Optimized scheduling method and device for multi-energy complementary system

Abstract

The invention provides an optimal scheduling method and device for a multi-energy complementary system. The optimal scheduling method comprises the following steps: acquiring the on-grid price of the multi-energy complementary system; and solving a pre-constructed optimal scheduling model according to the on-grid price of the multi-energy complementary system to obtain the generation power and the pumping power of the pumped storage power station in the multi-energy complementary system. And solving the preferentially constructed unit combination model according to the generation power and the pumping power of the pumped storage power station to obtain the state, the generation power and the pumping power of each pumped storage unit in the pumped storage power station. According to the method, each pumped storage unit is considered, the characteristics of the pumped storage units are fully combined, and the optimization scheduling precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of multi-energy complementarity, and in particular to an optimization scheduling method and device for a multi-energy complementarity system. Background Art

[0002] The multi-energy complementary system can include wind farms, photovoltaic power stations and pumped storage power stations. With the large-scale grid connection of wind farms, photovoltaic power stations and other new energy power generation, the operating conditions of pumped storage units in pumped storage power stations are more frequently changed, and the number of starts and stops is more, which will increase the wear of pumped storage units and greatly affect the reliability of pumped storage units and the economy of pumped storage power stations. Therefore, it is necessary to jointly dispatch wind farms, photovoltaic power stations and pumped storage power stations.

[0003] In the dispatching method provided by the relevant technology, the operating indicators such as the wind-solar complementary system, pumped storage power station, and power grid transmission power are used as constraints to obtain an optimal dispatching model with the minimum fluctuation of wind-solar complementary output, the minimum fluctuation of combined system output, and the tracking load curve. Finally, the model is solved and optimized using an improved simulated annealing algorithm. In the process of constructing the optimal dispatching model, due to the large number of operating conditions of the pumped storage units, the solution process of the optimal dispatching model is complicated, resulting in poor optimization dispatching accuracy. Summary of the invention

[0004] In order to solve the problem of poor optimization scheduling accuracy in the prior art, the present application provides an optimization scheduling method for a multi-energy complementary system, which may include:

[0005] Obtain the on-grid electricity price of multi-energy complementary systems.

[0006] The pre-constructed optimal dispatch model is solved according to the on-grid electricity price of the multi-energy complementary system to obtain the generating power and pumping power of the pumped-storage power station in the multi-energy complementary system.

[0007] The unit combination model constructed in priority is solved according to the generating power and pumping power of the pumped-storage power station to obtain the state, generating power and pumping power of each pumped-storage unit in the pumped-storage power station.

[0008] Optionally, a pre-built optimization dispatch model is solved according to the on-grid electricity price of the multi-energy complementary system to obtain the power generation power and pumping power of the pumped storage power station in the multi-energy complementary system, including:

[0009] According to the on-grid electricity price of the multi-energy complementary system, the continuous particle swarm algorithm is used to solve the optimization scheduling model to obtain the generating power and pumping power of the pumped storage power station in the multi-energy complementary system.

[0010] In some possible implementations, the unit commitment model includes a first objective function and a first constraint condition.

[0011] Among them, the first objective function aims to minimize the startup cost of the pumped storage power station.

[0012] The first constraint condition includes power plant power balance constraint and start / stop time constraint.

[0013] Furthermore, the first objective function satisfies:

[0014]

[0015] Among them, F1 represents the startup cost of the pumped storage power station, and N represents the number of pumped storage units; represents the startup cost of the i-th pumped storage unit in the power generation state, represents the startup cost of the i-th pumped storage unit in the pumping state; represents the number of starts of the i-th pumped storage unit in the power generation state, represents the number of starts of the i-th pumped storage unit in the pumping state, satisfying:

[0016]

[0017] Among them, U i,t represents the state of the i-th pumped storage unit in period t; U i,t =1 means that the i-th pumped storage unit is in the power generation state during the t period, U i,t =0 means that the i-th pumped storage unit is in shutdown state during period t, U i,t =-1 means that the i-th pumped storage unit is in the pumping state during period t; U i,t-1 represents the state of the i-th pumped storage unit in the t-1 period; U i,t-1 =1 means that the i-th pumped storage unit is in the power generation state during the t-1 period, U i,t-1 =0 means that the i-th pumped storage unit is in shutdown state during the period t-1, U i,t-1 =-1 means that the i-th pumped storage unit is in the pumping state during the t-1 period; T represents the total number of time periods.

[0018] Exemplarily, the power balance constraint of the power station satisfies:

[0019]

[0020] Among them, P i,t represents the generating power / pumping power of the i-th pumped storage unit in period t; P t It represents the generating power / pumping power of the pumped-storage power station in period t.

[0021] The start and stop time constraints are satisfied:

[0022]

[0023] in, represents the continuous operation time of the i-th pumped storage unit in period t, represents the downtime of the i-th pumped storage unit in period t, Indicates the minimum continuous operation time of the pumped storage unit. Indicates the minimum downtime of the pumped storage unit.

[0024] In some possible implementations, the unit combination model constructed preferentially is solved according to the power generation power and pumping power of the pumped storage power station to obtain the state, power generation power and pumping power of each pumped storage unit in the pumped storage power station, including:

[0025] Obtain the startup cost of the pumped-storage power station under power generation conditions and the startup cost of the pumped-storage power station under pumping conditions.

[0026] According to the startup cost of the pumped-storage power station under power generation conditions, the startup cost of the pumped-storage power station under pumping conditions, the power generation power and pumping power of the pumped-storage power station, the discrete particle swarm algorithm is used to solve the unit combination model to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

[0027] Furthermore, according to the startup cost of the pumped storage power station under power generation conditions, the startup cost of the pumped storage power station under pumping conditions, the power generation power and pumping power of the pumped storage power station, the discrete particle swarm algorithm is used to solve the unit combination model to obtain the state, power generation power and pumping power of each pumped storage unit, including:

[0028] Set the initial position and initial velocity of each particle in the particle population.

[0029] According to the initial position, initial speed, generating power and pumping power of the pumped storage power station, the load demand of the power grid is allocated to each pumped storage unit using the equal incremental rate method.

[0030] The fitness value of the particle population is calculated based on the first objective function and the first constraint condition.

[0031] The individual optimal solution and the group optimal solution in each iteration are updated according to the fitness value of the particle population, the speed of each particle is updated according to the continuous particle swarm algorithm, and the position of each particle is updated according to the tanh function.

[0032] When the number of iterations does not meet the maximum number of iterations, iterate again according to the individual optimal solution, the updated speed and position of each particle until the number of iterations reaches the maximum number of iterations, and output the group optimal solution; when the number of iterations meets the maximum number of iterations, output the group optimal solution; wherein the group optimal solution includes the state, power generation and pumping power of each pumped storage unit.

[0033] Optionally, set the initial position and initial velocity of each particle in the particle population, including:

[0034] Set the initial position of each particle as follows:

[0035]

[0036] Among them, U represents the initial position of each particle, U N,T Indicates the state of the Nth pumped storage unit in the T period;

[0037] Sets the initial velocity of each particle to a random number.

[0038] Optionally, update the position of each particle according to the tanh function, including:

[0039] Update the position of each particle as follows:

[0040]

[0041] in, represents the position of the particle in time period t in the kth iteration, V t k represents the velocity of the particle in the kth iteration in the t period; rand represents a random function in the interval [0,1]; tanh(V t k ) represents the tanh function, satisfying

[0042] In some other possible implementations, the optimization scheduling model includes a second objective function and a second constraint condition.

[0043] Among them, the second objective function aims to maximize the economic benefits of the multi-energy complementary system.

[0044] The second constraint conditions include system power balance constraint, spinning reserve constraint, power station power constraint and reservoir energy constraint.

[0045] Optionally, the second objective function satisfies:

[0046]

[0047] Among them, F2 represents the economic benefits of the multi-energy complementary system; T represents the total number of time periods; P t v represents the power generation of the photovoltaic power station in the multi-energy complementary system in time period t, 0≤P t v ≤P t vf , P t vf represents the resource quantity of the photovoltaic power station in period t; P t w represents the power generation of the wind farm in the multi-energy complementary system in period t, 0≤P t w ≤P t wf , P t wf represents the resource quantity of the wind farm in period t; P t g represents the power generation of the pumped storage power station in period t, P t gmin ≤P t g ≤P t gmax , P t gmin represents the minimum power generation of the pumped storage power station in period t, P t gmax represents the maximum power generation capacity of the pumped storage power station in period t; P t p represents the pumping power of the pumped storage power station in period t, P t pmin ≤P t p ≤P t pmax , P t pmax represents the maximum pumping power of the pumped storage power station in period t, P t pmin represents the minimum pumping power of the pumped storage power station in period t; P t r represents the abandoned power of the photovoltaic power station and the wind farm in period t; C t represents the on-grid electricity price of the multi-energy complementary system in period t, represents the pumped electricity price of the pumped storage power station in period t, It represents the penalty electricity price for power abandonment of photovoltaic power station and wind farm in period t.

[0048] The system power balance constraints meet:

[0049]

[0050] Among them, P t load Represents the load demand of the power grid; P t vf Represents the resource amount of the photovoltaic power station, P t wf Represents the resource volume of the wind farm.

[0051] The spinning reserve constraints satisfy:

[0052] (P t wmax -P t w )+(P t vmax -P t v )+(P t gmax -P t g )≥R t u

[0053] Among them, P t wmax It represents the maximum power generation of the wind farm in period t, P t vmax Represents the maximum power generation of the photovoltaic power station in period t; R t u Represents the spinning reserve capacity of the power grid.

[0054] The power constraints of the power station are satisfied:

[0055]

[0056] Among them, P max represents the total installed capacity of pumped storage power stations; represents the upper reservoir capacity of the pumped storage power station in period t, W represents the lower reservoir capacity of the pumped storage power station in period t; g It represents the water energy conversion coefficient of the pumped storage power station under power generation conditions, W p It represents the water energy conversion coefficient of the pumped storage power station under pumping conditions.

[0057] The reservoir energy constraints satisfy:

[0058]

[0059] Among them, E umin represents the minimum storage capacity of the upper reservoir of the pumped storage power station, E umax Indicates the maximum storage capacity of the upper reservoir of the pumped storage power station; Edmin It represents the minimum storage capacity of the lower reservoir of the pumped storage power station, E dmax It indicates the maximum value of the lower reservoir capacity of the pumped storage power station; represents the upper reservoir capacity of the pumped storage power station at time t+1, represents the lower reservoir capacity of the pumped storage power station in the t+1 period; t represents the current period number; It represents the upper reservoir capacity of the pumped storage power station in the initial dispatch period; It indicates the upper reservoir capacity of the pumped storage power station at the end of the dispatch period; It represents the lower reservoir capacity of the pumped storage power station in the initial dispatch period; It indicates the lower reservoir capacity of the pumped-storage power station at the end of the dispatching period.

[0060] On the other hand, the present application provides an optimization scheduling device for a multi-energy complementary system, comprising:

[0061] The acquisition module is used to obtain the on-grid electricity price of the multi-energy complementary system.

[0062] The first solving module is used to solve the pre-built optimization scheduling model according to the on-grid electricity price of the multi-energy complementary system to obtain the generating power and pumping power of the pumped storage power station in the multi-energy complementary system.

[0063] The second solving module is used to solve the unit combination model constructed preferentially according to the power generation power and pumping power of the pumped-storage power station, and obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

[0064] In some possible implementations, the first solution module is specifically used to:

[0065] According to the on-grid electricity price of the multi-energy complementary system, the continuous particle swarm algorithm is used to solve the optimization scheduling model to obtain the generating power and pumping power of the pumped storage power station in the multi-energy complementary system.

[0066] Optionally, the unit commitment model includes a first objective function and a first constraint condition.

[0067] Among them, the first objective function aims to minimize the startup cost of the pumped storage power station.

[0068] The first constraint condition includes power plant power balance constraint and start / stop time constraint.

[0069] Furthermore, the first objective function satisfies:

[0070]

[0071] Among them, F1 represents the startup cost of the pumped storage power station, and N represents the number of pumped storage units; represents the startup cost of the i-th pumped storage unit in the power generation state, represents the startup cost of the i-th pumped storage unit in the pumping state; represents the number of starts of the i-th pumped storage unit in the power generation state, represents the number of starts of the i-th pumped storage unit in the pumping state, satisfying:

[0072]

[0073] Among them, U i,t represents the state of the i-th pumped storage unit in period t; U i,t =1 means that the i-th pumped storage unit is in the power generation state during the t period, U i,t =0 means that the i-th pumped storage unit is in shutdown state during period t, U i,t =-1 means that the i-th pumped storage unit is in the pumping state during period t; U i,t-1 represents the state of the i-th pumped storage unit in the t-1 period; U i,t-1 =1 means that the i-th pumped storage unit is in the power generation state during the t-1 period, U i,t-1 =0 means that the i-th pumped storage unit is in shutdown state during the period t-1, U i,t-1 =-1 means that the i-th pumped storage unit is in the pumping state during the t-1 period; T represents the total number of time periods.

[0074] The power balance constraints of the power station meet the following requirements:

[0075]

[0076] Among them, P i,t P represents the generating power / pumping power of the i-th pumped storage unit in period t; t represents the generating power / pumping power of the pumped-storage power station in period t;

[0077] The start and stop time constraints are satisfied:

[0078]

[0079] in, represents the continuous operation time of the i-th pumped storage unit in period t, represents the downtime of the i-th pumped storage unit in period t, Indicates the minimum continuous operation time of the pumped storage unit. Indicates the minimum downtime of the pumped storage unit.

[0080] In some other possible implementations, the second solution module is specifically used to:

[0081] Obtain the startup cost of the pumped-storage power station under power generation conditions and the startup cost of the pumped-storage power station under pumping conditions.

[0082] According to the startup cost of the pumped-storage power station under power generation conditions, the startup cost of the pumped-storage power station under pumping conditions, the power generation power and pumping power of the pumped-storage power station, the discrete particle swarm algorithm is used to solve the unit combination model to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

[0083] Furthermore, the second solution module is specifically used for:

[0084] Set the initial position and initial velocity of each particle in the particle population.

[0085] According to the initial position, initial speed, generating power and pumping power of the pumped storage power station, the load demand of the power grid is allocated to each pumped storage unit using the equal incremental rate method.

[0086] The fitness value of the particle population is calculated based on the first objective function and the first constraint condition.

[0087] The individual optimal solution and the group optimal solution in each iteration are updated according to the fitness value of the particle population, the speed of each particle is updated according to the continuous particle swarm algorithm, and the position of each particle is updated according to the tanh function.

[0088] When the number of iterations does not meet the maximum number of iterations, iterate again according to the individual optimal solution, the updated speed and position of each particle until the number of iterations reaches the maximum number of iterations, and output the group optimal solution; when the number of iterations meets the maximum number of iterations, output the group optimal solution; wherein the group optimal solution includes the state, power generation and pumping power of each pumped storage unit.

[0089] Furthermore, the second solving module is specifically used to set the initial position of each particle according to the following formula:

[0090]

[0091] Among them, U represents the initial position of each particle, U N,T Indicates the status of the Nth pumped storage unit in the T period.

[0092] The second solving module is specifically used to set the initial velocity of each particle to a random number.

[0093] Optionally, the second solving module is used to update the position of each particle according to the following formula:

[0094]

[0095] in, represents the position of the particle in time period t in the kth iteration, V t k represents the velocity of the particle in the kth iteration in the t period; rand represents a random function in the interval [0,1]; tanh(V t k ) represents the tanh function, satisfying

[0096] Exemplarily, the optimization scheduling model includes a second objective function and a second constraint.

[0097] Among them, the second objective function aims to maximize the economic benefits of the multi-energy complementary system.

[0098] The second constraint conditions include system power balance constraint, spinning reserve constraint, power station power constraint and reservoir energy constraint.

[0099] Optionally, the second objective function satisfies:

[0100]

[0101] Among them, F2 represents the economic benefits of the multi-energy complementary system; T represents the total number of time periods; P t v represents the power generation of the photovoltaic power station in the multi-energy complementary system in time period t, 0≤P t v ≤P t vf , P t vf represents the resource quantity of the photovoltaic power station in period t; P t w represents the power generation of the wind farm in the multi-energy complementary system in period t, 0≤P t w ≤P t wf , P t wf represents the resource quantity of the wind farm in period t; P t g represents the power generation of the pumped storage power station in period t, P t gmin ≤P t g ≤P t gmax , P t gmin represents the minimum power generation of the pumped storage power station in period t, P t gmax represents the maximum power generation capacity of the pumped storage power station in period t; P tp represents the pumping power of the pumped storage power station in period t, P t pmin ≤P t p ≤P t pmax , P t pmax represents the maximum pumping power of the pumped storage power station in period t, P t pmin represents the minimum pumping power of the pumped storage power station in period t; P t r represents the abandoned power of the photovoltaic power station and the wind farm in period t; C t represents the on-grid electricity price of the multi-energy complementary system in period t, represents the pumped electricity price of the pumped storage power station in period t, represents the power abandonment penalty price of the photovoltaic power station and the wind farm in period t.

[0102] The system power balance constraints meet:

[0103]

[0104] Among them, P t load Represents the load demand of the power grid; P t vf Represents the resource amount of the photovoltaic power station, P t wf Represents the resource volume of the wind farm.

[0105] The spinning reserve constraints satisfy:

[0106] (P t wmax -P t w )+(P t vmax -P t v )+(P t gmax -P t g )≥R t u

[0107] Among them, P t wmax It represents the maximum power generation of the wind farm in period t, P t vmax Represents the maximum power generation of the photovoltaic power station in period t; R t u Represents the spinning reserve capacity of the power grid.

[0108] The power constraints of the power station are satisfied:

[0109]

[0110] Among them, P max represents the total installed capacity of pumped storage power stations; represents the upper reservoir capacity of the pumped storage power station in period t, W represents the lower reservoir capacity of the pumped storage power station in period t; g It represents the water energy conversion coefficient of the pumped storage power station under power generation conditions, W p It represents the water energy conversion coefficient of the pumped storage power station under pumping conditions.

[0111] The reservoir energy constraints satisfy:

[0112]

[0113] Among them, E umin represents the minimum storage capacity of the upper reservoir of the pumped storage power station, E umax Indicates the maximum storage capacity of the upper reservoir of the pumped storage power station; E dmin It represents the minimum storage capacity of the lower reservoir of the pumped storage power station, E dmax It indicates the maximum value of the lower reservoir capacity of the pumped storage power station; represents the upper reservoir capacity of the pumped storage power station at time t+1, represents the lower reservoir capacity of the pumped storage power station in the t+1 period; t represents the current period number; It represents the upper reservoir capacity of the pumped storage power station in the initial dispatch period; It indicates the upper reservoir capacity of the pumped storage power station at the end of the dispatch period; It represents the lower reservoir capacity of the pumped storage power station in the initial dispatch period; It indicates the lower reservoir capacity of the pumped-storage power station at the end of the dispatching period.

[0114] On the other hand, the present application also provides a computer device, including: one or more processors.

[0115] A processor is used to execute one or more programs.

[0116] When one or more programs are executed by one or more processors, the above-mentioned optimization scheduling method is implemented.

[0117] In another aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the above-mentioned optimization scheduling method is implemented.

[0118] Compared with the prior art, the beneficial effects of this application are:

[0119] In the optimization scheduling method provided by this application, the state, power generation and pumping power of each pumped storage unit in the pumped storage power station are obtained by combining the optimization scheduling model and the unit combination model. It can be seen that this application takes into account each pumped storage unit, fully combines the characteristics of the pumped storage unit, and improves the optimization scheduling accuracy.

[0120] This application uses a continuous particle swarm algorithm to solve the optimization scheduling model, which is used to solve the scheduling problem at the level of multi-energy complementary systems. At the same time, this application uses a discrete particle swarm algorithm to solve the unit combination model, which solves the unit combination problem at the level of pumped storage power stations. That is, this application uses a dual particle swarm optimization algorithm to achieve the optimal scheduling of multi-energy complementary systems and the combination of pumped storage units.

[0121] This application combines the combination of pumped storage units with the coordinated multi-energy complementary system to achieve comprehensive, efficient and economical optimization scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0123] Figure 1 A schematic flow chart of an optimization scheduling method in an embodiment of the present application;

[0124] Figure 2 A schematic diagram of the resource amount of a wind farm, the resource amount of a photovoltaic power station, and the load demand of a power grid in an embodiment of the present application;

[0125] Figure 3 A schematic diagram of the power generation power of a photovoltaic power station, the power generation power of a wind farm, the power generation power of a pumped-storage power station, and the pumping power in an embodiment of the present application;

[0126] Figure 4 A schematic diagram of the power generation power / pumping power of different pumped storage units in the embodiments of the present application;

[0127] Figure 5 Schematic diagram of the resource quantity of the wind farm, the resource quantity of the photovoltaic power station and the load demand of the power grid in the embodiment of the present application in the period t;

[0128] Figure 6 It is a schematic structural diagram of the optimization scheduling device in the embodiment of the present application. DETAILED DESCRIPTION

[0129] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0130] The terms "first", "second", etc. in the specification embodiments, claims, and drawings of the present application are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, including a series of steps or units. The method, system, product, or device is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0131] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0132] Embodiment 1:

[0133] The present application embodiment provides an optimization scheduling method for a multi-energy complementary system, such as Figure 1 The optimization scheduling method 100 comprises the following steps:

[0134] Step S1: Obtain the on-grid electricity price of the multi-energy complementary system.

[0135] Step S2: Solve the pre-constructed optimization dispatch model according to the on-grid electricity price of the multi-energy complementary system to obtain the generating power and pumping power of the pumped-storage power station in the multi-energy complementary system.

[0136] Step S3: Solve the unit combination model that is preferentially constructed according to the power generation power and pumping power of the pumped-storage power station to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

[0137] In some embodiments, in step S2, solving a pre-built optimization scheduling model according to the on-grid electricity price of the multi-energy complementary system to obtain the power generation power and pumping power of the pumped storage power station in the multi-energy complementary system includes:

[0138] According to the on-grid electricity price of the multi-energy complementary system, the continuous particle swarm algorithm is used to solve the optimization scheduling model to obtain the generating power and pumping power of the pumped storage power station in the multi-energy complementary system.

[0139] The continuous particle swarm optimization algorithm can also be called the standard particle swarm optimization algorithm (SPSO), which is a heuristic artificial intelligence optimization algorithm used to solve continuous variable optimization problems. It mainly uses the characteristics of birds and other organisms using group information to improve the efficiency of multi-dimensional search. It has the characteristics of fast calculation speed, high efficiency, and few parameters, so it is widely used. SPSO updates the speed and position of individual particles in the solution space according to the number of iterations and the group fitness value, so as to find the optimal solution of the search space.

[0140] Optionally, the unit commitment model includes a first objective function and a first constraint condition.

[0141] The first objective function aims to minimize the startup cost of the pumped storage power station. The first constraint condition includes the power balance constraint of the power station and the start-stop time constraint.

[0142] Furthermore, the first objective function satisfies:

[0143]

[0144] Among them, F1 represents the startup cost of the pumped storage power station, and N represents the number of pumped storage units; represents the startup cost of the i-th pumped storage unit in the power generation state, represents the startup cost of the i-th pumped storage unit in the pumping state; represents the number of starts of the i-th pumped storage unit in the power generation state, represents the number of starts of the i-th pumped storage unit in the pumping state, satisfying:

[0145]

[0146] Among them, U i,t represents the state of the i-th pumped storage unit in period t; U i,t =1 means that the i-th pumped storage unit is in the power generation state during the t period, U i,t =0 means that the i-th pumped storage unit is in shutdown state during period t, U i,t =-1 means that the i-th pumped storage unit is in the pumping state during period t; U i,t-1 represents the state of the i-th pumped storage unit in the t-1 period; U i,t-1 =1 means that the i-th pumped storage unit is in the power generation state during the t-1 period, Ui,t-1 =0 means that the i-th pumped storage unit is in shutdown state during the period t-1, U i,t-1 =-1 means that the i-th pumped storage unit is in the pumping state during the t-1 period; T represents the total number of time periods.

[0147] Exemplarily, the power balance constraint of the power station satisfies:

[0148]

[0149] Among them, P i,t P represents the generating power / pumping power of the i-th pumped storage unit in period t; t represents the generating power / pumping power of the pumped-storage power station in period t;

[0150] The start and stop time constraints are satisfied:

[0151]

[0152] in, represents the continuous operation time of the i-th pumped storage unit in period t, represents the downtime of the i-th pumped storage unit in period t, Indicates the minimum continuous operation time of the pumped storage unit. Indicates the minimum downtime of the pumped storage unit.

[0153] In other embodiments, the step S3 of solving the unit combination model constructed preferentially according to the power generation power and pumping power of the pumped-storage power station to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station includes:

[0154] Obtain the startup cost of the pumped-storage power station under power generation conditions and the startup cost of the pumped-storage power station under pumping conditions.

[0155] According to the startup cost of the pumped-storage power station under power generation conditions, the startup cost of the pumped-storage power station under pumping conditions, the power generation power and pumping power of the pumped-storage power station, the discrete particle swarm optimization (DPSO) algorithm is used to solve the unit combination model to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

[0156] Specifically, according to the startup cost of the pumped storage power station under power generation conditions, the startup cost of the pumped storage power station under pumping conditions, the power generation power and pumping power of the pumped storage power station, the discrete particle swarm algorithm is used to solve the unit combination model to obtain the state, power generation power and pumping power of each pumped storage unit, including:

[0157] Set the initial position and initial velocity of each particle in the particle population.

[0158] Specifically, the initial position of each particle is set as follows:

[0159]

[0160] Among them, U represents the initial position of each particle, U N,T Indicates the status of the Nth pumped storage unit in the T period.

[0161] You can also set the initial velocity of each particle to a random number.

[0162] According to the initial position, initial speed, generating power and pumping power of the pumped storage power station, the load demand of the power grid is allocated to each pumped storage unit using the equal incremental rate method.

[0163] The fitness value of the particle population is calculated based on the first objective function and the first constraint condition.

[0164] The individual optimal solution and the group optimal solution in each iteration are updated according to the fitness value, the speed of each particle is updated according to the continuous particle swarm algorithm, and the position of each particle is updated according to the tanh function.

[0165] Specifically, update the position of each particle as follows:

[0166]

[0167] in, represents the position of the particle in time period t in the kth iteration, V t k represents the velocity of the particle in the kth iteration in the t period. In the embodiment of the present application, V t k ∈[-6,6]. rand represents a random function in the interval [0,1]; tanh(V t k ) represents the tanh function, satisfying

[0168] When the number of iterations does not meet the maximum number of iterations, iterate again according to the individual optimal solution, the updated speed and position of each particle until the number of iterations reaches the maximum number of iterations, and output the group optimal solution; when the number of iterations meets the maximum number of iterations, output the group optimal solution; wherein the group optimal solution includes the state, power generation and pumping power of each pumped storage unit.

[0169] Exemplarily, in some other possible implementations, the optimization scheduling model includes a second objective function and a second constraint condition.

[0170] The second objective function aims to maximize the economic benefits of the multi-energy complementary system. The second constraint conditions include system power balance constraint, spinning reserve constraint, power station power constraint and reservoir energy constraint.

[0171] Optionally, the second objective function satisfies:

[0172]

[0173] Among them, F2 represents the economic benefits of the multi-energy complementary system; T represents the total number of time periods; P t v represents the power generation of the photovoltaic power station in the multi-energy complementary system in time period t, 0≤P t v ≤P t vf , P t vf represents the resource quantity of the photovoltaic power station in period t; P t w represents the power generation of the wind farm in the multi-energy complementary system in period t, 0≤P t w ≤P t wf , P t wf represents the resource quantity of the wind farm in period t; P t g represents the power generation of the pumped storage power station in period t, P t gmin ≤P t g ≤P t gmax , P t gmin represents the minimum power generation of the pumped storage power station in period t, P t gmax represents the maximum power generation capacity of the pumped storage power station in period t; P t p represents the pumping power of the pumped storage power station in period t, P t pmin ≤P t p ≤P t pmax , P t pmax represents the maximum pumping power of the pumped storage power station in period t, P t pmin represents the minimum pumping power of the pumped storage power station in period t; P t rrepresents the abandoned power of the photovoltaic power station and the wind farm in period t; C t represents the on-grid electricity price of the multi-energy complementary system in period t, represents the pumped electricity price of the pumped storage power station in period t, represents the power abandonment penalty price of the photovoltaic power station and the wind farm in period t.

[0174] The system power balance constraints meet:

[0175]

[0176] Among them, P t load Represents the load demand of the power grid; P t vf Represents the resource amount of the photovoltaic power station, P t wf Indicates the resource volume of the wind farm.

[0177] The spinning reserve constraints satisfy:

[0178] (P t wmax -P t w )+(P t vmax -P t v )+(P t gmax -P t g )≥R t u

[0179] Among them, P t wmax It represents the maximum power generation of the wind farm in period t, P t vmax Represents the maximum power generation of the photovoltaic power station in period t; R t u Represents the spinning reserve capacity of the power grid.

[0180] The power constraints of the power station are satisfied:

[0181]

[0182] Among them, P max represents the total installed capacity of pumped storage power stations; represents the upper reservoir capacity of the pumped storage power station in period t, W represents the lower reservoir capacity of the pumped storage power station in period t; g It represents the water energy conversion coefficient of the pumped storage power station under power generation conditions, Wp It represents the water energy conversion coefficient of the pumped storage power station under pumping conditions.

[0183] The reservoir energy constraints satisfy:

[0184]

[0185] Among them, E umin represents the minimum storage capacity of the upper reservoir of the pumped storage power station, E umax Indicates the maximum storage capacity of the upper reservoir of the pumped storage power station; E dmin It represents the minimum storage capacity of the lower reservoir of the pumped storage power station, E dmax It indicates the maximum value of the lower reservoir capacity of the pumped storage power station; represents the upper reservoir capacity of the pumped storage power station at time t+1, represents the lower reservoir capacity of the pumped storage power station in the t+1 period; t represents the current period number; It represents the upper reservoir capacity of the pumped storage power station in the initial dispatch period; It indicates the upper reservoir capacity of the pumped storage power station at the end of the dispatch period; It represents the lower reservoir capacity of the pumped storage power station in the initial dispatch period; It indicates the lower reservoir capacity of the pumped-storage power station at the end of the dispatching period.

[0186] In this embodiment, in the multi-energy complementary system, the installed capacity of the wind farm is 5350MW, and the installed capacity of the photovoltaic power station is 3150MW. The pumped storage power station can include 4 300MW pumped storage units (all of which can be reversible pump turbine units). The scheduling cycle is 24 hours a day, and the time scale is 15 minutes.

[0187] The main parameters of the pumped storage unit are shown in Table 1:

[0188] Table 1

[0189]

[0190]

[0191] In Table 1, Q(P) represents the fitting function, P represents the power generation of the pumped storage unit, Q represents the flow rate of the pumped storage unit, and a0, a1, and a2 represent the coefficients of the fitting function.

[0192] The parameters of the continuous particle swarm algorithm and the discrete particle swarm algorithm are shown in Table 2:

[0193] Table 2

[0194]

[0195] In the embodiment of the present application, the resource quantity of the wind farm, the resource quantity of the photovoltaic power station and the load demand of the power grid are as follows: Figure 2 The present embodiment does not consider the output change of conventional energy sources such as thermal power.

[0196] (1) Using SPSO to solve the optimization scheduling model

[0197] The power generation of photovoltaic power stations, the power generation of wind farms, the power generation and pumping power of pumped storage power stations are as follows: Figure 3 As shown. Figure 3 It can be seen that in this scenario, the pumped storage power station can be equivalent to a pumped storage unit, and the operation mode of each pumped storage unit is equivalent to "one pumping and three generating", with power generation and pumping operation durations of 45 and 29 periods respectively. Around the 8th to 20th period, the total output of conventional energy, wind farms, and photovoltaic power stations is small and cannot meet the load demand of the power grid. The pumped storage power station uses the reservoir capacity to generate electricity to fill the power gap. Around the 30th to 60th period, the output of wind farms and photovoltaic power stations is too much. The pumped storage power station uses excess wind and light resources and stores them in the upper reservoir in the form of hydropower. Around the 65th to 90th period, the pumped storage power station uses the pumped storage electricity to generate electricity to meet the large-capacity load demand. This shows that the pumped storage power station has the ability to load peaks and fill valleys, while reducing the impact of the fluctuation of the online power on the stable operation of the power system, it can also maximize the consumption of new energy.

[0198] (2) Solving the unit combination model based on DPSO

[0199] The operating indicators of the pumped storage unit can be shown in Table 3:

[0200] Table 3

[0201]

[0202] The comparison of the unit regulation intensity index considering unit combination and the index not considering unit combination is shown in Table 4:

[0203] Table 4

[0204]

[0205] In addition, it can be seen from Table 4 that when the entire pumped-storage power station is equivalent to a pumped-storage unit and the pumped-storage unit combination in the pumped-storage power station is not considered for scheduling, its start-stop and operation status are not consistent with the actual status of each pumped-storage unit in the pumped-storage power station. The average operating intensity of the pumped-storage unit after considering the pumped-storage unit combination is reduced by more than 25%, especially the intensity reduction in the power generation state is more obvious, and the intensity of the operating time and the number of power generation starts are reduced by 57.78% and 33.33% respectively. Therefore, the unit combination model can meet the more refined and reasonable dispatching requirements of pumped-storage units, and reasonably control the operation and regulation intensity of each pumped-storage unit while obtaining higher economic benefits; especially when there are obvious differences in the characteristics of each pumped-storage unit in the pumped-storage unit power station, it can significantly improve the operation efficiency and reliability of the pumped-storage power station.

[0206] In the embodiment of the present application, the power generation power / pumping power of different pumped storage units is as follows: Figure 4 As shown in Figure 2, the status of different pumped storage units is as follows: Figure 5 shown. Figure 5 In the figure, the blank part indicates that the pumped storage unit is in shutdown state. Figure 4 and Figure 5 It can be seen that the output of the pumped storage unit in each period meets the total load demand of the pumped storage power station well, and also meets the constraints such as the output limit of the pumped storage unit. In addition, the #1 unit has the longest power generation and pumping operation time, which are 45 and 29 periods respectively, and the number of starts is 4 times. Its "one pumping and three generating" operation mode is also consistent with the actual situation; at the same time, the #1 pumped storage unit has the largest output, bears more grid load, and the overall regulation intensity is relatively large. On the contrary, the #4 pumped storage unit has the smallest working regulation intensity, and the operation time and start-stop times are reduced by 95% and 75% respectively compared with the #1 pumped storage unit. Through analysis, it can be seen that the startup cost and water consumption capacity of the #4 pumped storage unit to the #1 pumped storage unit decrease successively, so the number of calls increases, and the peak-shaving tasks undertaken are gradually increasing. The differences in the start-stop status and output distribution of each pumped-storage unit are related to the start-stop and load distribution strategies affected by the startup cost and water consumption of the pumped-storage unit, indicating that the combination of pumped-storage units is related to the characteristics of each pumped-storage unit, verifying the feasibility and rationality of the unit combination model provided in the embodiment of the present application.

[0207] Embodiment 2:

[0208] Based on the same inventive concept, the embodiment of the present application also provides an optimization scheduling device for a multi-energy complementary system. Figure 6 As shown, the optimization scheduling device 200 includes:

[0209] The acquisition module 201 is used to acquire the on-grid electricity price of the multi-energy complementary system.

[0210] The first solving module 202 is used to solve the pre-built optimization scheduling model according to the on-grid electricity price of the multi-energy complementary system to obtain the generating power and pumping power of the pumped storage power station in the multi-energy complementary system.

[0211] The second solving module 203 is used to solve the unit combination model constructed preferentially according to the power generation power and pumping power of the pumped-storage power station, and obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

[0212] In some possible implementations, the first solution module 202 is specifically configured to:

[0213] According to the on-grid electricity price of the multi-energy complementary system, the continuous particle swarm algorithm is used to solve the optimization scheduling model to obtain the generating power and pumping power of the pumped storage power station in the multi-energy complementary system.

[0214] Optionally, the unit commitment model includes a first objective function and a first constraint condition.

[0215] Among them, the first objective function aims to minimize the startup cost of the pumped storage power station.

[0216] The first constraint condition includes power plant power balance constraint and start / stop time constraint.

[0217] Furthermore, the first objective function satisfies:

[0218]

[0219] Among them, F1 represents the startup cost of the pumped storage power station, and N represents the number of pumped storage units; represents the startup cost of the i-th pumped storage unit in the power generation state, represents the startup cost of the i-th pumped storage unit in the pumping state; St represents the number of starts of the i-th pumped storage unit in the power generation state, i p represents the number of starts of the i-th pumped storage unit in the pumping state, satisfying:

[0220]

[0221] Among them, U i,t represents the state of the i-th pumped storage unit in period t; U i,t =1 means that the i-th pumped storage unit is in the power generation state during the t period, U i,t =0 means that the i-th pumped storage unit is in shutdown state during period t, U i,t =-1 means that the i-th pumped storage unit is in the pumping state during period t; U i,t-1represents the state of the i-th pumped storage unit in the t-1 period; U i,t-1 =1 means that the i-th pumped storage unit is in the power generation state during the t-1 period, U i,t-1 =0 means that the i-th pumped storage unit is in shutdown state during the period t-1, U i,t-1 =-1 means that the i-th pumped storage unit is in the pumping state during the t-1 period; T represents the total number of time periods.

[0222] The power balance constraints of the power station meet the following requirements:

[0223]

[0224] Among them, P i,t P represents the generating power / pumping power of the i-th pumped storage unit in period t; t represents the generating power / pumping power of the pumped-storage power station in period t;

[0225] The start and stop time constraints are satisfied:

[0226]

[0227] in, represents the continuous operation time of the i-th pumped storage unit in period t, represents the downtime of the i-th pumped storage unit in period t, Indicates the minimum continuous operation time of the pumped storage unit. Indicates the minimum downtime of the pumped storage unit.

[0228] In some other possible implementations, the second solution module 203 is specifically used to:

[0229] Obtain the startup cost of the pumped-storage power station under power generation conditions and the startup cost of the pumped-storage power station under pumping conditions.

[0230] According to the startup cost of the pumped-storage power station under power generation conditions, the startup cost of the pumped-storage power station under pumping conditions, the power generation power and pumping power of the pumped-storage power station, the discrete particle swarm algorithm is used to solve the unit combination model to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

[0231] Furthermore, the second solution module 203 is specifically used for:

[0232] Set the initial position and initial velocity of each particle in the particle population.

[0233] Furthermore, the second solution module 203 is specifically used to set the initial position of each particle according to the following formula:

[0234]

[0235] Among them, U represents the initial position of each particle, U N,T Indicates the status of the Nth pumped storage unit in the T period.

[0236] The second solving module 203 is specifically used to set the initial velocity of each particle to a random number.

[0237] According to the initial position, initial speed, generating power and pumping power of the pumped storage power station, the load demand of the power grid is allocated to each pumped storage unit using the equal incremental rate method.

[0238] The fitness value of the particle population is calculated based on the first objective function and the first constraint condition.

[0239] The individual optimal solution and the group optimal solution in each iteration are updated according to the fitness value of the particle population, the speed of each particle is updated according to the continuous particle swarm algorithm, and the position of each particle is updated according to the tanh function.

[0240] Optionally, the second solving module 202 is used to update the position of each particle according to the following formula:

[0241]

[0242] in, represents the position of the particle in time period t in the kth iteration, V t k represents the velocity of the particle in the kth iteration in the t period; rand represents a random function in the interval [0,1]; tanh(V t k ) represents the tanh function, satisfying

[0243] When the number of iterations does not meet the maximum number of iterations, iterate again according to the individual optimal solution, the updated speed and position of each particle until the number of iterations reaches the maximum number of iterations, and output the group optimal solution; when the number of iterations meets the maximum number of iterations, output the group optimal solution; wherein the group optimal solution includes the state, power generation and pumping power of each pumped storage unit.

[0244] Exemplarily, the optimization scheduling model includes a second objective function and a second constraint.

[0245] Among them, the second objective function aims to maximize the economic benefits of the multi-energy complementary system.

[0246] The second constraint conditions include system power balance constraint, spinning reserve constraint, power station power constraint and reservoir energy constraint.

[0247] Optionally, the second objective function satisfies:

[0248]

[0249] Among them, F2 represents the economic benefits of the multi-energy complementary system; T represents the total number of time periods; P t v represents the power generation of the photovoltaic power station in the multi-energy complementary system in time period t, 0≤P t v ≤P t vf , P t vf represents the resource quantity of the photovoltaic power station in period t; P t w represents the power generation of the wind farm in the multi-energy complementary system in period t, 0≤P t w ≤P t wf , P t wf represents the resource quantity of the wind farm in period t; P t g represents the power generation of the pumped storage power station in period t, P t gmin ≤P t g ≤P t gmax , P t gmin represents the minimum power generation of the pumped storage power station in period t, P t gmax represents the maximum power generation capacity of the pumped storage power station in period t; P t p represents the pumping power of the pumped storage power station in period t, P t pmin ≤P t p ≤P t pmax , P t pmax represents the maximum pumping power of the pumped storage power station in period t, P t pmin represents the minimum pumping power of the pumped storage power station in period t; P t r represents the abandoned power of the photovoltaic power station and the wind farm in period t; C t represents the on-grid electricity price of the multi-energy complementary system in period t, represents the pumped electricity price of the pumped storage power station in period t, represents the power abandonment penalty price of the photovoltaic power station and the wind farm in period t.

[0250] The system power balance constraints meet:

[0251]

[0252] Among them, P t load Represents the load demand of the power grid; P t vf Represents the resource amount of the photovoltaic power station, P t wf Indicates the resource volume of the wind farm.

[0253] The spinning reserve constraints satisfy:

[0254] (P t wmax -P t w )+(P t vmax -P t v )+(P t gmax -P t g )≥R t u

[0255] Among them, P t wmax It represents the maximum power generation of the wind farm in period t, P t vmax Represents the maximum power generation of the photovoltaic power station in period t; R t u Represents the spinning reserve capacity of the power grid.

[0256] The power constraints of the power station are satisfied:

[0257]

[0258] Among them, P max represents the total installed capacity of pumped storage power stations; represents the upper reservoir capacity of the pumped storage power station in period t, W represents the lower reservoir capacity of the pumped storage power station in period t; g It represents the water energy conversion coefficient of the pumped storage power station under power generation conditions, W p It represents the water energy conversion coefficient of the pumped storage power station under pumping conditions.

[0259] The reservoir energy constraints satisfy:

[0260]

[0261] Among them, E umin represents the minimum storage capacity of the upper reservoir of the pumped storage power station, E umax Indicates the maximum storage capacity of the upper reservoir of the pumped storage power station; E dmin It represents the minimum storage capacity of the lower reservoir of the pumped storage power station, E dmax It indicates the maximum value of the lower reservoir capacity of the pumped storage power station; represents the upper reservoir capacity of the pumped storage power station at time t+1, represents the lower reservoir capacity of the pumped storage power station in the t+1 period; t represents the current period number; It represents the upper reservoir capacity of the pumped storage power station in the initial dispatch period; It indicates the upper reservoir capacity of the pumped storage power station at the end of the dispatch period; It represents the lower reservoir capacity of the pumped storage power station in the initial dispatch period; It indicates the lower reservoir capacity of the pumped-storage power station at the end of the dispatching period.

[0262] Embodiment 3:

[0263] Based on the same inventive concept, the embodiment of the present application also provides a computer device, which includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in a computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the optimization scheduling method provided in the above embodiment.

[0264] Embodiment 4:

[0265] Based on the same inventive concept, the embodiment of the present application also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the optimization scheduling method provided in the above embodiment.

[0266] Those skilled in the art will appreciate that the embodiments of the application may be provided as methods, systems, or computer program products. Therefore, the application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0267] The application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0268] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0269] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0270] The above are merely embodiments of the application and are not intended to limit the application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the application are included in the scope of the claims of the pending application.

Claims

1. An optimization scheduling method for a multi-energy complementary system, characterized in that: include: Obtaining the on-grid electricity price of the multi-energy complementary system; Solving a pre-built optimization dispatch model according to the on-grid electricity price of the multi-energy complementary system to obtain the power generation power and pumping power of the pumped-storage power station in the multi-energy complementary system; The unit combination model constructed preferentially is solved according to the power generation power and pumping power of the pumped-storage power station to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

2. The optimization scheduling method according to claim 1, characterized in that: The method of solving a pre-built optimization scheduling model according to the on-grid electricity price of the multi-energy complementary system to obtain the power generation power and pumping power of the pumped storage power station in the multi-energy complementary system includes: According to the on-grid electricity price of the multi-energy complementary system, the continuous particle swarm algorithm is used to solve the optimization scheduling model to obtain the generating power and pumping power of the pumped storage power station.

3. The optimization scheduling method according to claim 1, characterized in that: The unit commitment model includes a first objective function and a first constraint condition; Wherein, the first objective function aims to minimize the startup cost of the pumped storage power station; The first constraint condition includes a power plant power balance constraint and a start / stop time constraint.

4. The optimization scheduling method according to claim 3, characterized in that: The first objective function satisfies: Wherein, F1 represents the startup cost of the pumped storage power station, and N represents the number of the pumped storage units; represents the startup cost of the i-th pumped storage unit in the power generation state, represents the startup cost of the i-th pumped storage unit in the pumping state; represents the number of starts of the i-th pumped storage unit in the power generation state, represents the number of starts of the i-th pumped storage unit in the pumping state, satisfying: Among them, U i,t represents the state of the i-th pumped storage unit in period t; U i,t =1 means that the i-th pumped storage unit is in the power generation state during the t period, U i,t =0 means that the i-th pumped storage unit is in shutdown state during period t, U i,t =-1 means that the i-th pumped storage unit is in the pumping state during period t; U i,t-1 represents the state of the i-th pumped storage unit in the t-1 period; U i,t-1 =1 means that the i-th pumped storage unit is in the power generation state during the t-1 period, U i,t-1 =0 means that the i-th pumped storage unit is in shutdown state during the period t-1, U i,t-1 =-1 means that the i-th pumped storage unit is in the pumping state during the t-1 period; T represents the total number of time periods.

5. The optimization scheduling method according to claim 4, characterized in that: The power balance constraint of the power station satisfies: Among them, P i,t P represents the generating power / pumping power of the i-th pumped storage unit in period t; t represents the generating power / pumping power of the pumped-storage power station in period t; The start-stop time constraints satisfy: in, represents the continuous operation time of the i-th pumped storage unit in period t, represents the downtime of the i-th pumped storage unit in period t, Indicates the minimum continuous operation time of the pumped storage unit, Indicates the minimum downtime of the pumped storage unit.

6. The optimization scheduling method according to claim 4, characterized in that: The step of solving the unit combination model constructed preferentially according to the power generation power and pumping power of the pumped-storage power station to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station includes: Obtaining the startup cost of the pumped-storage power station under power generation conditions and the startup cost of the pumped-storage power station under pumping conditions; According to the startup cost of the pumped-storage power station under power generation conditions, the startup cost of the pumped-storage power station under pumping conditions, the power generation power and pumping power of the pumped-storage power station, a discrete particle swarm algorithm is used to solve the unit combination model to obtain the state, power generation power and pumping power of each pumped-storage unit.

7. The optimization scheduling method according to claim 6, characterized in that: The method adopts a discrete particle swarm algorithm to solve the unit combination model according to the startup cost of the pumped storage power station under power generation conditions, the startup cost of the pumped storage power station under pumping conditions, and the power generation power and pumping power of the pumped storage power station to obtain the state, power generation power and pumping power of each pumped storage unit, including: Set the initial position and initial velocity of each particle in the particle population; According to the initial position, the initial speed, the power generation power and the pumping power of the pumped storage power station, the load demand of the power grid is allocated to each of the pumped storage units by using the equal incremental rate method; Calculating a fitness value of a particle population based on the first objective function and the first constraint condition; The individual optimal solution and the group optimal solution in each iteration are updated according to the fitness value of the particle population, the speed of each particle is updated according to the continuous particle swarm algorithm, and the position of each particle is updated according to the tanh function; When the number of iterations does not meet the maximum number of iterations, iterate again according to the individual optimal solution, the updated speed and position of each particle until the number of iterations reaches the maximum number of iterations, and output the group optimal solution; when the number of iterations meets the maximum number of iterations, output the group optimal solution; wherein the group optimal solution includes the state, power generation power and pumping power of each pumped-storage unit.

8. The optimization scheduling method according to claim 7, characterized in that: The step of setting the initial position and initial velocity of each particle in the particle population includes: Set the initial position of each particle as follows: Among them, U represents the initial position of each particle, U N,T Indicates the state of the Nth pumped storage unit in the T period; Sets the initial velocity of each particle to a random number.

9. The optimization scheduling method according to claim 7, characterized in that: The updating of the position of each particle according to the tanh function comprises: Update the position of each particle as follows: in, represents the position of the particle in time period t in the kth iteration, V t k represents the velocity of the particle in the kth iteration in the t period; rand represents a random function in the interval [0,1]; tanh(V t k ) represents the tanh function, satisfying 10. The optimization scheduling method according to claim 1 or 2, characterized in that: The optimization scheduling model includes a second objective function and a second constraint condition; Wherein, the second objective function aims to maximize the economic benefits of the multi-energy complementary system; The second constraint conditions include system power balance constraint, spinning reserve constraint, power station power constraint and reservoir energy constraint.

11. The optimization scheduling method according to claim 10, characterized in that: The second objective function satisfies: Wherein, F2 represents the economic benefit of the multi-energy complementary system; T represents the total number of time periods; P t v represents the power generation of the photovoltaic power station in the multi-energy complementary system in time period t, 0≤P t v ≤P t vf , P t vf represents the resource amount of the photovoltaic power station in time period t; P t w represents the power generation of the wind farm in the multi-energy complementary system in period t, 0≤P t w ≤P t wf , P t wf represents the resource quantity of the wind farm in period t; P t g represents the power generation of the pumped storage power station in period t, P t gmin ≤P t g ≤P t gmax , P t gmin represents the minimum power generation of the pumped storage power station in period t, P t gmax represents the maximum power generation capacity of the pumped storage power station in period t; P t p represents the pumping power of the pumped storage power station in period t, P t pmin ≤P t p ≤P t pmax , P t pmax represents the maximum pumping power of the pumped storage power station in period t, P t pmin represents the minimum pumping power of the pumped storage power station in period t; P t r represents the abandoned power of the photovoltaic power station and the wind farm in period t; C t represents the on-grid electricity price of the multi-energy complementary system in period t, represents the pumped electricity price of the pumped storage power station in period t, represents the power abandonment penalty price of the photovoltaic power station and the wind farm in time period t.

12. The optimization scheduling method according to claim 11, characterized in that: The system power balance constraint satisfies: Among them, P t load represents the load demand of the power grid; P t vf represents the resource amount of the photovoltaic power station, P t wf represents the resource quantity of the wind farm; The spinning reserve constraints satisfy: (P t wmax -P t w )+(P t vmax -P t v )+(P t gmax -P t g )≥R t u Among them, P t wmax represents the maximum power generation of the wind farm in period t, P t vmax represents the maximum power generation of the photovoltaic power station in time period t; R t u representing the spinning reserve capacity of said power grid; The power plant power constraint satisfies: Among them, P max represents the total installed capacity of the pumped storage power station; represents the upper reservoir capacity of the pumped storage power station in period t, W represents the lower reservoir capacity of the pumped storage power station in period t; g represents the water energy conversion coefficient of the pumped storage power station under power generation conditions, W p represents the water energy conversion coefficient of the pumped storage power station under pumping conditions; The reservoir energy constraint satisfies: Among them, E umin represents the minimum storage capacity of the upper reservoir of the pumped storage power station, E umax represents the maximum value of the upper reservoir capacity of the pumped storage power station; E dmin represents the minimum storage capacity of the lower reservoir of the pumped storage power station, E dmax represents the maximum value of the lower reservoir capacity of the pumped-storage power station; represents the upper reservoir capacity of the pumped storage power station at time t+1, represents the lower reservoir capacity of the pumped storage power station in the t+1 period; t represents the current period number; represents the upper reservoir capacity of the pumped storage power station in the initial dispatching period; represents the upper reservoir capacity of the pumped storage power station at the end of the dispatching period; represents the lower reservoir capacity of the pumped storage power station in the initial dispatching period; It represents the lower reservoir capacity of the pumped-storage power station at the end of the scheduling period.

13. An optimization scheduling device for a multi-energy complementary system, characterized in that: include: An acquisition module, used for acquiring the on-grid electricity price of the multi-energy complementary system; A first solving module is used to solve a pre-built optimization scheduling model according to the on-grid electricity price of the multi-energy complementary system to obtain the power generation power and pumping power of the pumped storage power station in the multi-energy complementary system; The second solving module is used to solve the unit combination model constructed preferentially according to the power generation power and pumping power of the pumped-storage power station, and obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

14. The optimization scheduling device according to claim 13, characterized in that: The first solution module is specifically used for: According to the on-grid electricity price of the multi-energy complementary system, a continuous particle swarm algorithm is used to solve the optimization scheduling model to obtain the generating power and pumping power of the pumped storage power station in the multi-energy complementary system.

15. The optimization scheduling device according to claim 13, characterized in that: The unit commitment model includes a first objective function and a first constraint condition; Wherein, the first objective function aims to minimize the startup cost of the pumped storage power station; The first constraint condition includes a power plant power balance constraint and a start / stop time constraint.

16. The optimization scheduling device according to claim 15, characterized in that: The first objective function satisfies: Wherein, F1 represents the startup cost of the pumped storage power station, and N represents the number of the pumped storage units; represents the startup cost of the i-th pumped storage unit in the power generation state, represents the startup cost of the i-th pumped storage unit in the pumping state; represents the number of starts of the i-th pumped storage unit in the power generation state, represents the number of starts of the i-th pumped storage unit in the pumping state, satisfying: Among them, U i,t represents the state of the i-th pumped storage unit in period t; U i,t =1 means that the i-th pumped storage unit is in the power generation state during the t period, U i,t =0 means that the i-th pumped storage unit is in shutdown state during period t, U i,t =-1 means that the i-th pumped storage unit is in the pumping state during period t; U i,t-1 represents the state of the i-th pumped storage unit in the t-1 period; U i,t-1 =1 means that the i-th pumped storage unit is in the power generation state during the t-1 period, U i,t-1 =0 means that the i-th pumped storage unit is in shutdown state during the period t-1, U i,t-1 =-1 means that the i-th pumped storage unit is in the pumping state during the t-1 period; T represents the total number of time periods.

17. The optimization scheduling device according to claim 16, characterized in that: The power balance constraint of the power station satisfies: Among them, P i,t P represents the generating power / pumping power of the i-th pumped storage unit in period t; t represents the generating power / pumping power of the pumped-storage power station in period t; The start-stop time constraints satisfy: in, represents the continuous operation time of the i-th pumped storage unit in period t, represents the downtime of the i-th pumped storage unit in period t, Indicates the minimum continuous operation time of the pumped storage unit, Indicates the minimum downtime of the pumped storage unit.

18. The optimization scheduling device according to claim 16, characterized in that: The second solution module is specifically used for: Obtaining the startup cost of the pumped-storage power station under power generation conditions and the startup cost of the pumped-storage power station under pumping conditions; According to the startup cost of the pumped-storage power station under power generation conditions, the startup cost of the pumped-storage power station under pumping conditions, the power generation power and pumping power of the pumped-storage power station, a discrete particle swarm algorithm is used to solve the unit combination model to obtain the state, power generation power and pumping power of each pumped-storage unit in the pumped-storage power station.

19. The optimization scheduling device according to claim 18, characterized in that: The second solution module is specifically used for: Set the initial position and initial velocity of each particle in the particle population; According to the initial position, the initial speed, the power generation power and the pumping power of the pumped storage power station, the load demand of the power grid is allocated to each of the pumped storage units by using the equal incremental rate method; Calculating a fitness value of a particle population based on the first objective function and the first constraint condition; The individual optimal solution and the group optimal solution in each iteration are updated according to the fitness value of the particle population, the speed of each particle is updated according to the continuous particle swarm algorithm, and the position of each particle is updated according to the tanh function; When the number of iterations does not meet the maximum number of iterations, iterate again according to the individual optimal solution, the updated speed and position of each particle until the number of iterations reaches the maximum number of iterations, and output the group optimal solution; when the number of iterations meets the maximum number of iterations, output the group optimal solution; wherein the group optimal solution includes the state, power generation power and pumping power of each pumped-storage unit.

20. The optimization scheduling device according to claim 19, characterized in that: The second solution module is specifically used for: Set the initial position of each particle as follows: Among them, U represents the initial position of each particle, U N,T Indicates the state of the Nth pumped storage unit in the T period; Sets the initial velocity of each particle to a random number.

21. The optimization scheduling device according to claim 19, characterized in that: The second solution module is used for: Update the position of each particle as follows: in, represents the position of the particle in time period t in the kth iteration, V t k represents the velocity of the particle in the kth iteration in the t period; rand represents a random function in the interval [0,1]; tanh(V t k ) represents the tanh function, satisfying 22. The optimization scheduling device according to claim 13 or 14, characterized in that: The optimization scheduling model includes a second objective function and a second constraint condition; Wherein, the second objective function aims to maximize the economic benefits of the multi-energy complementary system; The second constraint conditions include system power balance constraint, spinning reserve constraint, power station power constraint and reservoir energy constraint.

23. The optimization scheduling device according to claim 22, characterized in that: The second objective function satisfies: Wherein, F2 represents the economic benefit of the multi-energy complementary system; T represents the total number of time periods; P t v represents the power generation of the photovoltaic power station in the multi-energy complementary system in time period t, 0≤P t v ≤P t vf , P t vf represents the resource amount of the photovoltaic power station in time period t; P t w represents the power generation of the wind farm in the multi-energy complementary system in period t, 0≤P t w ≤P t wf , P t wf represents the resource quantity of the wind farm in period t; P t g represents the power generation of the pumped storage power station in period t, P t gmin ≤P t g ≤P t gmax , P t gmin represents the minimum power generation of the pumped storage power station in period t, P t gmax represents the maximum power generation capacity of the pumped storage power station in period t; P t p represents the pumping power of the pumped storage power station in period t, P t pmin ≤P t p ≤P t pmax , P t pmax represents the maximum pumping power of the pumped storage power station in period t, P t pmin represents the minimum pumping power of the pumped storage power station in period t; P t r represents the abandoned power of the photovoltaic power station and the wind farm in period t; C t represents the on-grid electricity price of the multi-energy complementary system in period t, represents the pumped electricity price of the pumped storage power station in period t, represents the power abandonment penalty price of the photovoltaic power station and the wind farm in time period t.

24. The optimization scheduling device according to claim 23, characterized in that: The system power balance constraint satisfies: Among them, P t load represents the load demand of the power grid; P t vf represents the resource amount of the photovoltaic power station, P t wf represents the resource quantity of the wind farm; The spinning reserve constraints satisfy: (P t wmax -P t w )+(P t vmax -P t v )+(P t gmax -P t g )≥R t u Among them, P t wmax represents the maximum power generation of the wind farm in period t, P t vmax represents the maximum power generation of the photovoltaic power station in time period t; R t u representing the spinning reserve capacity of said power grid; The power plant power constraint satisfies: Among them, P max represents the total installed capacity of the pumped storage power station; represents the upper reservoir capacity of the pumped storage power station in period t, W represents the lower reservoir capacity of the pumped storage power station in period t; g represents the water energy conversion coefficient of the pumped storage power station under power generation conditions, W p represents the water energy conversion coefficient of the pumped storage power station under pumping conditions; The reservoir energy constraint satisfies: Among them, E umin represents the minimum storage capacity of the upper reservoir of the pumped storage power station, E umax represents the maximum value of the upper reservoir capacity of the pumped storage power station; E dmin represents the minimum storage capacity of the lower reservoir of the pumped storage power station, E dmax represents the maximum value of the lower reservoir capacity of the pumped-storage power station; represents the upper reservoir capacity of the pumped storage power station at time t+1, represents the lower reservoir capacity of the pumped storage power station in the t+1 period; t represents the current period number; represents the upper reservoir capacity of the pumped storage power station in the initial dispatching period; represents the upper reservoir capacity of the pumped storage power station at the end of the dispatching period; represents the lower reservoir capacity of the pumped storage power station in the initial dispatching period; It represents the lower reservoir capacity of the pumped-storage power station at the end of the scheduling period.

25. A computer device, characterized in that: include: one or more processors; a processor for storing one or more programs; When one or more programs are executed by one or more processors, the optimization scheduling method according to any one of claims 1 to 12 is implemented.

26. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the optimization scheduling method as claimed in any one of claims 1 to 12 is implemented.

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