the kth virtual photovoltaic power station; f k,t is the output coefficient of the kth virtual photovoltaic power station in period t; is the minimum output of the kth virtual photovoltaic power station during period t; is the maximum output of the kth virtual PV power station during period t; The output constraints of the hydropower station are as follows: Output constraints of hydropower station units: Where: are the minimum and maximum outputs allowed for the jth power station in the tth period, respectively; is the power generation flow of the jth hydropower station in the tth period; δ j is the water consumption rate of the ith hydropower station; k is the conversion coefficient; Reservoir water level constraints: Where: Z j,t is the water level of the jth hydropower station at the end of the tth period; is the upper limit of the water level of the jth hydropower station at the end of the tth period; is the lower limit of the water level of the jth hydropower station at the end of the tth period; Reservoir water level daily fluctuation constraints: Where: Z j,end is the end-of-day water level of the jth reservoir; Z j,begin is the initial water level of the jth reservoir, Z j,allow is the daily water level variation allowed for the jth reservoir; Downflow flow restriction: Where: Q j,t is the discharge flow of the j-th hydropower station in the t-th period; is the maximum discharge flow of the jth hydropower station in the tth period; is the minimum discharge flow of the j-th hydropower station in the t-th period; Power generation flow constraints: Where: is the maximum power generation flow of the jth hydropower station in the tth period; is the minimum power generation flow of the jth hydropower station in the tth period; Water balance constraints in the time dimension: Where: V j,t V is the final storage capacity of the jth hydropower station at the tth period; j,t-1 is the initial storage capacity of the jth hydropower station in the tth period; R j,t is the inflow of the jth hydropower station in the tth period; Water balance constraints in the spatial dimension: Where: I j,t is the interval flow of the jth hydropower station in the tth period; τ j-1 is the time it takes for the outflow from the j-1th hydropower station to reach the jth member reservoir; Water level storage capacity curve: Where: f v,z () is the reservoir water level and storage capacity interpolation function; Water level flow curve: Where: f Q,z () is the interpolation function of the discharge flow of the reservoir tailwater level; The transmission channel constraints between different sections in the power system are as follows: Where: is the transmission capacity of the dth transmission section; wN l,t is the output of the lth virtual wind power station in the tth period; sN k,t hN is the output of the kth virtual photovoltaic power station in the tth period; j,t is the output of the j-th hydropower station in the t-th period.

3. According to claim 1, a dispatching method for a water-wind-solar complementary power system based on cascade reservoirs is characterized in that: The power system dispatch strategy optimization model includes an upper-level dispatch model and a lower-level dispatch model; The upper-level dispatching model takes the highest matching degree between hydropower, wind power, photovoltaic power generation and load and the maximization of daily power generation as the objective function; The objective function of the lower-level dispatching model is to minimize the output fluctuation of the downstream hydropower stations.

4. According to claim 1, a dispatching method for a water-wind-solar hybrid power system based on cascade reservoirs is characterized in that: The upper-level dispatching model takes the highest matching degree between hydropower, wind power, photovoltaic power generation and load and the maximum daily power generation as the objective function: Where: ρ is the load tracking coefficient, t is the time period variable, T is the total number of time periods, N r,t is the power generation output in the tth period, L t is the power load in the tth period, is the average load level, l, k, j are the numbers of wind power, photovoltaic power generation and hydropower stations respectively; L, K, J are the total number of wind power, photovoltaic power generation and hydropower stations respectively; wN l,t is the output of the lth virtual wind power station in the tth period; sN k,t hN is the output of the kth virtual photovoltaic power station in the tth period; j,t is the output of the jth hydropower station in the tth period; E is the total power generation in the day; m t is the number of hours in period t.

5. According to claim 4, a dispatching method for a water-wind-solar hybrid power system based on cascade reservoirs is characterized in that: The objective function of the lower-level dispatching model is to minimize the output fluctuation of downstream hydropower stations: Where: F is the output fluctuation index of the downstream hydropower station; is the average output of downstream hydropower stations for external transmission; β is the proportion of electricity transmitted by downstream hydropower stations; k h is the conversion coefficient of the hth downstream hydropower station, is the power generation flow of the hth downstream hydropower station, is the net water head of the hth downstream hydropower station.

6. According to claim 4, a dispatching method for a water-wind-solar hybrid power system based on cascade reservoirs is characterized in that: The power system dispatch strategy optimization model is solved to obtain the dispatch optimization result, which specifically includes: The upper-layer scheduling model is solved by the PSO algorithm of the Sigmoid function to obtain the upper-layer stage scheduling optimization result; Based on the optimization results of the upper-level scheduling, the MSPOA algorithm is used to solve the lower-level scheduling model to obtain the reservoir water level operation plan of the hydropower station. If the reservoir water level of the downstream hydropower station in the reservoir water level operation plan exceeds the set range at any time, the upper-level scheduling model is re-solved until the reservoir water level of the downstream hydropower station meets the set range at any time of operation.

7. The method for dispatching a water-wind-solar hybrid power system based on cascade reservoirs according to claim 6, characterized in that: The upper-layer scheduling model is solved by the PSO algorithm of the Sigmoid function to obtain the upper-layer stage scheduling optimization result; Based on the optimization results of the upper-level scheduling, the lower-level scheduling model is solved by using the MSPOA algorithm to obtain the reservoir water level operation plan of the hydropower station. If the reservoir water level of the downstream hydropower station in the reservoir water level operation plan exceeds the set range at any time, the upper-level scheduling model is re-solved until the reservoir water level of the downstream hydropower station meets the set range at any time of operation, specifically including: Input the load demand of the power system and the output forecast curves obtained from the hydropower station, virtual wind power station, and virtual photovoltaic power station into the PSO algorithm of the Sigmoid function; Based on the Sigmoid function, the water storage curve of the hydropower station reservoir is initialized in the variable domain, and the water level is obtained as the initial state of the particle swarm; Calculate the initial fitness of particles and determine the initial global extreme value and initial individual extreme value; Based on the particle swarm update formula, the position and velocity of the particles are updated until the hydropower station, virtual wind power station, and virtual photovoltaic power station meet the working state constraints; Calculate the fitness value of the particle position and speed after updating, compare with the historical individual extreme value and the global extreme value, determine the current individual optimal position and the current global optimal position, output the obtained results and put them into the result cluster; repeatedly update the particle position and speed until the maximum number of cycles is reached; After the cycle is completed, the best result is selected from the result cluster to obtain the scheduling optimization result of the upper stage; Formulate an initial trajectory within the feasible solution domain under the total output process of the hydropower station that meets the optimization results of the upper stage dispatch; Determine the parameters of the MSPOA algorithm; The POA algorithm is used to solve the lower-level dispatch model, and the time step is gradually refined in stages to optimize the water level dispatch of the cascade power stations until the accuracy requirements are met, and the calculated reservoir water level operation plan of the hydropower station is output; If the reservoir water level of the downstream hydropower station in the reservoir water level operation plan exceeds the set range at any time of operation, the initialization of the reservoir water storage curve based on the Sigmoid function in the variable domain is readjusted, and the new water level is recalculated as the initial state of the particle swarm, and the upper scheduling model is solved again until the reservoir water level of the downstream hydropower station meets the set range at any time of operation.

8. A dispatching device for a water-wind-solar hybrid power system based on cascade reservoirs, comprising: The scheduling object screening module is used to compare the power generation of the hydropower station in the power system with the power generation threshold, and select the hydropower station with a power generation threshold greater than the power generation threshold to participate in the scheduling; all wind power stations in the power system are equivalent to a virtual wind power station for scheduling; all photovoltaic power stations in the power system are equivalent to a virtual photovoltaic power station for scheduling; The constraint condition establishment module is used to establish working status constraint conditions for the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations participating in the dispatch; The strategy model building module is used to build a power system dispatch strategy optimization model based on the working status constraints of the hydropower stations, virtual wind power stations, and virtual photovoltaic power stations involved in the dispatch; The optimization dispatch generation module is used to solve the power system dispatch strategy optimization model and obtain the dispatch optimization results.

9. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by the processor, the steps of the dispatching method of the water-wind-solar complementary power system based on cascade reservoirs as described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: It includes a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the dispatching method of the water-wind-solar complementary power system based on cascade reservoirs are implemented as described in any one of claims 1 to 7.

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