Integrated Scheduling Method and Device for Wind-Solar-Fire-Power Energy Storage Based on Particle Swarm Optimization Algorithm

Abstract

The present invention discloses a method and device for integrated scheduling of wind-solar-thermal-storage power systems based on a particle swarm optimization algorithm. The method includes: S1. Constructing a day-ahead revenue model for the integrated operation of the wind-solar-thermal-storage power system according to the integrated scheduling objective of the wind-solar-thermal-storage power system to obtain a revenue maximization objective function; S2. Solving the revenue maximization objective function by using the particle swarm optimization algorithm to obtain a global optimal solution; S3. Scheduling the wind-solar-thermal-storage power system according to the global optimal solution. It is possible to carry out integrated scheduling analysis of the wind-solar-thermal-storage power system according to the integrated scheduling optimization objective, and use the particle swarm optimization algorithm to solve the objective function, thereby improving the calculation efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of economic dispatch of energy bases, and particularly to a method and device for integrated dispatching of wind-solar-thermal-storage based on a particle swarm optimization algorithm. Background Art

[0002] With the continuous advancement of the construction of multi-energy complementary energy demonstration bases, the integrated wind-solar-thermal-storage mode may become the mainstream operation mode of large-scale energy bases. By preferentially using clean energy such as wind and solar energy, giving play to the regulation ability of thermal power units, and combining with reasonably configured energy storage facilities, the integrated wind-solar-thermal-storage turns renewable energy with volatility, randomness, and intermittency into a stable and reliable power output source, achieving the effect of complementary characteristics of wind-solar-thermal-storage within a day and reducing the peak shaving demand on the power grid.

[0003] When traditional mathematical programming methods are used to solve the problem of wind-solar-thermal-storage dispatching, they rely on accurate wind and solar power outputs and solve the output of thermal power units according to the outputs of new energy sources such as wind and solar energy. However, when solving complex optimization dispatching problems including the ramp constraints of thermal power units and the charge and discharge constraints of energy storage, integrated dispatching calculations cannot be performed, and it is difficult to be applied to the integrated operation of large-scale energy bases.

[0004] Aiming at the problem in the prior art that the respective outputs of wind-solar-thermal-storage cannot be flexibly and quickly calculated by integrated dispatching, there is currently no effective solution. Summary of the Invention

[0005] Embodiments of the present invention provide a method and device for integrated dispatching of wind-solar-thermal-storage based on a particle swarm optimization algorithm, which quickly and accurately calculate the integrated dispatching strategy of wind-solar-thermal-storage through the particle swarm optimization algorithm to solve the problem in the prior art that the respective outputs of wind-solar-thermal-storage cannot be flexibly and quickly calculated by integrated dispatching.

[0006] To achieve the above object, embodiments of the present invention provide a method for integrated dispatching of wind-solar-thermal-storage based on a particle swarm optimization algorithm, including: S1, constructing a day-ahead revenue model for the integrated operation of the wind-solar-thermal-storage integrated system according to the integrated dispatching objective of the wind-solar-thermal-storage integrated system to obtain a revenue maximization objective function; S2, using the particle swarm optimization algorithm to solve the revenue maximization objective function to obtain a global optimal solution; S3, dispatching the wind-solar-thermal-storage integrated system according to the global optimal solution.

[0007] Further optionally, solving the revenue maximization objective function by using the particle swarm optimization algorithm to obtain the global optimal solution includes: S201. Initialize the velocity and position of each particle, the particle swarm size, the maximum particle velocity, the learning factor, the maximum number of iterations, the maximum inertia weight, and the minimum inertia weight; S202. Calculate the maximization objective function value corresponding to each particle, obtain the individual optimal particle corresponding to each particle and the current global optimal particle according to the maximization objective function values of all particles, compare the current global optimal particle with the historical global optimal particle, and use the better particle as the latest global optimal particle; S203. Calculate the latest velocity and latest position after iteration of each particle according to the current inertia weight, current velocity, current position, learning factor, corresponding individual optimal particle, and latest global optimal particle of each particle; S204. Calculate the latest inertia weight according to the maximum inertia weight, minimum inertia weight, current iteration number, and maximum iteration number; S205. Determine whether each particle satisfies the constraint condition. If not, randomly generate a corresponding new particle and then determine whether the current iteration number reaches the maximum iteration number. If so, determine whether the current iteration number reaches the maximum iteration number; S206. If the current iteration number does not reach the maximum iteration number, use the latest global optimal particle as the historical global optimal particle, and use the latest inertia weight, latest velocity, and latest position as the current inertia weight, current velocity, and current position for the next iteration respectively, and repeat steps S202 - S205. If the current iteration number reaches the maximum iteration number, output the latest global optimal particle as the global optimal solution.

[0008] Further optionally, calculate the latest velocity and latest position after iteration of each particle according to the current inertia weight, current velocity, current position, learning factor, corresponding individual optimal particle, and current global optimal particle of each particle, and calculate through the following formula:

[0009] v n，i = w(t)v i + c1r1(p b，i - x i ) + c2r2(g b，i - x i )

[0010] x n，i = x i + v n，i

[0011] where v i , x i respectively represent the i-th current velocity and current position, v n，i , x n，i respectively represent the latest velocity and latest position after iterative update of the i-th particle; p b，i , gb，i They are the individual optimal particle and the current global optimal particle of the i-th particle respectively; w(t) is the dynamic inertia coefficient; c1 and c2 are learning factors, also known as acceleration constants; r1 and r2 are random numbers within the range of [0, 1], which increases the randomness of the particle flight pair.

[0012] Further optionally, the latest inertia weight is calculated according to the maximum inertia weight, the minimum inertia weight, the current iteration number and the maximum iteration number, and is calculated by the following formula:

[0013]

[0014] where, w max and w min are the maximum and minimum inertia weights respectively; t is the current iteration number; T max is the maximum iteration number.

[0015] Further optionally, the profit maximization objective function is:

[0016]

[0017] F net = p s，t ·1000·P net，t

[0018] P net，t = P l，t -(P i，t + P pv，t + P pw，t )

[0019] where, p c is the contract electricity price agreed upon by the energy base and the base for sending out electricity, usually a fixed constant; P i，t is the power generation power of the i-th thermal power unit at time t; P pv，t is the photovoltaic power generation power at time t; P pw，t is the wind power generation power at time t; Δe i is the energy storage discharge power; is the operating cost of the thermal power unit; is the cost of purchasing electricity from the power grid when the output of wind, light, thermal power and energy storage is insufficient to meet the DC sending demand; P l，t is the load demand to be sent out in t hours. P net，t is the power of purchasing electricity from the power grid; F net is the income of the base selling electricity to the local power grid after meeting the DC sending demand during the large-scale wind and light generation; p s，t takes the time-of-use electricity price for power generation and grid connection, and N is the total number of dispatching time periods, which is taken as 24 in the day-ahead dispatching.

[0020] On the other hand, a integrated scheduling device for wind, light, thermal and energy storage based on particle swarm optimization algorithm, comprising: an objective function construction module, configured to construct a day-ahead revenue model for the integrated operation of the wind-light-thermal-energy storage integrated system according to the integrated scheduling objective of the wind-light-thermal-energy storage integrated system, so as to obtain a revenue maximization objective function; an optimal solution solving module, configured to solve the revenue maximization objective function by using the particle swarm optimization algorithm to obtain a global optimal solution; and an actual scheduling module, configured to schedule the wind-light-thermal-energy storage integrated system according to the global optimal solution.

[0021] Further optionally, the optimal solution solving module includes: an initialization sub-module, configured to initialize the velocity and position of each particle, the particle swarm size, the maximum particle velocity, the learning factor, the maximum number of iterations, the maximum inertia weight and the minimum inertia weight; a global optimal solution updating sub-module, configured to calculate the maximization objective function value corresponding to each particle, obtain the individual optimal particle and the current global optimal particle corresponding to each particle according to the maximization objective function values of all particles, compare the global optimal particle with the historical global optimal particle, and use the better particle as the current global optimal particle; a velocity and position updating sub-module, configured to calculate the updated velocity and updated position of each particle after iteration according to the current inertia weight, the current velocity, the current position, the learning factor, the corresponding individual optimal particle and the latest global optimal particle of each particle; a weight updating sub-module, configured to calculate the latest inertia weight according to the maximum inertia weight, the minimum inertia weight, the current number of iterations and the maximum number of iterations; a judgment sub-module, configured to judge whether each particle satisfies the constraint condition, if not, randomly generate a corresponding new particle and then judge whether the current number of iterations reaches the maximum number of iterations, if so, judge whether the current number of iterations reaches the maximum number of iterations; and a repeated iteration sub-module, configured to, if the current number of iterations does not reach the maximum number of iterations, use the latest global optimal particle as the historical global optimal particle, use the latest inertia weight, the latest velocity and the latest position as the current inertia weight, the current velocity and the current position for the next iteration respectively, and repeat the operations of the global optimal solution updating sub-module, the velocity and position updating sub-module, the weight updating sub-module and the judgment sub-module, and if the current number of iterations reaches the maximum number of iterations, output the latest global optimal particle as the global optimal solution.

[0022] Further optionally, the updated velocity and updated position of each particle after iteration are calculated according to the current inertia weight, the current velocity, the current position, the learning factor, the corresponding individual optimal particle and the current global optimal particle of each particle, and are calculated by the following formula:

[0023] v n，i =w(t)v i +c1r1(p b，i -x i )+c2r2(g b，i -xi )

[0024] x n，i = x i + v n，i

[0025] wherein, v i and x i represent the current speed and current position of the i-th respectively, and v n，i and x n，i represent the latest speed and latest position of the i-th particle after iterative update respectively; p b，i and g b，i are the individual optimal particle and the current global optimal particle of the i-th particle respectively; w(t) is the dynamic inertia coefficient; c1 and c2 are learning factors, also known as acceleration constants; r1 and r2 are random numbers within the range of [0, 1], which increases the randomness of the particle flight pair.

[0026] Further optionally, the latest inertia weight is calculated according to the maximum inertia weight, minimum inertia weight, current iteration number and maximum iteration number, and is calculated by the following formula:

[0027]

[0028] wherein, w max and w min are the maximum and minimum inertia weights respectively; t is the current iteration number; T max is the maximum iteration number.

[0029] The above technical solutions have the following beneficial effects: The embodiments of the present invention are directed to the integrated operation optimization scheduling of large-scale energy bases for wind, light, fire, and storage. Based on the particle swarm optimization algorithm, an integrated scheduling strategy for wind, light, fire, and storage is designed and proposed, and integrated scheduling analysis of wind, light, fire, and storage can be carried out according to the integrated scheduling optimization objective; the particle swarm optimization algorithm adopted has the advantages of simple programming, intuitive and easy implementation, etc., and can quickly and accurately calculate the optimal scheduling scheme.

[0030] At the same time, the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above method is implemented.

[0031] At the same time, the present invention also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program for executing the above method.

[0032] To make the above and other objects, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given below and described in detail in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0034] Figure 1 It is a flowchart of the integrated scheduling method for wind-solar-thermal energy storage based on the particle swarm optimization algorithm provided by the embodiment of the present invention;

[0035] Figure 2 It is a flowchart of the method for solving the objective function using the particle swarm optimization algorithm provided by the embodiment of the present invention;

[0036] Figure 3 It is a flowchart of the particle swarm optimization algorithm provided by the embodiment of the present invention;

[0037] Figure 4 It is a schematic structural diagram of the integrated scheduling device for wind-solar-thermal energy storage based on the particle swarm optimization algorithm provided by the embodiment of the present invention;

[0038] Figure 5 It is a schematic structural diagram of the optimal solution solving module provided by the embodiment of the present invention.

[0039] Reference numerals: 100 - objective function construction module; 200 - optimal solution solving module; 2001 - initialization sub-module; 2002 - global optimal solution update module; 2003 - velocity and position update sub-module; 2004 - weight update sub-module; 2005 - judgment sub-module; 2006 - repeated iteration sub-module; 300 - actual scheduling module Detailed implementation manners

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.

[0041] To solve the problem in the prior art that it is impossible to flexibly and quickly perform integrated scheduling calculations on the respective outputs of wind, solar, thermal energy storage, the embodiment of the present invention provides an integrated scheduling method for wind-solar-thermal energy storage based on the particle swarm optimization algorithm. Figure 1 It is a flowchart of the integrated scheduling method for wind-solar-thermal energy storage based on the particle swarm optimization algorithm provided by the embodiment of the present invention. As Figure 1 shown, the method includes:

[0042] S1. Based on the integrated scheduling objective of the wind-solar-thermal-energy storage integrated system, construct a day-ahead revenue model for the operation of the wind-solar-thermal-energy storage integrated system to obtain the revenue maximization objective function;

[0043] The wind-solar-thermal-energy storage system includes four power generation methods: wind power generation, photovoltaic power generation, thermal power unit power generation, and energy storage device discharge. During actual power supply, clean energy such as wind and light is preferentially used for discharge. If the discharge amount cannot meet the DC power supply amount, thermal power units need to be called. Further, energy storage facilities need to be mobilized for power supply. If the above four power supply methods cannot meet the required power, power needs to be purchased from the power grid. Therefore, how to schedule the four discharge methods to maximize the final revenue needs to be calculated. The final scheduling objective is to maximize the revenue while considering the output constraints of thermal power units, ramp constraints, wind-solar output constraints, and energy storage SOC constraints. Therefore, a day-ahead revenue model for the operation of the wind-solar-thermal-energy storage integrated system and a revenue maximization objective function need to be established.

[0044] S2. Use the particle swarm optimization algorithm to solve the revenue maximization objective function to obtain the global optimal solution;

[0045] Use the particle swarm optimization algorithm to solve the above revenue maximization objective function to obtain the global optimal solution, which is the optimal output strategy of the four discharge methods.

[0046] The particle swarm optimization algorithm has the advantages of simple programming, intuitive and easy to implement, etc., and can quickly and accurately calculate the global optimal solution.

[0047] S3. Schedule the wind-solar-thermal-energy storage integrated system according to the global optimal solution.

[0048] Perform actual scheduling on the wind-solar-thermal-energy storage integrated system according to the optimal output of each discharge method to maximize the final revenue.

[0049] As an optional implementation method, Figure 2 This is the flowchart of the method for solving the objective function using the particle swarm optimization algorithm provided by the embodiment of the present invention. As Figure 2 shown, use the particle swarm optimization algorithm to solve the revenue maximization objective function to obtain the global optimal solution, including:

[0050] Figure 3 This is the flowchart of the particle swarm optimization algorithm provided by the embodiment of the present invention. As Figure 3 shown, the particle swarm optimization algorithm iteratively updates by giving random initial particles and continuously tracking the individual optimal solutions found by the particles themselves and the optimal solutions found by the entire population to obtain the global optimal solution.

[0051] S201. Initialize the velocity and position of each particle, the particle swarm size, the maximum particle velocity, the learning factors, the maximum number of iterations, the maximum inertia weight, and the minimum inertia weight;

[0052] In the initialization stage, the day-ahead output values of wind, light, thermal power, and energy storage are used as particles to form a particle swarm. According to the output constraints of thermal power units, the ramping constraints, the wind and light output constraints, and the energy storage SOC constraints, the allowable value ranges of various outputs are obtained, and the velocity and position of each particle are randomly initialized from them. At the same time, set the population size, the maximum velocity, the learning factors, the maximum number of iterations, the maximum inertia weight, and the minimum inertia weight.

[0053] S202. Calculate the maximization objective function value corresponding to each particle, obtain the individual optimal particle and the current global optimal particle corresponding to each particle according to the maximization objective function values of all particles, compare the current global optimal particle with the historical global optimal particle, and take the better particle as the latest global optimal particle;

[0054] Calculate the fitness function (the objective function in this embodiment) of each particle to obtain the fitness value (the objective function value in this embodiment), and find the individual extreme value (the individual optimal particle) and the global extreme value (the current global optimal particle). Among them, the individual extreme value is the optimal solution found by each particle, and a global value is found from these optimal solutions, which is the global optimal solution of this iteration. Compare with the historical global optimal solution to update the optimal solution.

[0055] S203. Calculate the latest velocity and latest position of each particle after iteration according to the current inertia weight, current velocity, current position, learning factors, the corresponding individual optimal particle, and the latest global optimal particle of each particle;

[0056] Update the position and velocity of each particle to enter the next iteration.

[0057] As a specific implementation manner, update the position and velocity of each particle through the following formula:

[0058] v n，i = w(t)v i + c1r1(p b，i - x i ) + c2r2(g b，i - x i )

[0059] x n，i = x i + v n，i

[0060] where v i and x i respectively represent the current velocity and current position of the i-th particle, and v n，i, x n，i respectively represent the latest velocity and the latest position of the \(i\)-th particle after iterative update; \(p\) b，i , \(g\) b，i are respectively the individual optimal particle of the \(i\)-th particle and the current global optimal particle; \(w(t)\) is the dynamic inertia coefficient; \(c_1\), \(c_2\) are learning factors, also known as acceleration constants; \(r_1\), \(r_2\) are random numbers within the range of \([0, 1]\), which increases the randomness of the particle flight pair.

[0061] S204. Calculate the latest inertia weight according to the maximum inertia weight, the minimum inertia weight, the current iteration number and the maximum iteration number;

[0062] Update the inertia weight for each iteration to enter the next iteration. The dynamic inertia weight will be updated according to the current iteration loop number. The inertia weight value will start from the maximum value and decrease in the form of an exponential function.

[0063] As a specific implementation manner, the inertia weight is updated by the following formula:

[0064]

[0065] where \(w\) max and \(w\) min are respectively the maximum and minimum inertia weights; \(t\) is the current iteration number; \(T\) max is the maximum iteration number.

[0066] S205. Determine whether each particle satisfies the constraint conditions. If not, randomly generate a corresponding new particle and then determine whether the current iteration number reaches the maximum iteration number. If so, determine whether the current iteration number reaches the maximum iteration number;

[0067] Judge in turn whether each particle satisfies the preset constraint conditions. For a single particle, if it satisfies the constraint conditions, it can directly judge whether the current iteration number satisfies the maximum iteration number; if it does not satisfy the constraint conditions, randomly generate a new particle and then judge whether the current iteration number satisfies the maximum iteration number. Among them, the randomly generated new particle needs to be randomly generated within the range of the constraint conditions.

[0068] As an optional implementation manner, before this step, perform boundary condition processing according to the constraint conditions.

[0069] S206. If the current iteration number does not reach the maximum iteration number, take the latest global optimal particle as the historical global optimal particle, and take the latest inertia weight, the latest velocity and the latest position as the current inertia weight, the current velocity and the current position for the next iteration respectively, and repeat steps S202 - S205. If the current iteration number reaches the maximum iteration number, output the latest global optimal particle as the global optimal solution.

[0070] Judge whether the current iteration count is equal to the maximum iteration count. If the maximum iteration count has been reached, output the latest global optimal particle corresponding to the current iteration as the global optimal solution of the objective function. If the maximum iteration count has not been reached, perform the next iteration calculation on the updated particle velocity, position, inertia weight, and global optimal particle until the maximum iteration count is satisfied to output the optimal solution.

[0071] As an alternative implementation, the revenue maximization objective function is:

[0072]

[0073] F net = p s，t ·1000·P net，t

[0074] P net，t = P l，t -(P i，t + P pv，t + P pw，t )

[0075] Where p c is the contract electricity price agreed upon by the energy base and the base's power delivery, usually a fixed constant; P i，t is the power generation of the i-th thermal power unit at time t; P pv，t is the photovoltaic power generation at time t; P pw，t is the wind power generation at time t; Δe i is the energy storage discharge power; is the operating cost of the thermal power unit; is the cost of purchasing electricity from the grid when the output of wind, light, thermal power, and energy storage is insufficient to meet the DC power delivery demand; P l，t is the load demand to be externally delivered at t hours. P net，t is the power purchased from the grid; F net is the revenue of the base selling electricity to the local grid after meeting the DC power delivery during the large-scale wind and light generation; p s，t takes the time-of-use electricity price for power generation and grid connection, and N is the total number of scheduling time periods, taking 24 in day-ahead scheduling.

[0076] As an alternative implementation, the cost of purchasing electricity from the grid is calculated by the following formula:

[0077]

[0078] Where p b，t is the time-of-use electricity price for the grid to sell electricity.

[0079] As an alternative implementation, the time-of-use electricity price model for grid power sales refers to the following formula, with the electricity price unit being RMB / kWh.

[0080]

[0081] As an alternative implementation, the operating cost of a thermal power unit is calculated by the following formula:

[0082]

[0083] The embodiment of the present invention also provides a wind-solar-thermal-storage integrated scheduling device based on the particle swarm optimization algorithm. Figure 4 FIG. is a schematic structural diagram of the wind-solar-thermal-storage integrated scheduling device based on the particle swarm optimization algorithm provided by the embodiment of the present invention. As Figure 4 shown, the device includes:

[0084] A target function construction module 100, configured to construct a day-ahead revenue model for the integrated operation of the wind-solar-thermal-storage integrated system according to the integrated scheduling target of the wind-solar-thermal-storage integrated system, and obtain a revenue maximization target function;

[0085] The wind-solar-thermal-storage system includes four power generation methods: wind power generation, photovoltaic power generation, thermal power unit power generation, and energy storage device discharge. During actual power supply, clean energy such as wind and light is preferentially used for discharge. If the discharge amount cannot meet the DC power supply amount, the thermal power unit needs to be called. Further, the energy storage facility needs to be mobilized for power supply. If none of the above four power supply methods can meet the required power, power needs to be purchased from the grid. Therefore, how to schedule the four discharge methods to maximize the final revenue needs to be calculated. The final scheduling target is to maximize the revenue while considering the output constraints of the thermal power unit, the ramp rate constraints, the output constraints of wind and light, and the energy storage SOC constraints. Therefore, a day-ahead revenue model for the integrated operation of the wind-solar-thermal-storage integrated system and a revenue maximization target function need to be established.

[0086] An optimal solution solving module 200, configured to solve the revenue maximization target function by using the particle swarm optimization algorithm to obtain a global optimal solution;

[0087] The particle swarm optimization algorithm is used to solve the above revenue maximization target function to obtain a global optimal solution, and the global optimal solution is the optimal output strategy for the four discharge methods.

[0088] The particle swarm optimization algorithm has the advantages of simple programming, intuitive and easy to implement, etc., and can quickly and accurately calculate the global optimal solution.

[0089] An actual scheduling module 300, configured to schedule the wind-solar-thermal-storage integrated system according to the global optimal solution.

[0090] According to the optimal output of each discharge mode, the integrated wind-solar-thermal-energy storage system is actually scheduled to maximize the final benefit.

[0091] As an alternative implementation, Figure 5 is a schematic structural diagram of the optimal solution solving module provided by the embodiment of the present invention. As shown in Figure 5 shown, the optimal solution solving module 200 includes:

[0092] An initialization sub-module 2001, which is used to initialize the velocity and position of each particle, the particle swarm size, the maximum particle velocity, the learning factor, the maximum number of iterations, the maximum inertia weight, and the minimum inertia weight;

[0093] In the initialization stage, the day-ahead output values of wind-solar-thermal-energy storage are used as particles to form a particle swarm. According to the output constraints of thermal power units, ramping constraints, wind-solar output constraints, and energy storage SOC constraints, the value ranges of various outputs are obtained, and the velocity and position of each particle are randomly initialized therefrom. At the same time, the population size, the maximum velocity, the learning factor, the maximum number of iterations, the maximum inertia weight, and the minimum inertia weight are set.

[0094] A global optimal solution update module 2002, which is used to calculate the maximized objective function value corresponding to each particle, obtain the individual optimal particle and the current global optimal particle corresponding to each particle according to the maximized objective function values of all particles, compare the global optimal particle with the historical global optimal particle, and use the better particle as the current global optimal particle;

[0095] Calculate the fitness function (the objective function in this embodiment) of each particle to obtain the fitness value (the objective function value in this embodiment), and find the individual extreme value (individual optimal particle) and the global extreme value (current global optimal particle). Among them, the individual extreme value is the optimal solution found by each particle, and a global value is found from these optimal solutions, which is the global optimal solution of this iteration. Compare with the historical global optimal solution to update the optimal solution.

[0096] A velocity-position update sub-module 2003, which is used to calculate the updated velocity and position of each particle after iteration according to the current inertia weight, current velocity, current position, learning factor, corresponding individual optimal particle, and the latest global optimal particle of each particle;

[0097] Update the position and velocity of each particle to enter the next iteration.

[0098] As a specific implementation, the position and velocity of each particle are updated by the following formula:

[0099] v n，i = w(t)v i + c1r1(p b，i - xi ) + c2r2(g b，i -x i )

[0100] x n，i = x i + v n，i

[0101] where v i and x i represent the i-th current speed and current position respectively, and v n，i and x n，i represent the latest speed and latest position of the i-th particle after iterative update respectively; p b，i and g b，i are the individual best particle and the current global best particle of the i-th particle respectively; w(t) is the dynamic inertia coefficient; c1 and c2 are learning factors, also known as acceleration constants; r1 and r2 are random numbers in the range of [0, 1], which increases the randomness of particle flight pairs.

[0102] The weight update sub-module 2004 is used to calculate the latest inertia weight according to the maximum inertia weight, minimum inertia weight, current iteration number and maximum iteration number;

[0103] Update the inertia weight for each iteration to enter the next iteration. The dynamic inertia weight will be updated according to the current iteration loop number. The inertia weight value will start with the maximum value and decrease in the form of an exponential function.

[0104] As a specific implementation, the inertia weight is updated by the following formula:

[0105]

[0106] where w max and w min are the maximum and minimum inertia weights respectively; t is the current iteration number; T max is the maximum iteration number.

[0107] The judgment sub-module 2005 is used to judge whether each particle satisfies the constraint conditions. If not, a corresponding new particle is randomly generated and then it is judged whether the current iteration number reaches the maximum iteration number. If so, it is judged whether the current iteration number reaches the maximum iteration number;

[0108] Judge in turn whether each particle satisfies the preset constraint conditions. For a single particle, if it satisfies the constraint conditions, it can directly judge whether the current iteration number satisfies the maximum iteration number; if it does not satisfy the constraint conditions, a new particle is randomly generated and then it is judged whether the current iteration number satisfies the maximum iteration number. Among them, the randomly generated new particle needs to be randomly generated within the range of the constraint conditions.

[0109] As an alternative implementation, before this step, boundary condition processing is performed according to the constraint conditions.

[0110] The repeated iteration sub-module 2006 is used to, if the current iteration number has not reached the maximum iteration number, take the latest global optimal particle as the historical global optimal particle, and take the latest inertia weight, the latest velocity, and the latest position as the current inertia weight, the current velocity, and the current position for the next iteration respectively, and repeat the operations of the global optimal solution update module, the velocity and position update sub-module, the weight update sub-module, and the judgment sub-module. If the current iteration number reaches the maximum iteration number, take the latest global optimal particle as the global optimal solution for output.

[0111] Judge whether the current iteration number is equal to the maximum iteration number. If it has reached the maximum iteration number, take the latest global optimal particle corresponding to the current iteration as the global optimal solution of the objective function for output; if it has not reached the maximum iteration number, perform the next iteration calculation on the updated particle velocity, position, inertia weight, and global optimal particle until the maximum iteration number is satisfied to output the optimal solution.

[0112] As an alternative implementation, the revenue maximization objective function is:

[0113]

[0114] F net =p s，t ·1000·P net，t

[0115] P net，t =P l，t -(P i，t +P pv，t +P pw，t )

[0116] Wherein, p c is the contract electricity price agreed upon by the energy base and the base for power transmission, usually a fixed constant; P i，t is the power generation power of the i-th thermal power unit at time t; P pv，t is the photovoltaic power generation power at time t; P pw，t is the wind power generation power at time t; Δe i is the energy storage discharge power; is the operating cost of the thermal power unit; is the power purchase cost from the power grid when the output of the wind, light, thermal power, and energy storage is insufficient to meet the DC transmission demand; P l，t is the load demand to be externally transmitted at t hours. P net，t is the power purchase power from the power grid; F netTo meet the revenue from selling electricity back to the local grid after DC transmission when the base is operating at full capacity; p s，t Take the time-of-use electricity price for power generation and grid connection. N is the total number of scheduling periods, and 24 is taken in day-ahead scheduling.

[0117] As an alternative implementation, the cost of purchasing electricity from the grid is calculated by the following formula:

[0118]

[0119] where p b，t is the time-of-use electricity price for the grid to sell electricity.

[0120] As an alternative implementation, the time-of-use electricity price model for the grid to sell electricity refers to the following formula, and the unit of electricity price is RMB / kWh.

[0121]

[0122] As an alternative implementation, the operating cost of thermal power units is calculated by the following formula:

[0123]

[0124] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is processed by a processor, it executes the above-mentioned integrated scheduling method for wind, light, thermal power, and energy storage based on the particle swarm optimization algorithm.

[0125] The above technical solutions have the following beneficial effects: The embodiment of the present invention aims at the integrated operation and optimization scheduling of large-scale energy bases with wind, light, thermal power, and energy storage. Based on the particle swarm optimization algorithm, it designs and proposes an integrated scheduling strategy for wind, light, thermal power, and energy storage, and can carry out integrated scheduling analysis of wind, light, thermal power, and energy storage according to the integrated scheduling optimization target; the particle swarm optimization algorithm adopted has the advantages of simple programming, intuitive and easy to implement, etc., and can quickly and accurately calculate the optimal scheduling plan; by improving the particle swarm optimization algorithm and introducing an inertia weight value that is dynamically updated in each iteration, it can avoid the traditional particle swarm algorithm from being easily trapped in local optimum in the solution process, which is beneficial to obtaining the global optimal scheduling strategy, and can be widely applied in engineering with strong universality.

[0126] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt 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.

[0127] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0128] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0130] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A method for integrated scheduling of wind, light, thermal and energy storage based on particle swarm optimization algorithm, characterized in that, Including: S1. Construct a day-ahead revenue model for the integrated operation of the wind-solar-thermal-storage integrated system according to the integrated scheduling objective of the wind-solar-thermal-storage integrated system, and obtain the revenue maximization objective function; S2. Use the particle swarm optimization algorithm to solve the revenue maximization objective function and obtain the global optimal solution; S3. Schedule the wind-solar-thermal-storage integrated system according to the global optimal solution; The step of using the particle swarm optimization algorithm to solve the revenue maximization objective function and obtain the global optimal solution includes: S201. Initialize the velocity and position of each particle, the particle swarm size, the maximum particle velocity, the learning factor, the maximum number of iterations, the maximum inertia weight, and the minimum inertia weight; S202. Calculate the maximization objective function value corresponding to each particle, obtain the individual optimal particle corresponding to each particle and the current global optimal particle according to the maximization objective function values of all particles, compare the current global optimal particle with the historical global optimal particle, and take the better particle as the latest global optimal particle; S203. Calculate the latest velocity and latest position of each particle after iteration according to the current inertia weight, current velocity, current position, learning factor, corresponding individual optimal particle, and the latest global optimal particle of each particle; S204. Calculate the latest inertia weight according to the maximum inertia weight, minimum inertia weight, current number of iterations, and maximum number of iterations; S205. Judge whether each particle satisfies the constraint conditions. If not, randomly generate a corresponding new particle and then judge whether the current number of iterations reaches the maximum number of iterations. If so, judge whether the current number of iterations reaches the maximum number of iterations; S206. If the current number of iterations does not reach the maximum number of iterations, take the latest global optimal particle as the historical global optimal particle, take the latest inertia weight, latest velocity, and latest position as the current inertia weight, current velocity, and current position for the next iteration respectively, and repeat steps S202 - S205. If the current number of iterations reaches the maximum number of iterations, take the latest global optimal particle as the global optimal solution and output it; The revenue maximization objective function is: Among them, is the contract electricity price for the energy base and the agreed electricity price for sending power from the base, usually a fixed constant; is the power generation of the i-th thermal power unit at time t; is the photovoltaic power generation at time t; is the wind power generation at time t; is the energy storage discharge power; is the operating cost of the thermal power unit; is the cost of purchasing electricity from the grid when the output of wind, light, thermal power and energy storage is insufficient to meet the DC transmission demand; is the load demand to be transmitted externally at t hours; is the power purchased from the grid; is the income from selling electricity to the local grid after the base meets the DC transmission demand during the large-scale generation of wind and light; takes the time-of-use electricity price for power generation and grid connection. N is the total number of dispatching time periods, and 24 is taken in the day-ahead dispatch.

2. The integrated scheduling method for wind, light, thermal and energy storage based on the particle swarm optimization algorithm according to claim 1, wherein, Calculate the latest velocity and latest position of each particle after iteration according to the current inertia weight, current velocity, current position, learning factor, corresponding individual optimal particle, and the current global optimal particle of each particle, and calculate through the following formula: Among them, , respectively represent the th current speed and current position, , respectively represent the th latest speed and latest position after the particle iteration update; , are respectively the individual optimal particle and the current global optimal particle of the th particle; is the dynamic inertia coefficient; , are the learning factors, also known as acceleration constants; , are random numbers within the range of [0, 1], which increases the randomness of the particle flight pair.

3. The integrated scheduling method for wind-solar-thermal energy storage based on the particle swarm optimization algorithm according to claim 1, wherein, Calculate the latest inertia weight according to the maximum inertia weight, minimum inertia weight, current number of iterations, and maximum number of iterations, and calculate through the following formula: wherein, and are the maximum and minimum inertia weights respectively; t is the current iteration number; is the maximum iteration number.

4. A wind-solar-thermal energy storage integrated scheduling device based on a particle swarm optimization algorithm, characterized in that, Including: An objective function construction module, which is used to construct a day-ahead revenue model for the integrated operation of the wind-solar-thermal-storage integrated system according to the integrated scheduling objective of the wind-solar-thermal-storage integrated system, and obtain the revenue maximization objective function; An optimal solution solving module, which is used to use the particle swarm optimization algorithm to solve the revenue maximization objective function and obtain the global optimal solution; An actual scheduling module, which is used to schedule the wind-solar-thermal-storage integrated system according to the global optimal solution; The optimal solution solving module includes: An initialization sub-module, which is used to initialize the velocity and position of each particle, the particle swarm size, the maximum particle velocity, the learning factor, the maximum number of iterations, the maximum inertia weight, and the minimum inertia weight; The global optimal solution update module is used to calculate the maximized objective function value corresponding to each particle, obtain the individual optimal particle and the current global optimal particle corresponding to each particle according to the maximized objective function values of all particles, compare the global optimal particle with the historical global optimal particle, and use the better particle as the current global optimal particle; The velocity and position update sub-module is used to calculate the updated velocity and position of each particle after iteration according to the current inertia weight, current velocity, current position, learning factor, corresponding individual optimal particle and the latest global optimal particle of each particle; The weight update sub-module is used to calculate the latest inertia weight according to the maximum inertia weight, minimum inertia weight, current iteration number and maximum iteration number; The judgment sub-module is used to judge whether each particle meets the constraint conditions. If not, a corresponding new particle is randomly generated and then it is judged whether the current iteration number reaches the maximum iteration number. If so, it is judged whether the current iteration number reaches the maximum iteration number; The repeated iteration sub-module is used to, if the current iteration number does not reach the maximum iteration number, use the latest global optimal particle as the historical global optimal particle, and use the latest inertia weight, latest velocity and latest position as the current inertia weight, current velocity and current position for the next iteration respectively, and repeat the operations of the global optimal solution update module, velocity and position update sub-module, weight update sub-module and judgment sub-module. If the current iteration number reaches the maximum iteration number, output the latest global optimal particle as the global optimal solution; The profit maximization objective function is: Among them, is the contract electricity price for the energy base and the agreed electricity price for sending power from the base, usually a fixed constant; is the power generation of the i-th thermal power unit at time t; is the photovoltaic power generation at time t; is the wind power generation at time t; is the energy storage discharge power; is the operating cost of the thermal power unit; is the cost of purchasing electricity from the grid when the output of wind, light, thermal power and energy storage is insufficient to meet the DC transmission demand; is the load demand to be transmitted externally at t hours; is the power purchased from the grid; is the revenue from selling electricity to the local grid after the base meets the DC transmission demand during the large-scale generation of wind and light; Take the time-of-use electricity price for power generation and grid connection. N is the total number of dispatching time periods, and 24 is taken in the day-ahead dispatching.

5. The integrated dispatching device for wind-solar-thermal energy storage based on the particle swarm optimization algorithm according to claim 4, characterized in that, The updated velocity and position of each particle after iteration are calculated according to the current inertia weight, current velocity, current position, learning factor, corresponding individual optimal particle and the current global optimal particle of each particle, and are calculated by the following formula: Among them, and respectively represent the th current speed and current position, and respectively represent the th latest speed and latest position after the particle iteration update; and are respectively the individual best particle and the current global best particle of the th particle; is the dynamic inertia coefficient; and are the learning factors, also known as acceleration constants; and are random numbers within the range of [0, 1], which increases the randomness of the particle flight pair.

6. The integrated dispatching device for wind-solar-thermal energy storage based on the particle swarm optimization algorithm according to claim 4, characterized in that, The latest inertia weight is calculated according to the maximum inertia weight, minimum inertia weight, current iteration number and maximum iteration number, and is calculated by the following formula: Among them, and are the maximum and minimum inertia weights respectively; t is the current iteration number; is the maximum iteration number.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is processed by the processor, it executes the integrated scheduling method of wind-solar-thermal-storage based on the particle swarm optimization algorithm according to any one of claims 1-3.

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